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

Popular Articles (February 2015)

Includes the top 50 most frequently downloaded documents for this publication according to the most recent monthly usage statistics.
  • 1. A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis

    Publication Year: 2010 , Page(s): 1 - 12
    Cited by:  Papers (143)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1299 KB) |  | HTML iconHTML  

    The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements. View full abstract»

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  • 2. Survey of Wireless Indoor Positioning Techniques and Systems

    Publication Year: 2007 , Page(s): 1067 - 1080
    Cited by:  Papers (465)  |  Patents (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (712 KB) |  | HTML iconHTML  

    Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented. View full abstract»

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  • 3. Gesture Recognition: A Survey

    Publication Year: 2007 , Page(s): 311 - 324
    Cited by:  Papers (193)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (199 KB) |  | HTML iconHTML  

    Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human-computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail. Existing challenges and future research possibilities are also highlighted View full abstract»

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  • 4. Social Security and Social Welfare Data Mining: An Overview

    Publication Year: 2012 , Page(s): 837 - 853
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (743 KB) |  | HTML iconHTML  

    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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  • 5. Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

    Publication Year: 2011 , Page(s): 765 - 781
    Cited by:  Papers (34)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (954 KB) |  | HTML iconHTML  

    Local binary pattern (LBP) is a nonparametric descriptor, which efficiently summarizes the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as diverse as face detection, face recognition, facial expression analysis, and demographic classification. This paper presents a comprehensive survey of LBP methodology, including several more recent variations. As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial image analysis, are also highlighted. View full abstract»

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  • 6. Educational Data Mining: A Review of the State of the Art

    Publication Year: 2010 , Page(s): 601 - 618
    Cited by:  Papers (35)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (410 KB) |  | HTML iconHTML  

    Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed. View full abstract»

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  • 7. A Review of Smart Homes—Past, Present, and Future

    Publication Year: 2012 , Page(s): 1190 - 1203
    Cited by:  Papers (10)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1084 KB) |  | HTML iconHTML  

    A smart home is an application of ubiquitous computing in which the home environment is monitored by ambient intelligence to provide context-aware services and facilitate remote home control. This paper presents an overview of previous smart home research as well as the associated technologies. A brief discussion on the building blocks of smart homes and their interrelationships is presented. It describes collective information about sensors, multimedia devices, communication protocols, and systems, which are widely used in smart home implementation. Special algorithms from different fields and their significance are explained according to their scope of use in smart homes. This paper also presents a concrete guideline for future researchers to follow in developing a practical and sustainable smart home. View full abstract»

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  • 8. Neural networks for classification: a survey

    Publication Year: 2000 , Page(s): 451 - 462
    Cited by:  Papers (235)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (152 KB)  

    Classification is one of the most active research and application areas of neural networks. The literature is vast and growing. This paper summarizes some of the most important developments in neural network classification research. Specifically, the issues of posterior probability estimation, the link between neural and conventional classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics View full abstract»

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

    Publication Year: 2011 , Page(s): 262 - 267
    Cited by:  Papers (40)
    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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  • 10. A survey on visual surveillance of object motion and behaviors

    Publication Year: 2004 , Page(s): 334 - 352
    Cited by:  Papers (548)  |  Patents (16)
    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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  • 11. Wearable Obstacle Avoidance Electronic Travel Aids for Blind: A Survey

    Publication Year: 2010 , Page(s): 25 - 35
    Cited by:  Papers (34)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (935 KB) |  | HTML iconHTML  

    The last decades a variety of portable or wearable navigation systems have been developed to assist visually impaired people during navigation in known or unknown, indoor or outdoor environments. There are three main categories of these systems: electronic travel aids (ETAs), electronic orientation aids (EOAs), and position locator devices (PLDs). This paper presents a comparative survey among portable/wearable obstacle detection/avoidance systems (a subcategory of ETAs) in an effort to inform the research community and users about the capabilities of these systems and about the progress in assistive technology for visually impaired people. The survey is based on various features and performance parameters of the systems that classify them in categories, giving qualitative-quantitative measures. Finally, it offers a ranking, which will serve only as a reference point and not as a critique on these systems. View full abstract»

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  • 12. A Survey on Visual Content-Based Video Indexing and Retrieval

    Publication Year: 2011 , Page(s): 797 - 819
    Cited by:  Papers (24)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (351 KB) |  | HTML iconHTML  

    Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions. View full abstract»

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  • 13. Video-Based Abnormal Human Behavior Recognition—A Review

    Publication Year: 2012 , Page(s): 865 - 878
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (693 KB) |  | HTML iconHTML  

    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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  • 14. Sensor-Based Activity Recognition

    Publication Year: 2012 , Page(s): 790 - 808
    Cited by:  Papers (7)
    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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  • 15. The life cycle CO2 emission performance of the DOE/NASA solar power satellite system: a comparison of alternative power generation systems in Japan

    Publication Year: 2005 , Page(s): 391 - 400
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (635 KB) |  | HTML iconHTML  

    Solar power generation and, in particular, space solar power generation seem to be one of the most promising electric power generation technologies for reducing emissions of global warming gases (denoted collectively as CO2 emissions below). Calculating the precise amount of net reduction in CO2 emissions of a solar power system over other alternative power systems requires careful life cycle considerations. For example, emissions from a space solar system must include the emissions from consuming rocket fuel during the launching the satellites, and the emissions from the energy consumed while producing the solar panels. In this paper, we calculate the CO2 emissions observed through the life cycle of a solar power satellite (SPS). This life cycle consists of the production of rocket fuel and solar panels and the construction of a Rectenna (power receiving antenna), satellite, and all other equipment listed in the Department of Energy/NASA reference system. The calculation also includes indirect CO2 emissions that occur in various stages of production of these materials. Our baseline scenario shows that the life cycle CO2 emissions for an SPS system per unit of energy generated are almost the same as the emissions for nuclear power systems and are much less than the life cycle emissions for LNG-fired and coal-fired power generation systems. Furthermore, our SPS-Breeder scenario, in which SPSs supply electricity for producing further SPS systems, shows significantly lower CO2 emissions. As electrical power generation constitutes one fourth of Japan's total CO2 emissions, reducing emissions from electric power generation is one of the most important issues on Japan's policy agenda for dealing with global warming. Our findings suggest that the SPS is the most effective alternative power generation technology. View full abstract»

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  • 16. A Comprehensive Survey of Multiagent Reinforcement Learning

    Publication Year: 2008 , Page(s): 156 - 172
    Cited by:  Papers (118)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (334 KB) |  | HTML iconHTML  

    Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning. A significant part of the research on multiagent learning concerns reinforcement learning techniques. This paper provides a comprehensive survey of multiagent reinforcement learning (MARL). A central issue in the field is the formal statement of the multiagent learning goal. Different viewpoints on this issue have led to the proposal of many different goals, among which two focal points can be distinguished: stability of the agents' learning dynamics, and adaptation to the changing behavior of the other agents. The MARL algorithms described in the literature aim---either explicitly or implicitly---at one of these two goals or at a combination of both, in a fully cooperative, fully competitive, or more general setting. A representative selection of these algorithms is discussed in detail in this paper, together with the specific issues that arise in each category. Additionally, the benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied. Finally, an outlook for the field is provided. View full abstract»

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  • 17. Wireless Sensor Network Reliability and Security in Factory Automation: A Survey

    Publication Year: 2012 , Page(s): 1243 - 1256
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (307 KB) |  | HTML iconHTML  

    Industries can benefit a lot from integrating sensors in industrial plants, structures, machinery, shop floors, and other critical places and utilizing their sensing and monitoring power, communicating and processing abilities to deliver sensed information. Proper use of wireless sensor networks (WSNs) can lower the rate of catastrophic failures, and improve the efficiency and productivity of factory operations. Ensuring reliability and providing adequate security in these crucial services provided by WSNs will reinforce their acceptability as a viable and dependable technology in the factory and industrial domain. In this paper, we examine the reliability and security challenges of WSNs and survey their practicality for industrial adoption. We discuss the unique characteristics that distinguish the factory environment from the rest, elaborate on security and reliability issues with their respective solution measures, and analyze the existing WSN architectures and standards. A number of challenges and interesting research issues have emerged from this study and have been reported for further investigation. View full abstract»

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  • 18. An overview of character recognition focused on off-line handwriting

    Publication Year: 2001 , Page(s): 216 - 233
    Cited by:  Papers (83)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (392 KB)  

    Character recognition (CR) has been extensively studied in the last half century and has progressed to a level that is sufficient to produce technology-driven applications. Now, rapidly growing computational power is enabling the implementation of the present CR methodologies and is creating an increasing demand in many emerging application domains which require more advanced methodologies. This paper serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques, with their superiorities and weaknesses, are reviewed. Finally, the current status of CR is discussed and directions for future research are suggested. Special attention is given to off-line handwriting recognition, since this area requires more research in order to reach the ultimate goal of machine simulation of human reading View full abstract»

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  • 19. Morphogenetic Robotics: An Emerging New Field in Developmental Robotics

    Publication Year: 2011 , Page(s): 145 - 160
    Cited by:  Papers (10)
    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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  • 20. Learn++: an incremental learning algorithm for supervised neural networks

    Publication Year: 2001 , Page(s): 497 - 508
    Cited by:  Papers (134)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (301 KB)  

    We introduce Learn++, an algorithm for incremental training of neural network (NN) pattern classifiers. The proposed algorithm enables supervised NN paradigms, such as the multilayer perceptron (MLP), to accommodate new data, including examples that correspond to previously unseen classes. Furthermore, the algorithm does not require access to previously used data during subsequent incremental learning sessions, yet at the same time, it does not forget previously acquired knowledge. Learn++ utilizes ensemble of classifiers by generating multiple hypotheses using training data sampled according to carefully tailored distributions. The outputs of the resulting classifiers are combined using a weighted majority voting procedure. We present simulation results on several benchmark datasets as well as a real-world classification task. Initial results indicate that the proposed algorithm works rather well in practice. A theoretical upper bound on the error of the classifiers constructed by Learn++ is also provided View full abstract»

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  • 21. Human-robot interaction in rescue robotics

    Publication Year: 2004 , Page(s): 138 - 153
    Cited by:  Papers (97)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (624 KB) |  | HTML iconHTML  

    Rescue robotics has been suggested by a recent DARPA/NSF study as an application domain for the research in human-robot integration (HRI). This paper provides a short tutorial on how robots are currently used in urban search and rescue (USAR) and discusses the HRI issues encountered over the past eight years. A domain theory of the search activity is formulated. The domain theory consists of two parts: 1) a workflow model identifying the major tasks, actions, and roles in robot-assisted search (e.g., a workflow model) and 2) a general information flow model of how data from the robot is fused by various team members into information and knowledge. The information flow model also captures the types of situation awareness needed by each agent in the rescue robot system. The article presents a synopsis of the major HRI issues in reducing the number of humans it takes to control a robot, maintaining performance with geographically distributed teams with intermittent communications, and encouraging acceptance within the existing social structure. View full abstract»

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  • 22. Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies

    Publication Year: 2008 , Page(s): 397 - 415
    Cited by:  Papers (39)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1236 KB) |  | HTML iconHTML  

    Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggregated to a scalar cost function. This can be mainly attributed to the fact that most conventional learning algorithms can only deal with a scalar cost function. Over the last decade, efforts on solving machine learning problems using the Pareto-based multiobjective optimization methodology have gained increasing impetus, particularly due to the great success of multiobjective optimization using evolutionary algorithms and other population-based stochastic search methods. It has been shown that Pareto-based multiobjective learning approaches are more powerful compared to learning algorithms with a scalar cost function in addressing various topics of machine learning, such as clustering, feature selection, improvement of generalization ability, knowledge extraction, and ensemble generation. One common benefit of the different multiobjective learning approaches is that a deeper insight into the learning problem can be gained by analyzing the Pareto front composed of multiple Pareto-optimal solutions. This paper provides an overview of the existing research on multiobjective machine learning, focusing on supervised learning. In addition, a number of case studies are provided to illustrate the major benefits of the Pareto-based approach to machine learning, e.g., how to identify interpretable models and models that can generalize on unseen data from the obtained Pareto-optimal solutions. Three approaches to Pareto-based multiobjective ensemble generation are compared and discussed in detail. Finally, potentially interesting topics in multiobjective machine learning are suggested. View full abstract»

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  • 23. Business Process Analysis and Optimization: Beyond Reengineering

    Publication Year: 2008 , Page(s): 69 - 82
    Cited by:  Papers (32)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (782 KB) |  | HTML iconHTML  

    There is an abundance of business process modeling techniques that capture and address different aspects of a business process. A limited number of these process models allow further quantitative analysis, and only a few enable structured process improvement. This paper reviews and classifies the main techniques for business process modeling with regard to their analysis and optimization capabilities. Three primary groups are identified, and a selection of representative business process modeling techniques is classified based on these. Similar classification is also presented for the analysis and optimization approaches for business processes that were identified in relevant literature. The main contribution of the paper is that it identifies which types of business process models are suitable for analysis and optimization, and also highlights the lack of such approaches. This paper offers a state-of-the-art review in the areas of business process modeling, analysis, and optimization-underlining that the latter two have not received enough coverage and support in the literature. View full abstract»

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  • 24. Automatic Video Classification: A Survey of the Literature

    Publication Year: 2008 , Page(s): 416 - 430
    Cited by:  Papers (34)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (302 KB) |  | HTML iconHTML  

    There is much video available today. To help viewers find video of interest, work has begun on methods of automatic video classification. In this paper, we survey the video classification literature. We find that features are drawn from three modalities - text, audio, and visual - and that a large variety of combinations of features and classification have been explored. We describe the general features chosen and summarize the research in this area. We conclude with ideas for further research. View full abstract»

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  • 25. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

    Publication Year: 2012 , Page(s): 463 - 484
    Cited by:  Papers (19)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1663 KB) |  | HTML iconHTML  

    Classifier learning with data-sets that suffer from imbalanced class distributions is a challenging problem in data mining community. This issue occurs when the number of examples that represent one class is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. In machine learning, the ensemble of classifiers are known to increase the accuracy of single classifiers by combining several of them, but neither of these learning techniques alone solve the class imbalance problem, to deal with this issue the ensemble learning algorithms have to be designed specifically. In this paper, our aim is to review the state of the art on ensemble techniques in the framework of imbalanced data-sets, with focus on two-class problems. We propose a taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based. In addition, we develop a thorough empirical comparison by the consideration of the most significant published approaches, within the families of the taxonomy proposed, to show whether any of them makes a difference. This comparison has shown the good behavior of the simplest approaches which combine random undersampling techniques with bagging or boosting ensembles. In addition, the positive synergy between sampling techniques and bagging has stood out. Furthermore, our results show empirically that ensemble-based algorithms are worthwhile since they outperform the mere use of preprocessing techniques before learning the classifier, therefore justifying the increase of complexity by means of a significant enhancement of the results. View full abstract»

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  • 26. Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems

    Publication Year: 2002 , Page(s): 1 - 13
    Cited by:  Papers (116)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (750 KB)  

    Traditionally, assignment and scheduling decisions are made separately at different levels of the production management framework. The combining of such decisions presents additional complexity and new problems. We present two new approaches to solve jointly the assignment and job-shop scheduling problems (with total or partial flexibility). The first one is the approach by localization (AL). It makes it possible to solve the problem of resource allocation and build an ideal assignment model (assignments schemata). The second one is an evolutionary approach controlled by the assignment model (generated by the first approach). In such an approach, we apply advanced genetic manipulations in order to enhance the solution quality. We also explain some of the practical and theoretical considerations in the construction of a more robust encoding that will enable us to solve the flexible job-shop problem by applying the genetic algorithms (GAs). Two examples are presented to show the efficiency of the two suggested methodologies View full abstract»

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  • 27. Random-Forests-Based Network Intrusion Detection Systems

    Publication Year: 2008 , Page(s): 649 - 659
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (427 KB) |  | HTML iconHTML  

    Prevention of security breaches completely using the existing security technologies is unrealistic. As a result, intrusion detection is an important component in network security. However, many current intrusion detection systems (IDSs) are rule-based systems, which have limitations to detect novel intrusions. Moreover, encoding rules is time-consuming and highly depends on the knowledge of known intrusions. Therefore, we propose new systematic frameworks that apply a data mining algorithm called random forests in misuse, anomaly, and hybrid-network-based IDSs. In misuse detection, patterns of intrusions are built automatically by the random forests algorithm over training data. After that, intrusions are detected by matching network activities against the patterns. In anomaly detection, novel intrusions are detected by the outlier detection mechanism of the random forests algorithm. After building the patterns of network services by the random forests algorithm, outliers related to the patterns are determined by the outlier detection algorithm. The hybrid detection system improves the detection performance by combining the advantages of the misuse and anomaly detection. We evaluate our approaches over the knowledge discovery and data mining 1999 (KDDpsila99) dataset. The experimental results demonstrate that the performance provided by the proposed misuse approach is better than the best KDDpsila99 result; compared to other reported unsupervised anomaly detection approaches, our anomaly detection approach achieves higher detection rate when the false positive rate is low; and the presented hybrid system can improve the overall performance of the aforementioned IDSs. View full abstract»

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  • 28. Machine Learning in Financial Crisis Prediction: A Survey

    Publication Year: 2012 , Page(s): 421 - 436
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1000 KB) |  | HTML iconHTML  

    For financial institutions, the ability to predict or forecast business failures is crucial, as incorrect decisions can have direct financial consequences. Bankruptcy prediction and credit scoring are the two major research problems in the accounting and finance domain. In the literature, a number of models have been developed to predict whether borrowers are in danger of bankruptcy and whether they should be considered a good or bad credit risk. Since the 1990s, machine-learning techniques, such as neural networks and decision trees, have been studied extensively as tools for bankruptcy prediction and credit score modeling. This paper reviews 130 related journal papers from the period between 1995 and 2010, focusing on the development of state-of-the-art machine-learning techniques, including hybrid and ensemble classifiers. Related studies are compared in terms of classifier design, datasets, baselines, and other experimental factors. This paper presents the current achievements and limitations associated with the development of bankruptcy-prediction and credit-scoring models employing machine learning. We also provide suggestions for future research. View full abstract»

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  • 29. Machine Learning Algorithms in Bipedal Robot Control

    Publication Year: 2012 , Page(s): 728 - 743
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (765 KB) |  | HTML iconHTML  

    Over the past decades, machine learning techniques, such as supervised learning, reinforcement learning, and unsupervised learning, have been increasingly used in the control engineering community. Various learning algorithms have been developed to achieve autonomous operation and intelligent decision making for many complex and challenging control problems. One of such problems is bipedal walking robot control. Although still in their early stages, learning techniques have demonstrated promising potential to build adaptive control systems for bipedal robots. This paper gives a review of recent advances on the state-of-the-art learning algorithms and their applications to bipedal robot control. The effects and limitations of different learning techniques are discussed through a representative selection of examples from the literature. Guidelines for future research on learning control of bipedal robots are provided in the end. View full abstract»

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  • 30. Sensors for Gesture Recognition Systems

    Publication Year: 2012 , Page(s): 277 - 290
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (889 KB) |  | HTML iconHTML  

    A gesture recognition system (GRS) is comprised of a gesture, gesture-capture device (sensor), tracking algorithm (for motion capture), feature extraction, and classification algorithm. With the impending movement toward natural communication with mechanical and software systems, it is important to examine the first apparatus that separates the human communicator and the device being controlled. Although there are numerous reviews of GRSs, a comprehensive analysis of the integration of sensors into GRSs and their impact on system performance is lacking in the professional literature. Thus, we have undertaken this effort. Determination of the sensor stimulus, context of use, and sensor platform are major preliminary design issues in GRSs. Thus, these three components form the basic structure of our taxonomy. We emphasize the relationship between these critical components and the design of the GRS in terms of its architectural functions and computational requirements. In this treatise, we consider sensors that are capable of capturing dynamic and static arm and hand gestures. Although we discuss various sensor types, our main focus is on visual sensors as we expect these to become the sensor of choice in the foreseeable future. We delineate the challenges ahead for their increased effectiveness in this application domain. We note as a special challenge, the development of sensors that take over many of the functions the GRS designer struggles with today. We believe our contribution, in this first survey on sensors for GRSs, can give valuable insights into this important research and development topic, and encourage advanced research directions and new approaches. View full abstract»

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  • 31. Unsupervised Construction of an Indoor Floor Plan Using a Smartphone

    Publication Year: 2012 , Page(s): 889 - 898
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1079 KB) |  | HTML iconHTML  

    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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  • 32. Discovering golden nuggets: data mining in financial application

    Publication Year: 2004 , Page(s): 513 - 522
    Cited by:  Papers (32)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (227 KB) |  | HTML iconHTML  

    With the increase of economic globalization and evolution of information technology, financial data are being generated and accumulated at an unprecedented pace. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of financial data to support companies and individuals in strategic planning and investment decision-making. Data mining techniques have been used to uncover hidden patterns and predict future trends and behaviors in financial markets. The competitive advantages achieved by data mining include increased revenue, reduced cost, and much improved marketplace responsiveness and awareness. There has been a large body of research and practice focusing on exploring data mining techniques to solve financial problems. In this paper, we describe data mining in the context of financial application from both technical and application perspectives. In addition, we compare different data mining techniques and discuss important data mining issues involved in specific financial applications. Finally, we highlight a number of challenges and trends for future research in this area. View full abstract»

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  • 33. Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods

    Publication Year: 2012 , Page(s): 133 - 149
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2785 KB) |  | HTML iconHTML  

    Plagiarism can be of many different natures, ranging from copying texts to adopting ideas, without giving credit to its originator. This paper presents a new taxonomy of plagiarism that highlights differences between literal plagiarism and intelligent plagiarism, from the plagiarist's behavioral point of view. The taxonomy supports deep understanding of different linguistic patterns in committing plagiarism, for example, changing texts into semantically equivalent but with different words and organization, shortening texts with concept generalization and specification, and adopting ideas and important contributions of others. Different textual features that characterize different plagiarism types are discussed. Systematic frameworks and methods of monolingual, extrinsic, intrinsic, and cross-lingual plagiarism detection are surveyed and correlated with plagiarism types, which are listed in the taxonomy. We conduct extensive study of state-of-the-art techniques for plagiarism detection, including character n-gram-based (CNG), vector-based (VEC), syntax-based (SYN), semantic-based (SEM), fuzzy-based (FUZZY), structural-based (STRUC), stylometric-based (STYLE), and cross-lingual techniques (CROSS). Our study corroborates that existing systems for plagiarism detection focus on copying text but fail to detect intelligent plagiarism when ideas are presented in different words. View full abstract»

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  • 34. Unsupervised Locating of WiFi Access Points Using Smartphones

    Publication Year: 2012 , Page(s): 1341 - 1353
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (849 KB) |  | HTML iconHTML  

    WiFi positioning systems require radio maps in the form of either RF fingerprints or positions of WiFi access points (APs). In particular, knowledge of the AP positions is essential to enable a locating mechanism as well as to understand the nature of underlying WiFi networks, such as density, connectivity, interference characteristics, and so on. In this paper, we propose an approach called Serendipity, which locates WiFi APs in an unsupervised manner using radio scans collected by ordinary smartphone users. From the radio scans, we extract dissimilarities between all pairs of WiFi APs and estimate relative positions of APs by analyzing the dissimilarities based on a multidimensional scaling technique. We then find the absolute positions with additional radio scans whose positions are known. The discovered positions of WiFi APs are used for the positioning of smartphones or the management of the WiFi networks. To validate the proposed approach, we conducted experiments on several indoor locations. View full abstract»

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  • 35. A Comparative Study of 3-D Face Recognition Under Expression Variations

    Publication Year: 2012 , Page(s): 710 - 727
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1185 KB) |  | HTML iconHTML  

    Research in face recognition has continuously been challenged by extrinsic (head pose, lighting conditions) and intrinsic (facial expression, aging) sources of variability. While many survey papers on face recognition exist, in this paper, we focus on a comparative study of 3-D face recognition under expression variations. As a first contribution, 3-D face databases with expressions are listed, and the most important ones are briefly presented and their complexity is quantified using the iterative closest point (ICP) baseline recognition algorithm. This allows to rank the databases according to their inherent difficulty for face-recognition tasks. This analysis reveals that the FRGC v2 database can be considered as the most challenging because of its size, the presence of expressions and outliers, and the time lapse between the recordings. Therefore, we recommend to use this database as a reference database to evaluate (expression-invariant) 3-D face-recognition algorithms. We also determine and quantify the most important factors that influence the performance. It appears that performance decreases 1) with the degree of nonfrontal pose, 2) for certain expression types, 3) with the magnitude of the expressions, 4) with an increasing number of expressions, and 5) for a higher number of gallery subjects. Future 3-D face-recognition algorithms should be evaluated on the basis of all these factors. As the second contribution, a survey of published 3-D face-recognition methods that deal with expression variations is given. These methods are subdivided into three classes depending on the way the expressions are handled. Region-based methods use expression-stable regions only, while other methods model the expressions either using an isometric or a statistical model. Isometric models assume the deformation because of expression variation to be (locally) isometric, meaning that the deformation preserves lengths along the surface. Statistical models learn how the facial sof- tissue deforms during expressions based on a training database with expression labels. Algorithmic performances are evaluated by the comparison of recognition rates for identification and verification. No statistical significant differences in class performance are found between any pair of classes. View full abstract»

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  • 36. Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

    Publication Year: 2006 , Page(s): 563 - 577
    Cited by:  Papers (148)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (181 KB) |  | HTML iconHTML  

    Manufacturing process planning is the process of selecting and sequencing manufacturing processes such that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is the process of selecting a process plan and assigning manufacturing resources for specific time periods to the set of manufacturing processes in the plan. It is, in fact, an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. Manufacturing process planning and scheduling are usually considered to be two separate and distinct phases. Traditional optimization approaches to these problems do not consider the constraints of both domains simultaneously and result in suboptimal solutions. Without considering real-time machine workloads and shop floor dynamics, process plans may become suboptimal or even invalid at the time of execution. Therefore, there is a need for the integration of manufacturing process-planning and scheduling systems for generating more realistic and effective plans. After describing the complexity of the manufacturing process-planning and scheduling problems, this paper reviews the research literature on manufacturing process planning, scheduling as well as their integration, particularly on agent-based approaches to these difficult problems. Major issues in these research areas are discussed, and research opportunities and challenges are identified View full abstract»

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  • 37. Decision-Making Assistance in Engineering-Change Management Process

    Publication Year: 2011 , Page(s): 344 - 349
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB) |  | HTML iconHTML  

    Effective engineering-change management (ECM) is a real challenge in mechanical engineering industry and manufacturing companies. Computer-aided design systems are usually connected to other systems such as ERP or product data management, but currently this integration does not provide effective means to manage engineering change (EC). While communication between multidisciplinary teams working on a project is known to have a significantly positive impact on the ECM, the communication between disciplines is generally performed solely through message exchange. Experts could feel the need to meet to agree on the requested changes, which in turn translates into longer design and manufacturing processes. There is a need for a system that assists human experts in making decisions about ECs. Such a system will considerably reduce the processing time following a change-request procedure. This paper proposes a collaborative tool named EchoMag, which assists designers and experts during the change-management process. The proposed system ensures the coherence of data between the various disciplines involved in the change process. EchoMag also assists experts in making decisions by proposing alternative solutions when change requests are not agreed upon. Software agents were used to implement EchoMag for which a prototype was developed. Results of the implementation are discussed. View full abstract»

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  • 38. A Survey of Glove-Based Systems and Their Applications

    Publication Year: 2008 , Page(s): 461 - 482
    Cited by:  Papers (45)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1938 KB) |  | HTML iconHTML  

    Hand movement data acquisition is used in many engineering applications ranging from the analysis of gestures to the biomedical sciences. Glove-based systems represent one of the most important efforts aimed at acquiring hand movement data. While they have been around for over three decades, they keep attracting the interest of researchers from increasingly diverse fields. This paper surveys such glove systems and their applications. It also analyzes the characteristics of the devices, provides a road map of the evolution of the technology, and discusses limitations of current technology and trends at the frontiers of research. A foremost goal of this paper is to provide readers who are new to the area with a basis for understanding glove systems technology and how it can be applied, while offering specialists an updated picture of the breadth of applications in several engineering and biomedical sciences areas. View full abstract»

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  • 39. Security Challenge and Defense in VoIP Infrastructures

    Publication Year: 2007 , Page(s): 1152 - 1162
    Cited by:  Papers (17)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (573 KB) |  | HTML iconHTML  

    Voice over Internet protocol (VoIP) has become a popular alternative to traditional public-switched telephone network (PSTN) networks that provides advantages of low cost and flexible advanced ldquodigitalrdquo features. The flexibility of the VoIP system and the convergence of voice and data networks brings with it additional security risks. These are in addition to the common security concerns faced by the underlying IP data network facilities that a VoIP system relies on. The result being that the VoIP network further complicates the security assurance mission faced by enterprises employing this technology. It is time to document various security issues that a VoIP infrastructure may face and analyze the challenges and solutions that may guide future research and development efforts. In this paper, we examine and investigate the concerns and requirements of VoIP security. After a thorough review of security issues and defense mechanisms, we focus on attacks and countermeasures unique to VoIP systems that are essential for current and future VoIP implantations. Then, we analyze two popular industry best practices for securing VoIP networks and conclude this paper with further discussion on future research directions. This paper aims to direct future research efforts and to offer helpful guidelines for practitioners. View full abstract»

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  • 40. System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics

    Publication Year: 2011 , Page(s): 869 - 884
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1532 KB) |  | HTML iconHTML  

    The battery management system (BMS) is an integral part of an automobile. It protects the battery from damage, predicts battery life, and maintains the battery in an operational condition. The BMS performs these tasks by integrating one or more of the functions, such as protecting the cell, thermal management, controlling the charge-discharge, determining the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of the battery, cell balancing, data acquisition, communication with on-board and off-board modules, as well as monitoring and storing historical data. In this paper, we propose a BMS that estimates the critical characteristics of the battery (such as SOC, SOH, and RUL) using a data-driven approach. Our estimation procedure is based on a modified Randles circuit model consisting of resistors, a capacitor, the Warburg impedance for electrochemical impedance spectroscopy test data, and a lumped parameter model for hybrid pulse power characterization test data. The resistors in a Randles circuit model usually characterize the self-discharge and internal resistance of the battery, the capacitor generally represents the charge stored in the battery, and the Warburg impedance represents the diffusion phenomenon. The Randles circuit parameters are estimated using a frequency-selective nonlinear least squares estimation technique, while the lumped parameter model parameters are estimated by the prediction error minimization method. We investigate the use of support vector machines (SVMs) to predict the capacity fade and power fade, which characterize the SOH of a battery, as well as estimate the SOC of the battery. An alternate procedure for estimating the power fade and energy fade from low-current Hybrid Pulse Power characterization (L-HPPC) test data using the lumped parameter battery model has been proposed. Predictions of RUL of the battery are obtained by support vector regression of the power fade and capacity fade estimates. Survival - - function estimates for reliability analysis of the battery are obtained using a hidden Markov model (HMM) trained using time-dependent estimates of capacity fade and power fade as observations. The proposed framework provides a systematic way for estimating relevant battery characteristics with a high-degree of accuracy. View full abstract»

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  • 41. Computational Intelligence in Urban Traffic Signal Control: A Survey

    Publication Year: 2012 , Page(s): 485 - 494
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (413 KB) |  | HTML iconHTML  

    Urban transportation system is a large complex nonlinear system. It consists of surface-way networks, freeway networks, and ramps with a mixed traffic flow of vehicles, bicycles, and pedestrians. Traffic congestions occur frequently, which affect daily life and pose all kinds of problems and challenges. Alleviation of traffic congestions not only improves travel safety and efficiencies but also reduces environmental pollution. Among all the solutions, traffic signal control (TSC) is commonly thought as the most important and effective method. TSC algorithms have evolved quickly, especially over the past several decades. As a result, several TSC systems have been widely implemented in the world, making TSC a major component of intelligent transportation system (ITS). In TSC and ITS, many new technologies can be adopted. Computational intelligence (CI), which mainly includes artificial neural networks, fuzzy systems, and evolutionary computation algorithms, brings flexibility, autonomy, and robustness to overcome nonlinearity and randomness of traffic systems. This paper surveys some commonly used CI paradigms, analyzes their applications in TSC systems for urban surface-way and freeway networks, and introduces current and potential issues of control and management of recurrent and nonrecurrent congestions in traffic networks, in order to provide valuable references for further research and development. View full abstract»

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  • 42. Iterative Learning Control: Brief Survey and Categorization

    Publication Year: 2007 , Page(s): 1099 - 1121
    Cited by:  Papers (256)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (490 KB) |  | HTML iconHTML  

    In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results. View full abstract»

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  • 43. Smartphone-Based Collaborative and Autonomous Radio Fingerprinting

    Publication Year: 2012 , Page(s): 112 - 122
    Cited by:  Papers (13)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (797 KB) |  | HTML iconHTML  

    Although active research has recently been conducted on received signal strength (RSS) fingerprint-based indoor localization, most of the current systems hardly overcome the costly and time-consuming offline training phase. In this paper, we propose an autonomous and collaborative RSS fingerprint collection and localization system. Mobile users track their position with inertial sensors and measure RSS from the surrounding access points. In this scenario, anonymous mobile users automatically collect data in daily life without purposefully surveying an entire building. The server progressively builds up a precise radio map as more users interact with their fingerprint data. The time drift error of inertial sensors is also compromised at run-time with the fingerprint-based localization, which runs with the collective fingerprints being currently built by the server. The proposed system has been implemented on a recent Android smartphone. The experiment results show that reasonable location accuracy is obtained with automatic fingerprinting in indoor environments. View full abstract»

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  • 44. Restoration of Services in Interdependent Infrastructure Systems: A Network Flows Approach

    Publication Year: 2007 , Page(s): 1303 - 1317
    Cited by:  Papers (25)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1174 KB) |  | HTML iconHTML  

    Modern society depends on the operations of civil infrastructure systems, such as transportation, energy, telecommunications, and water. These systems have become so interconnected, one relying on another, that disruption of one may lead to disruptions in all. The approach taken in this research is to model these systems by explicitly identifying these interconnections or interdependencies. Definitions of five types of infrastructure inter-dependencies are presented and incorporated into a network flows mathematical representation, i.e., an interdependent layer network model. Using the lower Manhattan region of New York, for illustrative purposes, the implementation of the model is shown. First, the data requirements are presented with realistic data on the interdependent infrastructure systems of power, telecommunications, and subways. Next, a scenario is given that causes major disruption in the services provided by these infrastructures and demonstrates the use of the model in guiding restoration of services. The paper concludes with a discussion of accomplishments and opportunities for future work. View full abstract»

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  • 45. Comparing Optical Flow Algorithms Using 6-DOF Motion of Real-World Rigid Objects

    Publication Year: 2012 , Page(s): 1752 - 1762
    Cited by:  Papers (3)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (868 KB) |  | HTML iconHTML  

    The application of optical flow algorithms to guidance and navigation problems has gained considerable interest in recent years. This paper summarizes the results of a comparative study on the accuracy of nine different optical flow (OF) algorithms using videos that are captured from an on-board camera during the flight of an autonomous aircraft model. The comparison among the algorithms relies on two formulas that are used both to calculate the ideal OF generated by the motion of a rigid body in the camera field of view and to estimate the linear and angular velocity from the OF. View full abstract»

    Open Access
  • 46. A Survey of Evolutionary Algorithms for Clustering

    Publication Year: 2009 , Page(s): 133 - 155
    Cited by:  Papers (81)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (377 KB) |  | HTML iconHTML  

    This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research. View full abstract»

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  • 47. Human Performance Issues and User Interface Design for Teleoperated Robots

    Publication Year: 2007 , Page(s): 1231 - 1245
    Cited by:  Papers (41)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (547 KB) |  | HTML iconHTML  

    In the future, it will become more common for humans to team up with robotic systems to perform tasks that humans cannot realistically accomplish alone. Even for autonomous and semiautonomous systems, teleoperation will be an important default mode. However, teleoperation can be a challenging task because the operator is remotely located. As a result, the operator's situation awareness of the remote environment can be compromised and the mission effectiveness can suffer. This paper presents a detailed examination of more than 150 papers covering human performance issues and suggested mitigation solutions. The paper summarizes the performance decrements caused by video images bandwidth, time lags, frame rates, lack of proprioception, frame of reference, two-dimensional views, attention switches, and motion effects. Suggested solutions and their limitations include stereoscopic displays, synthetic overlay, multimodal interfaces, and various predicative and decision support systems. View full abstract»

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  • 48. Reliability-Constrained Optimum Placement of Reclosers and Distributed Generators in Distribution Networks Using an Ant Colony System Algorithm

    Publication Year: 2008 , Page(s): 757 - 764
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (293 KB) |  | HTML iconHTML  

    Optimal placement of protection devices and distributed generators (DGs) in radial feeders is important to ensure power system reliability. Distributed generation is being adopted in distribution networks with one of the objectives being enhancement of system reliability. In this paper, an ant colony system algorithm is used to derive the optimal recloser and DG placement scheme for radial distribution networks. A composite reliability index is used as the objective function in the optimization procedure. Simulations are carried out based on two practical distribution systems to validate the effectiveness of the proposed method. Furthermore, comparative studies in relation to genetic algorithm are also conducted. View full abstract»

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  • 49. A Survey of Evolutionary Algorithms for Decision-Tree Induction

    Publication Year: 2012 , Page(s): 291 - 312
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (795 KB) |  | HTML iconHTML  

    This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research. View full abstract»

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  • 50. Ensemble Clustering for Internet Security Applications

    Publication Year: 2012 , Page(s): 1784 - 1796
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6698 KB) |  | HTML iconHTML  

    Due to their damage to Internet security, malware and phishing website detection has been the Internet security topics that are of great interests. Compared with malware attacks, phishing website fraud is a relatively new Internet crime. However, they share some common properties: 1) both malware samples and phishing websites are created at a rate of thousands per day driven by economic benefits; and 2) phishing websites represented by the term frequencies of the webpage content share similar characteristics with malware samples represented by the instruction frequencies of the program. Over the past few years, many clustering techniques have been employed for automatic malware and phishing website detection. In these techniques, the detection process is generally divided into two steps: 1) feature extraction, where representative features are extracted to capture the characteristics of the file samples or the websites; and 2) categorization, where intelligent techniques are used to automatically group the file samples or websites into different classes based on computational analysis of the feature representations. However, few have been applied in real industry products. In this paper, we develop an automatic categorization system to automatically group phishing websites or malware samples using a cluster ensemble by aggregating the clustering solutions that are generated by different base clustering algorithms. We propose a principled cluster ensemble framework to combine individual clustering solutions that are based on the consensus partition, which can not only be applied for malware categorization, but also for phishing website clustering. In addition, the domain knowledge in the form of sample-level/website-level constraints can be naturally incorporated into the ensemble framework. The case studies on large and real daily phishing websites and malware collection from the Kingsoft Internet Security Laboratory demonstrate the effectiveness and efficiency of - ur proposed method. 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. 

Authors should submit human-machine systems papers to the IEEE Transactions on Human-Machine Systems.

Authors should submit systems engineering papers to the IEEE Transactions on Systems, Man and Cybernetics: Systems.

Authors should submit cybernetics papers to the IEEE Transactions on Cybernetics.

Authors should submit social system papers to the IEEE Transactions on Computational Social Systems.

 

Full Aims & Scope

Meet Our Editors

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