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

Issue 2 • Date March 2010

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Displaying Results 1 - 15 of 15
  • 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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  • A Survey on the Application of Genetic Programming to Classification

    Page(s): 121 - 144
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (514 KB) |  | HTML iconHTML  

    Classification is one of the most researched questions in machine learning and data mining. A wide range of real problems have been stated as classification problems, for example credit scoring, bankruptcy prediction, medical diagnosis, pattern recognition, text categorization, software quality assessment, and many more. The use of evolutionary algorithms for training classifiers has been studied in the past few decades. Genetic programming (GP) is a flexible and powerful evolutionary technique with some features that can be very valuable and suitable for the evolution of classifiers. This paper surveys existing literature about the application of genetic programming to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers. View full abstract»

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  • A Review of Active Appearance Models

    Page(s): 145 - 158
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1869 KB) |  | HTML iconHTML  

    Active appearance model (AAM) is a powerful generative method for modeling deformable objects. The model decouples the shape and the texture variations of objects, which is followed by an efficient gradient-based model fitting method. Due to the flexible and simple framework, AAM has been widely applied in the fields of computer vision. However, difficulties are met when it is applied to various practical issues, which lead to a lot of prominent improvements to the model. Nevertheless, these difficulties and improvements have not been studied systematically. This motivates us to review the recent advances of AAM. This paper focuses on the improvements in the literature in turns of the problems suffered by AAM in practical applications. Therefore, these algorithms are summarized from three aspects, i.e., efficiency, discrimination, and robustness. Additionally, some applications and implementations of AAM are also enumerated. The main purpose of this paper is to serve as a guide for further research. View full abstract»

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  • Hybrid Petri Net Modeling and Schedulability Analysis of High Fusion Point Oil Transportation Under Tank Grouping Strategy for Crude Oil Operations in Refinery

    Page(s): 159 - 175
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (605 KB) |  | HTML iconHTML  

    There are varieties of constraints for a short-term scheduling problem of crude oil operations in a refinery. These constraints are difficult to model and complicate the short-term scheduling problem. Among them, oil residency time and high fusion point crude oil transportation constraints are the challenging ones. With high setup cost for high fusion point oil transportation, it is desired that the volume of high fusion point oil can be transported as much as possible by a single setup. This may result in late transportation of other types of crude oil, leading to the violation of crude oil residency time constraint. These constraints are ignored by existing methods in the literature. To solve this problem, this paper studies the problem in a control theory perspective by viewing an operation decision in the schedule as a control. With this idea, the system is modeled by a hybrid Petri net. With this model and tank grouping strategy, schedulability analysis is carried out and schedulability conditions are presented with tank charging and discharging costs being taken into consideration. These conditions are necessary for determining a refining schedule and can be used to check whether a target-refining schedule is realizable or not. If so, a feasible detailed schedule for the refining schedule can be easily obtained by creating the operation decisions one by one. View full abstract»

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  • Optimization of Spatiotemporal Clustering for Target Tracking From Multisensor Data

    Page(s): 176 - 188
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1165 KB) |  | HTML iconHTML  

    This study focuses on the information extraction from reported sensor data in the communication system of wide-area-search munitions (WASMs). Such sensor data could be erroneous and inconsistent. For example, two WASMs might detect the same target, but associate it with two different targets and tracks. Similarly, two WASMs might detect two distinct targets, but recognize them as the same target. The research challenge is how to fuse both accurate and inaccurate information broadcasted from WASMs, and reconstruct the battle space for accurate target tracking. For each of the detected target points, WASMs provide its location information, detection time, and directional velocity. We, herein, propose a target clustering approach to group target points detected by WASMs and identify the track of individual targets. Our approach differs from traditional clustering techniques as it performs clustering using the time and orientation information, in addition to the distance in the Euclidean space. Our approach employs a network modeling technique to reconstruct all target points and their feasible movement, and a new optimization technique to find the most probable target tracks. Our approach can also determine the optimal number of clusters (targets) automatically from the input data. In this study, distributed interactive simulation, a real-time simulation of a network's information exchange, is used to generate battle space test instances that are used in evaluating the proposed framework. Based on seven realistically simulated instances, the computational results show that our approach provides extremely accurate target-tracking results in a timely fashion. We also compare our results with those obtained using the k-means clustering technique. On average, our approach reconstructs the real target tracks with about 95% accuracy in less than 10 s, while the k-means clustering results yields about 80% accuracy in a similar computational time. View full abstract»

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  • Status-based Routing in Baggage Handling Systems: Searching Verses Learning

    Page(s): 189 - 200
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1984 KB) |  | HTML iconHTML  

    This study contributes to work in baggage handling system (BHS) control, specifically dynamic bag routing. Although studies in BHS agent-based control have examined the need for intelligent control, but there has not been an effort to explore the dynamic routing problem. As such, this study provides additional insight into how agents can learn to route in a BHS. This study describes a BHS status-based routing algorithm that applies learning methods to select criteria based on routing decisions. Although numerous studies have identified the need for dynamic routing, little analytic attention has been paid to intelligent agents for learning routing tables rather than manual creation of routing rules. We address this issue by demonstrating the ability of agents to learn how to route based on bag status, a robust method that is able to function in a variety of different BHS designs. View full abstract»

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  • Reducing the Probability of Bankruptcy Through Supply Chain Coordination

    Page(s): 201 - 215
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (566 KB) |  | HTML iconHTML  

    With the increasing interdependence among supply chain members, bankruptcy of a supply chain member may cause other member firms to get into financial difficulties. This paper investigates the methods for reducing the probability of bankruptcy through supply chain coordination. Based on the developed multiagent simulation model for a simple three-echelon supply chain, the effects of coordination mechanisms, such as information sharing (INS) and vendor-managed inventory (VMI), on reducing the occurrence of bankruptcy at each stage of the supply chain are examined. Simulation results show that such coordination mechanisms are effective in reducing the risk of bankruptcy. However, the key roles of these coordination mechanisms, e.g., the manufacturer in VMI and the retailer in INS, may be reluctant to cooperate since they gain less benefit or even suffer a loss from the coordination. Additional cooperation incentive measures, i.e., permissible delay in payment for INS and inventory subsidy for VMI, are thus proposed for the implementation of these coordination mechanisms, and simulation results confirm their validity. View full abstract»

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  • Microarray Data Classifier Consisting of k-Top-Scoring Rank-Comparison Decision Rules With a Variable Number of Genes

    Page(s): 216 - 226
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1142 KB) |  | HTML iconHTML  

    Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for phenotype classification of many diseases. Our proposed phenotype classifier is an ensemble method with k-top-scoring decision rules. Each rule involves a number of genes, a rank comparison relation among them, and a class label. Current classifiers, which are also ensemble methods, consist of k-top-scoring decision rules. Some of these classifiers fix the number of genes in each rule as a triple or a pair. In this paper, we generalize the number of genes involved in each rule. The number of genes in each rule ranges from 2 to N, respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Our algorithm saves resources by combining shorter rules in order to build a longer rule. It converges rapidly toward its high-scoring rule list by implementing several heuristics. The parameter k is determined by applying leave-one-out cross validation to the training dataset. View full abstract»

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  • An Object-Process-Based Modeling Language for Multiagent Systems

    Page(s): 227 - 241
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4001 KB) |  | HTML iconHTML  

    While a number of modeling languages for constructing multiagent systems (MASs) have been suggested, none of them is widely accepted and used. A prominent reason for this is the gap that exists between agent-oriented modeling languages and the agent-based system modeling needs, including accessibility, flexibility, and expressiveness. This paper addresses the need for such a language by proposing object-process methodology (OPM)/MAS, an agent modeling language (AML) that extends OPM with an intermediate metamodel of the MAS domain. Three case studies and a comparison to contemporary AMLs demonstrate the novelty and benefits of OPM/MAS. View full abstract»

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  • Special issue on semantics-enabled software engineering

    Page(s): 242
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    Page(s): 243
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    Page(s): 244
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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.

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Meet Our Editors

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