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Systems, Man and Cybernetics, IEEE Transactions on

Issue 3 • Date Mar 1994

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Displaying Results 1 - 15 of 15
  • Minimal consistent set (MCS) identification for optimal nearest neighbor decision systems design

    Publication Year: 1994 , Page(s): 511 - 517
    Cited by:  Papers (42)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (672 KB)  

    A new approach is presented in this study for tackling the problem of high computational demands of nearest neighbor (NN) based decision systems. The approach, based on the concept of an optimal subset selection from a given training data set, derives a consistent subset which is aimed to be minimal in size. This minimal consistent subset (MCS) selection, in contrast to most of the other previous attempts of this nature, leads to an unique solution irrespective of the initial order of presentation of the data. Further, consistency property is assured at every iteration. Also, unlike under most prior approaches, the samples are selected here in the order of significance of their contribution for enabling the consistency property. This provides insight into the relative significance of the samples in the training set. Experimental results based on a number of independent training and test data sets are presented and discussed to illustrate the methodology and bring to focus its benefits. These results show that the nearest neighbor decision system performance suffers little degradation when the given large training set is replaced by its much smaller MCS in the operational phase of testing with an independent test set. A direct experimental comparison with a prior approach is also furnished to further strengthen the case for the new methodology View full abstract»

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  • Discontinuity preserving regularization of inverse visual problems

    Publication Year: 1994 , Page(s): 455 - 469
    Cited by:  Papers (33)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1484 KB)  

    The method of Tikhonov regularization has been widely used to form well-posed inverse problems in low-level vision. The application of this technique usually results in a least squares approximation or a spline fitting of the parameter of interest. This is often adequate for estimating smooth parameter fields. However, when the parameter of interest has discontinuities the estimate formed by this technique will smooth over the discontinuities. Several techniques have been introduced to modify the regularization process to incorporate discontinuities. Many of these approaches however, will themselves be ill-posed or ill-conditioned. This paper presents a technique for incorporating discontinuities into the reconstruction problem while maintaining a well-posed and well-conditioned problem statement. The resulting computational problem is a convex functional minimization problem. This method is compared to previous approaches and examples are presented for the problems of reconstructing curves and surfaces with discontinuities and for estimating image data. Computational issues arising in both analog and digital implementations are also discussed View full abstract»

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  • Proof of correctness for ASOCS AA3 networks

    Publication Year: 1994 , Page(s): 503 - 510
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (660 KB)  

    Analyzes adaptive algorithm 3 (AA3) of adaptive self-organizing concurrent systems (ASOCS) and proves that AA3 correctly fulfills the rules presented. Several different models for ASOCS have been developed. AA3 uses a distributed mechanism for implementing rules so correctness is not obvious. An ASOCS is an adaptive network composed of many simple computing elements operating in parallel. An ASOCS operates in one of two modes: learning and processing. In learning mode, rules are presented to the ASOCS and incorporated in a self-organizing fashion. In processing mode, the ASOCS acts as a parallel hardware circuit that performs the function defined by the learned rules View full abstract»

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  • An analytical comparative study of a class of discrete linear basis transforms

    Publication Year: 1994 , Page(s): 531 - 535
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (344 KB)  

    For suitability to carry out a comparative study of discrete linear basis transforms, a parametric class of these transforms is considered. It is shown that under certain conditions on the parameters, every member of this class is superior to the Walsh-Hadamard transform for first-order stationary Markov process. For high correlation of this process, the class is proved to be inferior to the slant transform. A lower bound of the performance of this class is also found View full abstract»

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  • A connectionist approach for clustering with applications in image analysis

    Publication Year: 1994 , Page(s): 365 - 384
    Cited by:  Papers (1)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1772 KB)  

    A new neural network strategy for clustering is presented. The network works on the histogram and the process is similar to mode separation. The number of clusters are autonomously detected by the network and it overcomes some major difficulties encountered by mode separation techniques. Clustering is done by first selecting the prototypes and then assigning patterns to one of the prototypes based on its distance from the prototype and the distribution of data. The network does not employ weight learning and is therefore faster than existing unsupervised learning networks. The network was applied to a wide class of problems including gray level image reduction, color segmentation and remotely sensed image segmentation. The experimental results obtained are promising View full abstract»

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  • Collision avoidance of two general robot manipulators by minimum delay time

    Publication Year: 1994 , Page(s): 517 - 522
    Cited by:  Papers (15)  |  Patents (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (484 KB)  

    A simple time delay method for avoiding collisions between two general robot arms is proposed. Links of the robots are approximated by polyhedra and the danger of collision between two robots is expressed by distance functions defined between the robots. The collision map scheme, which can describe collisions between two robots effectively, is adopted. The minimum delay time value needed for collision avoidance is obtained by a simple procedure of following the boundary contour of collision region on collision map. To demonstrate the effectiveness of the proposed time delay method, a computer simulation study is shown where a collision is likely to occur realistically View full abstract»

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  • Uncertainty management for rule-based systems with applications to image analysis

    Publication Year: 1994 , Page(s): 470 - 481
    Cited by:  Patents (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (948 KB)  

    In image analysis, there have been few effective procedures that deal with a wide class of imagery acquired by different sensors under different environmental conditions. The success of a given classification algorithm is dependent upon pattern familiarity, background, and the image acquisition process. Thus, with the inaccuracies in the acquisition process, as well as incomplete or incorrect knowledge about the pattern classes, one cannot place complete confidence in the classifier outcome. There has been increasing success in making decisions under such uncertain conditions by using a rule-based approach with effective uncertainty management, which involves identifying the causes of uncertainty and developing mathematical models for the same. These are incorporated into the rule structure so that the result would be a set of choices or decisions, with a set of associated certainty values or confidences. This paper proposes a “unified” methodology to combine the uncertainties associated with evidence for a given proposition, which is then systematically propagated down the decision tree. The relative importance of propositions as well as the rules themselves have also been considered. Finally, the methodology has been applied to an ATR problem and the results, when compared to some existing methods, show the overall effectiveness of this approach View full abstract»

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  • Automating the layout of network diagrams with specified visual organization

    Publication Year: 1994 , Page(s): 440 - 454
    Cited by:  Papers (7)  |  Patents (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1068 KB)  

    Network diagrams are a familiar graphic form that can express many different kinds of information. The problem of automating network-diagram layout has therefore received much attention. Previous research on network-diagram layout has focused on the problem of aesthetically optimal layout, using such criteria as the number of link crossings, the sum of all link lengths, and total diagram area. In this paper the authors propose a restatement of the network-diagram layout problem in which layout-aesthetic concerns are subordinated to perceptual-organization concerns. The authors present a notation for describing the visual organization of a network diagram. This notation is used in reformulating the layout task as a constrained-optimization problem in which constraints are derived from a visual-organization specification and optimality criteria are derived from layout-aesthetic considerations. Two new heuristic algorithms are presented for this version of the layout problem: one algorithm uses a rule-based strategy for computing a layout; the other is a massively parallel genetic algorithm. The authors demonstrate the capabilities of the two algorithms by testing them on a variety of network-diagram layout problems View full abstract»

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  • On edge and line linking with connectionist models

    Publication Year: 1994 , Page(s): 413 - 428
    Cited by:  Papers (11)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1300 KB)  

    In this paper two connectionist models for mid-level vision problems, namely, edge and line linking, have been presented. The processing elements (PE) are arranged in the form of two-dimensional lattice in both the models. The models take the strengths and the corresponding directions of the fragmented edges (or lines) as the input. The state of each processing element is updated by the activations received from the neighboring processing elements. In one model, each neuron interacts with its eight neighbors, while in the other model, each neuron interacts over a larger neighborhood. After convergence, the output of the neurons represent the linked edge (or line) segments in the image. The first model directly produces the linked line segments, while the second model produces a diffused edge cover. The linked edge segments are found by finding out the spine of the diffused edge cover. The experimental results and the proof of convergence of the network models have also been provided View full abstract»

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  • A hybrid approach for robust diagnostics of cutting tools

    Publication Year: 1994 , Page(s): 482 - 492
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1152 KB)  

    A new multisensor based hybrid technique has been developed for robust diagnosis of cutting tools. The technique combines the concepts of pattern classification and real-time knowledge based systems (RTKBS) and draws upon their strengths; learning facility in the case of pattern classification and a higher level of reasoning in the case of RTKBS. It eliminates some of their major drawbacks: false alarms or delayed/lack of diagnosis in case of pattern classification and tedious knowledge base generation in case of RTKBS. It utilizes a dynamic distance classifier, developed upon a new separability criterion and a new definition of robust diagnosis for achieving these benefits. The promise of this technique has been proven concretely through an on-line diagnosis of drill wear. Its suitability for practical implementation is substantiated by the use of practical, inexpensive, machine-mounted sensors and low-cost delivery systems View full abstract»

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  • Subassembly identification and evaluation for assembly planning

    Publication Year: 1994 , Page(s): 493 - 503
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1016 KB)  

    This paper presents a method for the automatic generation of assembly sequences from a liaison graph representation of an assembly through the recursive decomposition of assembly into subassemblies. In order to increase the planning efficiency, the proposed assembly planning system automatically identifies and avoids those decomposition that incur physically infeasible assembly operation. This is achieved by merging those parts that can not be mutually separable at the current stage of assembly planning due to interconnection infeasibility as well as functional dependency. The above merging process transforms the original liaison graph into an abstract liaison graph with smaller number of nodes. Then, weights are assigned to each liaison of abstract liaison graph based on the stability and structural connectivity associated with liaison such that these weights are used to extract the tentative subassemblies. To select preferred subassemblies, the extracted tentative subassemblies are evaluated based on the subassembly selection indices defined in terms of mobility, structural preference, stability, and parallelism. Furthermore, by adjusting the assembly coefficients of subassembly selection indices according to given assembly environment, an optimal assembly sequence can be generated. The application of the proposed planning system to the tabletop vise assembly is illustrated as an example View full abstract»

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  • Robotic manipulator control of generalized contact force and position

    Publication Year: 1994 , Page(s): 523 - 531
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (652 KB)  

    Considers the problem of control of generalized contact forces with a manipulator controller that has traditionally been regarded as a noncontact task trajectory controller. The open-loop control of generalized forces, suitable for tasks in which only crude force control is required, is achieved through the manipulation of the generalized position inputs of the robotic manipulator. An algorithm is proposed which determines the appropriate manipulator generalized inputs, the only input signal available to the position controller, in order to generate prescribed generalized force and position trajectories during contact with the robot work environment. During noncontact motion of the manipulator, the robot is operated in the more usual generalized position control mode. The use of such a method to control generalized contact forces, although in an open-loop manner, permits a single control to be utilized for both noncontact and contact tasks. Thus, issues of stability during the transition to and from contact, as well as stability during sustained contact are avoided. Hence, the utilization of a single control for both noncontact and contact phases of a single task is seen to be advantageous. The stability of the robotic manipulator during object contact, implicitly assumed by the proposed control strategy, is established using the theory of singular perturbations. Experimental results of a two-degree-of-freedom direct drive manipulator during contact with a one-degree-of-freedom linear mechanical impedance illustrate the usefulness of the proposed method View full abstract»

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  • Knowledge-based seismogram processing by mental images

    Publication Year: 1994 , Page(s): 429 - 439
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (904 KB)  

    The impact of pictorial knowledge representation is demonstrated for two examples of time series analysis in seismology. The approaches perform a) automated recognition of known event signatures and b) high-resolution onset timing of later phases. Both methods work well under extreme conditions of noise and achieved human-like performance in recognizing known situations. Crucial for this success of pictorial knowledge representation was the design of suitably scaled images. They must be simple and robust enough to transform the complexity of “real life” data into a limited set of patterns. These patterns differ significantly from the initial data; they correspond more closely to the non-linear weighting of recognized impressions by an experienced scientist. Thus the author addresses the pictorial presentations as mental images. For both reported applications, part of their power comes by model-based image modifications. However, this enhancement is far from demanding a complete theory. Any fractional model already enhances the image adaptation, so mental images are best suited to deal with incomplete knowledge like any other artificial intelligence approach. Cognitive plausibility was found for both the non-linear image scalings and the model-based image modifications. In general, the author's method of pictorial knowledge representation conforms to the concept of mental images by Kosslyn. Any new task will demand the composition of new, dedicated image transformations where some generalized design criteria are derived from the author's applications View full abstract»

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  • Adaptive control of unknown plants using dynamical neural networks

    Publication Year: 1994 , Page(s): 400 - 412
    Cited by:  Papers (163)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (772 KB)  

    In this paper, we are dealing with the problem of controlling an unknown nonlinear dynamical system. The algorithm is divided into two phases. First a dynamical neural network identifier is employed to perform “black box” identification and then a dynamic state feedback is developed to appropriately control the unknown system. We apply the algorithm to control the speed of a nonlinearized DC motor, giving in this way an application insight. In the algorithm, not all the plant states are assumed to be available for measurement View full abstract»

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  • Self-organizing neural network as a fuzzy classifier

    Publication Year: 1994 , Page(s): 385 - 399
    Cited by:  Papers (29)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1216 KB)  

    This paper describes a self-organizing artificial neural network, based on Kohonen's model of self-organization, which is capable of handling fuzzy input and of providing fuzzy classification. Unlike conventional neural net models, this algorithm incorporates fuzzy set-theoretic concepts at various stages. The input vector consists of membership values for linguistic properties along with some contextual class membership information which is used during self-organization to permit efficient modeling of fuzzy (ambiguous) patterns. A new definition of gain factor for weight updating is proposed. An index of disorder involving mean square distance between the input and weight vectors is used to determine a measure of the ordering of the output space. This controls the number of sweeps required in the process. Incorporation of the concept of fuzzy partitioning allows natural self-organization of the input data, especially when they have ill-defined boundaries. The output of unknown test patterns is generated in terms of class membership values. Incorporation of fuzziness in input and output is seen to provide better performance as compared to the original Kohonen model and the hard version. The effectiveness of this algorithm is demonstrated on the speech recognition problem for various network array sizes, training sets and gain factors View full abstract»

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