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Systems, Man and Cybernetics, IEEE Transactions on

Issue 3 • Date May/Jun 1991

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Displaying Results 1 - 20 of 20
  • Visual detection with search: an empirical model

    Page(s): 596 - 606
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    A visual detection model, including search, is constructed from empirical data on foveal and off-axis contrast thresholds, fixation times, saccadic sizes, and eye response times. The model includes one parameter to account for scene complexity and human preparedness, and its accounts quantitatively for the effects of clutter on this parameter and on other variables. The resulting algorithm correlates well with published laboratory results at light levels of 1 fL or more also gives intuitively satisfying predictions of field performance View full abstract»

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  • Performance evaluation of a six-axis generalized force-reflecting teleoperator

    Page(s): 620 - 633
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    Recent work in real-time distributed computation and control has culminated in a prototype force-reflecting telemanipulation system having dissimilar master (cable-driven force-reflecting hand controller) and slave (PUMA 560 robot with custom controller), extremely high sampling rate (1000 Hz), and low loop computation delay (5 ms). In a series of experiments with this system and five trained test operators covering more than 100 h of teleoperation, performance in a series of generic and application-driven tasks with and without force feedback was measured, and with control shared between teleoperation and local sensor referenced control. Measurements defining task performance include 100-Hz recording of six-axis force-torque information, task completion time, and visual observation of predefined task errors. It is shown that all performance measures improved as capability was added along a spectrum of capabilities ranging from pure position control through force-reflecting teleoperation and shared control. Performance was maximal for the barehanded operator View full abstract»

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  • Adaptive performance optimization of loosely coupled processors

    Page(s): 607 - 619
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    Routing stationary Poisson arrivals at a common scheduler probabilistically to N heterogeneous processors with exponential service distributions is considered. The objective is to minimize the overall average response time. Based on the solution to the known parameter problem, an adaptive estimator is proposed for the scheduler to learn the optimal routing probabilities when the system parameters are not known a priori. The demand is assumed to be less than the overall capacity to ensure the existence of a stable solution. The adaptive estimator utilizes the data that the scheduler can gather on its own, without any communication overhead. It is designed to function well even when a subset of processors are useless and should not receive jobs. The adaptive estimator converges to the optimum with probability 1 and in the mean square sense. Simulation experiments are presented to demonstrate its performance in a variety of practical situations. An extension involving additional unknown arrivals at individual processors is worked out View full abstract»

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  • Character recognition in a sparse distributed memory

    Page(s): 674 - 678
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    P. Kanerva's (1988) sparse distributed memory model is applied to character recognition. The results of recognizing corrupted characters, using a sparse distributed memory with fewer than 10000 locations, is described: 100 corrupted test patterns were generated to recognize 18 template patterns. The performance of the model is evaluated. Insights about the behavior of sparse distributed memories as well as the model's applicability to character recognition are provided. It was found that a sparse distributed memory of small size can recognize patterns with considerable noise View full abstract»

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  • A knowledge exchange architecture for collaborative human-computer communication

    Page(s): 555 - 564
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    Based on accepted reference models of human and computer communication, a combined human-computer communication architecture is developed for exchanging knowledge. The architecture explicitly recognizes and prescribes a set of syntactic and semantic content functions needed to exchange knowledge for the achievement of collaborative problem solving. The functional components of this architecture are presented, and its potential for the development of more effective human-computer interaction is examined View full abstract»

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  • c-means clustering with the ll and l norms

    Page(s): 545 - 554
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    An extension of the hard and fuzzy c-means (HCM/FCM) clustering algorithms is described. Specifically, these models are extended to admit the case where the (dis)similarity measure on pairs of numerical vectors includes two members of the Minkowski or p-norm family, viz., the p=1 and p=∞ norms. In the absence of theoretically necessary conditions to guide a numerical solution of the nonlinear constrained optimization problem associated with this case, it is shown that a certain basis exchange algorithm can be used to find approximate critical points of the new objective functions. This method broadens the applications horizon of the FCM family by enabling users to match discontinuous multidimensional numerical data structures with similarity measures that have nonhyperelliptical topologies View full abstract»

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  • Optimized routing in flexible manufacturing systems with blocking

    Page(s): 589 - 595
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    Flexible manufacturing systems (FMSs) with limited storage capacity can be modeled as queuing networks with finite buffers and general service times. An approach is presented for improving the performance of such systems by distributing the routing probabilities across workstations in an optimal way. The proposed approach is suitable for online application in situations where analytic models are not available. Case studies have shown that reasonably good estimated optima can be obtained with very short computer run lengths in comparison with conventional simulation methods View full abstract»

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  • Classification-based reasoning

    Page(s): 644 - 659
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    A representation formalism for N-ary relations, quantification, and definition of concepts is described. Three types of conditions are associated with the concepts: (1) necessary and sufficient properties, (2) contingent properties, and (3) necessary properties. Also explained is how complex chains of inferences can be accomplished by representing existentially quantified sentences, and concepts denoted by restrictive relative clauses as classification hierarchies. The representation structures that make possible the inferences are explained first, followed by the reasoning algorithms that draw the inferences from the knowledge structures. All the ideas explained have been implemented and are part of the information retrieval component of a program called Snowy. An appendix contains a brief session with the program View full abstract»

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  • Algebraic, mathematical programming, and network models of the deterministic job-shop scheduling problem

    Page(s): 693 - 697
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    In the contemporary literature on deterministic machine scheduling, problems are formulated from three different, but equivalent, perspectives. Algebraic models provide a rigorous problem statement in the language of set theory and are typical of the more abstract development of scheduling theory in mathematics and computer science. Mathematical programming models rely on familiar concepts of nonlinear optimization and are generally the most accessible. Network models (disjunctive graphs) are best suited to the development of solution approaches and figure prominently in discussions of algorithm design and analysis. In this tutorial, it is shown how the minimum-makespan job-shop problem (n/m/G/Cmax) is realized in each of these three model forms. A common notation is developed and how the underlying structure and fundamental difficulty of the problem are expressed in each model is demonstrated View full abstract»

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  • Measures of confidence associated with combining classification results

    Page(s): 690 - 692
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    The problem under consideration is that of combining classification results from several classifiers. This problem was motivated by the US Navy's involvement in developing a system for integrating classification results from multiple sensors. As in any decision process one needs to quantify the uncertainty associated with the decision. A procedure based upon ranked lists from classification outputs is considered. Particular attention is given to the problem of assigning measures of confidence to the combined classification results where the interpretation is easily explained View full abstract»

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  • An analysis of a neural network with a fixed memory span

    Page(s): 683 - 690
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    A study is made of a discrete neural model with a fixed memory span h. The model is described by a system of coupled nonlinear delay differential equations of the Krasovskii-Razumikhin type. After establishing some of the general properties of the model the effects of the memory spans are discussed in the cellular and network levels. The results of some simulations are presented, showing a good agreement with theory. Activity profiles in both models agree for large h. For increasing h the neutral model with fixed memory span approaches the neural model with cumulative memory. However, the agreement between activity profiles of both models is lost for small memory spans h. For small h, the adaptation property of neurons to external inputs, and the causal property of the model disappear View full abstract»

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  • Resource allocation and performance evaluation in large human-machine organizations

    Page(s): 521 - 532
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    A methodology is presented for mapping the processes comprising a mission onto the capable resources within the organization such that the completion time of the terminal process in the mission is minimized. The authors then build a Petri net simulation directly from the output of the mapping algorithm to perform sensitivity analyses on the solution. This capability enables the analyst to study in an interactive way variations in the performance of the organization as a function of its workload capacity, the expertise distribution of its members, the task requirements, and the communication network linking the different resources in the organization View full abstract»

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  • A decision support system for the design of a large electronics test facility

    Page(s): 533 - 544
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    An optimization-based decision support system (DSS) for designing cost-efficient automatic test equipment (ATE) facilities is presented. The DSS combines efficient algorithms from cluster analysis, mixed-integer nonlinear programming, and closed queuing network theory in an iterative fashion to solve this problem. The DSS uses a hierarchical clustering algorithm to define test station configurations and to decompose a large, complex optimization problem into several moderately sized mixed-integer nonlinear programming problems. These problems are solved using a Lagrangian relaxation technique to determine the optimal distribution of the service workload among the test stations and to determine the number of test resources to be installed in each station; these solutions define the test facility design. The steady-state performance of these designs is evaluated using an approximate mean value analysis algorithm. The DSS allows sensitivity analysis with respect to changes in design objectives, test workload, and test facility configuration View full abstract»

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  • The adaptation of perceptrons with applications to inverse dynamics identification of unknown dynamic systems

    Page(s): 634 - 643
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    The authors propose a new class of adaptation algorithms for single- and multilayer perceptrons with discontinuous nonlinearities. The behavior of the proposed algorithms is shown on an application example and simulation results are included. The simulations were performed using the SIMNON package developed for purpose of simulation of nonlinear systems. The results can be used to control unknown dynamic systems using neural controllers. Indeed, many robust control algorithms utilize the inverse dynamics of the plant to be controlled. Thus, the proposed structures where the perceptrons are the inverse system model identifiers should constitute a part of the controller View full abstract»

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  • Structure from motion-a critical analysis of methods

    Page(s): 572 - 588
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    Existing methods for structure from motion are classified and analyzed and mathematical relationships among them are shown. Their behavior on the same synthetic data is evaluated, examining the effect of noise, initial estimates, and the amount of motion. The missing necessary polynomial constraint on the singular values of R.Y. Tsai and T.S. Huang's (1981) essential parameter matrix is shown, and related linear methods for less general SFM problems and their necessary polynomial constraints are derived. The experiments show that for data which the constraints are satisfied, linear methods work correctly and quickly, and where they are not applicable, polynomial methods may work but are slower View full abstract»

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  • Hardware complexity of binary distributed detection systems with isolated local Bayesian detectors

    Page(s): 565 - 571
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    The authors have devised a simple suboptimal decentralized binary detection system where each local detector independently, and the data function center, are minimizing Bayesian risks (not necessarily identical). They compare the hardware volume of a system with identical local risks to that of an optimal centralized system which minimizes the same global risk for several mathematically tractable cases. The results can be used by designers of distributed detection systems (even less restricted than the ones that the authors consider in this work) to obtain upper bounds on the number of (decentralized) detectors which are required in order to guarantee prespecified performance View full abstract»

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  • A novel model of autoassociative memory and its self-organization

    Page(s): 678 - 683
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    A network is presented which embodies the orthogonal type of association. A remarkable point of the network is that the weights of the connections between neurons can be determined directly from the correlation matrix derived from the prototype patterns, requiring no pseudoinverse calculation. As a result, the connection weights can also be obtained by an unsupervised, local learning procedure based on the conventional Hebbian principle View full abstract»

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  • Modeling the behavioral substrates of associate learning and memory: adaptive neural models

    Page(s): 510 - 520
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    Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms View full abstract»

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  • A survey of decision tree classifier methodology

    Page(s): 660 - 674
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    A survey is presented of current methods for decision tree classifier (DTC) designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, the subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed. The relation between decision trees and neutral networks (NN) is also discussed View full abstract»

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  • Outline for a theory of intelligence

    Page(s): 473 - 509
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    Intelligence is defined as that which produces successful behavior. Intelligence is assumed to result from natural selection. A model is proposed that integrates knowledge from research in both natural and artificial systems. The model consists of a hierarchical system architecture wherein: (1) control bandwidth decreases about an order of magnitude at each higher level, (2) perceptual resolution of spatial and temporal patterns contracts about an order-of-magnitude at each higher level, (3) goals expand in scope and planning horizons expand in space and time about an order-of-magnitude at each higher level, and (4) models of the world and memories of events expand their range in space and time by about an order-of-magnitude at each higher level. At each level, functional modules perform behavior generation (task decomposition planning and execution), world modeling, sensory processing, and value judgment. Sensory feedback control loops are closed at every level View full abstract»

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