• Title/Summary/Keyword: Artificial Neural Network

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A Study on Prediction of Attendance in Korean Baseball League Using Artificial Neural Network (인경신경망을 이용한 한국프로야구 관중 수요 예측에 관한 연구)

  • Park, Jinuk;Park, Sanghyun
    • KIPS Transactions on Software and Data Engineering
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    • v.6 no.12
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    • pp.565-572
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    • 2017
  • Traditional method for time series analysis, autoregressive integrated moving average (ARIMA) allows to mine significant patterns from the past observations using autocorrelation and to forecast future sequences. However, Korean baseball games do not have regular intervals to analyze relationship among the past attendance observations. To address this issue, we propose artificial neural network (ANN) based attendance prediction model using various measures including performance, team characteristics and social influences. We optimized ANNs using grid search to construct optimal model for regression problem. The evaluation shows that the optimal and ensemble model outperform the baseline model, linear regression model.

WEED DETECTION BY MACHINE VISION AND ARTIFICIAL NEURAL NETWORK

  • S. I. Cho;Lee, D. S.;J. Y. Jeong
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 2000.11b
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    • pp.270-278
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    • 2000
  • A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.

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A Development of Optimal Design Model for Initial Blank Shape Using Artificial Neural Network in Rectangular Case Forming with Large Aspect Ratio (세장비가 큰 사각케이스 성형 공정에서의 인공신경망을 적용한 초기 블랭크 형상 최적설계 모델 개발)

  • Kwak, M.J.;Park, J.W.;Park, K.T.;Kang, B.S.
    • Transactions of Materials Processing
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    • v.29 no.5
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    • pp.272-281
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    • 2020
  • As the thickness of mobile communication devices is getting thinner, the size of the internal parts is also getting smaller. Among them, the battery case requires a high-level deep drawing technique because it has a rectangular shape with a large aspect ratio. In this study, the initial blank shape was optimized to minimize earing in a multi-stage deep drawing process using an artificial neural network(ANN). There has been no reported case of applying artificial neural network technology to the initial blank optimal design for a square case with large aspect ratio. The training data for ANN were obtained though simulation, and the model reliability was verified by performing comparative study with regression model using random sample test and goodness-of-fit test. Finally, the optimal design of the initial blank shape was performed through the verified ANN model.

Classification of Tire Tread Wear Using Accelerometer Signals through an Artificial Neural Network (인공신경망을 이용한 가속도 센서 기반 타이어 트레드 마모도 판별 알고리즘)

  • Kim, Young-Jin;Kim, Hyeong-Jun;Han, Jun-Young;Lee, Suk
    • Journal of the Korean Society of Industry Convergence
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    • v.23 no.2_2
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    • pp.163-171
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    • 2020
  • The condition of tire tread is a key parameter closely related to the driving safety of a vehicle, which affects the contact force of the tire for braking, accelerating and cornering. The major factor influencing the contact force is tread wear, and the more tire tread wears out, the higher risk of losing control of a vehicle exits. The tire tread condition is generally checked by visual inspection that can be easily forgotten. In this paper, we propose the intelligent tire (iTire) system that consists of an acceleration sensor, a wireless signal transmission unit and a tread classifier. In addition, we also presents classification algorithm that transforms the acceleration signal into the frequency domain and extracts the features of several frequency bands as inputs to an artificial neural network. The artificial neural network for classifying tire wear was designed with an Multiple Layer Perceptron (MLP) model. Experiments showed that tread wear classification accuracy was over 80%.

CROSS-VALIDATION OF ARTIFICIAL NEURAL NETWORK FOR LANDSLIDE SUSCEPTIBILITY ANALYSIS: A CASE STUDY OF KOREA

  • LEE SARO;LEE MOUNG-JIN;WON JOONG-SUN
    • Proceedings of the KSRS Conference
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    • 2004.10a
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    • pp.298-301
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    • 2004
  • The aim of this study is to cross-validate of spatial probability model, artificial neural network at Boun, Korea, using a Geographic Information System (GIS). Landslide locations were identified in the Boun, Janghung and Youngin areas from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, forest cover and land use were constructed to spatial data-sets. The factors that influence landslide occurrence, such as slope, aspect and curvature of topography, were calculated from the topographic database. Topographic type, texture, material, drainage and effective soil thickness were extracted from the soil database, and type, diameter, age and density of forest were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using the landslide­occurrence factors by artificial neural network model. For the validation and cross-validation, the result of the analysis was applied to each study areas. The validation and cross-validate results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

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MINERAL POTENTIAL MAPPING AND VERIFICATION OF LIMESTONE DEPOSITS USING GIS AND ARTIFICIAL NEURAL NETWORK IN THE GANGREUNG AREA, KOREA

  • Oh, Hyun-Joo;Lee, Sa-Ro
    • Proceedings of the KSRS Conference
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    • v.2
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    • pp.710-712
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    • 2006
  • The aim of this study was to analyze limestone deposits potential using an artificial neural network and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential deposits in the Gangreung area, Korea. A spatial database considering deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The factors relating to 44 limestone deposits were the geological data, geochemical data and geophysical data. These factors were used with an artificial neural network to analyze mineral potential. Each factor’s weight was determined by the back-propagation training method. Training area was applied to analyze and verify the effect of training. Then the mineral deposit potential indices were calculated using the trained back-propagation weights, and potential map was constructed from GIS data. The mineral potential map was then verified by comparison with the known mineral deposit areas. The verification result gave accuracy of 87.31% for training area.

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Application of Artificial Neural Networks to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

  • Oh, Sang Hoon;Kim, Kyungmin;Harry, Ian W.;Hodge, Kari A.;Kim, Young-Min;Lee, Chang-Hwan;Lee, Hyun Kyu;Oh, John J.;Son, Edwin J.
    • The Bulletin of The Korean Astronomical Society
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    • v.39 no.2
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    • pp.107.1-107.1
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    • 2014
  • We apply a machine learning algorithm, artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. We also evaluate the gravitational-wave data within a few seconds of the selected short gamma-ray bursts' event times using the trained networks and obtain the false alarm probability. We suggest that artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.

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Displacement prediction in geotechnical engineering based on evolutionary neural network

  • Gao, Wei;He, T.Y.
    • Geomechanics and Engineering
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    • v.13 no.5
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    • pp.845-860
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    • 2017
  • It is very important to study displacement prediction in geotechnical engineering. Nowadays, the grey system method, time series analysis method and artificial neural network method are three main methods. Based on the brief introduction, the three methods are analyzed comprehensively. Their merits and demerits, applied ranges are revealed. To solve the shortcomings of the artificial neural network method, a new prediction method based on new evolutionary neural network is proposed. Finally, through two real engineering applications, the analysis of three main methods and the new evolutionary neural network method all have been verified. The results show that, the grey system method is a kind of exponential approximation to displacement sequence, and time series analysis is linear autoregression approximation, while artificial neural network is nonlinear autoregression approximation. Thus, the grey system method can suitably analyze the sequence, which has the exponential law, the time series method can suitably analyze the random sequence and the neural network method almostly can be applied in any sequences. Moreover, the prediction results of new evolutionary neural network method is the best, and its approximation sequence and the generalization prediction sequence are all coincided with the real displacement sequence well. Thus, the new evolutionary neural network method is an acceptable method to predict the measurement displacements of geotechnical engineering.

A study on the prediction of injection pressure and weight of injection-molded product using Artificial Neural Network (Artificial Neural Network를 이용한 사출압력과 사출성형품의 무게 예측에 대한 연구)

  • Yang, Dong-Cheol;Kim, Jong-Sun
    • Design & Manufacturing
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    • v.13 no.3
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    • pp.53-58
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    • 2019
  • This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was $0.49{\pm}0.23%$. In case of maximum injection pressure, error of the ANN was $1.40{\pm}1.19%$. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.

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

  • Choi, Hong;Kim, Tae-Kyung;Heo, Gyeong-Rin;Choi, Sung-Dae;Hur, Jang-Wook
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.18 no.9
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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.