Journal Publications

K. Derr, M. Manic, “Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms,” in IEEE Transactions on Industrial Informatics, accepted for publication, 2012.

Abstract - A mobile ad hoc network is a collection of independent nodes that communicate wirelessly with one another. This manuscript investigates nodes that are swarm robots with communications and sensing capabilities. Each robot in the swarm may operate in a distributed and decentralized manner to achieve some goal. This manuscript presents a novel approach to dynamically adapting control parameters to achieve mesh configuration stability. The presented approach to robot interaction is based on spring force laws (attraction and repulsion laws) to create near-optimal mesh like configurations. In prior work we presented the Extended Virtual Spring Mesh (EVSM) algorithm for the dispersion of robot swarms. This manuscript extends the EVSM framework by providing the first known study on the effects of adaptive, versus static, control parameters on robot swarm stability. Several new novelties are presented for the EVSM algorithm: 1) improved performance with adaptive control parameters, 2) achievable convergence, and 3) accelerated convergence with high formation effectiveness. Simulation results show that 120 robots reach convergence using adaptive control parameters more than twice as fast as with static control parameters in a multiple obstacle environment.

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O. Linda, M. Manic, "General Type-2 Fuzzy C-Means Algorithm for Uncertain Fuzzy Clustering," in IEEE Transaction on Fuzzy Systems, accepted for publication, 2012.

Abstract: Pattern recognition in real world data is subject to various sources of uncertainty that should be appropriately managed. The focus of this paper is the management of uncertainty associated with parameters of fuzzy clustering algorithms. Type-2 Fuzzy Sets (T2 FSs) received increased research interest in the past decade primarily due to their potential to model various uncertainties. However, because of the computational intensity of the processing of General T2 (GT2) Fuzzy Sets (FSs), only their constrained version, the Interval T2 (IT2) FSs, were typically used. Fortunately, the recently introduced concepts of -planes and zSlices allow for efficient representation and computation with GT2 FSs. Following this recent development, this paper presents a novel approach for uncertain fuzzy clustering using the General Type-2 Fuzzy C-Means (GT2 FCM) algorithm. The proposed method builds on top of the previously published IT2 FCM algorithm, which is extended via the -planes representation theorem. The fuzzifier parameter of the FCM algorithm can be expressed using linguistic terms such as "Small" or "High", modeled as T1 FSs. This linguistic fuzzifier value is then used to construct the GT2 FCM cluster membership functions. The linguistic uncertainty is transformed into uncertain fuzzy positions of the extracted clusters. The GT2 FCM algorithm was found to balance the performance of T1 FCM algorithms in various uncertain pattern recognition tasks and provide increased robustness in situations where noisy or insufficient training data are present.

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O. Linda, M. Manic, "Monotone Centroid Flow Algorithm for Type-Reduction of General Type-2 Fuzzy Sets," in IEEE Transaction on Fuzzy Systems, accepted for publication, 2011

Abstract: Recently, Type-2 Fuzzy Logic Systems (T2 FLSs) received increased research attention due to their potential to model and cope with the dynamic uncertainties ubiquitous in many engineering applications. However, because of the complex nature and the computational intensity of the inference process, only the constrained version of T2 FLSs, the Interval T2 FLSs, were typically used. Fortunately, the very recently introduced concepts of alpha-planes and zSlices allow for efficient representation as well as computationally fast inference process with General T2 (GT2) FLSs. This paper addresses the type-reduction phase in GT2 FLSs, using GT2 Fuzzy Sets (FSs) represented in the alpha-planes framework. The monotone property of centroids of a set of akpha-planes is derived and leveraged towards developing a simple to implement, but fast algorithm for type-reduction of GT2 FSs - the Monotone Centroid Flow (MCF) algorithm. When compared to the Centroid Flow (CF) algorithm previously developed by Zhai and Mendel, the MCF algorithm features the following advantages: 1) the MCF algorithm computes numerically identical centroid as the Karnik-Mendel (KM) iterative algorithms, unlike the approximated centroid obtained with CF algorithm, 2) the MCF algorithm is faster than the CF algorithm as well as the independent application of the KM algorithms, 3) the MCF algorithm is simple to implement, unlike the CF algorithm, which requires computation of the derivatives of the centroid, 4) the MCF algorithm completely eliminates the need to apply the KM iterative procedure to any alpha-planes of the GT2 FS. The performance of the algorithm is presented on benchmark problems and compared to the other type-reduction techniques available in literature.

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O. Linda, M. Manic, "Uncertainty-Robust Design of Interval Type-2 Fuzzy Logic Controller for Delta Parallel Robot," in IEEE Transaction on Industrial Informatics, vol. 7, no. 4, pp. 661-671, Nov. 2011.

Abstract: Type-2 Fuzzy Logic Controllers (T2 FLCs) have been recently applied in many engineering areas. While understanding the control potentials of T2 FLCs can still be considered an open question, researchers commonly claim superiority of T2 FLCs based on a limited exploration of the space of design parameters. The contribution of this work is based on a problem-driven design of uncertainty-robust Interval T2 (IT2) FLCs. The presented methodology starts with a baseline optimized T1 FLC. Next, a group of IT2 FLCs is designed using partially-dependent approach by symmetrically blurring the membership functions around the original T1 fuzzy sets. This constrained design space allows for its systematic exploration and analysis. The performance of the designed controllers was evaluated on delta parallel robot hardware under two kinds of commonly encountered uncertainties: i) sensory noise and ii) uncertain system parameters. The experimental results showed that IT2 FLCs provide improved control performance against T1 FLCs when appropriate design of IT2 fuzzy sets is performed. In addition, it was demonstrated that excessive amount of “type-2 fuzziness” in the IT2 FLC design leads in rapid performance degradation.

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D. Wijayasekara, M. Manic, P. Sabharwall, V. Utgikar, "Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique," in Nucl. Eng. Des. (2011).

Abstract: Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or over-learning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the testing error achieved was in the order of magnitude of within 10-5 to 10-3. It was also show that the absolute error achieved by EBaLM-OTR was an order of magnitude better than the lowest error achieved by EBaLM-THP.

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K. Derr, M. Manic, "Extended Virtual Spring Mesh (EVSM): The Distributed Self Organizing Mobile Ad Hoc Network for Area Exploration," in IEEE Trans. on Industrial Electronics (accepted for publication in 2011)

Abstract: Mobile Ad hoc NETworks (MANETs) are distributed self-organizing networks that can change locations and configure themselves on the fly. This paper focuses on an algorithmic approach for the deployment of a MANET within an enclosed area, such as a building in a disaster scenario, which can provide a robust communication infrastructure for search and rescue operations. While a virtual spring mesh (VSM) algorithm provides scalable, self-organizing and fault tolerant capabilities required by a MANET, the VSM lacks the MANET’s capabilities of deployment mechanisms for blanket coverage of an area and does not provide an obstacle avoidance mechanism. This paper presents a new technique, an Extended Virtual Spring Mesh (EVSM) algorithm that provides the following novelties: 1) new control laws for exploration and expansion to provide blanket coverage, 2) virtual adaptive springs enabling the mesh to expand as necessary, 3) adapts to communications disturbances by varying the density and movement of mobile nodes, and 4) new metrics to assess the performance of the EVSM algorithm. Simulation results show that EVSM provides up to 16% more coverage and is 3.5 times faster than VSM in environments with 8 obstacles.

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O. Linda, M. Manic, "Interval Type-2 fuzzy voter design for fault tolerant systems, Information Sciencs," vol. 181, issue: 14, pp. 2933-2950, July 2011.

Abstract: A voting scheme constitutes an essential component of many fault tolerant systems. Two types of voters are commonly used in applications of real-valued systems: the inexact majority and the amalgamating voters. The inexact majority voter effectively isolates erroneous modules and is capable of reporting benign outputs when a significant disagreement is detected. However, an application specific voter threshold must be provided. On the other hand, amalgamating voter, such as the weighted average voter, reduces the influence of faulty modules by averaging the input values together. Unlike the majority voters, amalgamating voters are not capable of producing benign outputs. In the past, a Type-1 (T1) fuzzy voting scheme was introduced, allowing for both smooth amalgamation of voter inputs and effective signalization of benign outputs. The presented paper proposes an extension to the fuzzy voting scheme via incorporating Interval Type-2 (IT2) fuzzy logic. The IT2 fuzzy logic allows for an improved handling of uncertain assumptions about the distributions of noisy and erroneous inputs which are essential for correct design of the fuzzy voting scheme. The proposed voter design features robust performance when the uncertainty assumptions dynamically change over time. The IT2 fuzzy voter architecture was compared against the average voter, inexact majority voter, and the T1 fuzzy voter using a refined experimental harness. The reported results demonstrate improved availability, safety and reliability of the presented IT2 fuzzy voting scheme.

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O. Linda, M. Manic, "Online Spatio-Temporal Risk Assessment for Intelligent Transportation Systems", IEEE Trans. on Intelligent Transportation Systems vol.12, no.1, Page(s): 194-200, March 2011.

Abstract: Due to modern pervasive wireless technologies and high-performance monitoring systems, the spatio-temporal information plays an important role in areas such as intelligent transportation systems (ITS), surveillance, scheduling, planning or industrial automation. The security or criminal/terrorist threat prevention in modern ITS, are one of today's most relevant concerns. This paper presents an algorithm for online spatio-temporal risk assessment in urban environments. In its first phase, the algorithm uses online Nearest Neighbor Clustering (NNC) algorithm to identify significant places. In the second phase, a fuzzy inference engine is employed to quantify the level of risk that each significant place poses to the place of interest (e.g. vehicle, person, building or an object of high assets). The contributions of the presented algorithm are as follows: i) correct recognition and extraction of the set of the most significant places, ii) dynamic adaptation of the solution to time-dependent traffic distributions, iii) parametric control by adjusting proximity, significance threshold and discount factor, and iv) online risk assessment. The performance of the algorithm was demonstrated on a problem of traffic density estimation and risk assessment in virtual urban environment.

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O. Linda, M. Manic, "Fuzzy Force-Feedback Augmentation for Manual Control of Multi-Robot System", IEEE Trans. on Industrial Electronics vol.58, no.8, Aug. 2011.

Abstract: Multi-robot systems represent an enticing area of research with numerous real world applications. Teams of multiple robots can achieve tasks that are more difficult or even impossible for single robot, e.g. environment exploration, search and rescue or surveillance operations. In previous work the authors developed a system for single-operator manual control of multi-robot system. However, such teleoperation systems commonly suffer from inadequate perception of the remote environment. This manuscript extends the previously presented work by adding a fuzzy force-feedback (FFF) augmentation for manual control of multi-robot system. The FFF augmentation delivers additional information to the operator. Moreover, it guides the operator towards a smooth control of the robotic group. The force feedback was generated by a system of fuzzy controllers monitoring the state of the multi-robot group. The performance of the system was evaluated in a virtual environment and the recorded forces were explored in various scenarios. The force feedback augmentation demonstrated the following improvements: i) operator's increased obstacle awareness, and ii) improved maneuvering performance.

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O. Linda, M. Manic, "Self-Organizing Fuzzy Haptic Teleoperation of Mobile Robot Using Sparse Sonar Data", IEEE Trans. on Industrial Electronics, vol.58, no.8, 2010.

Abstract: Mobile robot teleoperation has been used in many areas of industrial automation, such as explosives disposal, nuclear waste manipulation, freight handling or transportation. Here, the commonly provided audio-visual feedback often resulted in an inadequate perception of the remote environment. Haptic augmentation was shown to improve and positively enhance the control of the mobile robot. This paper presents a novel Self-Organizing Fuzzy Adaptive Mapping algorithm (SOFAMap) for a haptic teleoperation of mobile robots. The SOFAMap algorithm was specifically developed for a mobile robot with a rotational sonar sensory system, constituting an alternative to a traditionally used multi-sonar array. The main contributions of this work are: 1) development of a specific selforganizing environment mapping structure inspired by the Growing Neural Gas algorithm; 2) incorporating a fuzzy controller into the algorithm to adapt to robot's motion; 3) resolving typical issues such as sensor noise, communication time delay and low sampling rate. The experimental testing was performed in both virtual environment and on a real robotic platform, consisting of a Lego NXT mobile robot and a Novint Falcon 3-DOF haptic interface. The results showed that a highfidelity haptic feedback can be successfully generated using a simpler and more affordable rotational sonar sensory system, as opposed to the typical multi-sonar array. Further, it was demonstrated that the SOFAMap algorithm improves the operator's awareness of unstructured environments, making it applicable to wide range of mobile robot teleoperation systems.

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G. Navarro, M. Manic, "FuSnap: Fuzzy, Proportional, and Integral Snapshot Control for Disk Arrays," in IEEE Trans. on Industrial Electronics, vol. 58, no. 9, pp. 4436-4444, Sep. 2011.

This paper presents FuSnap, a fuzzy-logic-based controller that monitors and controls the snapshot process of a logical storage volume in a disk array. As disks do not linearly respond to the arrival rate of user accesses, FuSnap makes use of fuzzy logic as the means to achieve better control of their response time. The goal of the FuSnap controller is to reduce the response time caused by the copy-on-writes (CoWs) that occur during the snapping of a storage logical volume. The FuSnap controller, based on the response time of user accesses, makes the decision on whether to proceed with a CoW or a redirect-on-write when a source logical volume is being copied to a snapshot logical volume. The benefits of FuSnap approach are twofold. First, significant reductions in response time of user requests are obtained with the FuSnap approach over the traditional CoW snap approach. Second, these reductions in response time make the point-in-time copy of data a process less disruptive for database users. FuSnap was verified with two setups using Hewlett-Packard UniX workstations, one setup with eight and the other with 32 disks.

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R. McJunkin, R.L. Boring, M.A. McQueen, L.P. Shunn, J.L. Wright, D.I. Gertman, O. Linda, K. McCarty, M. Manic, "Concept of operations for data fusion visualization," in Proc. of European Safety and Reliability Conference (ESREL2011),Troyes, France, Sept. 19-22, 2011.

ABSTRACT: Data fusion for process control involves the presentation of synthesized sensor data in a manner that highlights the most important system states to an operator. The design of a data fusion interface must strike a balance between providing a process overview to the operator while still helping the operator pinpoint anomalies as needed. With the inclusion of a predictor system in the process control interface, additional design requirements must be considered, including the need to convey uncertainty regarding the prediction and to minimize nuisance alarms. This paper reviews these issues and establishes a design process for data fusion interfaces centered on creating a concept of operations as the basis for a design style guide..

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Peer-Reviewed Conference Publications

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2012

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U. Ravishankar, M. Manic, "Iterative Learning Heuristic Dynamic Programming (ILHDP) design of a Steam Power Plant Controller," in Proc. 38th Annual Conference of the IEEE Industrial Electronics Society, IEEE IECON 2012, Montreal, Canada, Oct. 25-28, 2012.

Abstract-This paper presents a new dynamic programming method called the Iterative Learning Heuristic Dynamic Programming (ILHDP). The ILHDP is an Iterative Learning Control (ILC) based Neural Dynamic Programming (NDP) algorithm. The NDP aspect of the ILHDP algorithm is borrowed from traditional Adaptive Critic Design (ACD) algorithms. Typical NDP algorithms in the ACD class of algorithms train a Model Network beforehand and use a Critic Network, as the gradient approximator, trained back-and-forth with the Action Network in each iteration to converge the Action Network towards the optimal control policy. The proposed ILHDP algorithm updates the Model Network continually based on newly obtained data sampled during each Action Network optimization step on the same experiment. This process of Model Network updating ensures better gradient approximation presented by the Model Network itself. The presented ILHDP is used for the design of a Steam Power Plant controller with respect to the Active-Power-to-Frequency droop characteristics. Test results indicated that the ILHDP designed controller was capable of stabilizing the output power of the Steam Power Plant to track the load with a maximum tracking error of 0.011 for abrupt load changes as fast as 15s. The Steam Power Plant was also subjected to large transient spikes for which the designed controller proved to recover the system back to stability.

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O. Linda, M. Manic, M. McQueen, "Improving Control System Cyber-State Awareness using Known Secure Sensor Measurements ," in Proc. 7th International Conference on Critical Information Infrastructure Security, accepted for publication, 2012.

Abstract: This paper presents design and simulation of a low cost and low false alarm rate method for improved cyber-state awareness of critical control systems - the Known Secure Sensor Measurements (KSSM) method. The KSSM concept relies on physical measurements to detect malicious falsification of the control systems state. The KSSM method can be incrementally integrated with already installed control systems for enhanced resilience. This paper reviews the previously developed theoretical KSSM concept and then describes a simulation of the KSSM system. A simulated control system network is integrated with the KSSM components. The effectiveness of detection of various intrusion scenarios is demonstrated on several control system network topologies.

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O. Linda, T. Vollmer, M. Manic, "Improving Cyber-Security of Smart Grid Systems via Anomaly Detection and Linguistic Domain Knowledge ," in Proc. IEEE Symposium on Resilience Control Systems, Salt Lake City, Utah, accepted for publication, August 2012.

Abstract: The planned large scale deployment of smart grid network devices will generate a large amount of information exchanged over various types of communication networks. The implementation of these critical systems will require appropriate cyber-security measures. A network anomaly detection solution is considered in this paper. In common network architectures multiple communications streams are simultaneously present, making it difficult to build an anomaly detection solution for the entire system. In addition, common anomaly detection algorithms require specification of a sensitivity threshold, which inevitably leads to a tradeoff between false positives and false negatives rates. In order to alleviate these issues, this paper proposes a novel anomaly detection architecture. The designed system applies a previously developed network security cyber-sensor method to individual selected communication streams allowing for learning accurate normal network behavior models. In addition, an Interval Type-2 Fuzzy Logic System (IT2 FLS) is used to model human background knowledge about the network system and to dynamically adjust the sensitivity threshold of the anomaly detection algorithms. The IT2 FLS was used to model the linguistic uncertainty in describing the relationship between various network communication attributes and the possibility of a cyber attack. The proposed method was tested on an experimental smart grid system demonstrating enhanced cyber-security.

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O. Linda, D. Wijayasekara, M. Manic, C. Rieger, "Computational Intelligence based Anomaly Detection for Building Energy Management Systems," in Proc. IEEE Symposium on Resilience Control Systems, Salt Lake City, Utah, accepted for publication, August 2012.

Abstract: In the past several decades Building Energy Management Systems (BEMSs) have become vital components of most modern buildings. BEMSs utilize advanced microprocessor technology combined with extensive sensor data collection and communication to minimize energy consumption while maintaining high human comfort levels. When properly tuned and operated, BEMSs can provide significant energy savings. However, the complexity of the acquired sensory data and the overwhelming amount of presented information renders them difficult to adjust or even understand by responsible building managers. This inevitably results in suboptimal BEMS operation and performance. To address this issue, this paper reports on a research effort that utilizes Computational Intelligence techniques to fuse multiple heterogeneous sources of BEMS data and to extract relevant actionable information. This actionable information can then be easily understood and acted upon by responsible building managers. In particular, this paper describes the use of anomaly detection algorithms for improving the understandability of BEMS data and for increasing the state-awareness of building managers. The developed system utilizes modified nearest neighbor clustering algorithm and fuzzy logic rule extraction technique to automatically build a model of normal BEMS operations and detect possible anomalous behavior. In addition, linguistic summaries based on fuzzy set representation of the input values are generated for the detected anomalies which increase the understandability of the presented results.

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D. Wijayasekara, M. Manic, J. L. Wright, M. McQueen "Mining Bug Databases for Unidentified Software Vulnerabilities," in Proc. of2012 IEEE Intl. Conference on Human System Iteraction, HSI 2012, 2012.

Abstract: Identifying software vulnerabilities is becoming more important as critical and sensitive systems increasingly rely on complex software systems. It has been suggested in previous work that some bugs are only identified as vulnerabilities long after the bug has been made public. These vulnerabilities are known as hidden impact vulnerabilities. This paper discusses existing bug data mining classifiers and present an analysis of vulnerability databases showing the necessity to mine common publicly available bug databases for hidden impact vulnerabilities. We present a vulnerability analysis from January 2006 to April 2011 for two well known software packages: Linux kernel and MySQL. We show that 32% (Linux) and 62% (MySQL) of vulnerabilities discovered in this time period were hidden impact vulnerabilities. We also show that the percentage of hidden impact vulnerabilities in the last two years has increased by 53% for Linux and 10% for MySQL. We then propose a hidden impact vulnerability identification methodology based on text mining classifier for bug databases. Finally, we discuss potential challenges faced by a development team when using such a classifier.

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O. Linda, M. Manic, "Improving Vehicle Fleet Fuel Economy via Learning Fuel Efficient Driving Behavior," in Proc. of 2012 IEEE Intl. Conference on Human System Iteraction, HSI 2012, 2012.

Abstract: Reducing the fuel consumption of road vehicles has the potential to decrease environmental impact of transportation as well as achieve significant economical benefits. This paper proposes a novel methodology for improving the fuel economy of vehicle fleets via learning fuel-efficient driving behaviors. Vehicle fleets composed of large number of heavy vehicles routinely perform runs with different drivers over a set of fixed routes. While all drivers might achieve on-time and safe driving performance their actual driving behaviors and the subsequent fuel economy can vary substantially. The proposed Intelligent Driver System (IDS) utilizes vehicle performance data combined with GPS information on fixed routes to incrementally build a model of the historically most fuel efficient driving behavior. During driving, the calculated optimal velocity for specific location is compared to the current vehicle state and a fuzzy logic PD controller is used to compute the optimal control action. The control action can be projected to the drivers via a specialized HMI or used directly as a predictive cruise control to achieve overall fuel economy improvements. The method has been validated on a simulated heavy vehicle model, showing potential for substantial fuel economy improvements.

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O. Linda, M. Manic, "On the Accuracy of Input-Output Uncertainty Modeling with Interval Type-2 Fuzzy Logic Systems," in Proc. of 2012 IEEE World Congress On Computational Intelligence, WCCI 2012 - FUZZ-IEEE 2012, 2012.

Abstract: Type-2 Fuzzy Logic Systems (T2 FLSs) have been commonly attributed with the capability to model various data uncertainties. Frequently, the input uncertainties of an FLS were modeled using T2 Fuzzy Sets (FSs) and the type-reduced centroid of the output FS was interpreted as a measure of uncertainty associated with the terminal real-valued output. However, the accuracy of this input-output uncertainty modeling has been rarely studied. It is well established that T2 FSs can be understood as a composition of a large number of embedded T1 FSs and thus model the uncertainty of selecting a specific T1 FSs. However, whether the same can be achieved with T2 FLS can be considered an open question. This paper contributes by presenting a study of the input-output uncertainty modeling capability of Interval T2 (IT2) FLSs. First, the Monte Carlo simulation technique is used to simulate linguistic uncertainties and to compute the aggregated output result. This simulation is then compared to the output bounds provided by the interval centroid as computed with classical IT2 FLS. It is demonstrated that the interval output of the IT2 FLS overestimates the output uncertainty range when compared to the results of the Monte Carlo simulation. To further understand this problem the concept of Equivalent Type-1 FSs is used. Finally, a detailed example is presented to demonstrate why the IT2 fuzzy inference process overestimates the output uncertainty.

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O. Linda, M. Manic, "Shadowed Type-2 Fuzzy Sets -Type-2 Fuzzy Sets with Shadowed Secondary Membership Functions," in Proc. of 2012 IEEE World Congress On Computational Intelligence, WCCI 2012 - FUZZ-IEEE 2012, 2012.

Abstract: General Type-2 (GT2) Fuzzy Sets (FSs) have been originally proposed to allow for modeling uncertainty associated with membership grades of Type-1 (T1) FSs. However, because of the computational complexity associated with the processing of GT2 FSs, only their constrained version, the Interval T2 (IT2) FSs, have been widely used. While IT2 FSs allow for fast processing, they lack the expressive power of GT2 FSs when describing the various uncertainties. In order to combine the better of both types, this paper proposes a novel class of T2 FSs - the Shadowed Type-2 (ST2) FSs. The ST2 FS is a T2 FS with secondary membership functions represented as Shadowed Sets (SSs), which were originally proposed by Pedrycz. Shadowed sets are directly induced by the T1 fuzzy membership functions and they conserve the amount of modeled uncertainty. In a similar manner, an ST2 FS is directly induced by a GT2 FS by transforming all the T1 fuzzy secondary membership functions into Shadowed Sets. The resulting ST2 FSs can thus better capture the uncertainty in the original GT2 FSs when compared to the constrained IT2 FSs. Additionally, ST2 FSs offer very efficient computational framework since the secondary membership grades can only take three values of 0, 1, or completely uncertain (shadowed) grade of [0,1]. This paper introduces the representation, the elementary set-theoretic operations and several methods for type-reduction and defuzzification of ST2 FSs. The modeling capability of ST2 SS was demonstrated on several examples.

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D. Wijayasekara, M. Manic, "Visual, Linguistic Data Mining Using Self-Organizing Maps," in Proc. of 2012 IEEE World Congress On Computational Intelligence, WCCI 2012 - IJCNN 2012, 2012.

Abstract: Data mining methods are becoming vital as the amount and complexity of available data is rapidly growing. Visual data mining methods aim at including a human observer in the loop and leveraging human perception for knowledge extraction. However, for large datasets, the rough knowledge gained via visualization is often times not sufficient. Thus, in such cases data summarization can provide a further insight into the problem at hand. Linguistic descriptors such as linguistic summaries and linguistic rules can be used in data summarization to further increase the understandability of datasets. This paper presents a visual linguistic data mining tool (VLS-SOM) that combines the visual data mining capability of the Self-Organizing Map (SOM) with the understandability of linguistic descriptors. This paper also presents new quality measures for ranking of predictive rules. The presented data mining tool enables users to 1) interactively derive summaries and rules about interesting behaviors of the data visualized though the SOM, 2) visualize linguistic descriptors and visually assess the importance of generated summaries and rules. The data mining tool was tested on two benchmark problems. The tool was helpful in identifying important features of the datasets. The visualization enabled the identification of the most important summaries. For classification, the visualization proved useful in identifying multiple rules that classify the dataset.

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U. Ravishanka, M. Manic, " The Adaptive Critic Learning Agent (ACLA) Algorithm: Towards Problem Independent Neural Network based Optimizers," in Proc. of 2012 IEEE World Congress On Computational Intelligence, WCCI 2012 - IJCNN 2012, 2012.

Abstract: This paper presents the development of a new neural network based optimizer called the Adaptive Critic Learning Agent (ACLA) algorithm. The ACLA algorithm is based on the traditional Adaptive Critic Design (ACD) algorithm and hence its name. Conventional neural network based optimizers use the principle of Hopfield/Tank Neural Networks (HTNN) to solve unimodal optimization problems. These neural networks require tailored structures for the specific optimization problem. The ACLA algorithm presented in this paper uses a general randomly initialized neural network to solve any unimodal optimization problem. This is achieved by extending the principles of the traditional ACD algorithm for the ACLA algorithm. Other attributes of the ACLA algorithm are related to the issues with swarm based optimizers such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). These issues are: 1) large memory requirements and 2) multiple parameters required to tune the algorithm's convergence performance. The ACLA algorithm resolves these issues by: 1) using only one neuron to reduce memory requirements and 2) using only a single learning coefficient parameter to tune the algorithm's convergence performance. The ACLA algorithm was tested and compared with three swarm based optimizers on two unimodal benchmark problems typically used for PSO and GA algorithms. Test results proved the ACLA algorithm to converge to solutions 7 orders greater than the swarm based algorithms. The ACLA algorithm was further tested on two multimodal benchmark problems to demonstrate its capability to converge to nearest local minima.

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U. Ravishanka, M. Manic, "A Direct Utility Adaptive Critic (DUAC) algorithm for power plant load management,," in in 2012 IEEE World Congress On Computational Intelligence, WCCI 2012 - IJCNN 2012 (accepted for publication), 2012.

Abstract: This paper presents a Direct Utility Adaptive Critic (DUAC) algorithm applied to a power plant load management problem. The DUAC algorithm is an enhancement of the original Heuristic Dynamic Programming (HDP) Adaptive Critic Design (ACD) algorithm into a simpler and more robust controller. Typical ACD algorithms model dynamic systems with time-delayed states and action inputs and due to this the Action Network training procedure is a complex BackPropagation-Through-Time (BPTT) process. Also required in typical ACD algorithms is a dedicated Critic Network training process for different control sequences before the Action Network training procedure. The DUAC algorithm, presented in this paper, simplifies the Adaptive Critic algorithm by 1) eliminating the complex BPTT process for training the Action Network and 2) replacing the Critic Network with the user-defined utility function directly. Due to these changes the utility-action gradient typically required to train the Action Network is based on direct result of two utility values with respect to two action inputs. The replacement of the Critic Network with the user-defined utility function ensures better control accuracy since Critic Network modeling provides only approximations of the utility function. The DUAC algorithm was tested for time-varying consumer loads on an RMS voltage analogous s-domain model of the power plant created in Simulink using the SimPowerSystems toolbox. Test results indicated that the DUAC algorithm was able to derive an Action Network that controlled the power plant model to an output RMS voltage fluctuation variance of the order of no more than 10e-3 . This result can prove to be an essential step in load balancing problems in Smart Grids.

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J. D. Hewlett, M. Manic, C. G. Rieger, " WESBES: A wireless embedded sensor for improving human comfort metrics using temporospatially correlated data," in Proc. IEEE Symposium on Resilience Control Systems, ISRCS 2012, Salt Lake City, Utah, Aug. 14-16, 2012.

Abstract —When utilized properly, energy management systems(EMS) can offer significant energy savings by optimizing the efficiency of heating, ventilation, and air-conditioning (HVAC)systems. However, difficulty often arises due to the constraints imposed by the need to maintain an acceptable level of comfort for a building's occupants. This challenge is compounded by the act that human comfort is difficult to define in a measurable way. One way to address this problem is to provide a building manager with direct feedback from the building's users. Still, this data is relative in nature, making it difficult to determine the actions that need to be taken, and while some useful comfort correlations have been devised, such as ASHRAE's Predicted Mean Vote index, they are rules of thumb that do not connect individual feedback with direct, diverse feedback sensing. As they are a correlation, quantifying effects of climate, age of building sand associated defects such as draftiness, are outside the realm of this correlation. Therefore, the contribution of this paper is the Wireless Embedded Smart Block for Environment Sensing (WESBES); an affordable wireless sensor platform that allows subjective human comfort data to be directly paired with temporospatially correlated objective sensor measurements for use in EMS. The described device offers a flexible research platform for analyzing the relationship between objective and subjective occupant feedback in order to formulate more meaningful measures of human comfort. It could also offer an affordable and expandable option for real world deployment in existing EMS.

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2011

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O. Linda, M. Manic, "Uncertainty Modeling with Interval Type-2 Fuzzy Logic Systems in Mobile Robotics," in Proc. IEEE IECON'11, 37th Annual Conference of the IEEE Industrial Electronics Society (accepted for publication), 2011.

Abstract: Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) have been commonly attributed with the capability to model and cope with dynamic uncertainties. However, the interpretation of this uncertainty modeling using the IT2 FLSs have been rarely addressed or taken into consideration during the design of the respective fuzzy controller. This paper extends the previously proposed method for incorporating the experimentally measured input uncertainty into the design of the IT2 FLS. Two novel uncertainty quantifiers are proposed to track the uncertainty modeling throughout the inference process: the antecedent uncertainty and the consequent uncertainty quantifiers. Further, the new IT2 FLS design method was used to design a wallfollowing navigation controller for an autonomous mobile robot. It is demonstrated that the new IT2 FLS design offers improved uncertainty modeling, when compared to classical design methodologies. It was shown that the modeled input uncertainty is more accurately reflected in the system output as the geometry of the type-reduced interval centroid. This uncertainty model provides valuable information about the uncertainty associated with the output decision.

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O. Linda, M. Manic, T. R. McJunkin, "Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine," in Proc. IEEE Symposium on Resilience Control Systems, Boise, Idaho, 2011.

Abstract: Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a fuzzy-neural data fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data fusion engine for each component of the control system. Each data fusion engine implements three-layered alarm system consisting of: 1) conventional threshold-based alarms, 2) anomalous behavior detector using self-organizing maps, and 3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

Paper: PDF Project

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O. Linda, T. Vollmer, M. Manic, J. Alves-Foss, "Towards Resilient Critical Infrastructures: Application of Type-2 Fuzzy Logic in Embedded Network Security Cyber Sensor," in Proc. IEEE Symposium on Resilience Control Systems, Boise, Idaho, 2011.

Abstract: Resiliency and cyber security of modern critical infrastructures is becoming increasingly important with the growing number of threats in the cyber environment. This paper proposes an extension to a previously developed fuzzy logic based anomaly detection network security cyber sensor via incorporating Type-2 Fuzzy Logic (T2 FL). In general, fuzzy logic provides a framework for system modeling in linguistic form capable of coping with imprecise and vague meaning of words. T2 FL is an extension of Type-1 FL which proved to be successful in modeling and minimizing the effects of various kinds of dynamic uncertainties. In this paper, T2 FL provides a basis for robust anomaly detection and cyber security state awareness. In addition, the proposed algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental cyber security test-bed.

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O. Linda, M. Manic, "Centroid Density of Intervalt Type-2 Fuzzy Sets: Comparing Stochastic and Deterministic Defuzzification," in Proc. of FUZZ-IEEE, Taipei, Taiwan, 27-30 June, 2011.

Abstract: Recently, Type-2 (T2) Fuzzy Logic Systems (FLSs) gained increased attention due to their capability to better describe, model and cope with the ubiquitous dynamic uncertainties in many engineering applications. By far the most widely used type of T2 FLSs are the Interval T2 (IT2) FLSs. This paper provides a comparative analysis of two fundamentally different approaches to defuzzification of IT2 Fuzzy Sets (FSs) - the deterministic Karnik-Mendel Iterative Procedure (KMIP) and the stochastic sampling defuzzifier. As previously demonstrated by other researchers, these defuzzification algorithms do not always compute identical output values. In the presented work, the concept of centroid density of an IT2 FS is introduced in order to explain such discrepancies. It was demonstrated that the stochastic sampling defuzzification method converges towards the center of gravity of the proposed centroid density function. On the other hand, the KMIP method calculates the midpoint of the interval centroid obtained according to the extension principle. Since the information about the centroid density is removed via application of the extension principle, the two methods produce inevitably different results. As further demonstrated, this difference significantly increases in case of non-symmetric IT2 FSs.

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U. Ravishankar, M. Manic, “A Hardware Suitable Integrated Neural System for Autonomous Vehicles”, Int. Joint Conference on Neural Networks, IJCNN 2011, San Jose, California, July 31-Aug.5, 2011.

Abstract: Current developments in autonomous vehicle systems typically consider solutions to single problems like road detection, road following and object recognition individually. The integration of these individual systems into a single package becomes difficult because they are less compatible. This paper introduces a generic Integrated Neural System for Autonomous Vehicles (INSAV) package solution with processing blocks that are compatible with each other and are also suitable for hardware implementation. The generic INSAV is designed to account for important problems such as road detection, road structure learning, path tracking and obstacle detection. The paper begins the design of the generic INSAV by building its two most important blocks: the Road Structuring and Path Tracking Blocks. The obtained results from implementing the two blocks demonstrate an average of 92% accuracy of segmenting the road from a given image frame and path tracking of straight roads for stable motion and obstacle detection.

Paper: PDF Project

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D. Wijayasekara, O. Linda, M. Manic, “CAVE-SOM: Immersive Visual Data Mining Using 3D Self-Organizing Maps”, Int. Joint Conference on Neural Networks, IJCNN 2011, San Jose, California, July 31-Aug.5, 2011.

Abstract: Data mining techniques are becoming indispensable as the amount and complexity of available data is rapidly growing. Visual data mining techniques attempt to include a human observer in the loop and leverage human perception for knowledge extraction. This is commonly allowed by performing a dimensionality reduction into a visually easy-to-perceive 2D space, which might result in significant loss of important spatial and topological information. To address this issue, this paper presents the design and implementation of a unique 3D visual data mining framework – CAVE-SOM. The CAVE-SOM system couples the Self-Organizing Map (SOM) algorithm with the immersive Cave Automated Virtual Environment (CAVE). The main advantages of the CAVE-SOM system are: i) utilizing a 3D SOM to perform dimensionality reduction of large multi-dimensional datasets, ii) immersive visualization of the trained 3D SOM, iii) ability to explore and interact with the multi-dimensional data in an intuitive and natural way. The CAVE-SOM system uses multiple visualization modes to guide the visual data mining process, for instance the data histograms, U-matrix, connections, separations, uniqueness and the input space view. The implemented CAVE-SOM framework was validated on several benchmark problems and then successfully applied to analysis of wind-power generation data. The knowledge extracted using the CAVE-SOM system can be used for further informed decision making and machine learning.

Paper: PDF Project

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O. Linda, M. Manic, “Uncertainty Modeling for Interval Type-2 Fuzzy Logic Systems Based on Sensor Characteristics,” in Proc of 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems, within IEEE Symposium Series on Computational Intelligence, T2FUZZ 2011 (SCCI 2011), pp.31-37, Paris, France, Apr.11-15, 2011.

Abstract: In the past decade Type-2 Fuzzy Logic Systems (T2 FLSs) gained increased research attention due to their potential to outperform Type-1 FLSs in applications with dynamic uncertainties. This advantage is typically attributed to the capability of T2 Fuzzy Sets (FSs) to better model the dynamic uncertainty and cope with its negative impacts. However, the accuracy, correctness and interpretation of such uncertainty modeling using the T2 FLSs have been rarely addressed or taken into account during the design of the respective fuzzy controller. The contribution of this paper is in analyzing the uncertainty modeling capabilities of the commonly used Interval T2 (IT2) FSs with uncertain parameters. In addition, a novel method for incorporating the experimentally measured input uncertainty into the design of the IT2 FLS is proposed. It is demonstrated that the novel IT2 FLS design method improves the accuracy of the input uncertainty model in the specific problem domain. As a consequence, the modeled uncertainty is then more accurately reflected in the output domain as the geometry of the typereduced centroid.

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T. Vollmer., J. A. Foss, M. Manic, “Autonomous Rule Creation for Intrusion Detection”, in Proc of 2011 IEEE Symposium on Computational Intelligence in Cyber Security, within IEEE Symposium Series on Computational Intelligence, CICS 2011 (SCCI 2011), pp.1-8, Paris, France, Apr.11-15, 2011.

Abstract: Many computational intelligence techniques for anomaly based network intrusion detection can be found in literature. Translating a newly discovered intrusion recognition criteria into a distributable rule can be a human intensive effort. This paper explores a multi-modal genetic algorithm solution for autonomous rule creation. This algorithm focuses on the process of creating rules once an intrusion has been identified, rather than the evolution of rules to provide a solution for intrusion detection. The algorithm was demonstrated on anomalous ICMP network packets (input) and Snort rules (output of the algorithm). Output rules were sorted according to a fitness value and any duplicates were removed. The experimental results on ten test cases demonstrated a 100 percent rule alert rate. Out of 33,804 test packets 3 produced false positives. Each test case produced a minimum of three rule variations that could be used as candidates for a production system.

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O. Linda, M. Manic, T. Vollmer, J. Wright, “Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor,” in Proc of 2011 IEEE Symposium on Computational Intelligence in Cyber Security, within IEEE Symposium Series on Computational Intelligence, CICS 2011 (SCCI 2011), pp.202-209, Paris, France, Apr.11-15, 2011.

Abstract: Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule base modeling the normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.

Paper: PDF Project

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T. R. McJunkin, M. Manic, "Evolutionary Adaptive Discovery of Phased Array Sensor Signal," in Proc. IEEE Conf. on Human System Interaction, Yokohama, Japan, 2011.

Abstract: Tomography, used to create images of the internal properties and features of an object, from phased array ultasonics is improved through many sophisiticated methonds of post processing of data. One approach used to improve tomographic results is to prescribe the collection of more data, from different points of few so that data fusion might have a richer data set to work from. This approach can lead to rapid increase in the data needed to be stored and processed. It also does not necessarily lead to have the needed data. This article describes a novel approach to utilizing the data aquired as a basis for adapting the sensors focusing parameters to locate more precisely the features in the material: specifically, two evolutionary methods of autofocusing on a returned signal are coupled with the derivations of the forumulas for spatially locating the feature are given. Test results of the two novel methods of evolutionary based focusing (EBF) illustrate the improved signal strength and correction of the position of feature using the optimized focal timing parameters, called Focused Delay Identification (FoDI).

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S. D. Stan, M. Manic, C. Szep, R. Balan, “Performance analysis of 3 DOF Delta parallel robot,” in Proc. IEEE Conf. on Human System Interaction, Yokohama, Japan, 2011.

Abstract: Parallel robots have inherent advantages for many applications in the fields of robotics. They offer high dynamic capabilities combined with high accuracy and stiffness. There are a lot of performance criteria which have to be taken into account and which are pose dependent. The main idea of this paper is to present the fundamentals for a performance evaluation of the 3 DOF Delta parallel robot. Therefore we discuss a large number of performance criteria dealing with workspace, quality transmission, manipulability, dexterity and stiffness.

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O. Linda, M. Manic, “Evaluating Uncertainty Resiliency of Type-2 Fuzzy Logic Controllers for Parallel Delta Robot,” in Proc. IEEE Conf. on Human System Interaction, Yokohama, Japan, 2011.

Abstract: As a consequence of recent theoretical advancements in Type-2 (T2) fuzzy logic, applications of T2 Fuzzy Logic Controllers (FLCs) are becoming increasingly popular in various engineering areas. Nevertheless, the qualitative comparison of Type-1 (T1) and T2 FLCs and the assessment of the potential of T2 fuzzy logic can still be considered open questions. Despite this fact, researchers commonly claim superiority of T2 FLC in uncertain conditions based on a very limited exploration of the design parameter space. This manuscript provides a systematic analysis of the uncertainty resiliency of T2 FLC used for position control of parallel delta robot. In order to allow for objective comparison among different T1 and T2 FLCs, the controllers were constructed using a partially-dependent approach. Here, the T2 FLC is created based on an initially optimized T1 FLC. In this, manner the constrained design space allows for its full systematic exploration and analysis. The performance of each controller was evaluated on the real parallel delta robot under various levels of dynamic uncertainty. The experimental results support the theoretical claims about the superiority of T2 FLC. However, it was also demonstrated that there is a clear upper bound on the amount of “type-2 fuzziness” in the controller design, which can result in performance improvement. Exceeding such upper bound leads to performance deterioration.

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2010

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T. Vollmer, T. Soule, M. Manic, "A Distance Measure Comparison to Improve Crowding in Multi-Modal Optimization Problems, " in IEEE ISRCS’10, the 3rd IEEE Symposium on Resilience Control Systems, Idaho Falls, Idaho, Aug. 10-12, 2010.

Abstract: Solving multi-modal optimization problems are of interest to researchers solving real world problems in areas such as control systems and power engineering tasks. Extensions of simple Genetic Algorithms, particularly types of crowding, have been developed to help solve these types of problems. This paper examines the performance of two distance measures, Mahalanobis and Euclidean, exercised in the processing of two different crowding type implementations against five minimization functions. Within the context of the experiments, empirical evidence shows that the statistical based Mahalanobis distance measure when used in Deterministic Crowding produces equivalent results to a Euclidean measure. In the case of Restricted Tournament selection, use of Mahalanobis found on average 40% more of the global optima, maintained a 35% higher peak count and produced an average final best fitness value that is 3 times better.

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O. Linda, M. Manic, "Comparative Analysis of Type-1 and Type-2 Fuzzy Control in Context of Learning Behaviors for Mobile Robotics", 2010 IECON, Phoenix, Arizona, 2010.

Abstract: Dynamic uncertainties, manifested as input noise or variable environment conditions, are an inherent part of most real world control applications. Type-2 Fuzzy Logic Systems (T2 FLS) proved to be able to cope with such uncertainty and reduce its negative effects. In this paper, the advantages of T2 FLS are demonstrated in the context of learning behaviors for mobile robotics. First, a T1 FLS is optimized using the Particle Swarm Optimization algorithm to mimic a wall-following behavior performed by an operator. Next, two interval T2 FLSs are constructed by blurring the fuzzy sets of the T1 FLS. The T2 controllers use T1 and T2 output, respectively. The performance of the fuzzy controllers is compared using sonar-equipped mobile robot in both noise-free and noisy environments. The quantified error measures clearly demonstrated the improvements provided by the T2 FLS. The fully T2 controller featured smaller performance deterioration, lower overshooting and overall 30% error reduction when applied to noisy environments.

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O. Linda, M. Manic, "Importance Sampling Based Defuzzification for General Type-2 Fuzzy Sets", 2010 IEEE World Congress On Computational Intelligence, WCCI 2010 - FUZZ-IEEE 2010, July 18-23, Barcelona Spain.

Abstract: General type-2 fuzzy logic systems (T2 FLS) constitute a powerful tool for coping with ubiquitous uncertainty in many engineering applications. However, the immense computational complexity associated with defuzzification of general T2 fuzzy sets still remains an unresolved issue and prohibits its practical use. This paper proposes a novel importance sampling based defuzzification method for general T2 FLS. Here, a subset from the domain of all embedded fuzzy sets is randomly sampled using a specific probability distribution function. The algorithm is compared with the previously published uniform sampling defuzzification method. Experimental results demonstrate that importance sampling substantially reduces the variance of the sampling defuzzification method. Comparison of T2FLS output surfaces showed that smoother and more stable response can be achieved with the proposed importance sampling based defuzzification method.

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J. L. Wright, M. Manic, "Neural Network Architecture Selection Analysis With Application to Cryptography Location", WCCI 2010 - IJCNN 2010, July 18-23, Barcelona Spain.

Abstract: When training a neural network it is tempting to experiment with architectures until a low total error is achieved. The danger in doing so is the creation of a network that loses generality by over-learning the training data; lower total error does not necessarily translate into a low total error in validation. The resulting network may keenly detect the samples used to train it, without being able to detect subtle variations in new data. In this paper, a method is presented for choosing the best neural network architecture for a given data set based on observation of its accuracy, precision, and mean square error. The method, based on [1], relies on k-fold cross validation to evaluate each network architecture k times to improve the reliability of the choice of the optimal architecture. The need for four separate divisions of the data set is demonstrated (testing, training, and validation, as normal, and an comparison set). Instead of measuring simply the total error the resulting discrete measures of accuracy, precision, false positive, and false negative are used. This method is then applied to the problem of locating cryptographic algorithms in compiled object code for two different CPU architectures to demonstrate the suitability of the method.

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K. McCarty, M. Manic, S. Cherry, M. McQueen, "A Temporal-Spatial Data Fusion Architecture for Monitoring Complex Systems", HSI'10, the 3rd International Conference on Human System Interaction, Rzeszow, Poland, May 13-15, 2010, IEEE Catalog #: CFP1021D-CDR, ISBN: 978-1-4244-7561-2

Abstract: Non-homogenous systems arise from the need to incorporate a variety of disparate systems into a cohesive functioning whole and may comprise many crucial elements of an industrialized, modern society. As a result they must be constantly monitored to ensure efficient functioning and avoid expensive breakdowns. In particular, inter-connected computer-based systems must increasingly be aware of cyber and physical threats that are dynamic and evolutionary in nature. However, difficulties arise in trying to ascertain threats and problems among the diverse sources of information generated by these systems. Finally, there is the question of how best to present this data to a human operator. Human systems require not just analysis, but presentation which encourages timely, proactive or corrective decisions. This paper presents a software architecture to solve these problems based upon data fusion using temporal-spatial relationships. As phase one of a three phase project, a prototype implementation of this architecture demonstrates application of this technique for a cohesive system. Test results showed the system capable of real-time fusion of physical, cyber and process data elements as well as analysis, display and interpretation of threats.

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J. L. Wright, M. Manic, "The Analysis of Dimensionality Reduction Techniques in Cryptographic Object Code Classification", HSI'10, the 3rd International Conference on Human System Interaction, Rzeszow, Poland, May 13-15, 2010

Abstract: This paper compares the application of three different dimension reduction techniques to the problem of classifying functions in object code form as being cryptographic in nature or not. A simple classifier is used to compare dimensionality reduction via sorted covariance, principal component analysis, and correlation-based feature subset selection. The analysis concentrates on the classification accuracy as the number of dimensions is increased. It is demonstrated that when discarding 90% of the measured dimensions, accuracy only suffers by 1% for this problem. By discarding dimensions, computational intelligence techniques can be applied with a drastic reduction in algorithmic complexity. The primary focus is on Intel IA32 instruction set, but analysis shows consistent results on the Sun SPARC instruction set.

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2009

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O. Linda, T. Vollmer, M. Manic,“Neural Network Based Intrusion Detection System for Critical Infrastructures,” IJCNN 2009, Int. Joint INNS-IEEE Conf. on Neural Networks, Atlanta, Georgia, June 14-19, 2009.

Abstract: Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

Paper: PDF Project

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O. Linda, M. Manic, “GNG-SVM Framework – Classifying Large Datasets with Support Vector Machines Using Growing Neural Gas,” IJCNN 2009, Int. Joint INNS-IEEE Conf. on Neural Networks, Atlanta, Georgia, June 14-19, 2009.

Abstract: Support Vector Machines (SVMs) represent a well known technique for data classification. However, the memory requirements for the training process make the SVMs unsuitable for classifying large datasets. Examples of existing approaches to this problem are sampling of the input datasets or clustering of similar inputs. On the other hand, the Growing Neural Gas algorithm (GNG) is a robust tool for cluster analysis, capable of learning the topology of the data. It overcomes most of the common issues of clustering techniques such as predefined number of clusters or beforehand specified cluster radius. This paper presents a solution to the problem of classifying large datasets via learning of the data topology. The described algorithm combines the GNG algorithm with the SVM solver into a specific algorithm for classification of large datasets – the GNG-SVM framework. The input dataset is first preprocessed with the GNG algorithm. A new reduced training dataset is created from the extracted topological knowledge. Because the size of the dataset is significantly reduced, the training process of the SVM solver becomes substantially less memory demanding. The performance of the proposed GNG-SVM framework is tested on both synthetic and standard benchmark real world datasets.

Paper: PDF Project

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O. Linda, T. Vollmer, M. Manic, “SVM-inspired Dynamic Safe Navigation using Convex Hull Construction,” ICIEA 2009, 4th IEEE Conference on Industrial Electronics and Applications, Xi'an, China, May 25-27, 2009.

Abstract: The navigation of mobile robots or unmanned autonomous vehicles (UAVs) in an environment full of obstacles has a significant impact on their safety. If the robot maneuvers too close to an obstacle, it increases the probability of an accident. Preventing this is crucial in dynamic environments, where the obstacles, such as other UAVs, are moving. This kind of safe navigation is needed in any autonomous movement application but it is of a vital importance in applications such as automated transportation of nuclear or chemical waste. This paper presents the Maximum Margin Search using a Convex Hull construction (MMS-CH), an algorithm for a fast construction of a maximum margin between sets of obstacles and its maintenance as the input data are dynamically altered. This calculation of the safest path is inspired by the Support Vector Machines (SVM). It utilizes the convex hull construction to preprocess the input data and uses the boundaries of the hulls to search for the optimal margin. The MMS-CH algorithm takes advantage of the elementary geometrical properties of the 2-dimensional Euclidean space resulting in 1) significant reduction of the problem complexity by eliminating irrelevant data; 2) computationally less expensive approach to maximum margin calculation than standard SVM-based techniques; and 3) inexpensive recomputation of the solution suitable for real time dynamic applications.

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T. Vollmer, M. Manic, “Computational Intelligence Based Prognostic Automotive System Model,” ICIEA 2009, 4th IEEE Conference on Industrial Electronics and Applications, Xi'an, China, May 25-27, 2009.

Abstract: In an ideal case physically oriented vehicle models can reduce the required practical knowledge of a vehicle designer. These types of models are effective cost reducing tools used in industrial development cycles. There are many variables that can be used as input both internal and external to model automobile performance. The focus of this paper is on those external variable factors such as environment conditions that are not controllable by a human but are instantaneously measurable and affect performance. This paper presents CI-PASM, A Computational Intelligence Based Prognostic \ Automotive System Model. Initial feature reduction was accomplished by a human expert. Principal Component Analysis was performed to further reduce the input set. Using expert chosen features, the CI-PASM algorithm produced results having an error at worst in the hundredths of a second. These output results were compared against a support vector machine implementation and were shown to be superior. The CI-PASM mean error was half that of the support vector machine error. Results from using PCA attributes and a support vector machine indicated that these are relevant alternative methods given different requirements..

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K. Derr, M. Manic, "Multi-Robot Multi-Target Particle Swarm Optimization Search in Noisy Wireless Environments", HSI 2009, 2nd IEEE Conference on Human System Interaction, Catania, Italy, 21-23 May 2009.

Abstract: Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown environment with the goal of finding a target(s) at an unknown location(s) using multiple small mobile robots.
This work demonstrates the use of a distributed PSO algorithm with a novel adaptive RSS weighting factor to guide robots for locating target(s) in high risk environments. The approach was developed and analyzed on multiple robot single and multiple target search. The approach was further enhanced by the multi-robot-multi-target search in noisy environments. The experimental results demonstrated how the availability of radio frequency signal can significantly affect robot search time to reach a target.

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T. Vollmer, M. Manic, “Human Interface for Cyber Security Anomaly Detection Systems,” HSI 2009, 2nd IEEE Conference on Human System Interaction, Catania, Italy, 21-23 May 2009.

Abstract: Low-level network traffic information is often times beyond the understanding of common system operators (byte counts, port numbers, packet data, etc.). However, anomaly based Intrusion Detection Systems (IDS) often provide such low-level, difficult to comprehend information. This paper details a Human Interface for Security Awareness (HISA) algorithm for interpreting cyber incident information to human operators from anomaly based intrusion detections systems. A similarity algorithm mapping anomaly results to signature based intrusion system rules is developed. Categorizations of attacks found in rules created for the Snort intrusion system were used as a basis of information to present to the user. A proof of concept system was developed using Perl native functions and custom modules. Testing with generated ICMP packets resulted in an identification accuracy of 60% proving the efficacy of the presented HISA algorithm.

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O. Linda, M. Manic, “Fuzzy manual control of multi-robot system with built-in swarm behavior,” HSI 2009,2nd IEEE Conference on Human System Interaction, Catania, Italy, 21-23 May 2009.

Abstract: Swarm robotics is a decentralized control architecture, where global behavior emerges as a result of local interactions between neighboring robots. The deficiency of the swarm behavior model is the stochastic nature of movement patterns, which reduces its applicability, when precise maneuvering is needed. This paper alleviates this problem by introducing fuzzy manual control of a multi-robot system utilizing the swarm behavior model. The built-in swarm behavior controls low level tasks such as formation keeping and obstacle avoidance. Fuzzy controller works as an adaptive intelligent mechanism for tuning the manual control signal received by the robots. The main advantages of the presented algorithm are: 1) deliberating the operator from low level maneuvering tasks; 2) single operator control of multi-robot group; 3) robustness, flexibility and scalability. The presented architecture was implemented and tested in a simulation environment. The introduced system can significantly improve the performance of search and rescue operations as well as exploration of dangerous environments.

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K. McCarty, M. Manic, “Adaptive Behavioral Control of Collaborative Robots in Hazardous Environments”, HSI 2009, 2nd IEEE Conference on Human System Interaction, Catania, Italy, 21-23 May 2009.

Abstract:Terrain exploration carries with it significant hazards. Robots attempting to map a piece of unknown terrain must be able to make decisions and react appropriately to dynamic and potentially hostile conditions. However, because of constraints on size and cost, robots may have limited ability to store and process necessary information. In addition, knowledge discovered by others may be difficult to share. This paper proposes a system using a powerful master controller, operating from a safe environment, directing the movements of numerous robots exploring a piece of terrain. The master controller processes the information from the robots, updates the decision process and distributes these updates back to the robots. This process allows for a cooperative, effective search environment while also maintaining a small processing footprint. It also allows the robot to employ adaptive, subsumptive behavioral modification as new information is made available. A test simulation of a hazardous environment demonstrates that even robots with little intrinsic intelligence can learn complex behaviors in order to reach their goal.

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C. Szep, S. D. Stan, V. Csibi, M. Manic, R. Balan, “Kinematics, Workspace, Design and Accuracy Analysis of RPRPR Medical Parallel Robot”, HSI 2009, 2nd IEEE Conference on Human System Interaction, Catania, Italy, 21-23 May 2009.

Abstract: In recent years, parallel robots find many applications in human-systems interaction, medical robots, rehabilitation, exoskeletons, to name a few. These applications are characterized by many imperatives, with robust precision and dynamic workspace computation as the two ultimate ones. Practical methods of kinematic’s calibration make use of the linear differential error of the kinematics’ model. This model is based on the Jacobian of the direct kinematics’ model with respect to parameters of this model. The definition of the robot accuracy is usually related to robot positioning, so that the accuracy is defined as a measure of robot ability to attain a required position with respect to a fixed absolute reference coordinate frame. Such a definition is easily extended to trajectory tracking. Then, accuracy can be defined as a measure of robot ability to track the prescribed trajectory with respect to the absolute coordinate frame.

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D. Verdes, S. D. Stan, M. Manic, R. Balan, V. Maties, “Kinematics analysis, Workspace, Design and Control of 3-RPS and TRIGLIDE medical parallel robots”, HSI 2009, 2nd IEEE Conference on Human System Interaction, Catania, Italy, 21-23 May 2009.

Abstract:Parallel robots find many applications in human-systems interaction, medical robots, rehabilitation, exoskeletons, to name a few. These applications are characterized by many imperatives, with robust precision and dynamic workspace computation as the two ultimate ones. This paper presents kinematic analysis, workspace, design and control to 3 degrees of freedom (DOF) parallel robots. Parallel robots have received considerable attention from both researchers and manufacturers over the past years because of their potential for high stiffness, low inertia and high speed capability. Therefore, the 3 DOF translation parallel robots provide high potential and good prospects for their practical implementation in human-systems interaction.

Paper: PDF

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C. I. Nichol, M. Manic, “Video Game Device Haptic Interface for Robotic Arc Welding”, HSI 2009, 2nd IEEE Conference on Human System Interaction, Catania, Italy, 21-23 May 2009.

Abstract:Recent advances in technology for video games have made a broad array of haptic feedback devices available at low cost. This paper presents a bi-manual haptic system to enable an operator to weld remotely using a commercially available haptic feedback video game device for the user interface. The system showed good performance in initial tests, demonstrating the utility of low cost input devices for remote haptic operations.

Paper: PDF

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A. Ridluan, O. Linda, M. Manic, A. Tokuhiro, “Artificial Neural Network to Support Thermohydraulic Design Optimization for Advanced Nuclear Heat Removal System” ICAAP'09, Tokyo, Japan, May 10th-14th, 2009.

Abstract:The U.S. Department of Energy (DOE) is leading a number of initiatives, including one known as the Next Generation Nuclear Plant (NGNP) project. One of the NGNP nuclear system concepts is the Very High Temperature (gascooled) Reactor (VHTR) that may be coupled to a hydrogen generating plant to support the anticipated hydrogen economy. For the NGNP, an efficient power conversion system using an Intermediate Heat Exchanger (IHX) is key to electricity and/or process heat generation (hydrogen production). Ideally, it’s desirable for the IHX to be compact and thermally efficient. However, traditional heat exchanger design practices do not assure that the design parameters are optimized. As part of NGNP heat exchanger design and optimization project, this research paper thus proposes developing a recurrenttype Artificial Neural Network (ANN), the Hopfield Network (HN) model, in whichthe activation function is modified, as a design optimization approach to support a NGNP thermal system candidate, the Printed Circuit Heat Exchanger (PCHE). Four quadratic functions, available in literature, were used to test the presented methodology. The results computed by an artificially intelligent approach were compared to another approach, the Genetic Algorithm (GA). The results show that the HN results are close to GA in optimization of multi-variable second-order equations.

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McCarty, K. , Manic, M., Goodwin, P., Piasecki, M., “Submission and Querying Tools for a Hydrologic Information Systems Database”, 8th International Conference on Hydroinformatics, Concepción, Chile, Jan. 12-16, 2009.

Abstract: The recent establishment of the WATERs information network in the US, has prompted a number of entities to joint this network beyond the initial selected set of test bed nodes. The state of Idaho is supporting the creation of a IdahoWaters node through its EPSCoR program with them aim of not only providing a single access point for Idaho water information but also to make these data holdings accessible nationwide through participation in the network. Given the many individuals institutions that will participate in this effort, means of data submission are an extremely important aspect when developing an information node of this type. This paper demonstrates an architecture for the submission as well as querying and presentation of large datasets of hydrologic data via the internet. Discussed are the necessary hardware and software configurations used to create databases for staging, permanent storage, online analytical processing and distribution. In addition software and tools for decision support as well as automation for data extraction, transformation and loading are presented. Finally application of this architecture is shown for a wide-scale, distributed, hydrologic-based, collaborative information network.

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S. D. Stan, M. Manic, R. Balan, V. Maties, Genetic algorithms for workspace optimization of planar medical parallel robot, IEEE International Conference on Emerging Trends in Computing, ICETIC 2009, Virudhanagara, Tamil Nadu, India, Jan. 8-10, 2009.

Abstract: The aim of this paper is to demonstrate the usefulness of the Genetic Algorithms (GA) optimization approach to optimize the 2-DOF planar medical parallel robot PR). Variations of the kinematic performances index do not remain constant throughout the robot’s workspace. Parallel robots potentials are fully exploited only when their structure is optimally dimensioned from geometric point of view. In other words, their performances heavily depend on their
geometry. Thus, optimization of the geometric parameters or optimal dimensioning became an important issue in the process of parallel robots performance improvement. In this paper, motivated by the advantages of GA techniques, we apply them to the 2-DOF parallel robot optimization problem. Genetic algorithms are in general the most robust type of evolutionary algorithms. The obtained results have demonstrated the use of GA in previously described type optimization problems improve the quality of the optimization outcome, resulting in a better and more realistic support for the decision maker.

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Ridluan, A., Manic, M., Tokuhiro, A., "EBaLM-THP- Artificial Neural Network Thermo-Hydraulic Prediction Tool for an Advanced Nuclear Components", Elsevier, Int. Journal on Nuclear Engineering and Design, Volume 239, Issue 2, February 2009, Pages 308-319

Abstract: In lieu of the worldwide energy demand, economics and consensus concern regarding climate change, nuclear power – specifically near-term nuclear power plant designs are receiving increased engineering attention. However, as the nuclear industry is emerging from a lull in component modeling and analyses, optimization for example using ANN has received little research attention. This paper presents a neural network approach, EBaLM, based on a specific combination of two training algorithms, error-back propagation (EBP), and Levenberg–Marquardt (LM), applied to a problem of thermohydraulics predictions (THPs) of advanced nuclear heat exchangers (HXs). The suitability of the EBaLM-THP algorithm was tested on two different reference problems in thermohydraulic design analysis; that is, convective heat transfer of supercritical CO2 through a single tube, and convective heat transfer through a printed circuit heat exchanger (PCHE) using CO2. Further, comparison of EBaLM-THP and a polynomial fitting approachwas considered.Within the defined reference problems, the neural network approach generated good results in both cases, in spite of highly fluctuating trends in the dataset used. In fact, the neural network approach demonstrated cumulative measure of the error one to three orders of magnitude smaller than that produce via polynomial fitting of 10th order.

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2008

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S. D. Stan, M. Manic, M. Maties, R. Balan, “Kinematics Analysis, Design, and Control of an Isoglide3 Parallel Robot (IG3PR)”, IECON08, The 34th Annual Conference of the IEEE Industrial Electronics Society, Orlando, Florida, pp.2636-2641, Nov. 10-13, 2008.

Abstract: The paper presents a novel structure of the Isoglide3 Parallel Robot (IG3PR), as an effective robotic device with three degrees of freedom manipulation. The IG3PR manipulator offers the characteristics, advantageous relative to the other parallel manipulators (light weight construction), while on the other hand alleviates some of the traditional weaknesses of parallel manipulators, (extensive use of spherical joints and coupling of the platform orientation and position). The presented IG3PR robot employs only revolute (rotary) and prismatic (sliding) joints to achieve the translational motion of the moving platform. The pivotal advantages of the presented parallel manipulator are the following: all of the actuators can be attached directly to the base; closed-form solutions are available for the forward and inverse kinematics; and the moving platform maintains the same orientation throughout the entire workspace. In addition to these comparative improvements, the paper presents an innovative user interface for high-level control of the Isoglide3 parallel robot. The novel IG3PR was verified and tested, and results in MATLAB, Simulink, and SimMechanics were presented.

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L. Golden, M. Manic, “Optical Fuzzy Logic for Low-Power Satellite Controller”, IECON08, The 34th Annual Conference of the IEEE Industrial Electronics Society, Orlando, Florida, pp.2647-2654, Nov. 10-13, 2008.

Abstract: Power consumption is often a major advantage of optics. When access to power is limited, such as for a satellite in geosynchronous orbit, power is often not only important, but critically important. Satellites rely on power for motion control, controlling yaw, pitch, and roll. An optical Fuzzy controller would overcome the problem of limited power. This paper explores the possibility of optical implementation of fuzzy logic for low-power optical fuzzy controllers. Recent advances in optical logic have suggested ways to overcome the problems that have plagued that field for over 40 years. In this paper the authors overviews recent advances in the optical implementation of Boolean logic and explores whether these or similar technologies might feasibly be applied to optical implementation of fuzzy logic. Specifically, the authors examines whether fuzzy logic might be productively implemented in an interferometric network in which weighting is accomplished by optical phase shifting of mutually coherent beams of light. This paper produces a optical fuzzy OR and lays the foundation for other fuzzy operators based on an interferometric network.

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E.J. William , B.K. Johnson, M. Manic, “Implementing an Intelligent Error Back Propagation (EBP) Relay in PSCADTM/EMTDC 4.2.1”, NAPS'08, North American Power Symposium 2008, University of Calgary, Calgary, Canada, pp. 1-6, Sept 28th to 30th, 2008.

Abstract: Power Systems Computer Aided Design (PSCADTM) is a graphical user interface for the Electromagnetic Transient Direct Current (EMTDC) type program. PSCADTM/EMTDC is used in this paper to simulate the injection of static single-line-to-ground (SLG) faults located at 120%, 100%, and 80% of the cable length on the ship, and protect the electrical system using the error back propagation (EBP) algorithm-based relay. Sequential and combinational digital logic is used to design and implement the EBP Relay in PSCADTM/EMTDC.

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D. Dudenhoeffer, M. Manic, "Fuzzy simulation for infrastructure effects uncertainty analysis", 20th European Modeling and Simulation Symposium (Simulation in Industry), EMSS08, Campora San Giovanni, Amantea (CS), Italy, Sep. 17-19, 2008.

Abstract: In this paper we propose a method for conducting infrastructure effects-based modeling in uncertain environments. Critical infrastructure is composed of intertwining physical and social networks. Events in one network often cascade to other networks creating a domino effect. This cascading effect is not always well understood due to uncertainties in the multiple levels of effect. To account for these uncertainties, we present a method using fuzzy finite state machines (FFSM).

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K. Derr, M. Manic, “DSTiPE Algorithm for Fuzzy Spatio-Temporal Risk Calculation in Wireless Environments”, 13th IEEE Conference on Emerging Technologies and Factory Automation, ETFA’08, Hamburg, Germany, Sep. 9-12, 2008.

Abstract: Time and location data play a very significant role in a variety of factory automation scenarios, such as automated vehicles and robots, their navigation, tracking, and monitoring, to services of optimization and security. Pervasive wireless capabilities combined with time and location information are enabling new applications in areas such as transportation systems, health care, elder care, military, emergency response, critical infrastructure, and law enforcement. A wireless object in proximity to some area for a duration of time may pose a risk hazard to the environment. This paper presents a novel fuzzy based spatio-temporal risk calculation DSTiPE method that a wireless object may present to the environment. The presented Matlab based application for cluster extraction is verified on a diagonal vehicle movement example.

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K. McCarty, M. Manic, "Descending Deviation Optimizaiton Techniques For Scheduling Problems," 13th IEEE Conf. on Emerging Technologies and Factory Automation - ETFA, Hamburg, Germany, Sep. 2008.

Abstract: In factory automation, production line scheduling entails a number of competing issues. Finding optimal configurations often requires use of local search techniques. Local search looks for a goal state employing heuristics and random local “probes” in order to move from state to state. All local search techniques, however, suffer from problems with local maxima, i.e. have the potential of getting “stuck” in a suboptimal state. While careful introduction of randomizations is certainly a recognized technique, it can also lead the algorithm even more astray. This paper describes a heuristic technique called Descending Deviation Optimizations (DDO) in which a gradually lowering-- randomization ceiling allows a local search technique to “bounce” randomly without going too far astray. An example applying the DDO to a local search technique and achieving significant improvement is shown.

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K. Derr, M. Manic, “Wireless Indoor Location Estimation Based on Neural Network RSS Signature Recognition (LENSR)”, ICIEA 2008, 3rd IEEE Conference on Industrial Electronics and Applications, Singapore, June 3-5, 2008.

Abstract: Location Based Services (LBS), context aware applications, and people and object tracking depend on the ability to locate mobile devices, also known as localization, in the wireless landscape. Localization enables a diverse set of applications that include, but are not limited to, vehicle guidance in an industrial environment, security monitoring, self-guided tours, personalized communications services, resource tracking, mobile commerce services, guiding emergency workers during fire emergencies, habitat monitoring, environmental surveillance, and receiving alerts. This paper presents a new neural network approach (LENSR) based on a competitive topological Counter Propagation Network CPN) with k-nearest neighborhood vector mapping, for indoor location estimation based on received signal strength. The advantage of this approach is both speed and accuracy. The tested accuracy of the algorithm was 90.6% within 1 meter and 96.4% within 1.5 meters. Several approaches for location estimation using WLAN technology were reviewed for comparison of results.

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K. McCarty, M. Manic, "Line-of-Sight Tracking Based Upon Modern Heuristic Approach," 3rd IEEE Conf. on Idustrial Electronics and Application - ICIEA, Singapore, June, 2008.

Abstract: Any autonomous vehicle must be able to successfully navigate a wide variety of situations and terrain conditions. As a result, proposed solutions usually involve a sophisticated and expensive implementation of both hardware and software. In many situations, however, truly autonomous operation may not be necessary or practical. Instead, equipping and training a vehicle to automatically follow a human-controlled lead vehicle is a viable alternative. While still autonomous, the vehicle relies upon its leader to handle the complex decisions with regards to course and speed. This paper presents a simple and elegant configuration, called FLoST for Fuzzy Line of Sight Tracking, based on inexpensive line-of-sight devices controlled by a heuristic to determine direction and speed of a follower. Unlike the alternative approach where the follower needs to undergo a complex training process, the follower using the approach presented in this paper primarily relies upon a human leader to provide direction, allowing for a much simpler and less expensive vehicle implementation while still being able to match or exceed the effectiveness of the autonomous design under similar conditions. Finally, three boundary cases of lead vehicle maneuvers (circle, spiral and weave) are presented to show the efficacy of this approach.

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S. D. Stan, M. Manic, M. Maties, R. Balan, “Evolutionary Approach to Optimal Design of 3 DOF Translation Exoskeleton and Medical Parallel Robots”, HSI 2008, IEEE Conference on Human System Interaction, Krakow, Poland, May 25-27, 2008.

Abstract: Parallel robots find many applications in human-systems interaction, medical robots, rehabilitation, exoskeletons, to name a few. These applications are characterized by many imperatives, with robust precision and dynamic workspace computation as the two ultimate ones. This paper presents a multi-objective optimum design procedure to 3 degrees of freedom (DOF) parallel robots with regards to four optimality criteria: workspace boundary, transmission quality index, stiffness. A kinematic optimization was performed to maximize the workspace of the parallel robot. In order to perform an optimal design of 3 DOF parallel robots, an objective function was developed first, and then Genetic Algorithms applied in order to optimize the objective function. The experimental results demonstrate the advantages of the presented optimization procedure in design of 3 DOF parallel robots, specifically TRIGLIDE and DELTA robots. These advantages are reflected in a presented framework for robust, precise, and dynamically calculated workspace boundaries. Therefore, the performances of the 3 DOF translation parallel robots provide high potential and good prospects for their practical implementation in human-systems interaction.

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K. McCarty, M. Manic, "Contextual Fuzzy Type-2 Hierarchies for Decision Trees (CoFuH-DT) - An Accelerated Data Mining Technique," IEEE Conf. on Human System Interaction - HSI, Polan, May, 2008.

Abstract: Advanced data mining techniques (ADMT) are very powerful tools for classification, understanding and prediction of object behaviors, providing descriptive relationships between objects such as a customer and a product they intend to buy. ADMT typically consists of classifiers and association techniques, among them, Decision Trees (DT). However, some important relationships are not readily apparent in a traditional decision tree. In addition, decision trees can grow quite large as the number of dimensions and their corresponding elements increase, requiring significant resources for processing. In either case, rules governing these relationships can be difficult to construct. This paper presents CoFuH-DT, a new algorithm for capturing intrinsic relationships among the nodes of DT, based upon a proposed concept of type-2 fuzzy “contexts”. This algorithm modifies a decision tree, first by generating type-1 fuzzy extensions of the underlying DT criteria or “conditions”; combining further those extensions into new abstractions overlaid with type-2 contexts. The resulting fuzzy type-2 classification is then able to capture intrinsic relationships that are otherwise non-intuitive. In addition, performing fuzzy setbased operations simplifies the decision tree much faster than traditional search techniques in order to aid in rule construction. Testing presented on a virtual store example demonstrates savings of multiple orders of magnitude in terms of nodes and applicable conditions resulting in 1) reduced complexity of decision tree, 2) ability to data mine difficult to detect interrelationships, 3) substantial acceleration of decision tree search, making it applicable for 4) real-time data mining of new knowledge.

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2007

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N. Guillermo, M. Manic, “NFuSA – Neuro-Fuzzy Algorithm for Sparing in RAID Systems”, The 33rd Annual Conference of the IEEE Industrial Electronics Society IECON07, Taipei, Taiwan, Nov. 2007.

Abstract: Sparing, the process of rebuilding data in case of disk failure, has been a target of research since early 1990‘s [1]. The problem that these specific hardware/software control systems typically face in sparing is the tradeoff between serving requests – user’s versus internal [2]. If the algorithm favors user requests, in the presence of heavy workloads, the internal data recovery gets preempted resulting in risky delay of the data sparing. On the other hand, favoring internal data recovery requests over the user requests can result in high response times per transaction that are unacceptable for the users of the RAID system. Intelligent, neuro-fuzzy controllers (NFCs) offer a way to improve the control process and enhance the ability of a system to achieve faster system response, while serving the internal requests at the same time. This paper presents the neuro-fuzzy enhancement of the traditional data recovery of a RAID system modeled with a Queue System with Vacations (QSV) [3]. Experimental results demonstrated better balancing between an acceptable response time for the user requests and the time for the data to be redundant again, resulting in both higher user satisfaction and better system reliability.

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E.J. William , M. Manic, B.K. Johnson, “ANN Relays Used to Determine Fault Locations on Shipboard Electrical Distribution Systems”, NAPS'07, North American Power Symposium 2007, New Mexico State University, Las Cruces, September 30 - October 2, 2007.

Abstract: This paper observes an Artificial Neural Network Algorithm (ANN) distance relay solution. It traces the location of the fault on a shipboard power system. The United States Naval Surface Warfare Center has been exploring methods for increasing the reliability for shipboard electrical distribution systems. The electrical distribution system is protected when faults are located and isolated as quickly as possible. The goal is to increase the availability of shipboard electrical distribution systems by locating and isolating faults. Thus, introducing an ANN relay to locate the fault occurrence on the electrical distribution system. Maintaining the integrity of a shipboard power system in the event of multiple simultaneous electrical faults is necessary to achieve continuity of service to all loads under adverse battle conditions.

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K. Derr, M. Manic, “Intelligent Control in Automation Based on Wireless Traffic Analysis”, 12th IEEE Conference on Emerging Technologies and Factory Automation, ETFA’07, Patras, Greece, Sep. 25-28, 2007.

Abstract: Wireless technology is a central component of many factory automation infrastructures in both the commercial and government sectors, providing connectivity among various components in industrial realms (distributed sensors, machines, mobile process controllers). However wireless technologies provide more threats to computer security than wired environments. The advantageous features of Bluetooth technology resulted in Bluetooth units shipments climbing to five million per week at the end of 2005 [1, 2]. This is why the real-time interpretation and understanding of Bluetooth traffic behavior is critical in both maintaining the integrity of computer systems and increasing the efficient use of this technology in control type applications. Although neuro-fuzzy approaches have been applied to wireless 802.11 behavior analysis in the past, a significantly different Bluetooth protocol framework has not been extensively explored using this technology. This paper presents a new neurofuzzy traffic analysis algorithm of this still new territory of Bluetooth traffic. Further enhancements of this algorithm are presented along with the comparison against the traditional, numerical approach. Through test examples, interesting Bluetooth traffic behavior characteristics were captured, and the comparative elegance of this computationally inexpensive approach was demonstrated. This analysis can be used to provide directions for future development and use of this prevailing technology in various control type applications, as well as making the use of it more secure.

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N. Guillermo, M. Manic, “Fuzzy Control of Sparing in Disk Arrays”, 12th IEEE Conference on Emerging Technologies and Factory Automation, ETFA’07, , Patras, Greece, Sep. 25-28, 2007.

Abstract: The redundancy regeneration (sparing or rebuild) algorithms in disk arrays face the problem of balancing between the data recovery activity within the array and the user workload acting upon the array at the same time [1]. If the algorithm favors the user workload so the user requests can always preempt the internal data recovery, then the data sparing can stall in the presence of a sustained workload. But on the contrary, if the data recovery is favored over the user requests, the latency of the user requests can be so high to reach unacceptable levels for the data transactions. Using computationally intelligent techniques, like fuzzy logic, better algorithms to balance the level of user requests and the internal data recovery can be achieved. The disk array and data recovery process are modeled using the queue systems with vacations (QSV) [2]. A fuzzy algorithm to control the sparing is presented in this paper. The results indicate that by using fuzzy logic, a better balancing is achieved between the need to have an acceptable response time for the user requests and the data recovered as soon as possible.

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N. Guillermo, M. Manic, “Predictive E-Mail Server Performability Analysis Based on Fuzzy Arithmetic”, 20th International Joint INNS-IEEE Conference on Neural Networks, IJCNN 2007, Orlando, Florida, Aug 12-17, 2007.

Abstract: The performability of disk arrays systems has been studied before. However, in the case of imprecise data, a fuzzy model can be the base for the performability analysis. This paper presents a performability analysis of an MSExchange-like e-mail server. The analysis is based on a Markov Reward model. The performability analysis is accomplished through the use of fuzzy arithmetic. Unlike traditional Markov Chains, Fuzzy Markov Chains can successfully handle uncertain, imprecise probabilities. In cases where the failure rates, repair rates, or the workload parameters are uncertain, Markov Chains enhanced with fuzzy arithmetic provide means for comprehensive predictive performability analysis of a system. This performability analysis provides a valuable guideline regarding required resources such as the number of mailboxes, and therefore, the number of users the mail server can support with regards to the reliability and performance of the disk array used by the mail server. The fuzzy arithmetic helps in better visualization and estimation of the range of number of users the mail server is capable of servicing over long periods of time.

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D. Dudenhoeffer, P. Permann, S. Woolsey, R. Timpany, T. McDermott, C. Miller, and M. Manic, “Interdependency Modeling and Emergency Response”, 2007 Summer Computer Simulation Conference, SCSC 2007, San Diego , California, July 15-18, 2007.

Abstract: In large-scale disaster events, infrastructure owners are faced with many challenges in deciding the allocation of resources for preparation and response actions. This decision process involves building situation awareness, evaluating course of action, and effecting response. This paper describes a modeling and simulation system called CIMS© that presents a visual environment for assessing the causal effects of events and actions in complex environments. Specifically, CIMS© provides a framework for evaluating cascading effects associated with infrastructure interdependencies, thus providing greater situational awareness to infrastructure owners and decision-makers. This paper first presents the area of interdependency analysis and then presents CIMS© as a network framework for simulating the interactions between multiple infrastructures. Also introduced is the integration of infrastructure simulation with a decision support systems.

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