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

Issue 3 • Date Aug. 2004

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  • Table of contents

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  • IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews [publication information]

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  • Modeling search in group decision support systems

    Page(s): 237 - 244
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (230 KB) |  | HTML iconHTML  

    Groups using group decision support systems (GDSS) to address particular tasks can be viewed as performing a search. Such tasks involve arriving at a solution or decision within the context of a complex search space, warranting the use of computerized decision support tools. The type of search undertaken by the groups appears to be a form of adaptive, rather than enumerative, search. Recently, efforts have been made to incorporate this adaptation into an analytical model of GDSS usage. One possible method for incorporating adaptation into an analytical model is to use an evolutionary algorithm, such as a genetic algorithm (GA), as an analogy for the group problem-solving process. In this paper, a test is made to determine whether GDSS behaves similarly to a GA process utilizing rank selection, uniform crossover, and uniform mutation operators. A Markov model for GAs is used to make this determination. Using GDSS experimental data, the best-fit transition probabilities are estimated and various hypotheses regarding the relation of GA parameters to GDSS functionality are proposed and tested. Implications for researchers in both GAs and group decision support systems are discussed. View full abstract»

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  • Introduction of independent players in a centrally planned market: decision support by long-term production costing

    Page(s): 245 - 256
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    This paper presents long-term production costing as an important decision tool that can be used to assess entry of exogenous players within an otherwise centrally planned, natural monopolistic market. The ex-monopolist emerges as the dominant participant of the restructured market, and continues to play a decisive role in price determination. In doing so, it can apply long-term production costing methods to decide the extent to which entry of independent players should be permissible. On the other hand, the independent entrants can use production costing to weigh their gains in view of the price decided by the ex-monopolist or the policy maker. The specific case of an energy market is examined through a suitably formulated long-term production costing model. A case study shows that transfer of some of the generating units to independent power producers (IPPs) can lead to considerable reduction of production cost and fuel heat consumption associated with utility owned generation. Further, if the choice of generating units to be transferred to IPPs is made with suitable care, then the marginal cost of utility generation can be reduced significantly; thereby lowering the price of electricity. View full abstract»

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  • Toward agency and ontology for web-based information retrieval

    Page(s): 257 - 269
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    This paper presents a system that selects information sources, browses websites, and monitors changes in selected websites. Its distinguishing features are 1) adopting a holistic approach for bolstering web-based information gathering using a society of agents and 2) exploiting features of ontology and lexical chain for processing user queries and filtering relevant information. By engineering web-based information gathering systems along the dimensions of agency and ontology, relations among concepts can be identified and established, and information filtering and monitoring tasks can be automated by information filtering agents (IFAs) and information monitoring agents (IMAs). IFAs and IMAs relieve users of the tasks of browsing and monitoring changes in websites and multiple web sites can be browsed and monitored in parallel by several IFAs and IMAs. By establishing ontological relations among (key)words 1) the query processing agent (QPA) selects appropriate URLs for a query and 2) IFAs search and filter information relevant to the query. Empirical results demonstrated that the QPA can find appropriate number of websites and IFAs are effective in filtering relevant information. View full abstract»

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  • Efficient web content delivery using proxy caching techniques

    Page(s): 270 - 280
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (177 KB) |  | HTML iconHTML  

    Web caching technology has been widely used to improve the performance of the Web infrastructure and reduce user-perceived network latencies. Proxy caching is a major Web caching technique that attempts to serve user Web requests from one or a network of proxies located between the end user and Web servers hosting the original copies of the requested objects. This paper surveys the main technical aspects of proxy caching and discusses recent developments in proxy caching research including caching the "uncacheable" and multimedia streaming objects, and various adaptive and integrated caching approaches. View full abstract»

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  • Fuzzy logic arbiters for multiple-bus multiprocessor systems

    Page(s): 281 - 292
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (466 KB) |  | HTML iconHTML  

    This paper describes and evaluates the use of fuzzy logic arbiters for multiple-bus shared memory multiprocessor system. Multiple-bus systems allow multiple and simultaneous bus transfer in addition to a high degree of fault tolerance. In such systems, arbiters are used to resolve conflicts to system resources, which are the shared memory modules and the buses. Typically, these conflicts are resolved by using two-stage arbitration schemes that employ policies such as random choice, daisy chaining, round-robin, etc. A new way of implementing these arbiters is the use of fuzzy logic to resolve resource request conflicts based on the system state and performance variables. This paper describes a new technique for implementation of fuzzy logic in the system arbiters and presents a simulation program that evaluates the system performance. The program is coded in such a way as to accommodate any arbitration scheme, from which the fixed priority and fuzzy priority have been implemented. Parameters affecting multiple-bus system performance are considered and used as inputs to the fuzzy arbiters. The inputs are fuzzified by using appropriate membership functions, and rules have been defined in such a way as to increase and distribute evenly the acceptance probability of each processor in the system. Results from the simulation program using a prioritized arbitration scheme are compared against other published results and show very close agreement. Furthermore, results show an increase in the acceptance probability of the processors using fuzzy arbiters. View full abstract»

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  • Learning algorithms for a class of neurofuzzy network and application

    Page(s): 293 - 301
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (343 KB) |  | HTML iconHTML  

    A class of neurofuzzy networks and a constructive, competition-based learning procedure is introduced. Given a set of training data, the learning procedure automatically adjusts the input space portion to cover the whole space and finds membership functions parameters for each input variable. The network processes data following fuzzy reasoning principles and, due to its structure, it is dual to a rule-based fuzzy inference system. The neurofuzzy model is used to forecast seasonal streamflow, a key step to plan and operate hydroelectric power plants and to price energy. A database of average monthly inflows of three Brazilian hydroelectric plants located at different river basins was used as source of training and test data. The performance of the neurofuzzy network is compared with period regression, a standard approach used by the electric power industry to forecast streamflows. Comparisons with multilayer perceptron, radial basis network and adaptive neural-fuzzy inference system are also included. The results show that the neurofuzzy network provides better one-step-ahead streamflow forecasting, with forecasting errors significantly lower than the other approaches. View full abstract»

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  • Model-reference adaptive control based on neurofuzzy networks

    Page(s): 302 - 309
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (373 KB) |  | HTML iconHTML  

    Model reference adaptive control (MRAC) is a popular approach to control linear systems, as it is relatively simple to implement. However, the performance of the linear MRAC deteriorates rapidly when the system becomes nonlinear. In this paper, a nonlinear MRAC based on neurofuzzy networks is derived. Neurofuzzy networks are chosen not only because they can approximate nonlinear functions with arbitrary accuracy, but also they are compact in their supports, and the weights of the network can be readily updated on-line. The implementation of the neurofuzzy network-based MRAC is discussed, and the local stability of the system controlled by the proposed controller is established. The performance of the neurofuzzy network-based MRAC is illustrated by examples involving both linear and nonlinear systems. View full abstract»

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  • A new point process transition density model for space-time event prediction

    Page(s): 310 - 324
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (421 KB) |  | HTML iconHTML  

    A new point process transition density model is proposed based on the theory of point patterns for predicting the likelihood of occurrence of spatial-temporal random events. The model provides a framework for discovering and incorporating event initiation preferences in terms of clusters of feature values. Components of the proposed model are specified taking into account additional behavioral assumptions such as the "journey to event" and "lingering period to resume act." Various feature selection techniques are presented in conjunction with the proposed model. Extending knowledge discovery into feature space allows for extrapolation beyond spatial or temporal continuity and is shown to be a major advantage of our model over traditional approaches. We examine the proposed model primarily in the context of predicting criminal events in space and time. View full abstract»

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  • Automatic visual recognition of deformable objects for grasping and manipulation

    Page(s): 325 - 333
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (618 KB) |  | HTML iconHTML  

    This paper describes a vision-based system that is able to automatically recognize deformable objects, to estimate their pose, and to select suitable picking points. A hierarchical self-organized neural network is used to segment color images based on texture information. A morphological analysis allows the recognition of the objects and the picking points extraction. The proposed approach is useful in all of the situations where texture properties are significant for detecting regions of interest on deformable objects. Several tests on a large number of images, acquired in real operative working conditions, demonstrate the effectiveness of the system. View full abstract»

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  • A survey on visual surveillance of object motion and behaviors

    Page(s): 334 - 352
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (307 KB) |  | HTML iconHTML  

    Visual surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc. In general, the processing framework of visual surveillance in dynamic scenes includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, understanding and description of behaviors, human identification, and fusion of data from multiple cameras. We review recent developments and general strategies of all these stages. Finally, we analyze possible research directions, e.g., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote surveillance. View full abstract»

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  • A simulation study on the evolution of hopping motions in animals

    Page(s): 353 - 362
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (324 KB) |  | HTML iconHTML  

    This work was designed to investigate biomechanical aspects of the evolution based on the hypothesis of dynamic cooperative interactions between the locomotion pattern and the body shape in the quadrupedal hopping and the bipedal hopping. The musculoskeletal sagittal-plane model used in the computer simulation consisted of several segments; foot, shank, thigh, trunk, forearm, upper arm, and tail. Two adjacent segments were connected by a hinge joint, and each joint angle was controlled by an extensor and a flexor muscle. The nervous system was represented by a rhythm pattern generator which consisted of 12 neuron models. The genetic algorithm was employed based on the natural selection theory to represent the evolutionary mechanism. The simulation results showed that although hopping could not be seen in the early evolution process, repeated manipulations of the selection and multiplication increased the step length and the locomotion speed and that the resulting hopping motion was close to that of living animals. It was suggested that the advantage of the quadrupedal hopping is high energy efficiency and that of the bipedal hopping is high stability due to the simple and easy motion control. The computational evolution method employed in this study can be a new powerful tool for investigation of the evolution process mostly due to its versatility. View full abstract»

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  • Neural-network-based predictive learning control of ram velocity in injection molding

    Page(s): 363 - 368
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    In this paper, we develop a predictive learning controller for ram velocity of injection molding based on neural networks. We first introduce a model of describing the injection molding, including the time horizon and the batch index. The feedback control plus biased function is proposed for controlling this plant. More specifically, a radial basis function (RBF) network is used to approximate the biased function based on the time horizon. The weights in the RBF are determined by a predictive control scheme based on the batch index. For this algorithm, relevant convergence is investigated. Simulation results reveal that the proposed control can achieve our claims. View full abstract»

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  • iJADE WeatherMAN: a weather forecasting system using intelligent multiagent-based fuzzy neuro network

    Page(s): 369 - 377
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    Weather forecasting has been one of the most challenging problems around the world for more than half a century. Not only because of its practical value in meteorology, but it is also a typical "unbiased" time series forecasting problem in scientific research. In this paper, we propose an innovative, intelligent multiagent-based environment, namely intelligent Java Agent Development Environment (iJADE), to provide an integrated and intelligent agent-based platform in the e-commerce environment. In addition to the facilities found in contemporary agent development platforms, which focus on the autonomy and mobility of the multiagents, iJADE provides an intelligent layer (known as the "conscious layer") to implement various AI functionalities in order to produce "smart" agents. From an implementation point of view, we introduce a weather forecasting system known as iJADE WeatherMAN - a weather forecasting system that uses fuzzy-neuro-based intelligent agents for automatic weather information gathering and filtering, and for time series weather prediction. Compared with the previous studies on single point sources using a similar network and other networks, such as the radial basis function network, learning vector quantization and the Naïve Bayesian network, our experimental results are very promising. This neural-based rainfall forecasting system is useful and can be used in parallel with traditional forecast methods that are used at the Hong Kong Observatory. View full abstract»

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  • IEEE Systems, Man, and Cybernetics Society Information

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  • IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews Information for authors

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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

Meet Our Editors

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