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Systems, Man and Cybernetics, IEEE Transactions on

Issue 1 • Date Jan 1994

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Displaying Results 1 - 18 of 18
  • An evidential reasoning approach for multiple-attribute decision making with uncertainty

    Page(s): 1 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1268 KB)  

    A new evidential reasoning based approach is proposed that may be used to deal with uncertain decision knowledge in multiple-attribute decision making (MADM) problems with both quantitative and qualitative attributes. This approach is based on an evaluation analysis model and the evidence combination rule of the Dempster-Shafer theory. It is akin to a preference modeling approach, comprising an evidential reasoning framework for evaluation and quantification of qualitative attributes. Two operational algorithms have been developed within this approach for combining multiple uncertain subjective judgments. Based on this approach and a traditional MADM method, a decision making procedure is proposed to rank alternatives in MADM problems with uncertainty. A numerical example is discussed to demonstrate the implementation of the proposed approach. A multiple-attribute motor cycle evaluation problem is then presented to illustrate the hybrid decision making procedure View full abstract»

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  • Two numerical issues in simulating constrained robot dynamics

    Page(s): 19 - 27
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (620 KB)  

    A common approach to formulating the dynamics of closed-chain mechanisms requires finding the forces of constraint at the loop closures. However, there are indications that this approach leads both to ill conditioned systems that must be inverted and to numerically unstable differential equations of motion. We derive a sufficient condition for ill conditioning of augmented dynamical systems-that the mechanism's trajectory passes through, or very near, a kinematic singularity. In singular regions the equations of motion are also numerically stiff, and they frequently require special numerical methods for computer solution. We propose a new method of calculating closed-chain dynamics, based on the systematic elimination of variables that are both redundant and that may adversely affect the computations. This approach produces numerically stable solutions of the differential equations of motion, and the equations are apparently much less stiff than the equations produced by the traditional force-closure approach View full abstract»

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  • A holistic feasibility study framework for decision systems

    Page(s): 100 - 106
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (696 KB)  

    We present a holistic framework for the feasibility study phase in decision systems development. The framework is comprehensive in addressing issues in the conduct of the study, the assessment needs in the study, and the deliverables of the study. The framework has highly generic applicability across decision-system applications. It incorporates a contemporary approach to the assessment of costs, benefits, and risks of proposed projects. The framework has demonstrated its effectiveness in systematizing and expediting the tasks of the feasibility study team View full abstract»

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  • Neural network control of a pneumatic robot arm

    Page(s): 28 - 38
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1004 KB)  

    A neural map algorithm has been employed to control a five-joint pneumatic robot arm and gripper through feedback from two video cameras. The pneumatically driven robot arm (SoftArm) employed in this investigation shares essential mechanical characteristics with skeletal muscle systems. To control the position of the arm, 200 neurons formed a network representing the three-dimensional workspace embedded in a four-dimensional system of coordinates from the two cameras, and learned a three-dimensional set of pressures corresponding to the end effector positions, as well as a set of 3×4 Jacobian matrices for interpolating between these positions. The gripper orientation was achieved through adaptation of a 1×4 Jacobian matrix for a fourth joint. Because of the properties of the rubber-tube actuators of the SoftArm, the position as a function of supplied pressure is nonlinear, nonseparable, and exhibits hysteresis. Nevertheless, through the neural network learning algorithm the position could be controlled to an accuracy of about one pixel (~3 mm) after 200 learning steps and the orientation could be controlled to two pixels after 800 learning steps. This was achieved through employment of a linear correction algorithm using the Jacobian matrices mentioned above. Applications of repeated corrections in each positioning and grasping step leads to a very robust control algorithm since the Jacobians learned by the network have to satisfy the weak requirement that the Jacobian yields a reduction of the distance between gripper and target. The neural network employed in the control of the SoftArm bears close analogies to a network which successfully models visual brain maps. It is concluded, therefore, from this fact and from the close analogy between the SoftArm and natural muscle systems that the successful solution of the control problem has implications for biological visuo-motor control View full abstract»

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  • Depth map construction from range-guided multiresolution stereo matching

    Page(s): 134 - 144
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    This paper describes a multiresolution method for the acquisition of a complete, relatively noise-free, and high-resolution depth map from a low-resolution laser range image and a stereo pair of high resolution intensity images. Depth information from the laser range data is used to constrain the initial search in stereo matching. The inter- and intralevel linkings of edges in the pyramid allow a process where the coarse laser depth information drives a multiresolution stereo-matching process to construct a high-resolution depth map. The motivation for this new approach to multi-sensor integration is to offset the advantages and disadvantages of traditional stereo matching and triangulation range finding approaches View full abstract»

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  • Visual field information in low-altitude visual flight by line-of-sight slaved helmet-mounted displays

    Page(s): 120 - 134
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1232 KB)  

    The pilot's ability to derive control-oriented visual field information from teleoperated helmet-mounted displays in nap-of-the-earth flight is investigated in this paper. The visual field with these types of displays, commonly used in Apache and Cobra helicopter night operations, originates from a relatively narrow field-of-view forward looking infrared radiation (FLIR) camera, gimbal-mounted at the nose of the aircraft and slaved to the pilot's line of sight, providing a wide-angle field of regard. Pilots have encountered considerable difficulties in controlling the aircraft by these devices. The experimental simulator results presented here indicate that part of these difficulties can be attributed both to the narrow camera field of view and to head/camera slaving system phase lags and errors. In the presence of voluntary head rotation, these shortcomings are shown to impair the control-oriented visual field information vital in vehicular control, such as the perception of the anticipated flight path or the vehicle yaw rate. Since the pilot will tend to minimize head rotation in the presence of slaving system imperfections, the full wide-angle field of regard of the line-of-sight slaved helmet-mounted display is not always fully utilized. The findings in this paper are valid for a general class of head-slaved displays which are used in teleoperation and virtual environments and in which correct self-motion estimation is an essential part of the operator task View full abstract»

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  • A metric space approach to the specification of the heuristic function for the A* algorithm

    Page(s): 159 - 166
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (840 KB)  

    Given a graph with arcs that have costs, the A* algorithm is designed to find the shortest path from a single node to a set of nodes. While the A* algorithm is well understood, it is somewhat limited in its application due to the fact that it is often difficult to specify the “heuristic function” so that A* exhibits desirable computational properties. In this paper a metric space approach to the specification of the heuristic function is introduced. It is shown how to specify an admissible and monotone heuristic function for a wide class of problem domains. In addition, when the cost structure for the underlying graph is specified via a metric, it is shown that admissible and monotone heuristic functions are easy to specify and further computational advantages can be obtained. Applications to an optimal parts distribution problem in flexible manufacturing systems and artificial intelligence planning problems are provided View full abstract»

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  • Stereo correspondence based on line matching in Hough space using dynamic programming

    Page(s): 144 - 152
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (840 KB)  

    This paper presents a method of using Hough space for solving the correspondence problem in stereo vision. It is shown that the line-matching problem in image space can readily be converted into a point-matching problem in Hough (ρ-θ) space. Dynamic programming can be used for searching the optimal matching, now in Hough space. The combination of multiple constraints, especially the natural embedding of the constraint of figural continuity, ensures the accuracy of the matching. The time complexity for searching in dynamic programming is O(pmn), where m and n are the numbers of the lines for each θ in the pair of stereo images, respectively, and p is the number of all possible line orientations. Since m and n are usually fairly small, the matching process is very efficient. Experimental results from both binocular and trinocular matchings are presented and analyzed View full abstract»

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  • Fuzzy critical path method

    Page(s): 48 - 57
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    There have been several attempts in the literature to apply fuzzy numbers to the critical path method. The result delivers the earliest expected time for each event of the project. This paper shows that further extension can be made by considering the interactive fuzzy subtraction and by observing that only the nonnegative part of the fuzzy numbers can have physical interpretation. Based on these two observations, the formulas for the latest allowable time and slack for each event are presented. The availability of fuzzy slacks provides enough information, at least for certain α-level of the slack, to identify the critical path in the network model of the project. Thus, practically we can generalize the critical path method by accepting imprecise, fuzzy data for the duration of the activities View full abstract»

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  • On the representation of uncertain information by multidimensional arrays

    Page(s): 107 - 111
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (436 KB)  

    A multidimensional approach is introduced to the representation of uncertain information in conjunction with the Dempster-Schafer theory. A multidimensional array, called a transition array, is defined, which stores the joint probabilities of the occurrences of a set of variables taking values in different sets. Using this array, it is shown how to compute the information regarding the probability of occurrences of the variables as certain matrix products View full abstract»

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  • Coping with limited on-board memory and communication bandwidth in mobile-robot systems

    Page(s): 58 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1372 KB)  

    Much effort has gone into studying navigation algorithms for mobile-robot systems. However, although mobile-robot systems often suffer from a lack of adequate on-board memory and communication bandwidth, little work has been done on techniques to solve these problems. Two algorithm-implementation strategies are examined to solve the memory-limitation and communication-bandwidth-limitation problems associated with the navigation of single or multiple robots in large dynamic environments. On-board main-memory-management mechanisms, cache policies, auxiliary-memory data structures, and two path planners are explored by simulations based on a new navigation algorithm. One- and two-level caches with one- and two-level planning, respectively, are investigated; these can easily be extended to schemes with more levels. The authors' results show that among the seven (three local and four global) cache policies studied, the predicted-window, aisle, and via-point policies overcame the above limitations without compromising robot performance. Therefore, one or more of these three policies can be used with implementation strategies to deal with the memory-limitation and communication-bandwidth-limitation problems encountered in real-world mobile-robot navigation. The authors' results can also be very useful in the domain of Intelligent Vehicle Highway Systems (IVHS), where the main memory of the on-board computer may be too small to hold all of the road network and other useful information View full abstract»

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  • Improving learning of genetic rule-based classifier systems

    Page(s): 152 - 159
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (728 KB)  

    A genetic classifier system is reviewed and used for learning rules for classification. Two new strategies are described that enable all the letters of the alphabet to be learned. A “remembering” strategy locks in good rules to overcome forgetting that otherwise occurs during learning. A “specializing” strategy fine tunes the search process for rules. Experiments and an encoding scheme are described. Results show, for the first time, that a genetic classifier-type system can learn to classify all the letters of the alphabet. Further, computer experiments show that the new strategies result in faster and more robust classification involving images of varying position, size, and shape View full abstract»

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  • RPCT algorithm and its VLSI implementation

    Page(s): 87 - 99
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (944 KB)  

    This paper presents the regional projection contour transformation (RPCT) which transforms a compound pattern or multicontour pattern into a unique outer contour. Two RPCT's, (1) diagonal-diagonal regional projection contour transformation and (2) horizontal-vertical regional projection contour transformation, are presented. They are applicable to a wide range of areas such as image analysis, pattern recognition, etc. A very large scale integration (VLSI) architecture to implement the RPCT has also been designed based on a canonical methodology which maps homogeneous dependence graphs into processor arrays. In this paper, a linear array has been designed, where an N/2-element vector is used to process a pattern with a size of N×N. It can speed up the recognition process considerably with a time complexity of O(N) compared with O(N2) when a uniprocessor is used View full abstract»

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  • The development and evaluation of an improved genetic algorithm based on migration and artificial selection

    Page(s): 73 - 86
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    Much research has been done in developing improved genetic algorithms (GA's). Past research has focused on the improvement of operators and parameter settings and indicates that premature convergence is still the preeminent problem in GA's. This paper presents an improved genetic algorithm based on migration and artificial selection (GAMAS). GAMAS is an algorithm whose architecture is specifically designed to confront the causes of premature convergence. Though based on simple genetic algorithms, GAMAS is not concerned with the evolution of a single population, but instead is concerned with macroevolution, or the creation of multiple populations or species, and the derivation of solutions from the combined evolutionary effects of these species. New concepts that are emphasized in this architecture are artificial selection, migration, and recycling. Experimental results show that GAMAS consistently outperforms simple genetic algorithms and alleviates the problem of premature convergence View full abstract»

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  • An efficient differential box-counting approach to compute fractal dimension of image

    Page(s): 115 - 120
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB)  

    Fractal dimension is an interesting feature proposed to characterize roughness and self-similarity in a picture. This feature has been used in texture segmentation and classification, shape analysis and other problems. An efficient differential box-counting approach to estimate fractal dimension is proposed in this note. By comparison with four other methods, it has been shown that the authors, method is both efficient and accurate. Practical results on artificial and natural textured images are presented View full abstract»

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  • The fringe distance measure: an easily calculated image distance measure with recognition results comparable to Gaussian blurring

    Page(s): 111 - 115
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (460 KB)  

    A fast simple distance measure for calculating the degree of dissimilarity between binary images is presented. The new distance measure, called the fringe distance, gives the same degree of distortion tolerance as optimized Gaussian blurring, but can be calculated much more rapidly. Template matching using the fringe distance executes about six times as fast as Gaussian blurring template matching on an IBM AT with coprocessor, and more than three times as fast on a SUN SPARCStation I with floating point accelerator. The greater speed means more templates can be used in process time limited applications, increasing recognition accuracy View full abstract»

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  • Genetic-based new fuzzy reasoning models with application to fuzzy control

    Page(s): 39 - 47
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    The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown in this paper that the performance of fuzzy control systems may be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate an optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller View full abstract»

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  • A comment on noise injection into inputs in backpropagation [and author's reply]

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    In the above paper (K. Matsuoka, IEEE Trans. SMC, vol. 22, no. 3, pp. 436-440, 1992), the author defines the sensitivity R for a feedforward network and proceeds to derive an expression for R. It is pointed out that an assumption in the equation for R is incorrect. In reply the author accepts that the comment is correct View full abstract»

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