• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.709-714
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• 2002

This paper discussed and analyzed results of different classification algorithms for land use classification in arid and semiarid areas using CBERS-1 image, which in case of our study is Shihezi Municipality, Xinjiang Province. Three types of classifiers are included in our experiment, including the Maximum Likelihood classifier, BP neural network classifier and Fuzzy-ARTMAP neural network classifier. The classification results showed that the classification accuracy of Fuzzy-ARTMAP was the best among three classifiers, increased by 10.69% and 6.84% than Maximum likelihood and BP neural network, respectively. Meanwhile, the result also confirmed the practicability of CBERS-1 image in land use survey.

• 한국멀티미디어학회논문지
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• 제22권7호
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• pp.790-803
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• 2019

The effects of different heat treatments on the sensory characteristics of abalones are studied in this study. In this paper, the sensory evaluation of abalone samples under different heat treatment conditions is carried out, and the evaluation results are analyzed. The three-dimensional (3D) scanning and reverse engineering are used in tooth modeling of the sensory evaluation of abalone samples under different heat treatment conditions. Besides, the chewing movement models are simplified into three modes, including the cutting mode, compressing mode and grinding mode, which are simulated using finite element simulation. The elastic modulus of the abalone samples is obtained through the compression testing using a texture analyzer to distinguish their material properties under different heat treatments and to obtain simulated mechanical parameters. Finally, taking the mechanical parameters of the finite element simulation of abalone chewing as input and sensory evaluation parameters as the output, BP neural network is established in which the sensory texture evaluation model of abalone samples is obtained. Through verification, the neural network prediction model can meet the requirements of food texture evaluation, with an average error of 9.12%.

• International Journal of Quality Innovation
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• 제3권2호
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• pp.113-131
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• 2002

The relationships among multi-dimensional data (such as medical examination data) with ambiguity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back-propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi-dimensional feature selection. The first one proposed is a feature selection procedure from the trained back-propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis-Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD: it can deal with a reduced model, which is the focus of this paper In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

• 대한전기학회논문지:전력기술부문A
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• 제51권2호
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• pp.76-82
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• 2002

This paper presents the method of sail-resistivity estimation using the backpropagation(BP) neural network. Existing estimation programs are expensive, and their estimation methods need complex techniques and take much time. Also, those programs have not become well spreaded in Korea yet. Soil resistivity estimation method using BP algorithm has studied for the reason mentioned above. This paper suggests the method which differs from expensive program or graphic technology requiring many input stages, complicated calculation and professional knowledge. The equivalent earth resistivity can be presented immediately after inputting apparent resistivity through the personal computer with a simplified Program without many Processing stages. This program has the advantages of reasonable accuracy, rapid processing time and confident of anti users.

• 한국통신학회논문지
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• 제16권11호
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• pp.1186-1193
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• 1991

본 논문에서는 신경회로망을 사용한 로보트 매니퓰레이터의 궤적 제어 방법을 제안하였다. 매니퓰레이터에 가해지는 토크는 신경회로망이 출력인 feedforward 토크와 보조제어기로 사용되는 비례 미분 제어기PD 제어기의 출력인 feedback 토크의 합이다. 제안된 전경 회로망은 다층 신경회로로서 시간 지연 요소를 가지며 PD 제어기의 오차 토크를 사용하여 매니퓰레이터 이동력학 모델을 학습한다. errror backpropagation(BP) 학습 신경회로 제어기를 사용해보므로서 매니퓰레이터 동특성에 대한 정보를 미리 필요로 하지 않으며, 연결 가중치 값에 그러한 정보가 저장된다. 확인될 신경회로망의 특성을 컴퓨터 시뮬레이션을 통하여 입증한다.

• 대한전기학회논문지
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• 제39권9호
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• pp.985-996
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• 1990

An inverse kinematic solution of a robot manipulator using multilayer perceptrons is proposed. Neural networks allow the solution of some complex nonlinear equations such as the inverse kinematics of a robot manipulator without the need for its model. However, the back-propagation (BP) learning rule for multilayer perceptrons has the major limitation of being too slow in learning to be practical. In this paper, a new algorithm named Dynamically Reconfiguring BP is proposed to improve its learning speed. It uses a modified version of Kohonen's Self-Organizing Feature Map (SOFM) to partition the input space and for each input point, select a subset of the hidden processing elements or neurons. A subset of the original network results from these selected neuron which learns the desired mapping for this small input region. It is this selective property that accelerates convergence as well as enhances resolution. This network was used to learn the parity function and further, to solve the inverse kinematic problem of a robot manipulator. The results demonstrate faster learning than the BP network.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.15-19
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• 2002

Automatic detection of interstitial lung disease using Neural Network is presented. The rounded opacities in the pneumoconiosis X-ray photo are picked up quickly by a back propagation (BP) neural network with several typical training patterns. The training patterns from 0.6 mm ${\O}$ to 4.0 mm ${\O}$ are made by simple circles. The total evaluation is done from the size and figure categorization. Mary simulation examples show that the proposed method gives much reliable result than traditional ones.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1022-1027
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• 1991

A new method to control a robot manipulator by neural networks is proposed. The controller is composed of both a PD controller and a neural network-based feedforward controller. MLP(multi-layer perceptron) neural network is used for the feedforward controller and trained by BP(back-propagation) learning rule. Error terms for BP learning rule are composed of the outputs of a PD controller and the acceleration errors of manipulator joints. We compare the proposed method with existing ones and contrast performances of them by simulation. Also, We discuss the real application of the proposed method in consideration of the learning time of the neural network and the time required for sensing the joint acceleration.

• Journal of Electrical Engineering and Technology
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• 제11권5호
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• pp.1289-1298
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• 2016

• 대한전기학회논문지
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• 제41권4호
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• pp.400-410
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• 1992

This paper deals with a design of neural network control architectures for robot motion. Three types of control architectures are designed as follows : 1) a neural network control architecture which has the same characteristics as computed torque method 2) a neural network control architecture for compensating the control error on computed torque method with fixed feedback gain 3) neural network adaptive control architecture. Computer simulation of PUMA manipulator with 6 links is conducted for robot motion in order to examine the proposed neural network control architectures.