• Title/Summary/Keyword: Artificial neural network

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A Training Case Study of Deep Learning Artificial Neural Networks for Teacher Educations (교사교육을 위한 딥러닝 인공신경망 교육 사례 연구)

  • Hur, Kyeong
    • 한국정보교육학회:학술대회논문집
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    • pp.385-391
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    • 2021
  • In this paper, a case of deep learning artificial neural network education was studied for artificial intelligence literacy education for preservice teachers and incumbent teachers. In addition, through the proposed educational case, we tried to explore the contents of artificial neural network principle education that elementary, middle and high school students can experience. To this end, first, an example of training on the principle of operation of an artificial neural network that recognizes two types of images is presented. And as an artificial neural network extension application education case, an artificial neural network education case for recognizing three types of images was presented. The number of output layers was changed according to the number of images to be recognized by the artificial neural network, and the cases implemented in a spreadsheet were divided and explained. In addition, in order to experience the operation results of the artificial neural network, we presented the educational contents to directly write the learning data necessary for the artificial neural network of the supervised learning method. In this paper, the implementation of the artificial neural network and the recognition test results are visually presented using a spreadsheet.

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

  • Hur, Kyeong
    • Journal of Practical Engineering Education
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    • v.13 no.2
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    • pp.233-242
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    • 2021
  • In this paper, as a liberal arts course for non-majors, we proposed a supervised learning artificial neural network parameter optimization method and a basic education method for activation function to design a basic artificial neural network subject curriculum. For this, a method of finding a parameter optimization solution in a spreadsheet without programming was applied. Through this training method, you can focus on the basic principles of artificial neural network operation and implementation. And, it is possible to increase the interest and educational effect of non-majors through the visualized data of the spreadsheet. The proposed contents consisted of artificial neurons with sigmoid and ReLU activation functions, supervised learning data generation, supervised learning artificial neural network configuration and parameter optimization, supervised learning artificial neural network implementation and performance analysis using spreadsheets, and education satisfaction analysis. In this paper, considering the optimization of negative parameters for the sigmoid neural network and the ReLU neuron artificial neural network, we propose a training method for the four performance analysis results on the parameter optimization of the artificial neural network, and conduct a training satisfaction analysis.

Comparative Study on Surrogate Modeling Methods for Rapid Electromagnetic Forming Analysis

  • Lee, Seungmin;Kang, Beom-Soo;Lee, Kyunghoon
    • Transactions of Materials Processing
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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
    • Journal of the Korea Society of Computer and Information
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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.

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

  • 조현경
    • Journal of Environmental Science International
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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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Precise Tracking Control of Parallel Robot using Artificial Neural Network (인공신경망을 이용한 병렬로봇의 정밀한 추적제어)

  • Song, Nak-Yun;Cho, Whang
    • Journal of the Korean Society for Precision Engineering
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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
    • Journal of the Korean Chemical Society
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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
    • Journal of the Korea Society of Computer and Information
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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.

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

  • Kim, Young-Sang;Jeong, Hyun-Chel;Huh, Jung-Won;Jeong, Gyeong-Hwan
    • Proceedings of the Korean Geotechical Society Conference
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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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A Study on a Fault Detection and Isolation Method of Nonlinear Systems using SVM and Neural Network (SVM과 신경회로망을 이용한 비선형시스템의 고장감지와 분류방법 연구)

  • Lee, In-Soo;Cho, Jung-Hwan;Seo, Hae-Moon;Nam, Yoon-Seok
    • Journal of Institute of Control, Robotics and Systems
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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.