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Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on

Issue 2 • Date March 2011

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Displaying Results 1 - 17 of 17
  • Table of contents

    Page(s): C1
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews publication information

    Page(s): C2
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  • Morphogenetic Robotics: An Emerging New Field in Developmental Robotics

    Page(s): 145 - 160
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1383 KB) |  | HTML iconHTML  

    Developmental robotics is also known as epigenetic robotics. We propose in this paper that there is one substantial difference between developmental robotics and epigenetic robotics, since epigenetic robotics concentrates primarily on modeling the development of cognitive elements of living systems in robotic systems, such as language, emotion, and social skills, while developmental robotics should also cover the modeling of neural and morphological development in single- and multirobot systems. With the recent rapid advances in evolutionary developmental biology and systems biology, increasing genetic and cellular principles underlying biological morphogenesis have been revealed. These principles are helpful not only in understanding biological development, but also in designing self-organizing, self-reconfigurable, and self-repairable engineered systems. In this paper, we propose morphogenetic robotics, an emerging new field in developmental robotics, is an important part of developmental robotics in addition to epigenetic robotics. By morphogenetic robotics, we mean a class of methodologies in robotics for designing self-organizing, self-reconfigurable, and self-repairable single- or multirobot systems, using genetic and cellular mechanisms governing biological morphogenesis. We categorize these methodologies into three areas, namely, morphogenetic swarm robotic systems, morphogenetic modular robots, and morphogenetic body and brain design for robots. Examples are provided for each of the three areas to illustrate the main ideas underlying the morphogenetic approaches to robotics. View full abstract»

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  • Optimizing Operator–Agent Interaction in Intelligent Adaptive Interface Design: A Conceptual Framework

    Page(s): 161 - 178
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (463 KB) |  | HTML iconHTML  

    Intelligent adaptive interfaces (IAIs) are emerging technologies that promise opportunities for enhancing performance in complex sociotechnical environments, such as multiple uninhabited aerial vehicle (UAV) control. However, a lack of established design guidelines for such advanced interfaces makes many designs costly and ineffective. In this paper, a generic conceptual framework for developing IAIs is proposed to guide interface design. The framework integrates a user-centered design approach with the concept of proactive use of adaptive intelligent agents (AIAs), aiming at maximizing overall system performance. Based on existing design approaches, identified challenges, and IAI design needs, the framework uses a multiple-agent hierarchical structure to allocate tasks between operators and agents for optimizing operator-agent interaction. These AIAs provide interface aids as a means of reducing operator workload, and increasing situation awareness and operational effectiveness. The framework and associated IAI models provide guidance to design a knowledge-based system, such as a UAV control station interface. View full abstract»

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  • A Case-Driven Ambient Intelligence System for Elderly in-Home Assistance Applications

    Page(s): 179 - 189
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (692 KB) |  | HTML iconHTML  

    Elderly in-home assistance (EHA) has traditionally been tackled by human caregivers to equip the elderly with homecare assistance in their daily living. The emerging ambience intelligence (AmI) technology suggests itself to be of great potential for EHA applications, owing to its effectiveness in building a context-aware environment that is sensitive and responsive to the presence of humans. This paper presents a case-driven AmI (C-AmI) system, aiming to sense, predict, reason, and act in response to the elderly activities of daily living (ADLs) at home. The C-AmI system architecture is developed by synthesizing various sensors, activity recognition, case-based reasoning, along with EHA-customized knowledge, within a coherent framework. An EHA information model is formulated through the activity recognition, case comprehension, and assistive action layers. The rough set theory is applied to model ADLs based on the sensor platform embedded in a smart home. Assistive actions are fulfilled with reference to a priori case solutions and implemented within the AmI system through human-object-environment interactions. Initial findings indicate the potential of C-AmI for enhancing context awareness of EHA applications. View full abstract»

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  • Time Series Clustering Via RPCL Network Ensemble With Different Representations

    Page(s): 190 - 199
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (400 KB) |  | HTML iconHTML  

    Time series clustering provides underpinning techniques for discovering the intrinsic structure and condensing/summarizing information conveyed in time series, which is demanded in various fields ranging from bioinformatics to video content understanding. In this paper, we present an unsupervised ensemble learning approach to time series clustering by combining rival-penalized competitive learning (RPCL) networks with different representations of time series. In our approach, the RPCL network ensemble is employed for clustering analyses based on different representations of time series whenever available, and an optimal selection function is applied to find out a final consensus partition from multiple partition candidates yielded by applying various consensus functions for the combination of competitive learning results. As a result, our approach first exploits its capability of the RPCL rule in clustering analysis of automatic model selection on individual representations and subsequently applies ensemble learning for the synergy of reconciling diverse partitions resulted from the use of different representations and augmenting RPCL networks in automatic model selection and overcoming its inherent limitation. Our approach has been evaluated on 16 benchmark time series data mining tasks with comparison to state-of-the-art time series clustering techniques. Simulation results demonstrate that our approach yields favorite results in clustering analysis of automatic model selection. View full abstract»

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  • Modeling Competition in the Telecommunications Market Based on Concepts of Population Biology

    Page(s): 200 - 210
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (620 KB) |  | HTML iconHTML  

    Based on concepts of ecology modeling and specifically on population biology, a methodology for describing a high-technology market's dynamics is developed and presented. The importance of the aforementioned methodology is its capability to estimate and forecast the degree of competition, market equilibrium, and market concentration, the latter expressed by corresponding market shares, in the high-technology environment. Evaluation of the presented methodology in the area of telecommunications led to accurate results, as compared to historical data, in a specific case study. Apart from a very good estimation of the market's behavior, this methodology presents a very good forecasting ability, which can provide valuable inputs for managerial and regulatory decisions and strategic planning, to the players of a high-technology market, described by high entry barriers. View full abstract»

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  • WASP: A System and Algorithms for Accurate Radio Localization Using Low-Cost Hardware

    Page(s): 211 - 222
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (505 KB) |  | HTML iconHTML  

    In this paper, we present a low-cost wireless sensor network platform, called wireless ad hoc system for positioning (WASP), that has been developed for high-accuracy localization and tracking. This platform uses the time of arrival (TOA) of beacon signals periodically transmitted by the nodes at known times for localization. The system was designed to have a unique tradeoff between hardware complexity and processing complexity to provide high accuracy at minimal cost in complex radio propagation environments. To enable the system to perform well in realistic environments, it was also necessary to develop novel extensions to existing algorithms for the measurement of TOA, localization, and tracking. In this paper, we describe the architecture, hardware, and algorithms of WASP and present results based on field trials conducted in different radio propagation environments. The results show that WASP achieves a ranging accuracy of 0.15 m outdoors and 0.5 m indoors when around 12 anchor nodes are used. These accuracies are achieved with operating range of up to 200 m outdoors and 30 m indoors. This compares favorably to other published results for systems operating in realistic environments. View full abstract»

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  • Hierarchical Fault Diagnosis and Health Monitoring in Satellites Formation Flight

    Page(s): 223 - 239
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (420 KB) |  | HTML iconHTML  

    Current spacecraft health monitoring and fault-diagnosis practices involve around-the-clock limit-checking and trend analysis on large amount of telemetry data. They do not scale well for future multiplatform space missions due the size of the telemetry data and an increasing need to make the long-duration missions cost-effective by limiting the operations team personnel. The need for efficient utilization of telemetry data achieved by employing machine learning and reasoning algorithms has been pointed out in the literature for enhancing diagnostic performance and assisting the less-experienced personnel in performing monitoring and diagnosis tasks. In this paper, we develop a systematic and transparent fault-diagnosis methodology within a hierarchical fault-diagnosis framework for a satellites formation flight. We present our proposed hierarchical decomposition framework through a novel Bayesian network, whose structure is developed from the knowledge of component health-state dependencies. We have developed a methodology for specifying the network parameters that utilizes both node fault-diagnosis performance data and domain experts' beliefs. Our proposed model development procedure reduces the demand for expert's time in eliciting probabilities significantly. Our proposed approach provides the ground personnel with an ability to perform diagnostic reasoning across a number of subsystems and components coherently. Due to the unavailability of real formation flight data, we demonstrate the effectiveness of our proposed methodology by using synthetic data of a leader-follower formation flight architecture. Although our proposed approach is developed from the satellite fault-diagnosis perspective, it is generic and is targeted toward other types of cooperative fleet vehicle diagnosis problems. View full abstract»

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  • Pareto-Optimal Design of Damping Controllers Using Modified Artificial Immune Algorithm

    Page(s): 240 - 250
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (870 KB) |  | HTML iconHTML  

    This paper presents two approaches for multiobjective simultaneous coordinated tuning of damping controllers, a modified artificial immune network (MAINet) algorithm and a multiobjective immune algorithm (MOIA). The weighted-sum approach is used to handle the multiobjective optimization problem in the MAINet, while the Pareto-optimization approach is used in the MOIA. To investigate the ability of the proposed algorithms in designing the damping controllers, one small and one large power systems are considered. Two power-system stabilizers (PSSs) are designed for the small power system, while one PSS for a generator and one supplementary controller for a static var compensator (SVC) are designed for the large power system. The simulation studies show that the controllers designed by MOIA perform better than those by MAINet in damping the power-system low-frequency oscillations. View full abstract»

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  • Normality Mining: Privacy Implications of Behavioral Profiles Drawn From GPS Enabled Mobile Phones

    Page(s): 251 - 261
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1833 KB) |  | HTML iconHTML  

    There is growing interest in the ways in which the location of a person can be utilized by new applications and services. Recent advances in mobile technologies have meant that the technical capability to record and transmit location data for processing is appearing in off-the-shelf handsets. This opens possibilities to profile people based on the places they visit, people they associate with, or other aspects of their complex routines determined through persistent tracking. It is possible that services offering customized information based on the results of such behavioral profiling could become commonplace. However, it may not be immediately apparent to the user that a wealth of information about them, potentially unrelated to the service, can be revealed. Further issues occur if the user agreed, while subscribing to the service, for data to be passed to third parties where it may be used to their detriment. Here, we report in detail on a short case study tracking four people, in three European member states, persistently for six weeks using mobile handsets. The GPS locations of these people have been mined to reveal places of interest and to create simple profiles. The information drawn from the profiling activity ranges from intuitive through special cases to insightful. In this paper, these results and further extensions to the technology are considered in light of European legislation to assess the privacy implications of this emerging technology. View full abstract»

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  • Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey

    Page(s): 262 - 267
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (307 KB) |  | HTML iconHTML  

    Wireless-sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensional optimization problems, and approached through bioinspired techniques. Particle swarm optimization (PSO) is a simple, effective, and computationally efficient optimization algorithm. It has been applied to address WSN issues such as optimal deployment, node localization, clustering, and data aggregation. This paper outlines issues in WSNs, introduces PSO, and discusses its suitability for WSN applications. It also presents a brief survey of how PSO is tailored to address these issues. View full abstract»

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  • A Decision-Support Model for Filtering RFID Read Data in Supply Chains

    Page(s): 268 - 273
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (280 KB) |  | HTML iconHTML  

    The introduction of radio-frequency identification (RFID) tags in supply chains engenders the need for incorporating and utilizing the additional generated data. It is generally assumed that these data, once generated, are complete and rife with necessary information for making decisions. The reality is, however, that these data are not error free. Common errors observed in these data include false positives and false negatives. Given that these data are among the set of primary inputs for decision-making purposes, the read-rate accuracy is of paramount importance for effectively managing supply chains incorporating such data. Although there are means by which the RFID tag read rate could be improved to a certain extent, the errors in read rate cannot be completely eliminated, and decision makers are left to deal with such data while managing the supply chain. We present and illustrate few algorithms that can be used to reduce false read rates. We consider models for filtering data that are already being gathered in RFID systems and utilize it to improve read-rate accuracy. We implement the proposed models and illustrate their performance. View full abstract»

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  • 3-D Palmprint Recognition With Joint Line and Orientation Features

    Page(s): 274 - 279
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (562 KB) |  | HTML iconHTML  

    2-D palmprint has been recognized as an effective biometric identifier in the past decade. Recently, 3-D palmprint recognition was proposed to further improve the performance of palmprint systems. This paper presents a simple yet efficient scheme for 3-D palmprint recognition. After calculating and enhancing the mean-curvature image of the 3-D palmprint data, we extract both line and orientation features from it. The two types of features are then fused at either score level or feature level for the final 3-D palmprint recognition. The experiments on The Hong Kong Polytechnic University 3-D palmprint database, which contains 8000 samples from 400 palms show that the proposed feature extraction and fusion methods lead to promising performance. View full abstract»

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  • Special issue on multimodal human-robot interfaces

    Page(s): 280
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  • IEEE Systems, Man, and Cybernetics Society Information

    Page(s): C3
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews Information for authors

    Page(s): C4
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Aims & Scope

Overview, tutorial and application papers concerning all areas of interest to the SMC Society: systems engineering, human factors and human machine systems, and cybernetics and computational intelligence. 

This Transactions ceased production in 2012. The current retitled publication is IEEE Transactions on Human-Machine Systems.

Full Aims & Scope

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Editor-in-Chief
Dr. Vladimir Marik
(until 31 December 2012)