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检索条件"任意字段=Conference on Neural Network and Distributed Processing"

共 2988 条记录，以下是151-160 订阅

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Pattern recognition of internal structural defects in industrial radiographic testing based on neural network

Pattern recognition of internal structural defects in indust...

引用

conference on neural network and distributed processing

作者： Ming, M Li, Z Tsing Hua Univ Dept Engn Phys Beijing 100084 Peoples R China

ISBN: (纸本)0819442836

It is shown that an artificial neural network can be used to classify internal structural defects in radiographic nondestructive testing. We design a series of images presenting phantoms to simulate three different classes of defects: porosity, crack, and slag. Features of these defects are selected from domains of geometry, gray statistics, frequency spectrum, and etc. Some of them are especially suitable for pattern recognition in the case of radiographic image. A three-layered neural network trained with back-propagation rule is developed to carry out the classification. The training and testing data for the net are the features extracted from digitized radiographic images. Results are presented with satisfactory recognition rate.

关键词： pattern recognition internal structural defects radiographic testing feature extraction neural network

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Low Complexity Artificial neural network Unit for Sugar Content Detection in Microwave Sensor System

Low Complexity Artificial Neural Network Unit for Sugar Cont...

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Annual Summit and conference of Asia-Pacific-Signal-and-Information-processing-Association (APSIPA)

作者： Leekul, Prapan Soontornwong, Parinya Chivapreecha, Sorwat King Mongkuts Inst Technol Ladkrabang Fac Engn Dept Telecommun Engn Bangkok 10520 Thailand Rajamungala Univ Technol Srivijaya Coll Ind Technol Dept Elect Engn Nakhon Sithammarat 80210 Thailand

ISBN: (纸本)9786163618238

This research presents a low complexity artificial neural network (ANN) unit that used for implementation of microwave sensor system in order to detect sugar content in tested solution. Generally, the hardware implementation of ANN suffers from the number of multiplication that used in network and the difficulty of implementing sigmoid activation function. In this paper, distributed arithmetic (DA) will be used to represent the multiplication and make our ANN unit multiplier less computation unit. An efficient digital implementation of sigmoid activation function by using piecewise linear approximation will be used. Therefore, low complexity ANN unit can be achieved and it will be used in a microwave sensor system for sugar content detection.

关键词： approximation theory chemical variables measurement computerised instrumentation distributed arithmetic microwave detectors neural nets piecewise linear techniques sugar transfer functions ANN unit multiplier artificial neural network digital implementation microwave sensor system multiplication piecewise linear approximation sigmoid activation function sugar content detection Abstracts Artificial neural networks Complexity theory Decision support systems Hardware Microwave sensors Sugar

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A distributed and multithreaded neural event driven simulation framework

A distributed and multithreaded neural event driven simulati...

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IASTED International conference on Parallel and distributed Computing and networks

作者： Mouraud, A Paugam-Moisy, H Puzenat, D Inst Cognit Sci CNRS UMR 5015 67 Blvd Pinel F-69675 Lyon France Univ Antilles Guyane Lab GRIMAAG Guadeloupe France

ISBN: (纸本)088986568X

In a Spiking neural networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of large scale neural networks can take advantage of distributing the neurons on a set of processors (either workstation cluster or parallel computer). This article presents a large scale SNN simulation framework able to gather the benefits of EDS and parallel computing. Two levels of parallelism are combined: distributed mapping of the neural topology, at the network level, and local multithreaded allocation of resources for simultaneous processing of events, at the neuron level. Based on the causality of events, a distributed solution is proposed for solving the complex problem of scheduling without synchronization barrier.

关键词： Spiking neural networks Event-Driven Simulations parallel computing multi-threading scheduling

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Beyond Concurrent Engineering: Parallel distributed Engineering for More Adaptability and Less Energy Consumption

Beyond Concurrent Engineering: Parallel Distributed Engineer...

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20th ISPE International conference on Concurrent Engineering

作者： Fukuda, Shuichi Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781614993025;9781614993018

A Parallel distributed processing (PDP) or neural network model is proposed to re-organize industries so that they can share knowledge and experience among them. This re-organized industry framework, which is called Parallel distributed Engineering here, develops interchangeable components at its intermediate level and combines them into final products to meet the requirements of customers. Thus, it brings forth greater flexibility to adapt to very frequently and extensively changing situations and what is more important, it reduces time, cost and energy consumption and increase productivity considerably. And it will satisfy customers more because their diverse requirements can be more precisely met.

关键词： Parallel distributed processing neural network interchangeable common components reduction of time cost and energy consumption increase of productivity

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neural network ADAPTIVE CONTROLLERS

NEURAL NETWORK ADAPTIVE CONTROLLERS

引用

INTERNATIONAL JOINT CONF ON neural networkS ( IJCNM-90 )

作者： SCOTT, RW COLLINS, DJ US Naval Postgraduate Sch Monterey CA USA

The application of parallel distributed processing (neural networks) to the problem of adaptive control is examined. Focusing on the backpropagation paradigm, a general form of a controller consistent with neural networks is developed and combined with linear least-squares parameter-estimation techniques to suggest a structure for neural network adaptive controllers. This neural network adaptive control structure is then applied to a number of estimation problems using the longitudinal motion of the A-4 aircraft as a model

关键词： Control systems, Adaptive

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A Parallel Radial Basis Probabilistic neural network for scalable data mining in distributed memory machines

A Parallel Radial Basis Probabilistic Neural Network for sca...

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IEEE 24th International conference on Tools with Artificial Intelligence (ICTAI)

作者： Kokkinos, Yiannis Margaritis, Konstantinos Univ Macedonia Parallel & Distributed Proc Lab Dept Appl Informat Thessaloniki 54006 Greece

ISBN: (纸本)9780769549156;9781479902279

This work presents scalable algorithms for basic construction of parallel Radial Basis Probabilistic neural networks. The final goal is to build a neural network that can efficiently be implemented in distributed memory machines. Thus a fast simple parallel training scheme for RBPNNs is studied, that is based almost solely on Gaussian summations which can by their part be efficiently mapped on parallel as well as on pipeline distributed machines. The suggested training scheme is tested for accuracy and performance and can guarantee simplicity, parallelization and linear speed ups in common parallel implementations, namely neuron parallel and pipelining studied here.

关键词： Parallel processing Radial Basis Probabilistic neural networks Large Scale

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Study of manufacturing system based on neural network multi-sensor data fusion and its application

Study of manufacturing system based on neural network multi-...

引用

IEEE International conference on Robotics, Intelligent Systems and Signal processing

作者： Jia, YP Wang, PX Li, Y Tai Yuan Heavy Machinery Inst Dept Machinery & Elect Engn Taiyuan 030024 Peoples R China

ISBN: (纸本)078037925X

A detailed introduction of intelligent inspection and monitoring system for Flexible Manufacturing System (FMS) based on distributed Artificial Intelligence(DAI) is given in this paper This paper deals with a multi-sensor system applied to mechanical fault diagnosing fusion pattern based on neural network, the uncertainty of diagnosis system can be decreased greatly. The methods for data preprocessing, features extraction and construction of the neural network are discussed The real-time data sampling, information fusion and control, management and decision, and operating process of the complex industrial systems are researched. The schemes of distributed hierarchical intelligent control, methods of multi-sensor in formation fusion and controller expert fuzzy neural networks are proposed for complex industrial process. Applying the redundancy technology and multi-sensor information fusion technology, the synchronized automated data acquisition, processing and malfunction warning is realized by computer. The results of application in a textile machinery plant for the FMS showed the effectiveness of the proposed hierarchical intelligent control system.

关键词： flexible manufacturing systems inspection fuzzy neural nets sensor fusion learning (artificial intelligence) fault diagnosis distributed control intelligent control fuzzy control diagnostic expert systems redundancy data acquisition textile industry flexible manufacturing system FMS fuzzy neural networks controller expert system multisensor data fusion intelligent inspection monitoring system distributed artificial intelligence DAI multisensor system mechanical fault diagnosing fusion pattern data preprocessing features extraction real time data sampling complex industrial systems decision making distributed hierarchical intelligent control system multisensor information fusion technology complex industrial process redundancy technology automated data acquisition malfunction warning textile machinery plant

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Stochastic Finite-Time Stability Analysis of Markovian Jumping neural networks With Mixed Time Delays 6

Stochastic Finite-Time Stability Analysis of Markovian Jumpi...

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IEEE Proceedings Sixth Int conference Intelligent Control Inform processing

作者： Huang, He Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China

ISBN: (纸本)9781479917174

The stochastic finite-time stability is studied in this paper for Markovian jumping neural networks with discrete and distributed delays. By defining a proper stochastic Lyapunov functional with mode-dependent Lyapunov matrices, a sufficient condition is derived such that the delayed Markovian jumping neural network under consideration is stochastically finite-time stable with respect to prescribed scalars. The stability criterion is delay- and mode-dependent, and can be readily checked by resorting to available algorithms. Two numerical examples are finally provided to show the application of the developed theory.

关键词： Markovian jumping neural networks finite-time stability time delays distributed delays

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PRIVACY-AWARE EDGE COMPUTING SYSTEM FOR PEOPLE TRACKING

PRIVACY-AWARE EDGE COMPUTING SYSTEM FOR PEOPLE TRACKING

引用

IEEE International conference on Image processing (ICIP)

作者： Yrjanainen, Jukka Ni, Xingyang Adhikari, Bishwo Huttunen, Heikki Tampere Univ Fac Informat Technol & Commun Sci Tampere Finland

ISBN: (纸本)9781728163956

This work studies the practical implementation of a distributed computer vision system for people tracking. A particular focus is on improved data privacy when compared to the traditional surveillance approaches. This is achieved by extracting a feature vector from each detected person by a neural network in real-time in the edge device and transmitting only the feature vector to the cloud, eliminating privacy-sensitive image data transmission and storage. The proposed solution is implemented in a network of Raspberry Pi single-board computers and Intel (R) neural Compute Stick accelerators. The system is tested in an environment where multiple edge devices are sending data to the cloud server for further analysis. In this context, we consider the spectrum of design and implementation aspects of real-time execution of multiple neural networks in a capacity limited edge computing environment.

关键词： Computer Vision Object Detection Re-Identification neural network Edge Computing

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Real Time Classification Analysis in distributed Acoustic Sensing Systems 26

Real Time Classification Analysis in Distributed Acoustic Se...

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26th IEEE Signal processing and Communications Applications conference (SIU)

作者： Maral, Hakan Akgun, Toygar Aktas, Metin ASELSAN Ulasim Guvenl Enerji & Otomasyon Sistemle Sekt Baskanligi Ankara Turkey TOBB Ekon & Teknol Univ Sogutozu MahSogutozu Cd 43 TR-06510 Ankara Turkey

ISBN: (纸本)9781538615010

In this paper, we present a real time treat classification approach to be used in a distributed acoustic sensing system that is developed for monitoring linear assets with a maximum length of 50 kms. The Convolutional Neuaral network (CNN) based deep learning approach is used for treat classification. The classification accuracies and execution times for neural networks with different architecture and complexity are measured. The proposed approach for classifying all the detected treats without decreasing the detection accuracy is introduced. The maximum allowable execution time for the network structure that is appropriate for the proposed approach is analyzed for the worst case scenario. Hence, the most appropriate network architecture selection can be performed based on classification accuracy and also applicability in real-time criterion.

关键词： distributed acoustic sensing phase-OTDR deep learning convolutional neural networks CNN threat detection threat classification real-time processing

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