• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.84-88
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• 2008

The UAV propulsion system that will be operated for long time at more than 40,000ft altitude should have not only fuel flow minimization but also high reliability and durability. If this UAV propulsion system may have faults, it is not easy to recover the system from the abnormal, and hence an accurate diagnostic technology must be needed to keep the operational reliability. For this purpose, the development of the health monitoring system which can monitor remotely the engine condition should be required. In this study, a fuzzy trend monitoring method for detecting the engine faults including mechanical faults was proposed through analyzing performance trends of measurement data. The trend monitoring is an engine conditioning method which can find engine faults by monitoring important measuring parameters such as fuel flow, exhaust gas temperatures, rotational speeds, vibration and etc. Using engine condition database as an input to be generated by linear regression analysis of real engine instrument data, an application of the fuzzy logic in diagnostics estimated the cause of fault in each component. According to study results, it was confirmed that the proposed trend monitoring method can improve reliability and durability of the propulsion system for a long endurance UAV to be operated at medium altitude.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.6
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• pp.562-576
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• 2013

Power TAC (Power Trading Agent Competition) is an agent-based simulation for competitions between electricity brokering agents on the smart grid. To win the competition, agents obtain electricity from the electricity wholesale market among the power plants. In this operation, a key to success is balancing the demand of the customer and the supply from the plants because any imbalance results in a significant penalty to the brokering agent. Given the bidding on the wholesale market requires the price and the quantity on the electricity, this paper proposes four different price estimation strategies: exponentially moving average, linear regression, fuzzy logic, and support vector regression. Our evaluations with the competition simulation show which strategy is better than which, and which strategy wins in the free-for-all situations. This result is a crucial component in designing an electricity brokering agent in both Power TAC and the real world.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.692-700
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• 2017

In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models' performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data.

• Computers and Concrete
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• v.12 no.3
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• pp.243-259
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• 2013

Recent developments in Artificial Intelligence (AI) and computational intelligence have made it viable in the construction industry and structural analysis. This study usesthe Adaptive Network-based Fuzzy Inference System (ANFIS) as a modelling tool to predict the strain in tie section for High Strength Self Compacting Concrete (HSSCC) deep beams. 3773 experimental data were collected. The input data andits corresponding strains in tie section as output data were recorded at all loading stages. Results from ANFIS are compared with the classical linear regression (LR). The comparison shows that the ANFIS's results are highly accurate, precise and satisfactory.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.132-137
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• 1993

In this paper, a fish drying process control system is proposed, which predicts the proper change with time in weight of the material fish and the drying conditions in advance, based on the performance of skilled worker. In order to implement a human expertise into an automated fish drying process control system, an experimental analysis is made and a model for the process is built. The proposed system divided into two procedures: The procedure before drying and the one during drying. The procedure before drying is for the prediction of necessary drying time. To estimate the necessary drying time, first, the proper change in weight for the product is obtained by using fuzzy reasoning. The condition part of the production rule consists of the factors of fish body and the expected degree of dryness. Kext, the necessary drying time is obtained by regression models. The variables employed in the models are the factors, inferred change in weight and drying conditions. The model for the procedure during drying is also proposed for more accurate estimation, which is described by a system of linear-differential equations.

• Proceedings of the KIEE Conference
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• summer
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• pp.683-685
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• 2004

This paper introduces a basic design for the short-term load forecasting system using a commercial data base. The proposed system uses a hybrid load forecasting method using fuzzy linear regression for forecasting of weekends and Monday and general exponential smoothing for forecasting of weekdays. The temperature sensitive is used to improve the accuracy of the load forecasting during the summer season. MS-SQL Sever has been used a commercial data base for the proposed system and the database is operated by ADO(ActiveX Data Objects) and RDO(Remote Data Object). Database has been constructed by altering the historical load data for the past 38 years. The weather iDormation is included in the database. The developed short-term load forecasting system is developed as a user friendly system based on GUI(Graphical User interface) using MFC(Microsoft Foundation Class). Test results show that the developed system efficiently performs short-term load forecasting.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.52-54
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• 2001

전력 수요 예측은 전력 수급 안정과 양질의 전력을 공급하기 위한 필수 기법이며 경쟁적인 전력 시장에서 전력요금과 밀접한 관련이 있다. 그러므로, 경쟁적인 전력시장 구조하의 시장 참여자에게 있어서 전력수요 예측은 매우 관심 있는 사항이다. 최근의 전력 수요 예측 기법으로 예측한 오차율을 살펴보면 특수일의 전력 수요 예측의 정확도가 평일 예측에 비해 낮으며 특히, 토요일 또는 월요일에 특수일이 오는 경우 예측의 정확도가 낮아지는 경향이 있다. 따라서, 찬 논문은 퍼지 선형회귀 분석법과 상대계수법을 병행하여 예측함으로써 특수일 수요 예측의 정확도를 개선하는 방법을 제시한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1657-1660
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• 2013

Short-term load forecasting for Chusok and New Year's consecutive holidays is very difficult, due to the irregular characteristics compared with ordinary weekdays and insufficient holidays historical data. During consecutive holidays of New Year and Chusok, most of industries reduce their operation rates and their electrical load levels. The correlation between businesses' operation rates and their loads during consecutive holidays of New Year and Chusok is analysed and short-term load forecasting algorithm for consecutive holidays considering businesses' operation rates of industries is proposed. Test results show that the proposed method improves the accuracy of short-term load forecasting over fuzzy linear regression method.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.51-53
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• 2001

전력 수요 예측은 전력 수급 안정과 양질의 전력을 공급하기 위한 필수 기법이며 경쟁적인 전력시장에서 전력요금과 밀접한 관련이 있다. 그러므로, 경쟁적인 전력시장 구조하의 시장 참여자에게 있어서 전력 수요 예측은 매우 관심 있는 사항이다. 최근의 전력 수요 예측 기법으로 예측한 오차율을 살펴보면 평일과는 다르게 특수일의 전력 수요예측은 평균 5%를 상회하는 수준으로 예측의 정확도가 평일 예측에 비해 크게 낮은데 이유는 특수일이 평일에 비하여 부하의 크기가 다소 낮게 나타나고 특수일 마다 계절적인 차이가 있으며 각각의 특수일 마다 고유한 부하의 특성이 있으므로 과거 데이터를 이용할 때 동일 특수일을 이용하게 되며 따라서 평일과는 다르게 일년 단위로 과거 데이터 값들이 취득되므로 오차율이 커진다. 따라서 데이터들을 퍼지화하여 선형계획법을 수행하여 평균 $2{\sim}3%$ 정도의 우수한 결과를 도출한 바 있다. 본 논문에서는 퍼지 선형회귀분석법을 이용한 예측 기법에 최소자승법을 도입하여 특수일 전력 수요예측의 정확도를 개선하였다.

• Journal of Korean Society of Transportation
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• v.16 no.2
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• pp.177-195
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• 1998

지능형교통체계(ITS:Intellegent Transport System)의 구현을 위한 가장 중요한 요소중의 하나는 교통정보의 생성이다. 교통정보의 생성은 루프 검지기, 폐쇄회로(CCTV), probe 차량, 경찰, 통신원 등을 수집된 제보자료들을 분석 및 가공함으로써 이루어진다. 그러나 이들 수집원은 주어진 시간에 있어 모든 네트웍을 통해서 자료가 완전히 수집되어지는 것은 아니다. 즉, 특정 지역에 수집원이 몰려 있는 경우가 있는 반면, 전혀 수집되어지지 않는 지역이 발생할 수도 있다. 이러한 공간적인 불균형적 특성은 동시에 발생한 다량의 자료를 처리하는 기술과 자료가 수집되지 않은 지역에 대한 처리기술을 요하게 된다. 본 논문은 전술한 바와 같은 사항에 대하여 ITS의 진행 단계별로 드러날 수 있는 문제점을 검토하고, 자료통합에 대한 일반적인 개념을 우선 설명한다. 다음에 특정시각에 주어진 자료의 통합을 위해 퍼지선형회귀모형(fuzzy linear regression model)과 데이터 퓨전(data fusion)기법의 내용을 소개하고, 신뢰성있는 단일 교통정보생성을 위한 테이터 퓨전 알고리즘을 제시한다. 또한 제시된 알고리즘을 토대로 가상의 자료를 이용하여 적용가능 봉? 타진해 보았다. 제시되어진 알고리즘은 향후 교통정보 수집환경이 어느 정도 형성된다고 볼 때, 예측치와 실측자료간의 자료검증을 통하여 신뢰도를 가질 경우 보다 광범위하게 사용되어질 수 있을 것으로 판단된다.