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  • Articles  (10)
  • neural networks  (10)
  • Springer  (10)
  • American Chemical Society
  • 2010-2014
  • 1995-1999  (10)
  • 1997  (10)
  • Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics  (10)
Collection
  • Articles  (10)
Publisher
  • Springer  (10)
  • American Chemical Society
Years
  • 2010-2014
  • 1995-1999  (10)
Year
Topic
  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent manufacturing 8 (1997), S. 227-234 
    ISSN: 1572-8145
    Keywords: Proportional hazards models ; neural networks ; accelerated life testing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Because of increased manufacturing competitiveness, new methods for reliability estimation are being developed. Intelligent manufacturing relies upon accurate component and product reliability estimates for determining warranty costs, as well as optimal maintenance, inspection, and replacement schedules. Accelerated life testing is one approach that is used for shortening the life of products or components or hastening their performance degradation with the purpose of obtaining data that may be used to predict device life or performance under normal operating conditions. The proportional hazards (PH) model is a non-parametric multiple regression approach for reliability estimation, in which a baseline hazard function is modified multiplicatively by covariates (i.e. applied stresses). While the PH model is a distribution-free approach, specific assumptions need to be made about the time behavior of the hazard rates. A neural network (NN) is particularly useful in pattern recognition problems that involve capturing and learning complex underlying (but consistent) trends in the data. Neural networks are highly non-linear, and in some cases are capable of producing better approximations than multiple regression. This paper reports on the comparison of PH and NN models for the analysis of time-dependent dielectric breakdown data for a metal-oxide-semiconductor integrated circuit. In this case, the NN model results in a better fit to the data based upon minimizing the mean square error of the predictions when using failure data from an elevated temperature and voltage to predict reliability at a lower temperature and voltage.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent manufacturing 8 (1997), S. 203-214 
    ISSN: 1572-8145
    Keywords: Feature recognition ; feature representation ; neural networks ; ART
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract A self-organizing neural network, ART2, based on adaptive resonance theory (ART), is applied to the problem of feature recognition from a boundary representation (B-rep) solid model. A modified face score vector calculation scheme is adopted to represent the features by continuous-valued vectors, suitable to be input to the network. The face score is a measure of the face complexity based upon the convexity or concavity of the surrounding region. The face score vector depicts the topological relations between a face and its neighbouring faces. The ART2 network clusters similar features together. The similarity of the features within a cluster is controlled by a vigilance parameter. A new feature presented to the net is associated with one of the existing clusters, if the feature is similar to the members of the cluster. Otherwise, the net creates a new cluster. An algorithm of the ART2 network is implemented and tested with nine different features. The results obtained indicate that the network has significant potential for application to the problem of feature recognition.
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent manufacturing 8 (1997), S. 177-190 
    ISSN: 1572-8145
    Keywords: Nesting ; stock cutting ; neural networks ; optimization ; genetic algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract In this study, two approaches are explored for the solution of the rectangular stock cutting problem: neuro-optimization, which integrates artificial neural networks and optimization methods; and genetic neuro-nesting, which combines artificial neural networks and genetic algorithms. In the first approach, an artificial neural network architecture is used to generate rectangular pattern configurations, to be used by the optimization model, with an acceptable scrap. Rectangular patterns of different sizes are selected as input to the network to generate the location and rotation of each pattern after they are combined. A mathematical programming model is used to determine the nesting of different sizes of rectangular patterns to meet the demand for rectangular blanks for a given planning horizon. The test data used in this study is generated randomly from a specific normal distribution. The average scrap percentage obtained is within acceptable limits. In the second approach, a genetic algorithm is used to generate sequences of the input patterns to be allocated on a finite width with infinite-length material. Each gene represents the sequence in which the patterns are to be allocated using the allocation algorithm developed. The scrap percentage of each allocation is used as an evaluation criterion for each gene for determining the best allocation while considering successive generations. The allocation algorithm uses the sliding method integrated with an artificial neural network based on the adaptive resonance theory (ART1) paradigm to allocate the patterns according to the sequence generated by the genetic algorithm. It slides an incoming pattern next to the allocated ones and keeps all scrap areas produced, which can be utilized in allocating a new pattern through the ART1 network. If there is a possible match with an incoming pattern and one of the scrap areas, the neural network selects the best match area and assigns the pattern. Both approaches gave satisfactory results. The second approach generated nests having packing densities in the range 95–97%. Improvement in packing densities was possible at the expense of excessive computational time. Parallel implementation of this unconventional approach could well bring a quick and satisfactory solution to this classical problem.
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent manufacturing 8 (1997), S. 167-175 
    ISSN: 1572-8145
    Keywords: Pattern recognition ; neural networks ; time series ; feature extraction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract This paper presents a new approach for automated parts recognition. It is based on the use of the signature and autocorrelation functions for feature extraction and a neural network for the analysis of recognition. The signature represents the shapes of boundaries detected in digitized binary images of the parts. The autocorrelation coefficients computed from the signature are invariant to transformations such as scaling, translation and rotation of the parts. These unique extracted features are fed to the neural network. A multilayer perceptron with two hidden layers, along with a backpropagation learning algorithm, is used as a pattern classifier. In addition, the position information of the part for a robot with a vision system is described to permit grasping and pick-up. Experimental results indicate that the proposed approach is appropriate for the accurate and fast recognition and inspection of parts in automated manufacturing systems.
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 295-317 
    ISSN: 1573-0409
    Keywords: nonholonomic systems ; mobile robots ; neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract A control structure that makes possible the integration of a kinematiccontroller and a neural network (NN) computed-torque controller fornonholonomic mobile robots is presented. A combined kinematic/torque controllaw is developed and stability is guaranteed by Lyapunov theory. Thiscontrol algorithm is applied to the practical point stabilization problemi.e., stabilization to a small neighborhood of the origin. The NN controllercan deal with unmodeled bounded disturbances and/or unstructured unmodeleddynamics in the vehicle. On-line NN weight tuning algorithms that do notrequire off-line learning yet guarantee small tracking errors and boundedcontrol signals are utilized.
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 181-193 
    ISSN: 1573-0409
    Keywords: robot manipulators ; adaptive control ; neural networks ; stability analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract In this paper a controller based on neural networks is proposed toachieve output trajectory tracking of rigid robot manipulators. Neuralnetworks used here are one hidden layer ones so that their outputs dependlinearly on the parameters. Our method uses a decomposed connectioniststructure. Each neural network approximate a separate element of thedynamical model. These approximations are used to perform an adaptive stablecontrol law. The controller is based on direct adaptive techniques and theLyapunov approach is used to derive the adaptation laws of the nets’parameters. By using an intrinsic physical property of the manipulator, thesystem is proved to be stable. The performance of the controller depends onthe quality of the approximation, i.e. on the inherent reconstruction errorsof the exact functions.
    Type of Medium: Electronic Resource
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  • 7
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 251-273 
    ISSN: 1573-0409
    Keywords: robot control ; adaptive behavior ; robust intelligent control ; multi-robot systems ; machine learning ; neural networks ; genetic algorithms ; cognitive architecture.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The objective of this paper is to present a cognitive architecture thatutilizes three different methodologies for adaptive, robust control ofrobots behaving intelligently in a team. The robots interact within a worldof objects, and obstacles, performing tasks robustly, while improving theirperformance through learning. The adaptive control of the robots has beenachieved by a novel control system. The Tropism-based cognitive architecturefor the individual behavior of robots in a colony is demonstrated throughexperimental investigation of the robot colony. This architecture is basedon representation of the likes and dislikes of the robots. It is shown thatthe novel architecture is not only robust, but also provides the robots withintelligent adaptive behavior. This objective is achieved by utilization ofthree different techniques of neural networks, machine learning, and geneticalgorithms. Each of these methodologies are applied to the tropismarchitecture, resulting in improvements in the task performance of the robotteam, demonstrating the adaptability and robustness of the proposed controlsystem.
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  • 8
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 157-180 
    ISSN: 1573-0409
    Keywords: robot ; PID control ; neural networks ; learning ; generalization
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract In this article, an approach for improving the performance of industrialrobots using multilayer feedforward neural networks is presented. Thecontroller based on this approach consists of two main components: a PIDcontrol and a neural network. The function of the neural network is tocomplement the PID control for the specific purpose of improving theperformance of the system over time. Analytical and experimental resultsconcerning this synthesis of neural networks and PID control are presented.The analytical results assert that the performance of PID-controlledindustrial robots can be improved through proper utilization of the learningand generalization ability of neural networks. The experimental results,obtained through actual implementation using a commercial industrial robot,demonstrate the effectiveness of such control synthesis for practicalapplications. The results of this work suggest that neural networks could beadded to existing PID-controlled industrial robots for performanceimprovement.
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  • 9
    Electronic Resource
    Electronic Resource
    Springer
    Journal of intelligent and robotic systems 18 (1997), S. 367-397 
    ISSN: 1573-0409
    Keywords: virtual reality ; human-machine system ; robotics ; neural networks ; collision avoidance ; trajectory planning ; conglomerate of spheres
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract This paper describes how virtual tools that represent real robot end-effectors are used in conjunction with a generalized conglomerate-of-spheres approach to collision avoidance in such a way that telerobotic trajectory planning can be accomplished using simple gesture phrases such as ‘put that there while avoiding that’. In this concept, an operator (or set of collaborators) need not train for cumbersome telemanipulation on several multiple-link robots, nor do robots need a priori knowledge of operator intent and exhaustive algorithms for evaluating every aspect of a detailed environment model. The human does what humans do best during task specification, while the robot does what machines do best during trajectory planning and execution. Four telerobotic stages were implemented to demonstrate this strategic supervision concept that will facilitate collaborative control between humans and machines. In the first stage, virtual reality tools are selected from a ‘toolbox’ by the operator(s) and then these virtual tools are computationally interwoven into the live video scene with depth correlation. Each virtual tool is a graphic representation of a robot end-effector (gripper, cutter, or other robot tool) that carries tool-use attributes on how to perform a task. An operator uses an instrumented glove to virtually retrieve the disembodied tool, in the shared scene, and place it near objects and obstacles while giving key-point gesture directives, such as ‘cut there while avoiding that’. Collaborators on a network may alter the plan by changing tools or tool positioning to achieve preferred results from their own perspectives. When parties agree, from wherever they reside geographically, the robot(s) create and execute appropriate trajectories suitable to their own particular links and joints. Stage two generates standard joint-interpolated trajectories, and later creates potential field trajectories if necessary. Stage three tests for collisions with obstacles identified by the operator and modeled as conglomerates of spheres. Stage four involves automatic grasping (or cutting etc.) once the robot camera acquires a close-up view of the object during approach. In this paper particular emphasis is placed on the conglomerate-of-spheres approach to collision detection as integrated with the virtual tools concept for a Puma 560 robot by the Virtual Tools and Robotics Group in the Computer Integrated Manufacturing Laboratory at The Pennsylvania State University (Penn State).
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  • 10
    Electronic Resource
    Electronic Resource
    Springer
    The international journal of advanced manufacturing technology 13 (1997), S. 587-599 
    ISSN: 1433-3015
    Keywords: Fuzzy logic ; neural networks ; Signal-to-noise ratio ; Taguchi parameter design
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract Fuzzy nets have been proposed to combine the learning ability of neural networks and the reasoning ability of fuzzy logic to deal with complex control systems. This paper presents a systematic way of identifying the significant factors and optimising the performance of a fuzzy-nets application. To present the methodology, a model of a truck backing up has been evaluated. Four factors were considered: 1. The number of training sets. 2. The number of fuzzy regions. 3. The membership functions. 4. The fuzzy reasoning methods which would affect the performance of the fuzzy-nets training scheme in nonlinear applications. The Taguchi parameter design was implemented with anL 9 (34) orthogonal array to identify the optimal combination for training consideration. Both raw and signal-to-noise (S/N) ratios were evaluated to identify the optimal combination for the performance of fuzzy-nets training with very limited variation. The performance of the proposed fuzzy-nets scheme for the model of the truck backing up was represented by the average errors between the truck and loading dock: 0.178 units and 0.204 degrees. The results demonstrate that the Taguchi parameter design is a robust approach for optimising the performance of the fuzzy-nets training scheme.
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