• Title/Summary/Keyword: Back-propagation network

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The Detection of Esophagitis by Using Back Propagation Network Algorithm

  • Seo, Kwang-Wook;Min, Byeong-Ro;Lee, Dae-Weon
    • Journal of Mechanical Science and Technology
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    • v.20 no.11
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    • pp.1873-1880
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    • 2006
  • The results of this study suggest the use of a Back Propagation Network (BPN) algorithm for the detection of esophageal erosions or abnormalities - which are the important signs of esophagitis - in the analysis of the color and textural aspects of clinical images obtained by endoscopy. The authors have investigated the optimization of the learning condition by the number of neurons in the hidden layer within the structure of the neural network. By optimizing learning parameters, we learned and have validated esophageal erosion images and/or ulcers functioning as the critical diagnostic criteria for esophagitis and associated abnormalities. Validation was established by using twenty clinical images. The success rates for detection of esophagitis during calibration and during validation were 97.91% and 96.83%, respectively.

Classification of ECG Arrhythmia Signals Using Back-Propagation Network (역전달 신경회로망을 이용한 심전도 파형의 부정맥 분류)

  • 권오철;최진영
    • Journal of Biomedical Engineering Research
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    • v.10 no.3
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    • pp.343-350
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    • 1989
  • A new algorithm classifying ECG Arrhythmia signals using Back-propagation network is proposed. The base-line of ECG signal is detected by high pass filter and probability density function then input data are normalized for learning and classifying. In addition, ECG data are scanned to classify Arrhythmia signal which is hard to find R-wave. A two-layer perceptron with one hidden layer along with error back-propagation learning rule is utilized as an artificial neural network. The proposed algorithm shows outstanding performance under circumstances of amplitude variation, baseline wander and noise contamination.

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Web-based Design Support System for Automotive Steel Pulley (웹 기반 자동차용 스틸 풀리 설계 지원 시스템)

  • Kim, Hyung-Jung;Lee, Kyung-Tae;Chun, Doo-Man;Ahn, Sung-Hoon;Jang, Jae-Duk
    • Transactions of the Korean Society of Automotive Engineers
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    • v.16 no.6
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    • pp.39-47
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    • 2008
  • In this research, a web-based design support system is constructed for the design process of automotive steel pulley to gather engineering knowledge from pulley design data. In the design search module, a clustering tool for design data is proposed using K-means clustering algorithm. To obtain correlational patterns between design and FEA (Finite Element Analysis) data, a Multi-layer Back Propagation Network (MBPN) is applied. With the analyzed patterns from a number of simulation data, an estimation of minimum von mises can be provided for given design parameters of pulleys. The case study revealed fast estimation of minimum stress in the pulley within 12% error.

A comparative study between the neural network and the winters' model in forecasting

  • Kim, Wanhee
    • Korean Management Science Review
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    • v.9 no.1
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    • pp.17-30
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    • 1992
  • This paper is organized as follows. Section 2 illustrates several applications of neural networks. Section 3 presents the theoretical aspects of the major neural network paradigms as well as the structure of the back -propagation network used in the study. Section 4 describes the experiment including data analysis, modeling, and the performance criteria followed by the detailed discussion of the experimental results. Future research avenues including advantages and limitations of neural network are presented in the last section.

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A Study on the Intelligent High Voltage Switchboard for Custormer (고압 수용가용 배전반의 intelligent화 연구)

  • Byun, Young-Bok;Joe, Ki-Youn;Koo, Heun-Hoi
    • Proceedings of the KIEE Conference
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    • 1994.07a
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    • pp.444-446
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    • 1994
  • This paper describes the design of a digital multifunction controller for the intelligent high voltage customer switchboard and proposes a relaying algorithm for high impedance faults using back-propagation neural network. The hardware design uses the three microprocessors and global memory architecture to achive real time operation and control 4 feeders. The controller uses a 64-point radix-4 DIF FFT algorithm to measure the harmonic and relay parameters. Synthesized fault current waveforms are used to train and test the back - propagation network.

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A neural network with local weight learning and its application to inverse kinematic robot solution (부분 학습구조의 신경회로와 로보트 역 기구학 해의 응용)

  • 이인숙;오세영
    • 제어로봇시스템학회:학술대회논문집
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    • 1990.10a
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    • pp.36-40
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    • 1990
  • Conventional back propagation learning is generally characterized by slow and rather inaccurate learning which makes it difficult to use in control applications. A new multilayer perception architecture and its learning algorithm is proposed that consists of a Kohonen front layer followed by a back propagation network. The Kohonen layer selects a subset of the hidden layer neurons for local tuning. This architecture has been tested on the inverse kinematic solution of robot manipulator while demonstrating its fast and accurate learning capabilities.

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Future Location Prediction of Human Through Back-propagation Network (오류-역전파 네트워크를 통한 인간의 미래 위치 예측)

  • Kim, SungYun;Koo, Hoon Jung;Song, Ha Yoon
    • Proceedings of the Korea Information Processing Society Conference
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    • 2012.11a
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    • pp.1732-1735
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    • 2012
  • 인간은 일주일 단위로 유사한 행동 패턴을 가진다고 한다. 이런 점에서 일주일 단위의 시간-공간 기록의 형태인 인간 이동 데이터를 이용하면, 인간의 행동 패턴을 유추해 낼 수 있다. 본 논문에서 인간의 행동을 유추하기 위해 BPN알고리즘을 사용하였다. BPN알고리즘에 대해 설명하고, 인간 이동의 예측에 관한 적용에 관한 BPN알고리즘의 설계 과정을 논의한다. 그리고 해당 실험의 결과와 분석을 제시한다.

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Prediction of flow boiling heat transfer coefficient in horizontal channels varying from conventional to small-diameter scales by genetic neural network

  • Zhang, Jing;Ma, Yichao;Wang, Mingjun;Zhang, Dalin;Qiu, Suizheng;Tian, Wenxi;Su, Guanghui
    • Nuclear Engineering and Technology
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    • v.51 no.8
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    • pp.1897-1904
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    • 2019
  • Three-layer back propagation network (BPN) and genetic neural network (GNN) were developed in this study to predict the flow boiling heat transfer coefficient (HTC) in conventional and small-diameter channels. The GNN has higher precision than BPN (with root mean square errors of 17.16% and 20.50%, respectively) and other correlations. The inputs include vapor quality x, mass flux G, heat flux q, diameter D and physical parameter φ, and the predicted flow boiling HTC is set as the outputs. Influences of input parameters on the flow boiling HTC are discussed based on the trained GNN: nucleate boiling promoted by a larger saturated pressure, a larger heat flux and a smaller diameter is dominant in small channels; convective boiling improved by a larger mass flux and a larger vapor quality is more significant in conventional channels. The HTC increases with pressure both in conventional and small channels. The HTC in conventional channels rises when mass flux increases but remains almost unaffected in small channels. A larger heat flux leads to the HTC growth in small channels and an increase of HTC was observed in conventional channels at a higher vapor quality. HTC increases inversely with diameter before dry out.

A Study on Performance Diagnostic of Smart UAV Gas Turbine Engine using Neural Network (신경회로망을 이용한 스마트 무인기용 가스터빈 엔진의 성능진단에 관한 연구)

  • Kong Chang-Duk;Ki Ja-Young;Lee Chang-Ho
    • Journal of the Korean Society of Propulsion Engineers
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    • v.10 no.2
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    • pp.15-22
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    • 2006
  • An intelligent performance diagnostic program using the Neural Network was proposed for PW206C turboshaft engine. It was selected as a power plant for the tilt rotor type Smart UAV(Unmanned Aerial Vehicle) which is being developed by KARI (Korea Aerospace Research Institute). For teeming the NN(Neural Network), a BPN(Back Propagation Network) with one hidden, one input and one output layer was used. The input layer has seven neurons: variations of measurement parameters such as SHP, MF, P2, T2, P4, T4 and T5, and the output layer uses 6 neurons: degradation ratios of flow capacities and efficiencies for compressor, compressor turbine and power turbine, respectively, Database for network teaming and test was constructed using a gas turbine performance simulation program. From application of the learned networks to diagnostics of the PW206C turboshaft engine, it was confirmed that the proposed diagnostics algorithm could detect well the single fault types such as compressor fouling and compressor turbine erosion.

High Performance Concrete Mixture Design using Artificial Neural Networks (신경망을 이용한 고성능 콘크리트의 배합설계)

  • 양승일;윤영수;이승훈;김규동
    • Proceedings of the Korea Concrete Institute Conference
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    • 2002.05a
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    • pp.545-550
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    • 2002
  • Concrete is one of the essential structural materials in the construction. But, concrete consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructor. Therefore, concrete mixes depend on experiences of experts. However, it is more and more difficult to determine concrete mixes design by empirical means because more ingredients like mineral and chemical admixtures are included. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network are used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength and slump are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

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