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최한수,정헌,Choi, Han-Soo,Jeong, Heon 전력전자학회 1998 전력전자학회 논문지 Vol.3 No.4
퍼지제어시스템에 영향을 미치는 요소들은 제어규칙, 소속함수, 퍼지추론, 비퍼지화 그리고 이 출력이득요소 이다. 구성요소들 각각에 의한 동조방법은 요소들 중 일부만을 동조하기 때문에 동조대상 이외의 요소들에 대한 오류와 파라메타의 부적절한 설정등에 의해 적절한 동조가 이루어지지 못할 수 있으며 각 요소들간의 상관관계를 고려하여 동조를 수행해야 하는 어려움이 있다. 입 출력단에서 작용하는 이득요소들은 제어시스템에 직접적인 영향을 미치기 때문에 이들의 선정은 신중을 기해야 한다. 본 연구에서는 퍼지제어시스템의 동조를 위한 제어기 스스로 입 출력이득요소를 산출하는 방법과 퍼지제어기의 구성요소들에 의해 얻어진 초기의 제어값들을 원하는 목표치에 빨리 수렴할 수 있도록 동조하는 방법을 제안하였다. In constructing fuzzy control systems. there are many parameters such as rule base. membership functions. inference m method. defuzzification. and I/O scaling factors. To control the system in properly using fuzzy logic. we have to consider t the correlation with those parameters. This paper deals with self-tuning of fuzzy control systems. The fuzzy controller h has parameters that are input and output scaling factors to effect control output. And we propose the looklongleftarrowup table b based self-tuning fuzy controller. We propose the PMTM(Plus-Minus Tuning Method) for self tuning method, self-tuning the initial look-up table to the appropriate table by adding and subtracting the values.
서울 관악구 도심지역 미세먼지(PM<sub>10</sub>) 관측 값을 활용한 딥러닝 기반의 농도변동 예측
최한수,강명주,김용철,최한나,Choi, Han-Soo,Kang, Myungjoo,Kim, Yong Cheol,Choi, Hanna 한국지하수토양환경학회 2020 지하수토양환경 Vol.25 No.3
Since fine dust (PM<sub>10</sub>) has a significant influence on soil and groundwater composition during dry and wet deposition processes, it is of a vital importance to understand the fate and transport of aerosol in geological environments. Fine dust is formed after the chemical reaction of several precursors, typically observed in short intervals within a few hours. In this study, deep learning approach was applied to predict the fate of fine dust in an urban area. Deep learning training was performed by combining convolutional neural network (CNN) and recurrent neural network (RNN) techniques. The PM<sub>10</sub> concentration after 1 hour was predicted based on three-hour data by setting SO<sub>2</sub>, CO, O<sub>3</sub>, NO<sub>2</sub>, and PM<sub>10</sub> as training data. The obtained coefficient of determination value, R<sup>2</sup>, was 0.8973 between predicted and measured values for the entire concentration range of PM<sub>10</sub>, suggesting deep learning method can be developed into a reliable and viable tool for prediction of fine dust concentration.
최한수(Han Soo Choi),강명구(Myung Koo Kang) 한국자동차공학회 2019 한국자동차공학회 학술대회 및 전시회 Vol.2019 No.11
Development of multistep gearbox is needed to improve driving agility and fuel efficiency of the ICE (internal combustion engine) car. But it will increase the weight and the volume of transmission. In order to solve this disadvantages, we proposed single clutch engagement(DVT, discretely variable trandmission). Applying dual clutch transmission here additionally, performance will be improved more. The gear ratio of No1 DVT is changed as(수식 본문참조) with exponential relation. The gear ratio of No2 DVT is changed as(수식 본문참조) in Type I and as(수식 본문참조), (수식 본문참조) in Type II. The volume and the final gear ratio of transmission can be changed after appling V to No1 DVT and W to No2 DVT. As a clinically modified form, alternating clutch engagement(DCT) gear shifting is applied in low speed driving. In high speed driving, DCT gear shifting in fast acceleration and DVT gear shifting in slow acceleration can be applied. Using tools mentioned above, gear ratio of multistep gearbox is getting closer toward high speed gear step. Automotive companies can choose the gear ratio change profile to fit their aim.