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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Issue 5 • Date Oct. 2005

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Displaying Results 1 - 25 of 25
  • Table of contents

    Publication Year: 2005 , Page(s): c1 - 857
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics publication information

    Publication Year: 2005 , Page(s): c2
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  • Rising Submissions

    Publication Year: 2005 , Page(s): 858
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  • A fuzzy ontology and its application to news summarization

    Publication Year: 2005 , Page(s): 859 - 880
    Cited by:  Papers (107)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1695 KB) |  | HTML iconHTML  

    In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization. View full abstract»

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  • Optimal static output feedback simultaneous regional pole placement

    Publication Year: 2005 , Page(s): 881 - 893
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (445 KB) |  | HTML iconHTML  

    The problem of optimal simultaneous regional pole placement for a collection of linear time-invariant systems via a single static output feedback controller is considered. The cost function to be minimized is a weighted sum of quadratic performance indices of the systems. The constrained region for each system can be the intersection of several open half-planes and/or open disks. This problem cannot be solved by the linear matrix inequality (LMI) approach since it is a nonconvex optimization problem. Based on the barrier method, we instead solve an auxiliary minimization problem to obtain an approximate solution to the original constrained optimization problem. Moreover, solution algorithms are provided for finding the optimal solution. Furthermore, a necessary and sufficient condition for the existence of admissible solutions to the simultaneous regional pole placement problem is derived. Finally, two examples are given for illustration. View full abstract»

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  • Vision sensor planning for 3-D model acquisition

    Publication Year: 2005 , Page(s): 894 - 904
    Cited by:  Papers (30)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3691 KB) |  | HTML iconHTML  

    A novel method is proposed in this paper for automatic acquisition of three-dimensional (3-D) models of unknown objects by an active vision system, in which the vision sensor is to be moved from one viewpoint to the next around the target to obtain its complete model. In each step, sensing parameters are determined automatically for incrementally building the 3-D target models. The method is developed by analyzing the target's trend surface, which is the regional feature of a surface for describing the global tendency of change. While previous approaches to trend analysis are usually focused on generating polynomial equations for interpreting regression surfaces in three dimensions, this paper proposes a new mathematical model for predicting the unknown area of the object surface. A uniform surface model is established by analyzing the surface curvatures. Furthermore, a criterion is defined to determine the exploration direction, and an algorithm is developed for determining the parameters of the next view. Implementation of the method is carried out to validate the proposed method. View full abstract»

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  • Incremental linear discriminant analysis for classification of data streams

    Publication Year: 2005 , Page(s): 905 - 914
    Cited by:  Papers (81)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (496 KB) |  | HTML iconHTML  

    This paper presents a constructive method for deriving an updated discriminant eigenspace for classification when bursts of data that contains new classes is being added to an initial discriminant eigenspace in the form of random chunks. Basically, we propose an incremental linear discriminant analysis (ILDA) in its two forms: a sequential ILDA and a Chunk ILDA. In experiments, we have tested ILDA using datasets with a small number of classes and small-dimensional features, as well as datasets with a large number of classes and large-dimensional features. We have compared the proposed ILDA against the traditional batch LDA in terms of discriminability, execution time and memory usage with the increasing volume of data addition. The results show that the proposed ILDA can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods. View full abstract»

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  • An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems

    Publication Year: 2005 , Page(s): 915 - 927
    Cited by:  Papers (35)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (613 KB) |  | HTML iconHTML  

    There are numerous combinations of neural networks (NNs) and evolutionary algorithms (EAs) used in classification problems. EAs have been used to train the networks, design their architecture, and select feature subsets. However, most of these combinations have been tested on only a few data sets and many comparisons are done inappropriately measuring the performance on training data or without using proper statistical tests to support the conclusions. This paper presents an empirical evaluation of eight combinations of EAs and NNs on 15 public-domain and artificial data sets. Our objective is to identify the methods that consistently produce accurate classifiers that generalize well. In most cases, the combinations of EAs and NNs perform equally well on the data sets we tried and were not more accurate than hand-designed neural networks trained with simple backpropagation. View full abstract»

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  • Evolutionary optimization of radial basis function classifiers for data mining applications

    Publication Year: 2005 , Page(s): 928 - 947
    Cited by:  Papers (33)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (925 KB) |  | HTML iconHTML  

    In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms. View full abstract»

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  • On the verification of intransitive noninterference in mulitlevel security

    Publication Year: 2005 , Page(s): 948 - 958
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (563 KB) |  | HTML iconHTML  

    We propose an algorithmic approach to the problem of verification of the property of intransitive noninterference (INI), using tools and concepts of discrete event systems (DES). INI can be used to characterize and solve several important security problems in multilevel security systems. In a previous work, we have established the notion of iP-observability, which precisely captures the property of INI. We have also developed an algorithm for checking iP-observability by indirectly checking P-observability for systems with at most three security levels. In this paper, we generalize the results for systems with any finite number of security levels by developing a direct method for checking iP-observability, based on an insightful observation that the iP function is a left congruence in terms of relations on formal languages. To demonstrate the applicability of our approach, we propose a formal method to detect denial of service vulnerabilities in security protocols based on INI. This method is illustrated using the TCP/IP protocol. The work extends the theory of supervisory control of DES to a new application domain. View full abstract»

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  • Discovering fuzzy time-interval sequential patterns in sequence databases

    Publication Year: 2005 , Page(s): 959 - 972
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (898 KB) |  | HTML iconHTML  

    Given a sequence database and minimum support threshold, the task of sequential pattern mining is to discover the complete set of sequential patterns in databases. From the discovered sequential patterns, we can know what items are frequently brought together and in what order they appear. However, they cannot tell us the time gaps between successive items in patterns. Accordingly, Chen et al. have proposed a generalization of sequential patterns, called time-interval sequential patterns, which reveals not only the order of items, but also the time intervals between successive items . An example of time-interval sequential pattern has a form like (A, I2, B, I1, C), meaning that we buy A first, then after an interval of I2 we buy B, and finally after an interval of I1 we buy C, where I2 and I1 are predetermined time ranges. Although this new type of pattern can alleviate the above concern, it causes the sharp boundary problem. That is, when a time interval is near the boundary of two predetermined time ranges, we either ignore or overemphasize it. Therefore, this paper uses the concept of fuzzy sets to extend the original research so that fuzzy time-interval sequential patterns are discovered from databases. Two efficient algorithms, the fuzzy time interval (FTI)-Apriori algorithm and the FTI-PrefixSpan algorithm, are developed for mining fuzzy time-interval sequential patterns. In our simulation results, we find that the second algorithm outperforms the first one, not only in computing time but also in scalability with respect to various parameters. View full abstract»

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  • MetricMap: an embedding technique for processing distance-based queries in metric spaces

    Publication Year: 2005 , Page(s): 973 - 987
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (568 KB) |  | HTML iconHTML  

    In this paper, we present an embedding technique, called MetricMap, which is capable of estimating distances in a pseudometric space. Given a database of objects and a distance function for the objects, which is a pseudometric, we map the objects to vectors in a pseudo-Euclidean space with a reasonably low dimension while preserving the distance between two objects approximately. Such an embedding technique can be used as an approximate oracle to process a broad class of distance-based queries. It is also adaptable to data mining applications such as data clustering and classification. We present the theory underlying MetricMap and conduct experiments to compare MetricMap with other methods including MVP-tree and M-tree in processing the distance-based queries. Experimental results on both protein and RNA data show the good performance and the superiority of MetricMap over the other methods. View full abstract»

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  • Fisher sequential classifiers

    Publication Year: 2005 , Page(s): 988 - 998
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (571 KB) |  | HTML iconHTML  

    This paper presents further discussion and development of the two-parameter Fisher criterion and describes its two modifications (weighted criterion and another multiclass form). The criteria are applied in two algorithms for training linear sequential classifiers. The main idea of the first algorithm is separating the outermost class from the others. The second algorithm, which is a generalization of the first one, is based on the idea of linear division of classes into two subsets. As linear division of classes is not always satisfactory, a piecewise-linear version of the sequential algorithm is proposed as well. The computational complexity of different algorithms is analyzed. All methods are verified on artificial and real-life data sets. View full abstract»

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  • Toward a systems- and control-oriented agent framework

    Publication Year: 2005 , Page(s): 999 - 1012
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1196 KB) |  | HTML iconHTML  

    This paper develops a systems- and control-oriented intelligent agent framework called the hybrid intelligent control agent (HICA), as well as its composition into specific kinds of multiagent systems. HICA is essentially developed around a hybrid control system core so that knowledge-based planning and coordination can be integrated with verified hybrid control primitives to achieve the coordinated control of multiple multimode dynamical systems. The scheme is applied to the control of teams of unmanned air and ground vehicles engaged in a pursuit-evasion war game. Results are demonstrated in simulation. View full abstract»

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  • The fuzzy clustering analysis based on AFS theory

    Publication Year: 2005 , Page(s): 1013 - 1027
    Cited by:  Papers (22)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (609 KB) |  | HTML iconHTML  

    In the framework of axiomatic fuzzy sets theory, we first study how to impersonally and automatically determine the membership functions for fuzzy sets according to original data and facts, and a new algorithmic framework of determining membership functions and their logic operations for fuzzy sets has been proposed. Then, we apply the proposed algorithmic framework to give a new clustering algorithm and show that the algorithm is feasible. A number of illustrative examples show that this approach offers a far more flexible and effective means for the intelligent systems in real-world applications. Compared with popular fuzzy clustering algorithms, such as c-means fuzzy algorithm and k-nearest-neighbor fuzzy algorithm, the new fuzzy clustering algorithm is more simple and understandable, the data types of the attributes can be various data types or subpreference relations, even descriptions of human intuition, and the distance function and the class number need not be given beforehand. View full abstract»

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  • Ontology-based structured cosine similarity in document summarization: with applications to mobile audio-based knowledge management

    Publication Year: 2005 , Page(s): 1028 - 1040
    Cited by:  Papers (14)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1401 KB) |  | HTML iconHTML  

    Development of algorithms for automated text categorization in massive text document sets is an important research area of data mining and knowledge discovery. Most of the text-clustering methods were grounded in the term-based measurement of distance or similarity, ignoring the structure of the documents. In this paper, we present a novel method named structured cosine similarity (SCS) that furnishes document clustering with a new way of modeling on document summarization, considering the structure of the documents so as to improve the performance of document clustering in terms of quality, stability, and efficiency. This study was motivated by the problem of clustering speech documents (of no rich document features) attained from the wireless experience oral sharing conducted by mobile workforce of enterprises, fulfilling audio-based knowledge management. In other words, this problem aims to facilitate knowledge acquisition and sharing by speech. The evaluations also show fairly promising results on our method of structured cosine similarity. View full abstract»

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  • Homography-based visual servo regulation of mobile robots

    Publication Year: 2005 , Page(s): 1041 - 1050
    Cited by:  Papers (50)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (512 KB) |  | HTML iconHTML  

    A monocular camera-based vision system attached to a mobile robot (i.e., the camera-in-hand configuration) is considered in this paper. By comparing corresponding target points of an object from two different camera images, geometric relationships are exploited to derive a transformation that relates the actual position and orientation of the mobile robot to a reference position and orientation. This transformation is used to synthesize a rotation and translation error system from the current position and orientation to the fixed reference position and orientation. Lyapunov-based techniques are used to construct an adaptive estimate to compensate for a constant, unmeasurable depth parameter, and to prove asymptotic regulation of the mobile robot. The contribution of this paper is that Lyapunov techniques are exploited to craft an adaptive controller that enables mobile robot position and orientation regulation despite the lack of an object model and the lack of depth information. Experimental results are provided to illustrate the performance of the controller. View full abstract»

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  • A repeatable inverse kinematics algorithm with linear invariant subspaces for mobile manipulators

    Publication Year: 2005 , Page(s): 1051 - 1057
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (403 KB) |  | HTML iconHTML  

    On the basis of a geometric characterization of repeatability we present a repeatable extended Jacobian inverse kinematics algorithm for mobile manipulators. The algorithm's dynamics have linear invariant subspaces in the configuration space. A standard Ritz approximation of platform controls results in a band-limited version of this algorithm. Computer simulations involving an RTR manipulator mounted on a kinematic car-type mobile platform are used in order to illustrate repeatability and performance of the algorithm. View full abstract»

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  • A method for segmentation of switching dynamic modes in time series

    Publication Year: 2005 , Page(s): 1058 - 1064
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (291 KB)  

    A method to identify switching dynamics in time series, based on Annealed Competition of Experts algorithm (ACE), has been developed by Kohlmorgen et al. Incorrect selection of embedding dimension and time delay of the signal significantly affect the performance of the ACE method, however. In this paper, we utilize systematic approaches based on mutual information and false nearest neighbor to determine appropriate embedding dimension and time delay. Moreover, we obtained further improvements to the original ACE method by incorporating a deterministic annealing approach as well as phase space closeness measure. Using these improved implementations, we have enhanced the performance of the ACE algorithm in determining the location of the switching of dynamic modes in the time series. The application of the improved ACE method to heart rate data obtained from rats during control and administration of double autonomic blockade conditions indicate that the improved ACE algorithm is able to segment dynamic mode changes with pinpoint accuracy and that its performance is superior to the original ACE algorithm. View full abstract»

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  • GA-fisher: a new LDA-based face recognition algorithm with selection of principal components

    Publication Year: 2005 , Page(s): 1065 - 1078
    Cited by:  Papers (25)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1390 KB) |  | HTML iconHTML  

    This paper addresses the dimension reduction problem in Fisherface for face recognition. When the number of training samples is less than the image dimension (total number of pixels), the within-class scatter matrix (Sw) in linear discriminant analysis (LDA) is singular, and principal component analysis (PCA) is suggested to employ in Fisherface for dimension reduction of Sw so that it becomes nonsingular. The popular method is to select the largest nonzero eigenvalues and the corresponding eigenvectors for LDA. To attenuate the illumination effect, some researchers suggested removing the three eigenvectors with the largest eigenvalues and the performance is improved. However, as far as we know, there is no systematic way to determine which eigenvalues should be used. Along this line, this paper proposes a theorem to interpret why PCA can be used in LDA and an automatic and systematic method to select the eigenvectors to be used in LDA using a genetic algorithm (GA). A GA-PCA is then developed. It is found that some small eigenvectors should also be used as part of the basis for dimension reduction. Using the GA-PCA to reduce the dimension, a GA-Fisher method is designed and developed. Compared with the traditional Fisherface method, the proposed GA-Fisher offers two additional advantages. First, optimal bases for dimensionality reduction are derived from GA-PCA. Second, the computational efficiency of LDA is improved by adding a whitening procedure after dimension reduction. The Face Recognition Technology (FERET) and Carnegie Mellon University Pose, Illumination, and Expression (CMU PIE) databases are used for evaluation. Experimental results show that almost 5% improvement compared with Fisherface can be obtained, and the results are encouraging. View full abstract»

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  • An empirical comparison of nine pattern classifiers

    Publication Year: 2005 , Page(s): 1079 - 1091
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1186 KB) |  | HTML iconHTML  

    There are many learning algorithms available in the field of pattern classification and people are still discovering new algorithms that they hope will work better. Any new learning algorithm, beside its theoretical foundation, needs to be justified in many aspects including accuracy and efficiency when applied to real life problems. In this paper, we report the empirical comparison of a recent algorithm RM, its new extensions and three classical classifiers in different aspects including classification accuracy, computational time and storage requirement. The comparison is performed in a standardized way and we believe that this would give a good insight into the algorithm RM and its extension. The experiments also show that nominal attributes do have an impact on the performance of those compared learning algorithms. View full abstract»

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  • Interval fuzzy modeling applied to Wiener models with uncertainties

    Publication Year: 2005 , Page(s): 1092 - 1095
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (211 KB) |  | HTML iconHTML  

    This correspondence addresses the problem of interval fuzzy model identification and its use in the case of the robust Wiener model. The method combines a fuzzy identification methodology with some ideas from linear programming theory. On a finite set of measured data, an optimality criterion which minimizes the maximum estimation error between the data and the proposed fuzzy model output is used. The min-max optimization problem can then be seen as a linear programming problem that is solved to estimate the parameters of the fuzzy model in each fuzzy domain. This results in lower and upper fuzzy models that define the confidence interval of the observed data. The model is called the interval fuzzy model and is used to approximate the static nonlinearity in the case of the Wiener model with uncertainties. The resulting model has the potential to be used in the areas of robust control and fault detection. View full abstract»

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  • Comments on "Adaptive fuzzy decentralized control for a class of large-scale nonlinear systems"

    Publication Year: 2005
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (53 KB) |  | HTML iconHTML  

    In a previous paper (see ibid., vol.34, no.1, p.770-5, 2004), direct and indirect adaptive output-feedback fuzzy decentralized controllers for a class of uncertain large-scale nonlinear systems were presented. A comment is made here to show that the proposed control schemes of the previous paper are not realizable. View full abstract»

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  • IEEE Systems, Man, and Cybernetics Society Information

    Publication Year: 2005 , Page(s): c3
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  • IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics Information for authors

    Publication Year: 2005 , Page(s): c4
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Aims & Scope

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics focuses on cybernetics, including communication and control across humans, machines and organizations at the structural or neural level

 

This Transaction ceased production in 2012. The current retitled publication is IEEE Transactions on Cybernetics.

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Meet Our Editors

Editor-in-Chief
Dr. Eugene Santos, Jr.
Thayer School of Engineering
Dartmouth College