• Title/Summary/Keyword: Artificial Neural Network

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Maximum Torque Control of IPMSM with Adaptive Learning Fuzzy-Neural Network (적응학습 퍼지-신경회로망에 의한 IPMSM의 최대토크 제어)

  • Ko, Jae-Sub;Choi, Jung-Sik;Lee, Jung-Ho;Chung, Dong-Hwa
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2006.05a
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    • pp.309-314
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    • 2006
  • Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

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

  • Jung, Jisoo;Yoon, Ji Won
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.26 no.5
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    • pp.1191-1199
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    • 2016
  • To ensure the fairness, journal reviewers use blind-review system which hides the author information of the journal. Even though the author information is blinded, we could identify the author by looking at the field of the journal or containing words and phrases in the text. In this paper, we collected 315 journals of 20 authors and extracted text data. Bag-of-words were generated after preprocessing and used as an input of artificial neural network. The experiment shows the possibility of circumventing the blind review through identifying the author of the journal. By the experiment, we demonstrate the limitation of the current blind-review system and emphasize the necessity of robust blind-review system.

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

  • Lee, Jeong-Ick;Koh, Byung-Kab
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.16 no.4
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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
    • Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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    • 2003.04a
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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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The prediction of fatigue life of muffler by artificial neural network (인공신경망을 이용한 머플러의 피로 수명 예측)

  • Park, Soon-Cheol;Kang, Sung-Su;Yoon, Jin-Ho;Kim, Gug-Yong
    • Journal of Advanced Marine Engineering and Technology
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    • v.37 no.8
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    • pp.869-876
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    • 2013
  • In order to estimate the fatigue life of mufflers at the early stage of researches and designs, the new prediction process was developed by the artificial neural network, which has the algorism of weldment properties. Bending fatigue test was carried out for defining the characteristics of muffler weldment fatigue life and damage. For considering and predicting mechanical and fatigue properties of the muffler, the maximum stress of weldment was adapted as the variable of artificial neural network training. Also, it was compared with the fatigue life predicting results using fatigue notch factors, for proving the newly developed process of the artificial neural network.

Development of Artificial Neural Network Model for the Prediction of Descending Time of Room Air Temperature (실온하강신간 예측을 위한 신경망 모델의 개발)

  • 양인호;김광우
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.12 no.11
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    • pp.1038-1047
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    • 2000
  • The objective of this study is to develop an optimized Artificial Neural Network(ANN) model to predict the descending time of room air temperature. For this, program for predicting room air temperature and ANN program using generalized delta rule were collected through simulation for predicting room air temperature. ANN was trained and the ANN model having the optimized values-learning rate, moment, bias, number of hidden layer, and number of neuron of hidden layer was presented.

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Mongolian Car Plate Recognition using Neural Network

  • Ragchaabazar, Bud;Kim, SooHyung;Na, In Seop
    • Smart Media Journal
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    • v.2 no.4
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    • pp.20-26
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    • 2013
  • This paper presents an approach to Mongolian car plate recognition using artificial neural network. Our proposed method consists of two steps: detection and recognition. In detection step, we implement Flood fill algorithm. In recognition step we proceed to segment the plate for each Cyrillic character, and use an Artificial Neural Network (ANN) machine - learning algorithm to recognize the character. We have learned the theory of ANN and implemented it without using any library. A total of 150 vehicles images obtained from community entrance gates have been tested. The recognition algorithm shows an accuracy rate of 89.75%.

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Inspection of Automotive Oil-Seals Using Artificial Neural Network and Vision System (인공신경망과 비전 시스템을 이용한 자동차용 오일씰의 검사)

  • 노병국;김기대
    • Journal of the Korean Society for Precision Engineering
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    • v.21 no.8
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    • pp.83-88
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    • 2004
  • The Classification of defected oil-seals using a vision system with the artificial neural network is presented. The artificial neural network fur classification consists of 27 input nodes, 10 hidden nodes, and one output node. The selection of the number of the input nodes is based on an observation that the difference among the defected, non-defected, and smeared oil-seals is greatly pronounced in the 26 step gray-scale level thresholding. The number of the hidden nodes is chosen as a result of a trade-off between accuracy and computing time. The back-propagation algorithm is used for teaching the network. The proposed network is capable of successfully classifying the defected from the smeared oil-seals which tend to be classified as the defected ones using the binary thresholding. It is envisaged that the proposed method improves the reliability and productivity of the automotive vision inspection system.

Prediction of Deep Excavation-induced Ground Surface Movements Using Artificial Neural Network (인공신경망기법을 이용한 깊은 굴착에 따른 지표변위 예측)

  • 유충식;최병석
    • Journal of the Korean Geotechnical Society
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    • v.20 no.3
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    • pp.53-65
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    • 2004
  • This paper presents the prediction of deep excavation-induced ground surface movements using artificial neural network(ANN) technique, which is of prime importance in the damage assessment of adjacent buildings. A finite element model, which can realistically replicate deep excavation-induced ground movements, was employed to perform a parametric study on deep excavations with emphasis on ground movements. The result of the finite element analysis formed a basis for the Artificial Neural Network(ANN) system development. It was shown that the developed ANN system can be effective for a first-order prediction of ground movements associated with deep-excavation.

River Stage Forecasting Model Combining Wavelet Packet Transform and Artificial Neural Network (웨이블릿 패킷변환과 신경망을 결합한 하천수위 예측모델)

  • Seo, Youngmin
    • Journal of Environmental Science International
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    • v.24 no.8
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    • pp.1023-1036
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    • 2015
  • A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.