• 제목/요약/키워드: Artificial Neural Network

검색결과 3,014건 처리시간 0.025초

인공 신경회로망을 이용한 추적 제어기의 구성 및 최적 추적 제어기와의 비교 연구 (Design of tracking controller Using Artificial Neural Network & comparison with an Optimal Track ing Controller)

  • 박영문;이규원;최면송
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1993년도 하계학술대회 논문집 A
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    • pp.51-53
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    • 1993
  • This paper proposes a design of the tracking controller using artificial neural network and the compare the result with a result of optimal controller. In practical use, conventional Optimal controller has some limits. First, optimal controller can be designed only for linear system. Second, for many systems state observation is difficult or sometimes impossible. But the controller using artificial neural network does not need mathmatical model of the system including state observation, so it can be used for both linear and nonlinear system with no additional cost for nonlinearity. Designed multi layer neural network controller is composed of two parts, feedforward controller gives a steady state input & feedback controller gives transient input via minimizing the quadratic cost function. From the comparison of the results of the simulation of linear & nonlinear plant, the plant controlled by using neural network controller shows the trajectory similar to that of the plant controlled by an optimal controller.

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인공신경망을 이용한 주조 스테인리스강의 열취화 민감도 평가 (Evaluation of Thermal Embrittlement Susceptibility in Cast Austenitic Stainless Steel Using Artificial Neural Network)

  • 김철;박흥배;진태은;정일석
    • 대한기계학회논문집A
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    • 제28권4호
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    • pp.460-466
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    • 2004
  • Cast austenitic stainless steel is used for several components, such as primary coolant piping, elbow, pump casing and valve bodies in light water reactors. These components are subject to thermal aging at the reactor operating temperature. Thermal aging results in spinodal decomposition of the delta-ferrite leading to increased strength and decreased toughness. This study shows that ferrite content can be predicted by use of the artificial neural network. The neural network has trained teaming data of chemical components and ferrite contents using backpropagation learning process. The predicted results of the ferrite content using trained neural network are in good agreement with experimental ones.

인공신경망을 이용한 이면비드 예측 및 용접성 평가 (Back-bead Prediction and Weldability Estimation Using An Artificial Neural Network)

  • 이정익;고병갑
    • 한국공작기계학회논문집
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    • 제16권4호
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    • pp.79-86
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    • 2007
  • The shape of excessive penetration mainly depends on welding conditions(welding current and welding voltage), and welding process(groove gap and welding speed). These conditions are the major affecting factors to width and height of back bead. In this paper, back-bead prediction and weldability estimation using artificial neural network were investigated. Results are as follows. 1) If groove gap, welding current, welding voltage and welding speed will be previously determined as a welding condition, width and height of back bead can be predicted by artificial neural network system without experimental measurement. 2) From the result applied to three weld quality levels(ISO 5817), both experimented measurement using vision sensor and predicted mean values by artificial neural network showed good agreement. 3) The width and height of back bead are proportional to groove gap, welding current and welding voltage, but welding speed. is not.

Development and application of artificial neural network for landslide susceptibility mapping and its verfication at Janghung, Korea

  • Yu, Young-Tae;Lee, Moung-Jin;Won, Joong-Sun
    • 한국GIS학회:학술대회논문집
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    • 한국GIS학회 2003년도 공동 춘계학술대회 논문집
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    • pp.77-82
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    • 2003
  • The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural network and to apply the developed techniques to the study area of janghung in Korea. Landslide locations were identified in the study area from interpretation of satellite image and field survey data, and a spatial database of the topography, soil, forest and land use were consturced. The 13 landslide-related factors were extracted from the spatial database. Using those factors, landslide susceptibility was analyzed by artificial neural network methods, and the susceptibility map was made with a e15 program. For this, the weights of each factor were determinated in 5 cases by the backpropagation method, which is a type of artificial neural network method. Then the landslide susceptibility indexes were calculated using the weights and the susceptibility maps were made with a GIS to the 5 cases. A GIS was used to efficiently analyze the vast amount of data, and an artificial neural network was turned out be an effective tool to analyze the landslide susceptibility.

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인공신경망을 이용한 머플러의 피로 수명 예측 (The prediction of fatigue life of muffler by artificial neural network)

  • 박순철;강성수;윤진호;김국용
    • Journal of Advanced Marine Engineering and Technology
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    • 제37권8호
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    • pp.869-876
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    • 2013
  • 본 연구에서는 초기 개발 단계에서 신속하면서도 정확한 머플러의 피로 수명을 예측하기 위하여 인공신경망을 통해서 용접부 특성을 고려한 머플러의 피로 수명을 예측하는 프로세스를 개발하였다. 머플러 피로와 파손 특성을 파악하기 위하여 굽힘 피로 시험을 수행하였다. 머플러 용접부 특성을 고려하기 위하여 인공신경망 학습 변수로 용접부 최대 응력을 선정하였으며 이를 이용하여 피로수명을 예측하였다. 인공신경망과 기존 피로 노치 계수를 이용한 피로 수명 예측 결과 비교를 통하여 인공신경망을 이용한 머플러 피로 수명 예측 방법의 타당성을 검증하였다.

난방시스템 및 개구부의 통합제어를 위한 규칙기반제어법 및 인공신경망기반제어법의 성능비교 (Development of Integrated Control Methods for the Heating Device and Surface Openings based on the Performance Tests of the Rule-Based and Artificial-Neural-Network-Based Control Logics)

  • 문진우
    • KIEAE Journal
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    • 제14권3호
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    • pp.97-103
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    • 2014
  • This study aimed at developing integrated logic for controlling heating device and openings of the double skin facade buildings. Two major logics were developed-rule-based control logic and artificial neural network based control logic. The rule based logic represented the widely applied conventional method while the artificial neural network based logic meant the optimal method. Applying the optimal method, the predictive and adaptive controls were feasible for supplying the advanced thermal indoor environment. Comparative performance tests were conducted using the numerical computer simulation tools such as MATLAB (Matrix Laboratory) and TRNSYS (Transient Systems Simulation). Analysis on the test results in the test module revealed that the artificial neural network-based control logics provided more comfortable and stable temperature conditions based on the optimal control of the heating device and opening conditions of the double skin facades. However, the amount of heat supply to the indoor space by the optimal method was increased for the better thermal conditioning. The number of on/off moments of the heating device, on the other hand, was significantly reduced. Therefore, the optimal logic is expected to beneficial to create more comfortable thermal environment and to potentially prevent system degradation.

Artificial Neural Network를 이용한 논문 저자 식별 (Author Identification Using Artificial Neural Network)

  • 정지수;윤지원
    • 정보보호학회논문지
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    • 제26권5호
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    • pp.1191-1199
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    • 2016
  • 논문 심사는 공정성을 확보하기 위하여 누가, 누구의 논문을 리뷰하는지 알 수 없도록 블라인드 리뷰를 시행한다. 하지만 일반적으로 논문은 저자의 연구 분야뿐만 아니라 저자가 자주 사용하는 단어, 어휘 등으로 이루어지기 때문에 저자의 정보를 숨기더라도 논문의 내용을 통해 저자를 파악할 수 있다. 본 논문에서는 저자 20명의 논문 315편을 수집하고 텍스트를 추출하여 데이터 정제 작업을 수행하였다. 그리고 정제 작업을 통해 추출된 단어를 추출해내어 인공신경망(artificial neural network)을 통한 분류를 진행함으로써 블라인드 리뷰(blind review)의 우회 가능성을 보였다. 실험을 통해 기존 블라인드 리뷰 시스템의 한계점을 보임으로써 향후 더욱 안전한 블라인드 리뷰 시스템의 필요성을 강조하였다.

An Experimental Investigation of the Application of Artificial Neural Network Techniques to Predict the Cyclic Polarization Curves of AL-6XN Alloy with Sensitization

  • Jung, Kwang-Hu;Kim, Seong-Jong
    • Corrosion Science and Technology
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    • 제20권2호
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    • pp.62-68
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    • 2021
  • Artificial neural network techniques show an excellent ability to predict the data (output) for various complex characteristics (input). It is primarily specialized to solve nonlinear relationship problems. This study is an experimental investigation that applies artificial neural network techniques and an experimental design to predict the cyclic polarization curves of the super-austenitic stainless steel AL-6XN alloy with sensitization. A cyclic polarization test was conducted in a 3.5% NaCl solution based on an experimental design matrix with various factors (degree of sensitization, temperature, pH) and their levels, and a total of 36 cyclic polarization data were acquired. The 36 cyclic polarization patterns were used as training data for the artificial neural network model. As a result, the supervised learning algorithms with back-propagation showed high learning and prediction performances. The model showed an excellent training performance (R2=0.998) and a considerable prediction performance (R2=0.812) for the conditions that were not included in the training data.

인공신경망을 이용한 머신러닝 기반의 연료펌프 고장예지 연구 (Study of Fuel Pump Failure Prognostic Based on Machine Learning Using Artificial Neural Network)

  • 최홍;김태경;허경린;최성대;허장욱
    • 한국기계가공학회지
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    • 제18권9호
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    • pp.52-57
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    • 2019
  • The key technology of the fourth industrial revolution is artificial intelligence and machine learning. In this study, FMEA was performed on fuel pumps used as key items in most systems to identify major failure components, and artificial neural networks were built using big data. The main failure mode of the fuel pump identified by the test was coil damage due to overheating. Based on the artificial neural network built, machine learning was conducted to predict the failure and the mean error rate was 4.9% when the number of hidden nodes in the artificial neural network was three and the temperature increased to $140^{\circ}C$ rapidly.

Artificial neural network calculations for a receding contact problem

  • Yaylaci, Ecren Uzun;Yaylaci, Murat;Olmez, Hasan;Birinci, Ahmet
    • Computers and Concrete
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    • 제25권6호
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    • pp.551-563
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    • 2020
  • This paper investigates the artificial neural network (ANN) to predict the dimensionless parameters for the maximum contact pressures and contact areas of a contact problem. Firstly, the problem is formulated and solved theoretically by using Theory of Elasticity and Integral Transform Technique. Secondly, the contact problem has been extended based on the ANN. The multilayer perceptron (MLP) with three-layer was used to calculate the contact distances. External load, distance between the two quarter planes, layer heights and material properties were created by giving examples of different values were used at the training and test stages of ANN. Program code was rewritten in C++. Different types of network structures were used in the training process. The accuracy of the trained neural networks for the case was tested using 173 new data which were generated via theoretical solutions so as to determine the best network model. As a result, minimum deviation value (difference between theoretical and C++ ANN results) of was obtained for the network model. Theoretical results were compared with artificial neural network results and well agreements between them were achieved.