• 한국정밀공학회지
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• 제13권11호
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• pp.57-63
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• 1996

Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.

• 한국정보과학회논문지:소프트웨어및응용
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• 제27권11호
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• pp.1082-1087
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• 2000

신경회로망 구조의 정제(精製)는 회로망의 일반화능력이나 효율성의 관점에서 중요한 문제이다. 본 논문에서는 feed-forward neural networks로부터 은닉지식을 추출하는 방법을 사용하여 네트워크 재구성을 통한 정제방법을 제안한다. 먼저, 효율적인 if-then rule 추출방법을 제시하고 그 추출된 룰들을 사용하여 룰기반 네트워크로 변환하는 과정을 보여준다. 생성된 룰기반 네트워크 fully connected network에 비하여 상당히 축소된 연결 복잡도를 가지게 되며 일반적으로 더 우수한 일반화능력을 가지게 된다. 본 연구는 도메인 지식이 없이 데이타만 사용하여 어떻게 정제된 룰기반 신경망회로를 생성하고 있는가를 보여준다. 도메인 데이타들에 대한 실험결과도 제시하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 C
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• pp.2208-2210
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• 2005

전문적인 지식이 없는 UHF 부분방전 측정장치 사용자를 위해 자동으로 측정된 신호로부터 GIS 내부의 결함을 추론할 수 있는 신경망회로 엔진을 연구하였다. 측정된 방전신호로부터 적절한 변수들을 계산하고 이를 신경망회로를 이용하여 미리 분류한 GIS 결함들 중 가장 유사한 결함을 자동으로 표현하는 기능을 엔진이 가지도록 하였다. 특히 본 엔진은 3상 일괄형 GIS나 GIS의 전압 위상에 동기되지 않은 부분방전 측정시스템에도 방전 원인을 잘 추론함을 실험을 통하여 확인하였다.

• 한국정밀공학회지
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• 제22권7호
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• pp.94-101
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• 2005

This work proposes a new method for describing the hysteresis non-linearity of a piezoelectric actuator. The hysteresis behaviour of piezoelectric actuators, including the minor loop trajectory, are modeled by geometrical relationship between a reference major loop and its minor loops. This hysteresis model is transformed into inverse hysteresis model in order to output compensated voltage with regard to the given input displacement. A feedforward neural network, which is trained by a feedback PID control module, is incorporated to the inverse hysteresis model to compensate unknown dynamics of the piezoelectric system. To show the feasibility of the proposed feedforward-feedback controller, some experiments have been carried out and the tracking performance was compared to that of simple PTD controller.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2006년도 추계학술대회
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• pp.305-306
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• 2006

This paper introduces a radio-wave interference assessment of wind turbines that were planned to be installed at Homi-Cape in Pohang region where wind resource has been evaluated worthwhile developing a wind farm. In that area, AM radio station with two antennas and a harbor radar facility are located so that radio-wave coupling is inevitable if the wind farm is designed without considering radio-wave environmental impact. A low-frequency analysis using MoM (Method of Moment) is used to examine interference effect caused by wind turbines and an optimal layout minimizes coupling effect is presented.

• 한국정밀공학회지
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• 제19권12호
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• pp.100-108
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• 2002

This paper describes the development of adult authentication system using numerical recognition. Nowadays, the automats are very popular and they are dealing in many item suck as coffee, soft drinks, alcoholic drinks and cigarettes, etc. Among these items, some are harmful to the minor, and so the sale of these to the minor must be prohibited. In relation to this, adult authentication system is required to be equipped to the automat which deals in items harmful to minor. According to these demands, we develop the adult authentication system. This system capture the image of a residence certificate card by the identification card-reader, and recognize its numbers and identify it as adult or minor by main computer, where numeral recognition is accomplished by using image processing methods and neural network recognizer. The characteristic test of the system is carried out, and its result reveals that the system has the error of less than 1%. Thus, It is thought that the system can be used for identifying adult in the automats.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.299-302
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• 2000

Various types of measuring devices are used for reverse engineering and inspection in different fields of industry such as automotive, aerospace, computer graphics, and home appliance. In order to measure a part easily and efficiently, it is important to select appropriate measuring device considering the characteristics of each measuring machine and part information. In this research, an optimal measuring device selection system using neural networks is proposed. There are two major steps: Firstly, the measuring information such as curvature, normal, type of surface, edge, and facet approximation is extracted from the CAD model. Second, the best suitable measuring device is proposed using the neural network system based on the knowledge of the measuring parameters and the measuring resources. An example of machine selection is implemented to evaluate the performance of the system.

• 신재생에너지
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• 제4권4호
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• pp.23-29
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• 2008

Wind farm development project contains high business risks because that a wind farm, which is to be operating for 20 years, has to be designed and assessed only relying on a year or little more in-situ wind data. Accordingly, long-term correction of short-term measurement data is one of most important process in wind resource assessment for project feasibility investigation. This paper shows comparison of general Measure-Correlate-Prediction models and neural network, and presents new method using neural network for increasing prediction accuracy by accommodating multiple reference data. The proposed method would be interim step to complete long-term correction methodology for Korea, complicated Monsoon country where seasonal and diurnal variation of local meteorology is very wide.

• 한국정밀공학회지
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• 제19권9호
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• pp.106-110
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• 2002

A need fer stable and comfortable cabins in the high-speed passenger ships has increased. For active control of the motion of the ship cabin, a few control algorithms have been applied to the three dimensional real models in the vibration basin. Experimental results show that the feedforward neural network with a linear feedback controller is one of the promising control algorithms for this active control.

• 한국신재생에너지학회:학술대회논문집
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• 한국신재생에너지학회 2006년도 춘계학술대회
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• pp.323-324
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• 2006

In the present paper a forecasting system of wind power generation for Walryong Site, Jejudo is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model, KIER forecaster is constructed based on statistical models and is trained with wind speed data observed at Gosan Weather Station nearby Walryong Si to. Due to short period of measurements at Walryong Site for training statistical model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict technique. Three-hour advanced forecast ins shows good agreement with the measurement at Walryong site with the correlation factor 0.88 and MAE(mean absolute error) 15% under.