• Title/Summary/Keyword: Multi-layer back propagation

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Web-based Design Support System for Automotive Steel Pulley (웹 기반 자동차용 스틸 풀리 설계 지원 시스템)

  • Kim, Hyung-Jung;Lee, Kyung-Tae;Chun, Doo-Man;Ahn, Sung-Hoon;Jang, Jae-Duk
    • Transactions of the Korean Society of Automotive Engineers
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    • v.16 no.6
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    • pp.39-47
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    • 2008
  • In this research, a web-based design support system is constructed for the design process of automotive steel pulley to gather engineering knowledge from pulley design data. In the design search module, a clustering tool for design data is proposed using K-means clustering algorithm. To obtain correlational patterns between design and FEA (Finite Element Analysis) data, a Multi-layer Back Propagation Network (MBPN) is applied. With the analyzed patterns from a number of simulation data, an estimation of minimum von mises can be provided for given design parameters of pulleys. The case study revealed fast estimation of minimum stress in the pulley within 12% error.

Site Application of Artificial Neural Network for Tunnel Construction (인공신경망을 이용한 터널시공에서 현장 적용성)

  • Song, Joohyeon;Chae, Hwiyoung;Chun, Byungsik
    • Journal of the Korean GEO-environmental Society
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    • v.13 no.8
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    • pp.25-33
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    • 2012
  • Although it is important to reflect the accurate information of the ground condition in the tunnel design, the analysis and design are conducted by limited information because it is very difficult to consider various geographies and geotechnical conditions. When the tunnel is under construction, examination of accurate safety and prediction of behavior are overcome the limits of predicting behavior by Artificial Neural Network in this study. First, construct the suitable structure after the data of field was made sure by the multi-layer back propagation, then apply with algorithm. Employ the result of measured data from database, and consider the influence factor of tunnel, like supporting pattern, RMR, Q, the types of rock, excavation length, excavation shape, excavation over, to carry out the reliable analysis through field applicability of Artificial Neural Network. After studying, using the ANN model to predict the shearing displacement, convergence displacement, underground displacement, Rock bolt output follow the excavation over of tunnel construction field, then determine the field applicability with ANN through field measured value and comparison analysis when tunnel is being constructed.

Development of On-line Grading System Using Two Surface Images of Dried Oak Mushrooms (양면영상을 이용한 온라인 검표고 등급판정 시스템 개발)

  • Hwang, H.;Lee, C. H.;Kim, S. C.
    • Journal of Biosystems Engineering
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    • v.24 no.2
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    • pp.153-158
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    • 1999
  • As a basic research for the development of the automatic grading and sorting system for dried oak mushrooms, the device to acquire both cap and gill side images of mushroom has been developed and neural network based side recognition and quality grading has been proposed via inputting both side images. 20 quality grades have been selected considering the requirement of grade classifications imposed by the mushroom company. Developed DC motor driven‘V’type reversing device for the image acquisition of both side images of mushroom showed more than 95% success. Most error was caused by very small size mushrooms with a radius of around 1cm. However, it required a further research to reduce the reversing time. Grading and side recognition were performed via inputting normalized size factors and average gray levels of $8{\times}8$ grids converted from the raw images of both surfaces to the multi-layer back propagation(BP) network. Accuracy of the grading showed about 88.5% and the total grading time including reversing operation was around 2 seconds.

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A Study on the Damping Loads Prediction to prevent Harmonic Resonance during the Power System Restoration (전력계통의 정전복구시 고조파 공진억제를 위한 완충부하투입량 예측에 관한 연구)

  • Lee, Heung-Jae;Yu, Won-Kun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.62 no.7
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    • pp.913-917
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    • 2013
  • During the restoration process of primary restorative transmission system, some over voltages may happen due to nonlinear interaction between unloaded transformers and transmission systems. These over voltages caused by harmonic resonance can be suppressed by inserting damping loads before energizing transformers. But it is very difficult to predict the occurrence possibility of harmonic resonance and complex simulation must be repeated to estimate the sufficient damping loads. This paper presents a damping loads prediction system to prevent harmonic resonance. Detailed analysis of the relationship between harmonic resonance and the amount of damping loads is discussed. The prediction system is developed using a curve fitting and a neural network based on this relationship. A curve fitting used a Gaussian function based on non-linear least square method and multi-layer back-propagation neural network is applied. The system is applied to primary restorative transmission lines in korean power system and the result showed satisfactory performance.

A Study on the Number Recognition using Cellular Neural Network (Cellular Neural Network을 이용한 숫자인식에 관한 연구)

  • 전흥우;김명관;정금섭
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.6
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    • pp.819-826
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    • 2002
  • Cellular neural networks(CNN) are neural networks that have locally connected characteristics and real-time image processing. Locally connected characteristics are suitable for VLSI implementation. It also has applications in such areas as image processing and pattern recognition. In this thesis cellular neural networks are used for feature detection in number recognition at the stage of re-processing. The four or six directional shadow detectors are used in numbers recognition. At the stage of classification, this result of feature detection was simulated by using a multi-layer back Propagation neural network. The experiments indicate that the CNN feature detectors capture good features for number recognition tasks.

A Study on Instrumentation Results Analysis Using Artificial Neural Network in Tunnel Area (인공신경망을 이용한 터널시공 시 계측결과 분석에 관한 연구)

  • Lee, Jong-Hwi;Han, Dong-Geun;Byun, Yo-Seph;Chun, Byung-Sik
    • Proceedings of the Korean Geotechical Society Conference
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    • 2010.09b
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    • pp.21-31
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    • 2010
  • Although it is important to reflect the accurate information of the ground condition in the tunnel design, the analysis and design are conducted by limited information because it is very difficult to get it practically on considering various geography and geotechnical condition. So construction management of information concept is required to manage immediately on the field condition because it is very time-consuming to establish the countermeasure of underground reinforcement and the pattern change of Bo. Therefore, when construction is on tunnel area, examination of accurate safety and prediction of behavior is performed to overcomes the limit of predicting behavior by using Artificial Neural Network(ANN) in this study. Firstly, the field data was secured. Secondly, suitable structure was made on multi-layer perceptrons among the ANN. Thirdly, learning algorithm-propagated applies to ANN. The data for the learn of field application using ANN was used by considering impact factors, which influenced the behavior of tunnel, and performing credibility analysis. crown displacement, spring displacement, subsurfacement, and rock bolt axial force are predicted at the tunnel construction and on-site application was confirmed by using ANN from analyzing and comparing with measurement value of on-site. In this study, the data from Seoul Highway $\bigcirc\bigcirc$ tunnel section was applied to the ANN Theory, and the analysis on the investigate value and the reasoning for the value associated with field application was performed.

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A Study on Intelligent Control of Real-Time Working Motion Generation of Bipped Robot (2족 보행로봇의 실시간 작업동작 생성을 위한 지능제어에 관한 연구)

  • Kim, Min-Seong;Jo, Sang-Young;Koo, Young-Mok;Jeong, Yang-Gun;Han, Sung-Hyun
    • Journal of the Korean Society of Industry Convergence
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    • v.19 no.1
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    • pp.1-9
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    • 2016
  • In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.

Development of an Artificial Neural Expert System for Rational Determination of Lateral Earth Pressure Coefficient (합리적인 측압계수 결정을 위한 인공신경 전문가 시스템의 개발)

  • 문상호;문현구
    • Journal of the Korean Geotechnical Society
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    • v.15 no.1
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    • pp.99-112
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    • 1999
  • By using 92 values of lateral earth pressure coefficient(K) measured in Korea, the tendency of K with varying depth is analyzed and compared with the range of K defined by Hoek and Brown. The horizontal stress is generally larger than the vertical stress in Korea : About 84 % of K values are above 1. In this study, the theory of elasto-plasticity is applied to analyze the variation of K values, and the results are compared with those of numerical analysis. This reveals that the erosion, sedimentation and weathering of earth crust are important factors in the determination of K values. Surface erosion, large lateral pressure and good rock mass increase the K values, but sedimentation decreases the K values. This study enable us to analyze the effects of geological processes on the K values, especially at shallow depth where underground excavation takes place. A neural network expert system using multi-layer back-propagation algorithm is developed to predict the K values. The neural network model has a correlation coefficient above 0.996 when it is compared with measured data. The comparison with 9 measured data which are not included in the back-propagation learning has shown an average inference error of 20% and the correlation coefficient above 0.95. The expert system developed in this study can be used for reliable determination of K values.

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