• Journal of Information Technology Applications and Management
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• 제14권2호
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• pp.151-168
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• 2007

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제22권3호
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• pp.369-382
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• 2008

서울산업대학교에서 2005학년도부터 실시한 대학수학의 수준별 수업 진행 상황과 그 결과에 따른 학생들의 학업성취도를 분석하고, 향후 보완 추진해야할 과제들과 대학수학 교육의 발전을 위한 방법을 제안하고자 한다.

• 기술혁신학회지
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• 제5권1호
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• pp.1-15
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• 2002

This study reviews the new Perspectives of science and technology Policy based on "the innovation system ap-proach" . It examines the theories of innovation and the economic rationale of government intervention of the in-novation system approach and compares them with those of traditional nee-classical approach. It also examines the basic theme of science and technology Policy of "the innovation system approach" It argues that the enhancement of innovating capability, the transformation of innovation system coping with changing technological and econom-ic environments, and the policy learning of the government and innovators are very important and peculiar sub-jects of the science and technology Policy based on "the innovation system approach".ovation system approach".uot;.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.592-600
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• 2006

The pattern recognition of the complicated grasping operation based on electroencephalography (simply named as EEG) is very helpful on realtime control of the robotic hand. In the paper, a new spectral feature analysis method based on Band Pass Filter (simply named as BPF) and Power Spectral Analysis (simply named as PSA) is presented for discriminating the complicated grasping operations. By analyzing the spectral features of grasping operations with the use of the two-channel EEG measurement system and the pattern recognition of the BP neural network, the degree of recognition by the traditional spectral feature method based on FFT and the new spectral features method based on BPF and PSA could be compared. The results show that the proposed method provides highly improved performance than the traditional one because the new method has two obvious advantages such as high recognition capability and the fast learning speed.

• 제어로봇시스템학회논문지
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• 제7권3호
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• pp.229-237
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• 2001

In this paper, we propose two design methods of neural networks controller for the height control of helicopter, one is the design of neural network controller having learning capability and the other is the design of more improved neural network controller. Through the simulation results, we show that the proposed controllers have controllers have enhanced control performance(rapid response, effectiveness and safety) than the typical neural networks controller in the height control of helicopter.

• 전자공학회논문지D
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• 제34D권6호
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• pp.65-74
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• 1997

In this paper, SA-multiplexed filter is designed using SA (simulated ananealing) to recognize music notation patterns varying in size, shape, position and having considerably many similar shapes for optical pattern recognition system. This filter has correlation resutls at wanted location and can identify same class, classify similar class for scale-varianted or rotation-varianted music notation patterns havng learning process. Also, the optimum filter is oriented to analyze on the similar discrimination at acquired position using SA and enhances optical diffractive efficiency as well as peak beam intensity. Compared with POF *(phase only filter), cosine-BPOF(cosine-binary phase only filter), that has excellent discrimination capability even if the different rate is 0.1% quantitatively.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.82-89
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• 1997

In this paper, an adaptive speed controller with an FNN(Feedforward Neural Network) is proposed for servo motor drives. Generally, the motor system has nonlinearities in friction, load disturbance and magnetic saturation. It is necessary to treat the nonlinearities for improving performance in servo control. The FNN can be applied to control and identify a nonlinear dynamical system by learning capability. In this study, at first, a robust speed controller is developed by Lyapunov stability theory. However, the control input has discontinuity which generates an inherent chattering. To solve the problem and to improve the performances, the FNN is introduced to convert the discontinuous input to continuous one in error boundary. The FNN is applied to identify the inverse dynamics of the motor and to control the motor using coordination of feedforward control combined with inverse motor dynamics identification. The proposed controller is developed for an SR motor which has highly nonlinear characteristics and it is compared with an MRAC(Model Reference Adaptive Controller). Experiments on an SR motor illustrate te validity of the proposed controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.465-467
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• 1995

High impedance fault can not be easily detected by conventional method. But if it would not be detected and cleared quickly, it can result in fires, and electric shock. In this paper, ANN, which has learning capability, is used for high impedance fault detection. The potential of the neural network approach is demonstrated by simulation using KEPCO's measured data. Among ANN models used in this paper, CPN shows better result than BPN in respect of convergence and reliability.

• 한국지능시스템학회논문지
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• 제4권1호
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• pp.35-49
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• 1994

It is well known that, for nominimum phase systems, a conventional linear controller of PID type or an adaptive controller of this structure shows limitation in achieving a satisfactory performance under tight specifications. In this paper, we combine a neuro-controller with a PI-controller with off-line learning capability provided by the Genetic Algorithm to propose a novel neuro-controller to control nonminimum phase systems effectively. The simulation results show that our proposed model is more efficient with faster rising time and less undershoot effect when the performances of the proposed controller and a conventional form are compared.

• 수산경영론집
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• 제33권1호
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• pp.19-41
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• 2002

Recently, the number of Internet shopping malls increases dramatically Internet shopping malls offer direct sales by electronic catalogs. As compared with to physical stores and paper catalogs, electronic catalogs differ in terms of the varieties and types of products offered, promotional efforts, service, interface, ordering and delivering, and so on. This paper analysed the features of electronic catalogs for fisheries by 45 variables. By descriptive statistics of electronic catalogs for fisheries, most electron)c catalogs had sufficient product related information. But promotion and transaction security related features were scarce. And some development technologies of electronic catalogs for fisheries were obsolete. By factor analysis, there were 9 factors of electronic catalogs for fisheries, that was, design of product pages, transaction information, playfulness, convenience of product selection, interface design, design of homepages, product information, learning capability, other electronic catalog related factor. Thus in standardizing electronic catalog for fisheries products, the above 9 factors should be reflected significantly.