• 제목/요약/키워드: artificial neural network

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스프레드시트를 활용한 지도학습 인공신경망 매개변수 최적화와 활성화함수 기초교육방법 (Supervised Learning Artificial Neural Network Parameter Optimization and Activation Function Basic Training Method using Spreadsheets)

  • 허경
    • 실천공학교육논문지
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    • v.13 no.2
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    • pp.233-242
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    • 2021
  • 본 논문에서는 비전공자들을 위한 교양과정으로, 기초 인공신경망 과목 커리큘럼을 설계하기 위해, 지도학습 인공신경망 매개변수 최적화 방법과 활성화함수에 대한 기초 교육 방법을 제안하였다. 이를 위해, 프로그래밍 없이, 매개 변수 최적화 해를 스프레드시트로 찾는 방법을 적용하였다. 본 교육 방법을 통해, 인공신경망 동작 및 구현의 기초 원리 교육에 집중할 수 있다. 그리고, 스프레드시트의 시각화된 데이터를 통해 비전공자들의 관심과 교육 효과를 높일 수 있다. 제안한 내용은 인공뉴런과 Sigmoid, ReLU 활성화 함수, 지도학습데이터의 생성, 지도학습 인공신경망 구성과 매개변수 최적화, 스프레드시트를 이용한 지도학습 인공신경망 구현 및 성능 분석 그리고 교육 만족도 분석으로 구성되었다. 본 논문에서는 Sigmoid 뉴런 인공신경망과 ReLU 뉴런 인공신경망에 대해 음수허용 매개변수 최적화를 고려하여, 인공신경망 매개변수 최적화에 대한 네가지 성능분석결과를 교육하는 방법을 제안하고 교육 만족도 분석을 실시하였다.

Comparative Study on Surrogate Modeling Methods for Rapid Electromagnetic Forming Analysis

  • Lee, Seungmin;Kang, Beom-Soo;Lee, Kyunghoon
    • 소성가공
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    • v.27 no.1
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    • pp.28-36
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    • 2018
  • Electromagnetic forming is a type of high-speed forming process to deform a workpiece through a Lorentz force. As the high strain rate in an electromagnetic-forming simulation causes infeasibility in determining constitutive parameters, we employed inverse parameter estimation in the previous study. However, the inverse parameter estimation process required us to spend considerable time, which leads to an increase in computational cost. To overcome the computational obstacle, in this research, we applied two types of surrogate modeling methods and compared them to each other to evaluate which model is best for the electromagnetic-forming simulation. We exploited an artificial neural network and we reduced-order modeling methods. During the construction of a reduced-order model, we extracted orthogonal bases with proper orthogonal decomposition and predicted basis coefficients by utilizing an artificial neural network. After the construction of the surrogate models, we verified the artificial neural network and reduced-order models through training and testing samples. As a result, we determined the artificial neural network model is slightly more accurate than the reduced-order model. However, the construction of the artificial neural network model requires a considerably larger amount of time than that of the reduced-order model. Thus, a reduced order modeling method is more efficient than an artificial neural network for estimating the electromagnetic forming and for the rapid approximation of structural simulations which needs repetitive runs.

Design Of Intrusion Detection System Using Background Machine Learning

  • Kim, Hyung-Hoon;Cho, Jeong-Ran
    • 한국컴퓨터정보학회논문지
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    • v.24 no.5
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    • pp.149-156
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    • 2019
  • The existing subtract image based intrusion detection system for CCTV digital images has a problem that it can not distinguish intruders from moving backgrounds that exist in the natural environment. In this paper, we tried to solve the problems of existing system by designing real - time intrusion detection system for CCTV digital image by combining subtract image based intrusion detection method and background learning artificial neural network technology. Our proposed system consists of three steps: subtract image based intrusion detection, background artificial neural network learning stage, and background artificial neural network evaluation stage. The final intrusion detection result is a combination of result of the subtract image based intrusion detection and the final intrusion detection result of the background artificial neural network. The step of subtract image based intrusion detection is a step of determining the occurrence of intrusion by obtaining a difference image between the background cumulative average image and the current frame image. In the background artificial neural network learning, the background is learned in a situation in which no intrusion occurs, and it is learned by dividing into a detection window unit set by the user. In the background artificial neural network evaluation, the learned background artificial neural network is used to produce background recognition or intrusion detection in the detection window unit. The proposed background learning intrusion detection system is able to detect intrusion more precisely than existing subtract image based intrusion detection system and adaptively execute machine learning on the background so that it can be operated as highly practical intrusion detection system.

인공신경망기법을 이용한 하천수질인자의 예측모델링 - BOD와 DO를 중심으로- (Predictive Modeling of River Water Quality Factors Using Artificial Neural Network Technique - Focusing on BOD and DO-)

  • 조현경
    • 한국환경과학회지
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    • v.9 no.6
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    • pp.455-462
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    • 2000
  • This study aims at the development of the model for a forecasting of water quality in river basins using artificial neural network technique. Water quality by Artificial Neural Network Model forecasted and compared with observed values at the Sangju q and Dalsung stations in Nakdong river basin. For it, a multi-layer neural network was constructed to forecast river water quality. The neural network learns continuous-valued input and output data. Input data was selected as BOD, CO discharge and precipitation. As a result, it showed that method III of three methods was suitable more han other methods by statistical test(ME, MSE, Bias and VER). Therefore, it showed that Artificial Neural Network Model was suitable for forecasting river water quality.

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인공신경망을 이용한 병렬로봇의 정밀한 추적제어 (Precise Tracking Control of Parallel Robot using Artificial Neural Network)

  • 송낙윤;조황
    • 한국정밀공학회지
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    • v.16 no.1
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    • pp.200-209
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    • 1999
  • This paper presents a precise tracking control scheme for the proposed parallel robot using artificial neural network. This control scheme is composed of three feedback controllers and one feedforward controller. Conventional PD controller and artificial neural network are used as feedback and feedforward controller respectively. A backpropagation learning strategy is applied to the training of artificial neural network, and PD controller outputs are used as target outputs. The PD controllers are designed at the robot dynamics based on inter-relationship between active joints and moving platform. Feedback controllers insure the total stability of system, and feedforward controller generates the control signal for trajectory tracking. The precise tracking performance of proposed control scheme is proved by computer simulation.

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Correlation of Liquid-Liquid Equilibrium of Four Binary Hydrocarbon-Water Systems, Using an Improved Artificial Neural Network Model

  • Lv, Hui-Chao;Shen, Yan-Hong
    • 대한화학회지
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    • v.57 no.3
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    • pp.370-376
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    • 2013
  • A back propagation artificial neural network model with one hidden layer is established to correlate the liquid-liquid equilibrium data of hydrocarbon-water systems. The model has four inputs and two outputs. The network is systematically trained with 48 data points in the range of 283.15 to 405.37K. Statistical analyses show that the optimised neural network model can yield excellent agreement with experimental data(the average absolute deviations equal to 0.037% and 0.0012% for the correlated mole fractions of hydrocarbon in two coexisting liquid phases respectively). The comparison in terms of average absolute deviation between the correlated mole fractions for each binary system and literature results indicates that the artificial neural network model gives far better results. This study also shows that artificial neural network model could be developed for the phase equilibria for a family of hydrocarbon-water binaries.

Design and Implementation of Routing System Using Artificial Neural Network

  • Kim, Jun-Yeong;Kim, Seog-Gyu
    • 한국컴퓨터정보학회논문지
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    • v.22 no.12
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    • pp.137-143
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    • 2017
  • In this paper, we propose optimal route searching algorithm using ANN(Artificial Neural Network) and implement route searching system. Our proposed scheme shows that the route using artificial neural network is almost same as the route using Dijkstra's algorithm but the time in our propose algorithm is shorter than that of existing Dijkstra's algorithm. Proposed route searching method using artificial neural network has better performance than exiting route searching method because it use several weight value in making different routes. Through simulation, we show that our proposed routing system improves the performance and reduces time to make route irrespective of the number of hidden layers.

심층혼합처리된 개량토의 일축압축강도 추정을 위한 인공신경망의 적용 (Application of Artificial Neural Network Theory for Evaluation of Unconfined Compression Strength of Deep Cement Mixing Treated Soil)

  • 김영상;정현철;허정원;정경환
    • 한국지반공학회:학술대회논문집
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    • pp.1159-1164
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    • 2006
  • In this paper an artificial neural network model is developed to estimate the unconfined compression strength of Deep Cement Mixing(DCM) treated soil. A database which consists of a number of unconfined compression test result compiled from 9 clay sites is used to train and test of the artificial neural network model. Developed neural network model requires water content of soil, unit weight of soil, passing percent of #200 sieve, weight of cement, w-c ratio as input variables. It is found that the developed artificial neural network model can predict more precise and reliable unconfined compression strength than the conventional empirical models.

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SVM과 신경회로망을 이용한 비선형시스템의 고장감지와 분류방법 연구 (A Study on a Fault Detection and Isolation Method of Nonlinear Systems using SVM and Neural Network)

  • 이인수;조정환;서해문;남윤석
    • 제어로봇시스템학회논문지
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    • v.18 no.6
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    • pp.540-545
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    • 2012
  • In this paper, we propose a fault diagnosis method using artificial neural network and SVM (Support Vector Machine) to detect and isolate faults in the nonlinear systems. The proposed algorithm consists of two main parts: fault detection through threshold testing using a artificial neural network and fault isolation by SVM fault classifier. In the proposed method a fault is detected when the errors between the actual system output and the artificial neural network nominal system output cross a predetermined threshold. Once a fault in the nonlinear system is detected the SVM fault classifier isolates the fault. The computer simulation results demonstrate the effectiveness of the proposed SVM and artificial neural network based fault diagnosis method.

신경회로망을 이용한 미케니컬 실의 이상상태 감시 (Monitoring of Mechanical Seal Failure with Artificial Neural Network)

  • Lee, W.K.;Lim, S.J.;Namgung, S.
    • 한국정밀공학회지
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    • v.12 no.12
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    • pp.30-37
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    • 1995
  • The mechanical seals, which are installed in rotating machines like pump and compressor, are gengrally used as sealing devices in the many fields of industries. The failure of mechanical seals such as leakage,fast and severe wear, excessive torque, and squeaking results in big problems. To monitor the failure of mechanical seals and to propose the proper monitoring techniques with artificial neural network, sliding wear experiments were conducted. Torque and temperature of the mechanical seals were measured during experiments. Optical microstructure was observed for the wear processing after every 10 minute sliding at rotation speed of 1750 rpm and scanning electron microscopy was also observed. During the experiment, the variation of torque and temperature that meant an abnormal phenomenon, was observed. That experimental data recorded were applied to the developed monitoring system with artificial neural network. This study concludes that torque and temperature of mechanical seals wil be used to identify and to monitor the condition of sliding motion of mechanical seals. An availability to monitor the mechanical seal failure with artificial neural network was confirmed.

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