• Title/Summary/Keyword: Back propagation network (BPN)

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Injection Mold Cooling Circuit Optimization by Back-Propagation Algorithm (오류역전파 알고리즘을 이용한 사출성형 금형 냉각회로 최적화)

  • Rhee, B.O.;Tae, J.S.;Choi, J.H.
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.18 no.4
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    • pp.430-435
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    • 2009
  • The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. The cooling circuit optimization problem that was once solved by a response surface method with 4 design variables. It took too much time for the optimization as an industrial design tool. It is desirable to reduce the optimization time. Therefore, we tried the back-propagation algorithm of artificial neural network(BPN) to find an optimum solution in the cooling circuit design in this research. We tried various ways to select training points for the BPN. The same optimum solution was obtained by applying the BPN with reduced number of training points by the fractional factorial design.

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Personalized Wire and Wireless News Retrieval System Using Intelligent Agent (지능형 에이전트를 이용한 개인화된 유.무선 뉴스 검색 시스템)

  • Han, Seon-Mi;Woo, Jin-Woon
    • The KIPS Transactions:PartB
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    • v.8B no.6
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    • pp.609-616
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    • 2001
  • Today, as the Internet is popularized, information and news retrieval are generalized. However due to the tremendous amount and variety of information, many users appeal the difficulties of information retrieval. Thus in this paper, we propose a news retrieval system, which filters news articles using an intelligent agent with the learning ability of BPN (back propagation neural network). This system also uses a profile to accomodate the personalized news retrieval. This system consists of two major agents, collection agent and learning agent. The collection agent gathers the articles from several news sites, analyzes them, and stores into a database. The learning agent builds the BPN based on the personalized data. In addition, considering the popularity of the wireless internet due to the rapid development of communication technologies, we made this system provide the service through the wireless internet.

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Role of Artificial Neural Networks in Multidisciplinary Optimization and Axiomatic Design

  • Lee, Jong-Soo
    • Proceedings of the KSME Conference
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    • 2008.11a
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    • pp.695-700
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    • 2008
  • Artificial neural network (ANN) has been extensively used in areas of nonlinear system modeling, analysis and design applications. Basically, ANN has its distinct capabilities of implementing system identification and/or function approximation using a number of input/output patterns that can be obtained via numerical and/or experimental manners. The paper describes a role of ANN, especially a back-propagation neural network (BPN) in the context of engineering analysis, design and optimization. Fundamental mechanism of BPN is briefly summarized in terms of training procedure and function approximation. The BPN based causality analysis (CA) is further discussed to realize the problem decomposition in the context of multidisciplinary design optimization. Such CA is also applied to quantitatively evaluate the uncoupled or decoupled design matrix in the context of axiomatic design with the independence axiom.

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Acoustic Emission Source Characterization and Fracture Behavior of Finite-width Plate with a Circular Hole Defect using Artificial Neural Network (인공신경회로망을 이용한 원공결함을 갖는 유한 폭 판재의 음향방출 음원특성과 파괴거동에 관한 연구)

  • Rhee, Zhang-Kyu;Woo, Chang-Ki
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.18 no.2
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    • pp.170-177
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    • 2009
  • The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

Future Location Prediction of Human Through Back-propagation Network (오류-역전파 네트워크를 통한 인간의 미래 위치 예측)

  • Kim, SungYun;Koo, Hoon Jung;Song, Ha Yoon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2012.11a
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    • pp.1732-1735
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    • 2012
  • 인간은 일주일 단위로 유사한 행동 패턴을 가진다고 한다. 이런 점에서 일주일 단위의 시간-공간 기록의 형태인 인간 이동 데이터를 이용하면, 인간의 행동 패턴을 유추해 낼 수 있다. 본 논문에서 인간의 행동을 유추하기 위해 BPN알고리즘을 사용하였다. BPN알고리즘에 대해 설명하고, 인간 이동의 예측에 관한 적용에 관한 BPN알고리즘의 설계 과정을 논의한다. 그리고 해당 실험의 결과와 분석을 제시한다.

A Performance Analysis of Video Smoke Detection based on Back-Propagation Neural Network (오류 역전파 신경망 기반의 연기 검출 성능 분석)

  • Im, Jae-Yoo;Kim, Won-Ho
    • Journal of Satellite, Information and Communications
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    • v.9 no.4
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    • pp.26-31
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    • 2014
  • In this paper, we present performance analysis of video smoke detection based on BPN-Network that is using multi-smoke feature, and Neural Network. Conventional smoke detection method consist of simple or mixed functions using color, temporal, spatial characteristics. However, most of all, they don't consider the early fire conditions. In this paper, we analysis the smoke color and motion characteristics, and revised distinguish the candidate smoke region. Smoke diffusion, transparency and shape features are used for detection stage. Then it apply the BPN-Network (Back-Propagation Neural Network). The simulation results showed 91.31% accuracy and 2.62% of false detection rate.

The Detection of Esophagitis by Using Back Propagation Network Algorithm

  • Seo, Kwang-Wook;Min, Byeong-Ro;Lee, Dae-Weon
    • Journal of Mechanical Science and Technology
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    • v.20 no.11
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    • pp.1873-1880
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    • 2006
  • The results of this study suggest the use of a Back Propagation Network (BPN) algorithm for the detection of esophageal erosions or abnormalities - which are the important signs of esophagitis - in the analysis of the color and textural aspects of clinical images obtained by endoscopy. The authors have investigated the optimization of the learning condition by the number of neurons in the hidden layer within the structure of the neural network. By optimizing learning parameters, we learned and have validated esophageal erosion images and/or ulcers functioning as the critical diagnostic criteria for esophagitis and associated abnormalities. Validation was established by using twenty clinical images. The success rates for detection of esophagitis during calibration and during validation were 97.91% and 96.83%, respectively.

BPN Based Approximate Optimization for Constraint Feasibility (구속조건의 가용성을 보장하는 신경망기반 근사최적설계)

  • Lee, Jong-Soo;Jeong, Hee-Seok;Kwak, No-Sung
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2007.04a
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    • pp.141-144
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    • 2007
  • Given a number of training data, a traditional BPN is normally trained by minimizing the absolute difference between target outputs and approximate outputs. When BPN is used as a meta-model for inequality constraint function, approximate optimal solutions are sometimes actually infeasible in a case where they are active at the constraint boundary. The paper describes the development of the efficient BPN based meta-model that enhances the constraint feasibility of approximate optimal solution. The modified BPN based meta-model is obtained by including the decision condition between lower/upper bounds of a constraint and an approximate value. The proposed approach is verified through a simple mathematical function and a ten-bar planar truss problem.

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Optimization of Transonic Airfoil Using GA Based on Neural Network and Multiple Regression Model (유전 알고리듬과 반응표면을 이용한 천음속 익형의 최적설계)

  • Kim, Yun-Sik;Kim, Jong-Hun;Lee, Jong-Soo
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.26 no.12
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    • pp.2556-2564
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    • 2002
  • The design of airfoil had practiced by repeat tests in its first stage, though an airfoil has as been designed based on simulations according to techniques of computational fluid dynamics. Here, using of traditional optimization is unsuitable because a state of flux is hypersensitive to the shape of airfoil. Therefore the paper optimized the shape of airfoil in transonic region using a genetic algorithm (GA). Response surfaces are based on back propagation neural network (BPN) and regression model. Training data of BPN and regression model were obtained by computational fluid dynamic analysis using CFD-ACE, and each analysis has been designed by design of experiments.

A Study on Performance Diagnostics of a Gas Turbine Engine Using Neural Network (신경회로망을 적용한 가스터빈 엔진의 성능진단 연구)

  • 공창덕;고성희;기자영;강명철
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • 2003.10a
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    • pp.267-270
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    • 2003
  • An intelligent performance diagnostic computer program of a gas turbine using the NN(Neural Network) was developed. Recently on-condition performance monitoring of major gas path components using the GPA(Gas Path Analysis) method has been performed in analyzing of engine faults. However because the types and severities of engine faults are various and complex, it is not easy that all fault conditions of the engine would be monitored only by the GPA approach. Therefore in order to solve this problem, application of using the NNs for learning and diagnosis would be required. Among then, a BPN (Back Propagation Neural Network) with one hidden layer, which can use an updating learning rate, was proposed for diagnostics of PT6A-62 turboprop engine in this work.

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