• 한국환경과학회지
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• 제8권4호
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• pp.443-449
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• 1999

This paper describe the working of algal culture system under batch and continuous feeding effluents in biological treatment process. The main objective of this study was the determination of fundamental opeating parameters such as dilution rates, light intensity, biomass concentration, nutrients contents, which engender an effective nutrient and organic waste removal process. The results of this research indicate that the algae system will remove effectively nutrient and organic waste. In batch cultures, 91.8% dissolved orthophosphate and 83.3% ammonia nitrogen were removed from the sewage in ten days. In continuous flow systems, a detention time of 2.5 days was found adequate to remove 91% T-P, 87% T-N and 95% $NH_3-N$. At 22-28$^{\circ}C$, 60 rpm, with an intensity of 3500 Lux, the specific growth rate, k was 0.59/day in batch experiments. The optimal growth temperature and nutrients rate (N/P) were respectively $25^{\circ}C$ and 3~5. With an abundant supply of untrients, it was possible to sustain substantial population densities in the temperature range of 22~28$^{\circ}C$.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.502-506
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• 1992

In this study, we proposed the fuzzy modeling method and designed a model-based logic controller for Activated and Sludge Process(A.S.P.) in sewage treatment. The identification of the structure of fuzzy implications is carreid out by use of fuzzy c-means clustering algorithm. And to identify the parameters of fuzzy implications, we used the complex and the least square method. To tune the premise parameters automatically the complex method is implemented. The model-based fuzzy controller is designed by rules generated from the identified A.S.P. fuzzy model. The feasibility of the proposed approach is evaluated through the identification of the fuzzy model to describe an input-output relation of the A.S.P.. The performance of identified model-based fuzzy controller is evaluated through the computer simulations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2911-2913
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• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.101-110
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• 2003

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

• 대한전기학회논문지:전력기술부문A
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• 제48권6호
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• pp.789-799
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• 1999

In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.566-568
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• 1998

In this paper, we suggest a optimal design method of Fuzzy-Neural Networks model for complex and nonlinear systems. FNNs have the stucture of fusion of both fuzzy inference with linguistic variables and Neural Networks. The network structure uses the simpified inference as fuzzy inference system and the BP algorithm as learning procedure. And we use a clustering algorithm to find initial parameters of membership function. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance index, we use the time series data for gas furnace and the sewage treatment process.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2929-2931
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• 1999

In this paper, an adaptive fuzzy inference and HCM(Hard C-Means) clustering method are used for on-line fuzzy modeling of nonlinear and complex system. Here HCM clustering method is utilized for determining the initial parameter of membership function of fuzzy premise rules and also avoiding overflow phenomenon during the identification of consequence parameters. To obtain the on-line model structure of fuzzy systems. we use the recursive least square method for the consequent parameter identification. And the proposed on-line identification algorithm is carried out and is evaluated for sewage treatment process system.

• 한국지능시스템학회논문지
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• 제25권2호
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• pp.119-125
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• 2015

정수장, 하수처리장, 폐수처리장의 배출수 처리공정에서 고 농도의 슬러지 선별, 이송 및 약품 투입량 조절을 위한 기준으로 슬러지 농도계가 사용되고 있다. 그러나 슬러지에 함유된 이물질이 혼입될 경우 감쇄량이 증가하거나 초음파가 수신부에 전달되지 않아 실제 농도값 보다 높은 값을 출력하거나 헌팅현상이 발생한다. 또한 단일 센서에 슬러지 포착 또는 고장 등의 문제로 배출수 공정 자동화에 어려움이 많았다. 이러한 문제점을 개선하기 위해 초음파 다중빔 농도계를 개발하여 사용하고 있으나 특정 초음파 빔의 농도 측정값에 오류가 발생할 경우 전체 농도시스템의 성능이 떨어지는 단점이 있다. 따라서 본 논문에서는 초음파 다중빔 농도계 간의 신뢰성을 판단하고, 신뢰성이 높은 다중빔 농도계만을 사용하여 슬러지 농도 예측값의 성능 향상방안을 제시하였다. 예측 알고리즘으로는 뉴로-퍼지모델을 적용하였으며 다양한 실험을 통하여 제안된 방법의 타당성을 검증하였다.

• 조명전기설비학회논문지
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• 제15권4호
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• pp.37-42
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• 2001

본 논문에서는 하수처리장에서 활성슬러지를 통한 하수의 처리 과정을 표현할 수 있는 반응 모델을 제안한다. 제안된 모델은 관리대상 물리량들에 영향을 미치는 요소들을 고려하여 퍼지 규칙의 조합 형태로 설계되었다. 생물학적 산소요구량과 부유 물질량은 입력 및 출력 변수에 공통으로 사용되었으며, 폭기량은 입력 변수로 선정되었다. 입력 변수들에 대한 소속 함수들은 계산의 편리를 위해 삼각형 형태로 설계했으며, 운전데이터를 기준으로 소속도를 선정하였다. 시뮬레이션을 통해 제안된 모델의 타당성을 확인해 본 결과, 퍼지 모델의 출력은 운전데이터와 거의 동일하였다.