• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.307-310
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• 2003

In the maritime container terminal, LMTT(Linear Motor-based Transfer Technology) is horizontal transfer system for the yard automation, which has been proposed to take the place of AGV(Automated Guided Vehicle). The system is based on PMLSM (Permanent Magnetic Linear Synchronous Motor) that is consists of stator modules on the rail and shuttle car (mover). Because of large variant of mover's weight by loading and unloading containers, the difference of each characteristic of stator modules, and a stator module's trouble etc., LMCPS (Linear Motor Conveyance Positioning System) is considered as that the system is changed its model suddenly and variously. In this paper, we will introduce the soft-computing method of a multi-step prediction control for LMCPS using DR-FNN (Dynamically-constructed Recurrent Fuzzy Neural Network). The proposed control system is used two networks for multi-step prediction. Consequently, the system has an ability to adapt for external disturbance, cogging force, force ripple, and sudden changes of itself.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.488-491
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• 2005

This paper has been proposed an engine oil changing system automatically using artificial intelligence. As you know, It is very difficult to forecast the time that exchange engine oil exactly. Because, It does not necessary to change the engine oil when color of engine is black or distance is more than 3000 km. In order to forecast to optimal engine oil replacement time, We must to consider color of engine oil, greasy, mad condition, quick starting condition and quick braking condition. Therefore, in this paper, to overcome those problems, we, developed an expert system that it can forecast to exchange time of engine oil automatically using fuzzy rules and neural networks.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.475-478
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• 2005

본 논문에서는 자기구성 퍼지다항식 뉴럴 네트워크(SOFPNN)를 구성하고 있는 퍼지 다항식뉴론(FPM)의 구조와 파라미터를 유전자 알고리즘을 이용하여 최적화시킨 새로운 개념의 진화론적 최적 고급 자기구성 퍼지 다항식 뉴릴 네트워크를 소개한다. 기존의 자기구성 퍼지 다항식 뉴럴 네트워크에서 모델을 설계할 때에는 설계자의 주관적인 특징과 시행착오에 의해서 모델을 구축하였다. 이러한 설계자의 경험을 배제하고 객관적이고 효율적인 모델을 구축하기 위해서 본 논문에서는 FPH의 파라미터들을 최적화 알고리즘인 유전자 알고리즘을 이용하여 동조하였다. 즉, 모델을 구축하는데 기본이 되는 FPN의 각각의 파라미터들-입력변수의 수, 다항식 차수, 입력변수, 멤버쉽 함수의 수, 그리고 멤버쉽 함수의 정점-을 동조함으로써 기존의 모델에 비해서 구조적으로 그리고 파라미터적으로 최적화된 네트워크를 생성할 수 있다. 뿐만 아니라 주어진 데이터의 특성을 모델 구축에 반영하고자 멤버쉽 함수의 정점 역시 유전자 알고리즘으로 동조하였다. 실험적 예제를 통하여 제안된 모델의 성능을 확인한 결과 기존의 퍼지모델 및 신경망 모델에 비해서 아주 우수한 근사화 능력과 일반화 능력을 가짐을 알 수 있다.

• Geomechanics and Engineering
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• v.13 no.2
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• pp.237-254
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• 2017

Estimation of slope stability is a very important task in geotechnical engineering. However, its estimation using conventional and soft computing methods has several drawbacks. Use of conventional limit equilibrium methods for the evaluation of slope stability is very tedious and time consuming, while the use of soft computing approaches like Artificial Neural Networks and Fuzzy Logic are black box approaches. Multiple Regression (MR) analysis provides an alternative to conventional and soft computing methods, for the evaluation of slope stability. MR models provide a simplified equation, which can be used to calculate critical factor of safety of slopes without adopting any iterative procedure, thereby reducing the time and complexity involved in the evaluation of slope stability. In the present study, a multiple regression model has been developed and tested its accuracy in the estimation of slope stability using real field data. Here, two separate multiple regression models have been developed for dry and wet slopes. Further, the accuracy of these developed models have been compared and validated with respect to conventional limit equilibrium methods in terms of Mean Square Error (MSE) & Coefficient of determination ($R^2$). As the developed MR models here are not based on any region specific data and covers wide range of parametric variations, they can be directly applied to any real slopes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.334-337
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• 2004

차세대 생산 시스템(Next Generation Manufacturing System： NGMS)의 핵심 개념은 분산 생산 시스템과 다품종 소량의 유연 생산 시스템의 지원이다. 이러한 시스템의 구성을 위하여 실시간 데이터에 기반한 예측 모델이 필수적인데, 이러한 예측 기능을 통하여 생산공정의 관리와 운영, 특히 전체 공정관리를 효율적으로 수행할 수 있다. 한편, 공정으로부터 전송된 데이터는 특정한 형태의 지식으로 표현된다. 이러한 지식들은 시스템에 대한 다양한 정보를 가지고 있으므로 정보를 이용하여 시스템 상태를 빠르고 쉽게 진단할 수 있다. 공정 진단은 현재 공정 상태에서 생산되는 제품의 품질을 추정할 수 있는 정보로 활용된다. 본 논문에서는 이러한 개념이 바탕이 되어 공정관리 시스템을 설계하였다. 제안된 시스템의 적용 대상은 반도체 제조 공정의 단위 공정인 에칭 공정이다. 에칭 공정은 공정 중에 연속적인 검사가 수행되지 않고 최종 제품에 대한 검사가 수행되므로 불량 원인을 찾는 것이 쉽지 않다. 따라서 본 논문에서는 공정관리를 위한 의사지원시스템을 통해 공정의 연속적인 간접진단을 수행하고자 하였다. 본 연구에서 사용된 의사지원시스템은 각 공정에서 얻어지는 데이터와 경험적 지식을 토대로 공정시스템의 해석과 진단이 가능한 시스템이다.

• Journal of Aerospace System Engineering
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• v.7 no.1
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• pp.8-18
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.1060-1069
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• 2016

In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.107-110
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• 2003

산업의 발달과 인구의 증가로 인한 물 사용량 증가와 다양한 폐수들이 끊임없이 발생하고 있다. 회사나 공장들은 이러한 폐수를 처리하기 위한 하ㆍ폐수처리장의 효율적인 운전을 위하여 관리ㆍ제어 시스템을 도입하고 있는 추세이다. 본 논문에서는 김해에 설치되어 있는 축산 폐수를 처리하는 파일럿 플랜트의 공정상태를 원격으로 관리할 수 있는 모니터링 시스템을 바탕으로 퍼지와 신경망을 이용한 실시간 원격 진단 및 제어 시스템을 설계하였다. 또한 여러 경우의 고장 사례를 원격 진단ㆍ제어 시스템에 접목시킴으로써 진단시스템의 성능을 더욱 향상 시켰다. 이러한 진단ㆍ제어 시스템을 이용하여 관리자는 공정상태를 항상 모니터링 할 수 있으며, 진단ㆍ제어 시스템에서 제공하는 경고 및 제어 값을 축산폐수플랜트에 전송함으로써 공정을 보다 효율적이고 안정적으로 진단ㆍ제어할 수 있다.

• Science of Emotion and Sensibility
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• v.5 no.2
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• pp.73-78
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• 2002

The unique colors of paper, that is, blue, green, red, and yellow were used in the estimation of color from the subjective feeling. The monochrome with unique color or the unique color surrounded with the background color was presented. subject gazed the monochrome or the unique color, which was tailed target rotor. The target and background color were the complementary color each other. The various ratios of the area of gazed color and background were taken. Subject answered the level of subjective feeling consisted of pair of adjective items for unique color presented. With the use of the subjective feeling for the target color presented, the estimation of the unique color was cai\ulcornerlied out due to Fuzzy theory and neural networks. The results of color difference between unique color presented and the estimated color gave very small value for the case without background, while the results of the case with background color depended on the ratio of area of presented color and background color till the ration of 2:1, The relation showed the Kirschman's law, The color difference saturated In the increase of area of background with the ratio more than 2:1.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.261-265
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• 2002

The unique colors of paper, that is, blue, green, red, and yellow were used in the estimation of color from the subjective feeling. The monochrome with unique color or the unique color surrounded with the background color was presented. Subject gazed the monochrome or the unique color, which was called target color. The target and background color were the complementary color each other. The various ratios of the area of gazed color and background were taken. Subject answered the level of subjective feeling consisted of pair of adjective items for unique color presented. With the use of the subjective feeling fer the target color presented, the estimation of the unique color was carried out due to Fuzzy theory and neural networks. The results of color difference between unique color presented and the estimated color gave very small value for the case without background, while the results of the case with background color depended on the ratio of area of presented color and background color till the ration of 2:1, The relation showed the Kirschman's law. The color difference saturated in the increase of area of background with the ratio more than 2:1.