http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
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.
최한수(Hansoo Choi),이경웅(Kyoung-Woong Lee) 제어로봇시스템학회 2012 제어·로봇·시스템학회 논문지 Vol.18 No.12
This study proposes a method for improvement of PD type fuzzy controller. The method includes self tuner using gradient algorithm that is one of the optimization algorithms. The proposed controller improves simple Takagi-Sugeno type FLC (Fuzzy Logic Control) system. The simple Takagi-Sugeno type FLC system changes nonlinear characteristic to linear parameters of consequent membership function. The simple FLC system could control the system by calibrating parameter of consequent membership function that changes the system response. While the determination on parameter of the simple FLC system works well only partially, the proposed method is needed to determine parameters that work for overall response. The simple FLC system doesn’t predict the response characteristics. While the simple FLC system works just like proportional part of PID, our system includes derivative part to predict the next response. The proposed controller is constructed with P part and D part FLC system that characteristic parameter on system response is changed by self tuner for effective response. Since the proposed controller doesn’t include integral part, it can’t eliminate steady state error. So we include a gain to eliminate the steady state error.
Gradient Descent 알고리즘을 이용한 퍼지제어기의 멤버십함수 동조 방법
최한수(Choi, Hansoo) 한국산학기술학회 2014 한국산학기술학회논문지 Vol.15 No.12
본 연구에서는 gradient descent 알고리즘을 퍼지제어기의 동조를 위해 멤버십함수의 폭을 해석하는데 이용하였 으며 이 해석은 퍼지 제어규칙의 전건부와 후건부 퍼지변수들을 변화시켜 보다 개선된 제어 효과를 얻기 위해 사용된다. 이 방법은 제어기의 파라미터들이 gradient descent 알고리즘의 반복 과정에서 제어변수를 선택하는 것이다. 본 논문에서 는 궤환 목표치 제어를 위해 7개의 멤버십함수와 49개의 규칙 그리고 2개의 입력과 1개의 출력을 갖는 FLC을 사용하였다. 추론은 Min - Max 합성법을 이용하였고 멤버십함수는 13개의 양자화 레벨에 대한 삼각 형태를 채택하였다. In this study, the gradient descent algorithm was used for FLC analysis and the algorithm was used to represent the effects of nonlinear parameters, which alter the antecedent and consequence fuzzy variables of FLC. The controller parameters choose the control variable by iteration for gradient descent algorithm. The FLC consists of 7 membership functions, 49 rules and a two inputs - one output system. The system adopted the Min-Max inference method and triangle type membership function with a 13 quantization level.
최한수(Hansoo Choi),이경웅(Kyoung-Woong Lee) 제어로봇시스템학회 2011 제어·로봇·시스템학회 논문지 Vol.17 No.3
This paper presents a Takagi-Sugeno (T-S) type Fuzzy Logic Controller (FLC) with only 3 rules. The choice of parameters of FLC is very difficult job on design FLC. Therefore, the choice of appropriate linguistic variable is an important part of the design of fuzzy controller. However, since fuzzy controller is nonlinear, it is difficult to analyze mathematically the affection of the linguistic variable. So this choice is depend on the expert"s experience and trial and error method. In this paper, we propose the method to choose the consequence linear equation"s parameter of T-S type FLC. The parameters of consequence linear equations of FLC are tuned according to the system error that is the input of FLC. The full equation of T-S type FLC is presented and using this equation, the relation between output and parameters can represented. The parameters are tuned with gradient algorithm. The parameters are changed depending on output. Ths simulation results demonstrate the usefulness of this T-S type 3 rule fuzzy controller.