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

Issue 6 • Date Nov. 2012

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Displaying Results 1 - 25 of 100
  • Table of contents

    Page(s): C1 - 4
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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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  • Sensor-Based Activity Recognition

    Page(s): 790 - 808
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (516 KB) |  | HTML iconHTML  

    Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research. View full abstract»

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  • A Sensor Technology Survey for a Stress-Aware Trading Process

    Page(s): 809 - 824
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    The role of the global economy is fundamentally important to our daily lives. The stock markets reflect the state of the economy on a daily basis. Traders are the workers within the stock markets who deal with numbers, statistics, company analysis, news, and many other factors that influence the economy in real time. However, while making significant decisions within their workplace, traders must also deal with their own emotions. In fact, traders have one of the most stressful professional occupations. This survey merges current knowledge about stress effects and sensor technology by reviewing, comparing, and highlighting relevant existing research and commercial products that are available on the market. This assessment is made in order to establish how sensor technology can support traders to avoid poor decision making during the trading process. The purpose of this paper is: 1) to review the studies about the impact of stress on the decision-making process and on biological stress parameters that are applied in sensor design; 2) to compare different ways to measure stress by using sensors that are currently available in the market according to basic biometric principles under trading context; and 3) to suggest new directions in the use of sensor technology in stock markets. View full abstract»

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  • Brain–Machine Interfaces: Basis and Advances

    Page(s): 825 - 836
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    During the past 20 years, scientists have focused their efforts in the quest of real solutions in which the neural signals produced inside the human brain could be connected with computers or artificial prostheses that in a near future could be used to restore the mobility and communication abilities of patients with some damage in the central nervous system. In this paper, the procedure to control an artificial device with the thought is explained; the techniques used to extract the neural activity from the brain are classified and compared to establish their advantages, drawbacks, and future development. In addition, the main breakthroughs so far in brain-machine interfaces are described. View full abstract»

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  • Social Security and Social Welfare Data Mining: An Overview

    Page(s): 837 - 853
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    The importance of social security and social welfare business has been increasingly recognized in more and more countries. It impinges on a large proportion of the population and affects government service policies and people's life quality. Typical welfare countries, such as Australia and Canada, have accumulated a huge amount of social security and social welfare data. Emerging business issues such as fraudulent outlays, and customer service and performance improvements challenge existing policies, as well as techniques and systems including data matching and business intelligence reporting systems. The need for a deep understanding of customers and customer-government interactions through advanced data analytics has been increasingly recognized by the community at large. So far, however, no substantial work on the mining of social security and social welfare data has been reported. For the first time in data mining and machine learning, and to the best of our knowledge, this paper draws a comprehensive overall picture and summarizes the corresponding techniques and illustrations to analyze social security/welfare data, namely, social security data mining (SSDM), based on a thorough review of a large number of related references from the past half century. In particular, we introduce an SSDM framework, including business and research issues, social security/welfare services and data, as well as challenges, goals, and tasks in mining social security/welfare data. A summary of SSDM case studies is also presented with substantial citations that direct readers to more specific techniques and practices about SSDM. View full abstract»

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  • Either, Or: Exploration of an Emerging Decision Theory

    Page(s): 854 - 864
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    A novel decision theory is emerging out of sparse findings in economics, mathematics and, most importantly, psychology and computational cognitive science. It rejects a fundamental assumption of the theory of rational decision making, namely, that uncertain belief rests on independent assessments of utility and probability, and includes envisioning possibilities within its scope. Several researchers working with these premises, independently of one another, have remarked that when decision is made, the positive features of the alternative that will be chosen are highlighted, and that this alternative is opposed to a loosing alternative, whose unpleasant aspects are stressed. By doing so, decision makers construct a coherent framework that provides them with a sense of direction in spite of an uncertain future. This paper frames together contributions from different disciplines, often unknown to one another, with the hope of improving the coordination of research efforts. Furthermore, it discusses the status of this emerging theory with respect to our current idea of rationality. This collection might be useful in order to develop theories and models of decision making in uncertain situations, where consequences are unknown and possibilities must be conceived. It does not provide a simple solution, but it may lay a base for future developments. View full abstract»

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  • Video-Based Abnormal Human Behavior Recognition—A Review

    Page(s): 865 - 878
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    Modeling human behaviors and activity patterns for recognition or detection of special event has attracted significant research interest in recent years. Diverse methods that are abound for building intelligent vision systems aimed at scene understanding and making correct semantic inference from the observed dynamics of moving targets. Most applications are in surveillance, video content retrieval, and human-computer interfaces. This paper presents not only an update extending previous related surveys, but also a focus on contextual abnormal human behavior detection especially in video surveillance applications. The main purpose of this survey is to extensively identify existing methods and characterize the literature in a manner that brings key challenges to attention. View full abstract»

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  • Mani-Web: Large-Scale Web Graph Embedding via Laplacian Eigenmap Approximation

    Page(s): 879 - 888
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    The Web as a graph can be embedded in a low-dimensional space where its geometry can be visualized and studied in order to mine interesting patterns such as web communities. The existing algorithms operate on small-to-medium-scale graphs; thus, we propose a close to linear time algorithm called Mani-Web suitable for large-scale graphs. The result is similar to the one produced by the manifold-learning technique Laplacian eigenmap that is tested on artificial manifolds and real web-graphs. Mani-Web can also be used as a general-purpose manifold-learning/dimensionality-reduction technique as long as the data can be represented as a graph. View full abstract»

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  • Unsupervised Construction of an Indoor Floor Plan Using a Smartphone

    Page(s): 889 - 898
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    Indoor pedestrian tracking extends location-based services to indoor environments. Typical indoor positioning systems employ a training/positioning model using Wi-Fi fingerprints. While these approaches have practical results in terms of accuracy and coverage, they require an indoor map, which is typically not available to the average user and involves significant training costs. A practical indoor pedestrian tracking approach should consider the indoor environment without a pretrained database or floor plan. In this paper, we present an indoor pedestrian tracking system, called SmartSLAM, which automatically constructs an indoor floor plan and radio fingerprint map for anonymous buildings using a smartphone. The scheme employs odometry tracing using inertial sensors, an observation model using Wi-Fi signals, and a Bayesian estimation for floor-plan construction. SmartSLAM is a true simultaneous localization and mapping implementation that does not necessitate additional devices, such as laser rangefinders or wheel encoders. We implemented the scheme on off-the-shelf smartphones and evaluated the performance in our university buildings. Despite inherent tracking errors from noisy sensors, SmartSLAM successfully constructed indoor floor plans. View full abstract»

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  • Modeling the Strategic Process of Decision-Making Support Systems Implementations: A System Dynamics Approach Review

    Page(s): 899 - 912
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    Implementing decision-making support systems (DMSS) is considered an organizationally complex and risky task that is influenced by dynamic technical and social-political issues. Consequently, DMSS implementation failures, with associated economic loses, are still reported. While several statistics-based (static) quantitative models of successful factors and qualitative (descriptive) models to implement DMSS are available, few quantitative dynamic models have been posed. In this paper, we illustrate how a dynamic simulation model of the DMSS implementation process can be designed. We use a system dynamics approach via an extended methodology, which is called critical realism-based methodology for studying soft systems dynamics. Validation is realized through 1) the theoretical validity of the model, 2) the model's capability in reproducing historical DMSS implementation paths, and 3) the model's capability in predicting new DMSS implementation paths from new cases. Simulation results suggest the adequacy of using these modeling methods to complement the knowledge on the DMSS implementation processes. View full abstract»

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  • Predicting DNA Motifs by Using Evolutionary Multiobjective Optimization

    Page(s): 913 - 925
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    Bioinformatics and computational biology include researchers from many areas: biochemists, physicists, mathematicians, and engineers. The scale of the problems that are discussed ranges from small molecules to complex systems, where many organisms coexist. However, among all these issues, we can highlight genomics, which studies the genomes of microorganisms, plants, and animals. Predicting common patterns, i.e., motifs, in a set of deoxyribonucleic acid (DNA) sequences is one of the important sequence analysis problems, and it has not yet been resolved in an efficient manner. In this study, we study the application of evolutionary multiobjective optimization to solve the motif discovery problem, applied to the specific task of discovering novel transcription factor binding sites in DNA sequences. For this, we have designed, adapted, configured, and evaluated several types of multiobjective metaheuristics. After a detailed study, the results indicate that these metaheuristics are appropriate for discovering motifs. To find good approximations to the Pareto front, we use the hypervolume indicator, which has been successfully integrated into evolutionary algorithms. Besides the hypervolume indicator, we also use the coverage relation to ensure: Which is the best Pareto front? New results have been obtained, which significantly improve those published in previous research works. View full abstract»

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  • Energy-Efficiency-Based Gait Control System Architecture and Algorithm for Biped Robots

    Page(s): 926 - 933
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (506 KB) |  | HTML iconHTML  

    A novel systematic architecture and algorithm of gait control based on energy-efficiency optimization is represented, aiming at the fatal problem of high energy consumption for biped robots walking in unstructured environments. By designing an optimal controller to minimize the energy criterion, the proposed method provides a remarkable descend rate of energy consumption in the trunk-rotation walking mechanism. The proposed algorithm is able to optimize the trunk trajectory by minimizing the energy-related cost function while guaranteeing zero-moment point (ZMP) criterion. Simulations and experimental results show the validity of the method. View full abstract»

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  • Graph-Based Multiprototype Competitive Learning and Its Applications

    Page(s): 934 - 946
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    Partitioning nonlinearly separable datasets is a basic problem that is associated with data clustering. In this paper, a novel approach that is termed graph-based multiprototype competitive learning (GMPCL) is proposed to handle this problem. A graph-based method is employed to produce an initial, coarse clustering. After that, a multiprototype competitive learning is introduced to refine the coarse clustering and discover clusters of an arbitrary shape. The GMPCL algorithm is further extended to deal with high-dimensional data clustering, i.e., the fast graph-based multiprototype competitive learning (FGMPCL) algorithm. An experimental comparison has been performed by the exploitation of both synthetic and real-world datasets to validate the effectiveness of the proposed methods. Additionally, we apply our GMPCL/FGMPCL to two computer-vision tasks, namely, automatic color image segmentation and video clustering. Experimental results show that GMPCL/FGMPCL provide an effective and efficient tool with application to computer vision. View full abstract»

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  • Application-Oriented Intelligent Middleware for Distributed Sensing and Control

    Page(s): 947 - 956
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    In this paper, wireless sensor networks are proposed as a distributed sensing and control (DSC) approach for productivity and safety improvement of harsh and dynamic industrial systems, such as factory automation, oil and gas industries, and wind farms. The proposed approach focuses on DSC middleware, which considers both application requirements and network resource constraints. By embedding complex application knowledge at different levels and configuring network topology in real time, the DSC system can accomplish effective task assignment, optimal network deployment, and device-level intelligence. IEC 61499 function blocks and intelligent agents are employed as modeling tools for the middleware implementation. View full abstract»

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  • An Efficient Evolutionary Approach to Parameter Identification in a Building Thermal Model

    Page(s): 957 - 969
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (855 KB) |  | HTML iconHTML  

    Thermal models of buildings are often used to identify energy savings within a building. Given that a significant proportion of that energy is typically used to maintain building temperature, establishing the optimal control of the buildings thermal system is important. This requires an understanding of the thermal dynamics of the building, which is often obtained from physical thermal models. However, these models require detailed building parameters to be specified and these can often be difficult to determine. In this paper, we propose an evolutionary approach to parameter identification for thermal models that are formulated as an optimization task. A state-of-the-art evolutionary algorithm, i.e., SaNSDE+, has been developed. A fitness function is defined, which quantifies the difference between the energy-consumption time-series data that are derived from the identified parameters and that given by simulation with a set of predetermined target model parameters. In comparison with a conventional genetic algorithm, fast evolutionary programming, and two state-of-the-art evolutionary algorithms, our experimental results show that the proposed SaNSDE+ has significantly improved both the solution quality and the convergence speed, suggesting this is an effective tool for parameter identification for simulated building thermal models. View full abstract»

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  • Classification of Upper Limb Motion Trajectories Using Shape Features

    Page(s): 970 - 982
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    To understand and interpret human motion is a very active research area nowadays because of its importance in sports sciences, health care, and video surveillance. However, classification of human motion patterns is still a challenging topic because of the variations in kinetics and kinematics of human movements. In this paper, we present a novel algorithm for automatic classification of motion trajectories of human upper limbs. The proposed scheme starts from transforming 3-D positions and rotations of the shoulder/elbow/wrist joints into 2-D trajectories. Discriminative features of these 2-D trajectories are, then, extracted using a probabilistic shape-context method. Afterward, these features are classified using a k-means clustering algorithm. Experimental results demonstrate the superiority of the proposed method over the state-of-the-art techniques. View full abstract»

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  • A Negotiation-Based Capacity-Planning Model

    Page(s): 983 - 993
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    Capacity planning deals with the conflicts among multiple factories. This study employs a negotiation framework to allow autonomous budget allocation among factories and make full use of manufacturing resources capacity scattered over individual factories. Factories are modeled as intelligent entities that exchange offer messages with one another. This study investigates the effects of the attitudes of a factory, while it bargains with other factories over the budget. Furthermore, individual factories apply a capacity-planning optimization model and a genetic algorithm to revise their capacity plan right after receiving new messages from other factories. This paper makes a contribution in successfully building a negotiation-based capacity-planning model applied to a multiple-factory environment. The outcome of the experiments shows the efficiency of the proposed model and the effect of different negotiation attitudes. View full abstract»

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  • A New Intelligent Agent-Based AGC Design With Real-Time Application

    Page(s): 994 - 1002
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    Automatic generation control (AGC) is one of the important control problems in electric power system design and operation, and is becoming more significant today because of increasing renewable energy sources such as wind farms. The power fluctuation caused by a high penetration of wind farms negatively contributes to the power imbalance and frequency deviation. In this paper, a new intelligent agent-based control scheme, using Bayesian networks (BNs), is addressed to design AGC system in a multiarea power system. Model independence and flexibility in specifying the control objectives identify the proposed approach as an attractive solution for AGC design in a real-world power system. The BN also provides a robust probabilistic method of reasoning under uncertainty, and moreover, using multiagent structure in the proposed control framework realizes parallel computation and a high degree of scalability. The proposed control scheme is examined on the 10-machine New England test power system. An experimental real-time implementation is also performed on the aggregated model of West Japan power system. View full abstract»

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  • Color Trend Forecasting of Fashionable Products with Very Few Historical Data

    Page(s): 1003 - 1010
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (372 KB) |  | HTML iconHTML  

    In time-series forecasting, statistical methods and various newly emerged models, such as artificial neural network (ANN) and grey model (GM), are often used. No matter which forecasting method one would apply, it is always a huge challenge to make a sound forecasting decision under the condition of having very few historical data. Unfortunately, in fashion color trend forecasting, the availability of data is always very limited owing to the short selling season and life of products. This motivates us to examine different forecasting models for their performances in predicting color trend of fashionable product under the condition of having very few data. By employing real sales data from a fashion company, we examine various forecasting models, namely ANN, GM, Markov regime switching, and GM+ANN hybrid models, in the domain of color trend forecasting with a limited amount of historical data. Comparisons are made among these models. Insights on the appropriate choice of forecasting models are generated. View full abstract»

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  • Kernel Ridge Regression with Lagged-Dependent Variable: Applications to Prediction of Internal Bond Strength in a Medium Density Fiberboard Process

    Page(s): 1011 - 1020
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    Medium density fiberboard (MDF) is one of the most popular products in wood composites industry. Kernel-based regression approaches such as the support vector machine for regression have been used to predict the final product quality characteristics of MDF. However, existing approaches for the prediction do not consider the autocorrelation of observations while exploring the nonlinearity of data. To avoid such a problem, this paper proposes a kernel-based regression model with lagged-dependent variables (LDVs) to consider both autocorrelations of response variables and the nonlinearity of data. We will explore the nonlinear relationship between the response and both independent variables and past response variables using various kernel functions. In this case, it will be difficult to apply existing kernel trick because of LDVs. We derive the kernel ridge estimators with LDVs using a new mapping idea so that the nonlinear mapping does not have to be computed explicitly. In addition, the centering technique of the individual mapped data in the feature space is derived to consider an intercept term in kernel ridge regression (KRR) with LDVs. The performances of the proposed approaches are compared with those of popular approaches such as KRR, ordinary least squares (OLS) with LDVs using simulated and real-life datasets. Experimental results show that the proposed approaches perform better than KRR or ridge regression and yield consistently better results than OLS with LDVs, implying that it can be used as a promising alternative when there are autocorrelations of response variables. View full abstract»

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  • Robust and Effective Component-Based Banknote Recognition for the Blind

    Page(s): 1021 - 1030
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (888 KB) |  | HTML iconHTML  

    We develop a novel camera-based computer vision technology to automatically recognize banknotes to assist visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate; 2) robustness: handles a variety of currency designs and bills in various conditions; 3) high efficiency: recognizes banknotes quickly; and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using speeded up robust features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system are evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users. View full abstract»

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  • A Fuzzy Intimacy Space Model to Develop Human–Robot Attitudinal Relationship

    Page(s): 1031 - 1041
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    This paper presents a novel method to develop a human-robot attitudinal relationship based on intimacy. For a robot to estimate human's intimacy, we propose a fuzzy space model to classify intimate human behavioral patterns. Proxemic, tactile, and oculesic behavioral features, which are dominantly used for intimacy exchange in human-human communication, are analyzed to develop the 3-D intimacy space. The proposed model provides social standards to develop an intimacy-based attitudinal relationship between a human and a robot. We analyze the generality of our model through a sample interaction scenario and discuss how intimacy can be incrementally learnt for a long-term interaction between a robot and a human. View full abstract»

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  • A Novel Approach to Optimization of Refining Schedules for Crude Oil Operations in Refinery

    Page(s): 1042 - 1053
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    Short-term scheduling for crude oil operations is a combinatorial problem and involves extreme detail. Thus, it is very complicated and, up to now, there is no efficient technique and software tool for it. To search for efficient techniques, a two-layer hierarchical solution is proposed for it. At the upper level, one finds a realizable refining schedule to optimize some objectives. At the lower level, a detailed schedule is obtained to realize it. A methodology has been presented to solve the lower level problem from a control perspective by the authors of this paper. In this paper, the upper level problem for finding optimal refining schedules is addressed, and a novel method is proposed based on the results obtained at the lower level. This method solves a linear programming problem to determine the maximal production rate and a transportation problem to optimally assign crude oil types and volume to the distillers. This way, the method is computationally very efficient. An industrial case study is presented to show the application of the proposed method. View full abstract»

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  • Autonomous Application Recovery in Distributed Intelligent Automation and Control Systems

    Page(s): 1054 - 1070
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1386 KB) |  | HTML iconHTML  

    Over the past decade, a clear trend toward distributed automation in industrial systems was observable. This means that applications are executed at heterogeneous control devices and communication networks. One of the main drivers of this development was the availability of cheap computing and communication resources. Moreover, a strong market demand for operation and adaptation of automation and control services with no downtime is also often requested. As a result, appropriate approaches recovering and (re)configuring automation and control devices as well as even their services and functions during full operation are needed. The relatively new standard IEC 61499 “Function Blocks” provides a reference model for the development and implementation of distributed industrial process measurement and control systems (IPMCSs). It provides a scalable and open architecture to model distributed automation and control applications. The high-level goals of IEC 61499 can be summarized as interoperability, (re)configurability, and portability of distributed applications for IPMCS. Therefore, it provides a very good basis for dynamic (re)configuration and recovery of applications and status information in heterogeneous IPMCS and may master some of the shortcomings of present-day systems. The main purpose of this paper is to present and discuss a general concept for autonomous recovery of applications within the context of distributed automation and control systems which has been implemented using the IEC 61499 reference model. View full abstract»

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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)