• Title/Summary/Keyword: simulated network

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Development of Distributed Generic Simulator (GenSim) through Invention of Simulated Network (simNetwork)

  • Koo, Cheol-Hea;Lee, Hoon-Hee;Cheon, Yee-Jin
    • Journal of Astronomy and Space Sciences
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    • v.28 no.3
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    • pp.241-252
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    • 2011
  • A simulated network protocol provides the capability of distributed simulation to a generic simulator. Through this, full coverage of management of data and service handling among separated simulators is achieved. The distributed simulation environment is much more conducive to handling simulation load balancing and hazard treatment than a standalone computer. According to the simulated network protocol, one simulator takes on the role of server and the other simulators take on the role of client, and client is controlled by server. The purpose of the simulated network protocol is to seamlessly connect multiple simulator instances into a single simulation environment. This paper presents the development of a simulated network (simNetwork) that provides the capability of distributed simulation to a generic simulator (GenSim), which is a software simulator of satellites that has been developed by the Korea Aerospace Research Institute since 2010, to use as a flight software validation bench for future satellite development.

Generating Mechanisms of Initial and Candidate Solutions in Simulated Annealing for Packet Communication Network Design Problems (패킷 통신 네트워크 설계를 위한 시뮬레이티드 애닐링 방법에서 초기해와 후보해 생성방법)

  • Yim Dong-Soon;Woo Hoon-Shik
    • Journal of the Korean Operations Research and Management Science Society
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    • v.29 no.3
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    • pp.145-155
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    • 2004
  • The design of a communication network has long been a challenging optimization problem. Since the optimal design of a network topology is a well known as a NP-complete problem, many researches have been conducted to obtain near optimal solutions in polynomial time instead of exact optimal solutions. All of these researches suggested diverse heuristic algorithms that can be applied to network design problems. Among these algorithms, a simulated annealing algorithm has been proved to guarantee a good solution for many NP-complete problems. in applying the simulated annealing algorithms to network design problems, generating mechanisms for initial solutions and candidate solutions play an important role in terms of goodness of a solution and efficiency. This study aims at analyzing these mechanisms through experiments, and then suggesting reliable mechanisms.

Classification of Environmentally Distorted Acoustic Signals in Shallow Water Using Neural Networks : Application to Simulated and Measured Signal

  • Na, Young-Nam;Park, Joung-Soo;Chang, Duck-Hong;Kim, Chun-Duck
    • The Journal of the Acoustical Society of Korea
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    • v.17 no.1E
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    • pp.54-65
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    • 1998
  • This study attempts to test the classifying performance of a neural network and thereby examine its applicability to the signals distorted in a shallow water environment. Linear frequency modulated(LFM) signals are simulated by using an acoustic model and also measured through sea experiment. The network is constructed to have three layers and trained on both data sets. To get normalized power spectra as feature vectors, the study considers the three transforms : shot-time Fourier transform (STFT), wavelet transform (WT) and pseudo Wigner-Ville distribution (PWVD). After trained on the simulated signals over water depth, the network gives over 95% performance with the signal to noise ratio (SNR) being up to-10 dB. Among the transforms, the PWVD presents the best performance particularly in a highly noisy condition. The network performs worse with the summer sound speed profile than with the winter profile. It is also expected to present much different performance by the variation of bottom property. When the network is trained on the measured signals, it gives a little better results than that trained on the simulated data. In conclusion, the simulated signals are successfully applied to training a network, and the trained network performs well in classifying the signals distorted by a surrounding environment and corrupted by noise.

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The Comparison of Neural Network Learning Paradigms: Backpropagation, Simulated Annealing, Genetic Algorithm, and Tabu Search

  • Chen Ming-Kuen
    • Proceedings of the Korean Society for Quality Management Conference
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    • 1998.11a
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    • pp.696-704
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    • 1998
  • Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

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Inverse Analysis Approach to Flow Stress Evaluation by Small Punch Test (소형펀치 시험과 역해석에 의한 재료의 유동응력 결정)

  • Cheon, Jin-Sik
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.24 no.7 s.178
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    • pp.1753-1762
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    • 2000
  • An inverse method is presented to obtain material's flow properties by using small punch test. This procedure employs, as the objective function of inverse analysis, the balance of measured load-di splacement response and calculated one during deformation. In order to guarantee convergence to global minimum, simulated annealing method was adopted to optimize the current objective function. In addition, artificial neural network was used to predict the load-displacement response under given material parameters which is the most time consuming and limits applications of global optimization methods to these kinds of problems. By implementing the simulated annealing for optimization along with calculating load-displacement curve by neural network, material parameters were identified irrespective of initial values within very short time for simulated test data. We also tested the present method for error-containing experimental data and showed that the flow properties of material were well predicted.

Comparison of neural network algorithms for the optimal routing in a Multistage Interconnection Network (MIN의 최적경로 배정을 위한 신경회로망 알고리즘의 비교)

  • Kim, Seong-Su;Gong, Seong-Gon
    • Proceedings of the KIEE Conference
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    • 1995.11a
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    • pp.569-571
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    • 1995
  • This paper compares the simulated annealing and the Hopfield neural network method for an optimal routing in a multistage interconnection network(MIN). The MIN provides a multiple number of paths for ATM cells to avoid cell conflict. Exhaustive search always finds the optimal path, but with heavy computation. Although greedy method sets up a path quickly, the path found need not be optimal. The simulated annealing can find an sub optimal path in time comparable with the greedy method.

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Development of a Modified Random Signal-based Learning using Simulated Annealing

  • Han, Chang-Wook;Lee, Yeunghak
    • Journal of Multimedia Information System
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    • v.2 no.1
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    • pp.179-186
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    • 2015
  • This paper describes the application of a simulated annealing to a random signal-based learning. The simulated annealing is used to generate the reinforcement signal which is used in the random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural network. It is poor at hill-climbing, whereas simulated annealing has an ability of probabilistic hill-climbing. Therefore, hybridizing a random signal-based learning with the simulated annealing can produce better performance than before. The validity of the proposed algorithm is confirmed by applying it to two different examples. One is finding the minimum of the nonlinear function. And the other is the optimization of fuzzy control rules using inverted pendulum.

An Optimization Method Wsing Simulated Annealing for Universal Learning Network

  • Murata, Junichi;Tajiri, Akihito;Hirasawa, Kotaro;Ohbayashi, Masanao
    • 제어로봇시스템학회:학술대회논문집
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    • 1995.10a
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    • pp.183-186
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    • 1995
  • A method is presented for optimization of Universal Learning Networks (ULN), where, together with gradient method, Simulated Annealing (SA) is employed to elude local minima. The effectiveness of the method is shown by its application to control of a crane system.

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Microstructure Characteristics of ZnO of ZnO Varistors Simulated by Voronoi Network

  • Han, Se-Won;He, Jin-Liang;Hwang, Hui-Dong;Kang, Hyung-Boo
    • The Korean Journal of Ceramics
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    • v.3 no.4
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    • pp.239-244
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    • 1997
  • The Voronoi network can be used to effectively simulate the microstructure of ZnO varistors. The nonuniformity in microstructure of simulated ZnO varistor can be changed by setting different disorder degree of Voronoi network. In the region of disorder degree larger than 3 where the simulated microstructures are similar to those the actual ones of ZnO varistors, a chaotic phenomenon exists in the microstructure characteristics. This chaotic property can simulate the original behavior of nonuniformity of electrical characteristics caused by microstructures of ZnO varistors.

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Neural Network Based Classification of Time-Varying Signals Distorted by Shallow Water Environment (천해환경에 의해 변형된 시변신호의 신경망을 통한 식별)

  • Na, Young-Nam;Shim, Tae-Bo;Chang, Duck-Hong;Kim, Chun-Duck
    • Proceedings of the Acoustical Society of Korea Conference
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    • 1997.06a
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    • pp.27-34
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    • 1997
  • In this study , we tried to test the classification performance of a neural netow and thereby to examine its applicability to the signals distorted by a shallow water einvironment . We conducted an acoustic experiment iin a shallow sea near Pohang, Korea in which water depth is about 60m. The signals, on which the network has been tested, is ilinear frequency modulated ones centered on one of the frequencies, 200, 400, 600 and 800 Hz, each being swept up or down with bandwidth 100Hz. we considered two transforms, STFT(short-time Fourier transform) and PWVD (pseudo Wigner-Ville distribution), form which power spectra were derived. The training signals were simulated using an acoutic model based on the Fourier synthesis scheme. When the network has been trained on the measured signals of center frequency 600Hz,it gave a little better results than that trained onthe simulated . With the center frequencies varied, the overall performance reached over 90% except one case of center frequency 800Hz. With the feature extraction techniques(STFT and PWVD) varied,the network showed performance comparable to each other . In conclusion , the signals which have been simulated with water depth were successully applied to training a neural network, and the trained network performed well in classifying the signals distorted by a surrounding environment and corrupted by noise.

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