• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.33-37
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• 1990

In this paper, a new method for lung area segmentation in chest radiographs has been presented. The movivation of this study is to include fuzzy informations about the relation between the image date structure and the area to be segmented in the segmentation process efficiently. The proposed method approached the segmentation problem in the perspective of pattern classification, using trainable pattern classifier, multi-layer perceptron. Having been trained with 10 samples, this method gives acceptable segmentation results, and also demonstrated the desirable property of giving better results as the training continues with more training samples.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.7
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• pp.15-25
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• 2011

This proposes an online loss minimization algorithm for series PI(SPI) based interior permanent magnet synchronous motor(IPMSM) drive to yield high efficiency and high dynamic performance over wide speed range. The loss minimization algorithm is developed based on the motor model. In order to minimize the controllable electrical losses of the motor and thereby maximize the operating efficiency, the d-axis armature current is controlled optimally according to the operating speed and load conditions. For vector control purpose, a SPI is used as a speed controller which enables the utilization of the reluctance torque to achieve high dynamic performance as well as to operate the motor over a wide speed range. Also, this paper proposes current control of model reference adaptive fuzzy controller(MFC), and estimation of speed using artificial neural network(ANN) controller. The proposed efficiency optimization control, SPI, MFC, ANN in this paper is applied to IPMSM drive system, the validity of this paper is proved by analyzing response characteristics in variety operating conditions.

• Journal of Advanced Navigation Technology
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• v.16 no.4
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• pp.586-592
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• 2012

One approach of the control of a nonlinear system that has gained some success employs a fuzzy structure in cooperation with a neural network(ANFIS). The traditional ANFIS can only model and control the process in single-dimensional output nature in spite of multi-dimensional input. The membership function parameters are tuned using a combination of least squares estimation and back-propagation algorithm. In the case of a mobile robot, we need to drive left and right wheel respectively. In this paper, we proposed the control system architecture for a mobile robotic system that employs the 2-input 2-output ANFIS controller for trajectory tracking. Simulation results and preliminary evaluation show that the proposed architecture is a feasible one for mobile robotic systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.865-870
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• 2004

Development of new information and traffic technology causes fast-growing in the field of information-based system. At recent, development of LBS(Location Based Service) makes a remarkable growth of industry as GPS(Global Positioning System) becomes wide-spread and location information becomes more important. However, there is a problem like infringement of privacy when location information is used improperly［1］. In this paper, LBS platform is proposed in order to prevent infringement of privacy. To implement, we classify user path as pattern in a zone of user life. Thereupon, location information is provided according to user' specific situation.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.21-24
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• 2001

본 논문은 신경 회로망을 이용하여, 전화망 대역의 음성, 즉, narrowband 음성에서 wideband 음성을 복원하고자 했다. BP 알고리즘을 사용하는 기존의 신경 회로망의 경우에는 음성과 같이 복잡하고 크기가 큰 훈련데이터에 대해서는 훈련이 제대로 되지 않는 단점이 있다. 그러므로 븐 논문에서는 이를 해결하기 위해 입력으로 들어온 LPC 켑스트럼 벡터를 k-means 알고리즘을 이용하여 미리 정한 개수의 cluster로 나눈 다음, 각각의 cluster에 대해 독립적인 신경 회로망을 적용했다 이로 인해 각각의 신경 회로망은 제한되고 서로 상관관계가 많은 음성들만 훈련하면 되므로, 기존의 신경 회로망에서 생기는 훈련의 정체를 개선할 수 있었다. 또 clustering 과정에서 생기는 오류를 보완하기 위해 후보신경 로망들의 출력에 fuzzy 개념을 적용해서 최종 출력을 내도록 했다 실험 결과에서, 제안한 알고리즘은 기존의 codebook mapping 알고리즘보다 스펙트럼 거리척도에 의한 비교 및 주관적인 음질 평가 양쪽에서 개선된 성능을 보였다.

• Environmental Engineering Research
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• v.14 no.2
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• pp.111-119
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• 2009

A prototype GIS-based decision support system (DSS) was developed by using a database management system (DBMS), a model management system (MMS), a knowledge-based system (KBS), a graphical user interface (GUI), and a geographical information system (GIS). The method of selecting a dispersion model or a modeling scheme, originally devised by Park and Seok, was developed using our GIS-based DSS. The performances of candidate models or modeling schemes were evaluated by using a single index(statistical score) derived by applying fuzzy inference to statistical measures between the measured and predicted concentrations. The fumigation dispersion model performed better than the models such as industrial source complex short term model(ISCST) and atmospheric dispersion model system(ADMS) for the prediction of the ground level $SO_2$ (1 hr) concentration in a coastal area. However, its coincidence level between actual and calculated values was poor. The neural network models were found to improve the accuracy of predicted ground level $SO_2$ concentration significantly, compared to the fumigation models. The GIS-based DSS may serve as a useful tool for selecting the best prediction model, even for complex terrains.

• Proceedings of the KWS Conference
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• 2009.11a
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• pp.111-111
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• 2009

전 세계적으로 환경 보호의 차원에서 자동차 업체는 자동차의 연비 향상을 위한 차체의 경량화가 큰 이슈로 대두되고 있다. 이를 위해 알루미늄과 같은 경량화 소재를 이용하여 차체 조립에 투입하고자 연구 중에 있다. 이와 같은 레이저 용접 공정이 현장에 적용되기 위해서는 용접부의 품질을 실시간으로 모니터링하고 품질을 판단하여야 생산성을 극대화 할 수 있다. 그러므로 본 연구에서는 알루미늄 AA5182 알루미늄 판재의 용가 와이어를 이용한 레이저 용접에서 용접부를 모니터링 할 수 있는 시스템을 구축하였다. 이를 위하여 레이저는 4kW급 Nd:YAG 레이저를 사용하였고, 차체용 알루미늄 판재 AA5182 1.4t를 AA5356 와이어를 이용하여 용접을 수행하였다. 모니터링 센서로는 반응 범위가 190 mn~680 nm인 센서를 이용하였고, 용접 중 센서로부터 발생된 출력전류를, 신호 증폭기와 DAQ 보드를 통해 초당 10,000 samples/sec로 계측하였다. 다양한 용접조건을 이용하여 실험을 수행하였고 이를 정량적으로 분석하였다. 계측된 신호와 용접 품질은 비선형적 관계를 가지고 있으므로 본 연구에서는 용접 품질을 예측하는 방법으로 퍼지 패턴인식 알고리즘을 이용하는 방법과 계측 신호를 이용한 인장강도 예측모델을 이용하여 병렬로 품질평가를 할 수 있는 알고리즘을 구현하였다. 이를 위하여 계측된 신호와 용접 품질과의 관계를 이용하여 퍼지 규칙 베이스 정의하였고, 신경회로망 모델을 이용하여 인장강도 예측모델을 제시하였다. 또한 품질 평가 알고리즘을 기반으로 레이저 용접부의 품질평가가 가능한 GUI 프로그램을 구현하였다.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.773-779
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• 1996

Dried oak mushroom have complex and various visual features. Grading and sorting of dried oak mushrooms has been done by the human expert. Though actions involved in human grading looked simple, a decision making underneath the simple action comes from the result of the complex neural processing of the visual image. Through processing details involved in human visual recognition has not been fully investigated yet, it might say human can recognize objects via one of three ways such as extracting specific features or just image itself without extracting those features or in a combined manner. In most cases, extracting some special quantitative features from the camera image requires complex algorithms and processing of the gray level image requires the heavy computing load. This fact can be worse especially in dealing with nonuniform, irregular and fuzzy shaped agricultural products, resulting in poor performance because of the sensitiveness to the crisp criteria or specific ules set up by algorithms. Also restriction of the real time processing often forces to use binary segmentation but in that case some important information of the object can be lost. In this paper, the neuro net based real time recognition algorithm was proposed without extracting any visual feature but using only the directly captured raw gray images. Specially formated adaptable size of grids was proposed for the network input. The compensation of illumination was also done to accomodate the variable lighting environment. The proposed grading scheme showed very successful results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.292-297
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• 2000

This paper proposes a new concept of coordinating green time which controls 10 traffic intersection systems. For instance, if we have a baseball game at 8 pm today, traffic volume toward the baseball game at 8 pm today, traffic volume toward the baseball game will be increased 1 hour or 1 hour and 30 minutes before the baseball game. At that time we can not predict optimal green time Even though there have smart elctro-sensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dosen't consider coordinating green time.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.471-480
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• 2003

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. Among the reasons for its complexity are the system size, conflicting requirements of estimator accuracy, reliability in the presence of transducer noise and data communication failures, adaptability to change in the network topology and cost minimization. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs) which is widely used in areas such as: optimization of the objective function, learning of neural networks, tuning of fuzzy membership functions, machine learning, system identification and control. This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).