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Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on

Issue 5 • Date Sep 1998

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Displaying Results 1 - 17 of 17
  • Obstacle avoidance in a dynamic environment: a collision cone approach

    Page(s): 562 - 574
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    A novel collision cone approach is proposed as an aid to collision detection and avoidance between irregularly shaped moving objects with unknown trajectories. It is shown that the collision cone can be effectively used to determine whether collision between a robot and an obstacle (both moving in a dynamic environment) is imminent. No restrictions are placed on the shapes of either the robot or the obstacle, i.e., they can both be of any arbitrary shape. The collision cone concept is developed in a phased manner starting from existing analytical results that enable prediction of collision between two moving point objects. These results are extended to predict collision between a point and a circular object, between a point and an irregularly shaped object, between two circular objects, and finally between two irregularly shaped objects. Using the collision cone approach, several strategies that the robot can follow in order to avoid collision, are presented. A discussion on how the shapes of the robot and obstacles can be approximated in order to reduce computational burden is also presented. A number of examples are given to illustrate both collision prediction and avoidance strategies of the robot View full abstract»

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  • An invariant performance measure for surface reconstruction using the volume between two surfaces

    Page(s): 601 - 612
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (496 KB)  

    In this paper, we propose the volume between two surfaces normalized by the surface area (interpreted as average distance between two surfaces) as an invariant quantitative measure for comparing surface reconstruction results of the explicit form, z(x,y). The invariant property of the volume quantity provides the same measure with respect to an arbitrary coordinate system. By normalizing the volume by the surface area, the values of the measure can be compared for different size of images. We also present a novel computationally simple and efficient way of computing the volume between two surfaces and the surface area using a least-squared-error plane approximation of a surface patch defined over a rectangular grid. Experiments indicate that the method gives equivalent performance as other more complicated and computationally expensive methods. The advantages of this new method are that computation is simple and efficient View full abstract»

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  • Performability of systems based on renewal process models

    Page(s): 691 - 698
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    A system of N statistically identical machines is modeled using renewal theory through a unique architecture and state space. The model is very useful in evaluating flexible manufacturing systems (FMS) in particular. Each machine consists of multiple part types that are subject to individual failure. A multiple stage repair process composed of integrable sojourn time distributions requires access to spare parts to repair down machines. A performability measure is used to gauge system effectiveness for this partially degradable system, and an example is given View full abstract»

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  • Learning Bayesian networks probabilities from longitudinal data

    Page(s): 629 - 636
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    Many real applications of Bayesian networks (BN) concern problems in which several observations are collected over time on a certain number of similar plants. This situation is typical of the context of medical monitoring, in which several measurements of the relevant physiological quantities are available over time on a population of patients under treatment, and the conditional probabilities that describe the model are usually obtained from the available data through a suitable learning algorithm. In situations with small data sets for each plant, it is useful to reinforce the parameter estimation process of the BN by taking into account the observations obtained from other similar plants. On the other hand, a desirable feature to be preserved is the ability to learn individualized conditional probability tables, rather than pooling together all the available data. In this work we apply a Bayesian hierarchical model able to preserve individual parameterization, and, at the same time, to allow the conditionals of each plant to borrow strength from all the experience contained in the data-base. A testing example and an application in the context of diabetes monitoring will be shown View full abstract»

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  • An annealing framework with learning memory

    Page(s): 648 - 661
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    Simulated annealing can be viewed as a process that generates a sequence of Markov chains, i.e., it keeps no memory about the states visited in the past of the process. This property makes simulated annealing time-consuming in exploring needless states and difficult in controlling the temperature and transition number. In this paper, we propose a new annealing model with memory that records important information about the states visited in the past. After mapping applications onto a physical system containing particles with discrete states, the new annealing method systematically explores the configuration space, learns the energy information of it, and converges to a well-optimized state. Such energy information is encoded in a learning scheme. The scheme generates states distributed in Boltzmann-style probability according to the energy information recorded in it. Moreover, with the assistance of the learning scheme, controlling over the annealing process become simple and deterministic. From qualitative and quantitative analyses in this paper, we can see that this convenient framework provides an efficient technique for combinatorial optimization problems and good confidence in the solution quality View full abstract»

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  • Inferring new design rules by machine learning: a case study of topological optimization

    Page(s): 575 - 585
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    This paper presents a machine learning approach to the topological optimization of computer networks. Traditionally formulated as an integer program, this problem is well known to be a very difficult one, only solvable by means of heuristic methods. This paper addresses the specific problem of inferring new design rules that can reduce the cost of the network, or reduce the message delay below some acceptable threshold. More specifically, it extends a recent approach using a rule-based system in order to prevent the risk of combinatorial explosion and to reduce the search space of feasible network topologies. This extension essentially implements an efficient inductive learning algorithm leading to the refinement of existing rules and to the discovery of new rules from examples, defined as network topologies satisfying a given reliability constraint. The contribution of this paper is the integration of learning capabilities into topological optimization of computer networks. Computational results confirm the efficiency of the discovered rules View full abstract»

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  • An experiment on texture segmentation using modulated wavelets

    Page(s): 720 - 725
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (268 KB)  

    A textured image is considered to be produced by a smooth signal, representing in part the illumination environment, modulated by some frequencies determined by surface characteristics of the textures. Through expansion of the smooth signal into wavelets, a textured image may be decomposed into modulated “wavelets” providing multiresolution information. It is shown that the corresponding wavelet coefficients can be obtained efficiently by using the standard wavelet transform while the associated h and g filters are modulated accordingly. A set of multichannel filters is designed through the use of a modulated “wavelet' multiresolution decomposition, providing both spatial frequency and orientation selectivity. Based on these multichannel amplitude responses as discriminating features, an image containing multiple textures can be effectively segmented. The potential of this approach is shown by experimental results View full abstract»

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  • Estimating face-pose consistency based on synthetic view space

    Page(s): 613 - 628
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    The visual appearance of an object in space is an image configuration projected from a subset of connected faces of the object. It is believed that face perception and face integration play a key role in object recognition in human vision. This paper presents a novel approach for calculating viewpoint consistency for three-dimensional (3D) object recognition, which utilizes the perceptual models of face grouping and face integration. In the approach, faces are used as perceptual entities in accordance with the visual perception of shape constancy and face-pose consistency. To accommodate the perceptual knowledge of face visibility of objects, a synthetic view space (SVS) is developed. SVS is an abstractive perceptual space which partitions and synthesizes the conventional metric view sphere into a synthetic view box in which only a very limited set of synthetic views (s-views) need to be considered in estimating face-pose consistency. The s-views are structurally organized in a network, the view-connectivity net (VCN), which describes all the possible connections and constraints of the s-views in SVS. VCN provides a meaningful mechanism in pruning the search space of SVS during estimating face-pose consistency. The method has been successfully used for recognizing a class of industrial parts View full abstract»

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  • Identification of incompletely specified multiple-valued Kleenean functions

    Page(s): 637 - 647
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    This paper focuses on incompletely specified multiple-valued Kleenean functions (1991). It is easy to verify that they do not have functional completeness in the class of all functions on the unit interval. Therefore, not all incompletely specified functions on the unit interval are incompletely specified multiple-valued Kleenean functions. In this paper, we will clarify a necessary and sufficient condition for an incompletely specified function to be an incompletely specified multiple-valued Kleenean function. Further, we show an algorithm which derives one of the logic formulas representing the incompletely specified multiple-valued Kleenean function. In considering the application of multiple-valued Kleenean functions, we will show an example which suggests the possibility that input-output data can be described abstractly in terms of multiple-valued Kleenean functions View full abstract»

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  • Using graph parsing for automatic graph drawing

    Page(s): 545 - 561
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (460 KB)  

    This paper presents a procedure for automatically drawing directed graphs. Our system, Clan-based Graph Drawing Tool (CG), uses a unique clan-based graph decomposition to determine intrinsic substructures (clans) in the graph and to produce a parse tree. The tree is given attributes that specify the node layout. CG then uses tree properties with the addition of “routing nodes” to route the edges. The objective of the system is to provide, automatically, an aesthetically pleasing visual layout for arbitrary directed graphs. The prototype has shown the strengths of this approach. The innovative strategy of clan-based graph decomposition is the first digraph drawing technique to analyze locality in the graph in two dimensions. The typical approach to drawing digraphs uses a single dimension, level, to arrange the nodes View full abstract»

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  • Extended sequential algorithms for detecting changes in acting stochastic processes

    Page(s): 703 - 710
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    We present, analyze, and numerically evaluate extended algorithms for detecting changes from an acting stochastic process to a number of possible alternatives. The algorithms are sequential, requiring minimal memory capacity and operational complexity, and they incorporate decision thresholds. The performance of the algorithms is controlled by the selection of the thresholds. Asymptotically, the first algorithmic extension detects the acting process correctly in an expected stopping time sense. In addition, the probability of error induced by a reinitialization algorithmic extension converges asymptotically to zero, when the acting process changes infrequently (with order inversely proportional to the value of the decision thresholds). The presented algorithmic systems are quite powerful and their applications are numerous, ranging from industrial quality control to traffic and performance monitoring in highspeed networks View full abstract»

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  • Two-sided (intuitionistic) fuzzy reasoning

    Page(s): 662 - 677
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (948 KB)  

    Fuzzy propositional logic structure is summarized; different forms of fuzzy propositional expressions and their relations are discussed. Two-sided fuzzy proposition is defined in propositional logic algebra as the counterpart of intuitionistic fuzzy set. The concept of two-sided fuzzy reasoning is discussed and its mathematical structure is developed. Using two-sided fuzzy propositions, human decision-making can be closely simulated by considering his perception of both (somewhat opposite) sides of the subject matter simultaneously. Multiuniverse two-sided fuzzy propositions are presented and multiuniverse operations are defined. Two-sided fuzzy if-then rules are investigated under different interpretations of fuzzy implications, Approximate of these different implications and the inferences in the output universe are investigated, and associated error terms are identified View full abstract»

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  • Fuzzy control design for the trajectory tracking in phase plane

    Page(s): 710 - 719
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    Based on the concept of sliding mode control, the paper designs a fuzzy logic control for a linear system to achieve the trajectory tracking in the phase plane. That is, the system's state (e, e˙) is controlled to track the prespecified trajectory which is composed of several nonisoclinal segments. Each segment of the prespecified trajectory denotes the relation between the tracking error e and the error change e˙ in the corresponding region of the phase plane. In the paper, the prespecified trajectory is regarded as a sliding surface. A direct piecewise linear output formulation of the fuzzy logic controller is synthesized to achieve the hitting motion such that the trajectory tracking is completed region by region. We summarize the above analysis to be a fuzzy logic control design algorithm and give a practical example to illustrate its applicability View full abstract»

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  • Nonlinear system input structure identification: two stage fuzzy curves and surfaces

    Page(s): 678 - 684
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    When modeling a complex, poorly defined, nonlinear problem with hundreds of possible inputs, we must identify the significant inputs before any known nonlinear modeling techniques can be applied. We introduce two stage fuzzy curves and surfaces and use them to 1) automatically and quickly order large numbers of inputs according to their significance, 2) eliminate spurious inputs, and 3) eliminate inputs dependent on the significant inputs View full abstract»

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  • Extending GENET with lazy arc consistency

    Page(s): 698 - 703
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    Many important applications, such as graph coloring, scheduling and production planning, can be solved by GENET, a local search method which is used to solve binary constraint satisfaction problems (CSPs). Where complete search methods are typically augmented with consistency methods to reduce the search, local search methods are not. We propose a consistency technique, lazy arc consistency, which is suitable for use within GENET. We show it can improve the efficiency of the GENET search on some instances of binary CSPs, and does not suffer the overhead of full arc consistency View full abstract»

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  • The MDS-ANAVA technique for assessing knowledge representation differences between skill groups

    Page(s): 586 - 600
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    Knowledge representation is one of important factors that determine human performance on cognitive tasks. Due to different levels of experience, different groups of people may develop different knowledge representations which lead to different levels of performance on cognitive tasks. If knowledge representation differences exist between skill groups such as experts and novices, those differences can be used to guide the training of novices for skill acquisition, and to assist the design of jobs and tools for performance enhancement. A technique is presented in this paper for assessing knowledge representation differences between skill groups, based on multidimensional scaling (MDS) of dissimilarity data and analysis of angular variance (ANAVA). The MDS-ANAVA technique was applied to two sets of dissimilarity data that were obtained from ten experts and ten novices in the computer domain, one set concerning 23 concepts in C computer programming, and another set concerning 21 concepts in the UNIX operating system. Knowledge representation differences from the MDS-ANAVA technique are compared with those from the hierarchical clustering technique. The MDS-ANAVA technique shows several advantages to the hierarchical clustering technique in testing the statistical significance of knowledge representation differences between skill groups and revealing features underlying knowledge representations of skill groups View full abstract»

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  • Tuning and analysis of a fuzzy PI controller based on gain and phase margins

    Page(s): 685 - 691
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    The nonlinearity of the simplest fuzzy PI controller with different inference methods has been discussed and it shows that Mamdani's minimum inference method is the most general inference method for the fuzzy PI controller. In addition, a novel tuning method based on gain and phase margins has been proposed to determine the weighting coefficients of the fuzzy PI controllers. With the proposed tuning formula, the nature of the fuzzy PI controller has been analyzed and it shows the desired characteristics in terms of gain and phase margins when controlling linear plants. Numerical simulations are presented to show the validity of the proposed tuning method View full abstract»

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Aims & Scope

The fields of systems engineering and human machine systems: systems engineering includes efforts that involve issue formulation, issue analysis and modeling, and decision making and issue interpretation at any of the lifecycle phases associated with the definition, development, and implementation of large systems.

 

This Transactions ceased production in 2012. The current retitled publication is IEEE Transactions on Systems, Man, and Cybernetics: Systems.

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Meet Our Editors

Editor-in-Chief
Dr. Witold Pedrycz
University of Alberta