• 상하수도학회지
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• 제22권2호
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• pp.213-218
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• 2008

Prediction models for post-chlorination require complicated information of reaction time, chlorine dosage considering flow rate as well as environmental conditions such as turbidity, temperature and pH. In order to operate post-chlorination process effectively, the correlations between inlet and outlet of clear well were investigated to develop prediction models of chlorine dosages in post-chlorination process. Correlations of environmental conditions including turbidity and chlorine dosage were investigated to predict residual chlorine at the outlet of clear well. A linear regression model and autoregressive model were developed to apply for the post-chlorination which take place time delay due to detention in clear well tank. The results from autoregressive model show the correlationship of 0.915~0.995. Consequently, the autoregressive model developed in this study would be applicable for real time control for post chlorination process. As a result, the autoregressive model for post chlorination which take place time delay and have multi parameters to control system would contribute to water treatment automation system by applying the process control algorithm.

• 한국정밀공학회지
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• 제9권2호
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• pp.138-144
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• 1992

Conventionally, model equation for cutting process has been used at adaptive control. But in this paper, the cutting force is discerned by piezo electric dynamometer and is controlled adaptively using fuzzy inferance so that the constant load feeding is possible. Main conclusions are as follows : (1) with proper design of fuzzy label, more active cutting force control is possible. (2) adaptive control is possible with only qualitative knowledge instead of model equation of cutting process.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.282-285
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• 1996

In the water purification plant, the raw water is promptly purified by injecting chemicals. The amount of chemicals is directly related to water quality such as turbidity, temperature, pH and alkalinity. At present, however, the process of chemical reaction to the turbidity has not been clarified as yet. Since the process of coagulant dosage has no feedback signal, the amount of chemical can not be calculated from water quality data which were sensed from the plant. Accordingly, it has to be judged and determined by Jar-Test data which were made by skilled operators. In this paper, it is concerned to model and control the coagulant dosing process using jar-test results in order to predict optimum dosage of coagulant, PAC(Polymerized Aluminium Chloride). The considering relations to the reaction of coagulation and flocculation, the five independent variables(turbidity, temperature, pH, Alkalinity of the raw water, PAC feed rate) are selected out and they are put into calculation to develope a neural network model and a fuzzy model for coagulant dosing process in water purification system. These model are utilized to predict optimum coagulant dosage which can minimize the water turbidity in flocculator. The efficacy of the proposed control schemes was examined by the field test.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2333-2335
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• 2000

The main motivation of this research is to develop an intelligent control strategy for Activated Sludge Process(ASP). ASP is a complex and nonlinear dynamic system because of the characteristic of a wastewater, the change of an influent flow rate, weather conditions, and etc. The mathematical model of ASP also includes uncertainties which are ignored or not considered by process engineer or controller designer. The ASP is generally controlled by a PID controller that consists of fixed proportional, integral, and derivative gain values. The PID gains can be adjusted by the expert in the ASP. The ASP model based on Matlab$^{(R)}$5.3/Simulink$^{(R)}$3.0 is developed in this paper. Various control methods are applied to the ASP model and the control results are disscussed. Three control methods are designed and tested: conventional PID controller, fuzzy logic control approach to modify setpoints, and fuzzy-PID control method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.934-938
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• 1989

This paper considers the model predictive control (MPC) problems in nonlinear processes or systems. The MPC method determines the control law such that the predicted output based on the identified process model is equal to the reference output which consists of both the process output at current time and the setting value called as the command generator. In this paper, the nonlinear MPC software for a chemical reactor is developed and analized from the point of view of practical applications.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.899-902
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• 1993

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.299-302
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• 1996

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

• 한국콘텐츠학회논문지
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• 제10권5호
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• pp.351-359
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• 2010

최근 들어, 유비쿼터스 기술을 활용한 U-시티의 관심이 증대되고 있는 가운데 많은 산업분야에서 이를 활용하여 사람들의 삶의 질을 향상시키고 있다. 다양한 U-시티 서비스 중에서도 교통 분야 서비스는 다른 U-시티 서비스 중에서도 가장 활발히 적용되고 있다. 본 연구에서는 교통 분야 중에서도 유비쿼터스 기술을 이용하여 교통정보제어 서비스를 위한 비즈니스모델과 비즈니스 모델 프로세스를 제안하고자 한다. 이를 위해, 기존의 비즈니스 모델에 대해 살펴보고 교통정보제어서비스가 무엇인지를 알아보았다. 또한, 비즈니스 모델을 도출하기 위해 대표 서비스를 이용하여 시나리오를 제시하였다. 제시한 시나리오를 기반으로, 유비쿼터스 기술을 활용한 U-교통정보제어서비스의 비즈니스 모델 프로세스를 도출하였다. 본 연구 결과, 교통정보제어서비스에서 4개의 대표 서비스를 도출하였다. 도출된 세부 서비스에서 이해 관계자, 수익자, 수익가치 모델을 도출하여 유비쿼터스 기술을 활용한 U-교통정보제어서비스의 비즈니스 모델을 도출하였다.

• 디지털융복합연구
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• 제10권6호
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• pp.189-196
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• 2012

본 논문에서는 학습자들의 다자간 협력학습을 위한 스콤 기반 시퀀싱 & 네비게이션 모델을 제안한다. 이 모델은 정형적 접근 방법을 기반으로 하고 있으며, 협력학습을 효율적이고 그래픽적으로 정의하기 위하여 스콤에서의 콘텐츠 집합 모델과 시퀀싱 및 네비게이션 모델에 관하여 ICN(Information Control Net) 모델을 기반으로 정의한다. ICN 모델은 프로세스를 기반으로 각 요소들의 제어 흐름을 표현하는 모델인데, 본 논문에서는 이러한 ICN 모델을 확장한 SCOSNCN(SCO Sequencing & Navigation Control Net) 모델을 활용하여 프로세스의 실행 순서 및 학습 활동을 정의하고 협력학습에 필요한 콘텐츠와 그에 따른 시퀀싱 & 네비게이션 모델 관련 사항들을 정의한다. SCOSNCN 모델에서는 협력학습을 지원하기 위해 각각의 액티비티에 교수자 및 학습자를 정의하고, 정의되어진 액티비티의 선행, 후행 조건 및 네비게이션 조건 등을 명시하여 협력학습을 위한 시퀀싱 & 네비게이션 모델을 제시한다. 또한, 협력학습 정의에 필요한 시퀀싱 & 네비게이션 기본 요소 및 역할, 그리고 이에 대한 규칙 등을 제안한다. 이에 스콤 기반 협력학습을 위한 시퀀싱 & 네비게이션 모델을 바탕으로 스콤 기반 협력학습시스템 아키텍처와 실례를 제안함으로서 향후 교수자 및 학습자뿐만 아니라 e-러닝 산업 분야 및 교육에 있어 학습 콘텐츠의 정의 및 협력학습을 통한 교육의 효율성 향상에 기여하고자 한다.

• Journal of the Korean Statistical Society
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• 제33권4호
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• pp.381-399
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• 2004

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for process quality improvement. SPC re-duces process variability by detecting and eliminating special causes of process variation, while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been need for an integrated process control (IPC) procedure which combines the two strategies. This paper considers a scheme that simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an IMA(1,1) model with a step shift. The EPC part of the scheme adjusts the process, while the SPC part of the scheme detects the occurrence of a special cause. For adjusting the process repeated adjustment is applied according to the predicted deviation from target. For detecting special causes the exponentially weighted moving average control chart is applied to the observed deviations. It was assumed that the adjustment under the presence of a special cause may increase the process variability or change the system gain. Reasonable choices of parameters for the IPC procedure are considered in the context of the mean squared deviation as well as the average run length.