• International Journal of Naval Architecture and Ocean Engineering
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• 제9권4호
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• pp.373-381
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• 2017

A Collaborative Optimization (CO) methodology for ring-stiffened composite material pressure hull of underwater vehicle is proposed. Structural stability and material strength are both examined. Lamination parameters of laminated plates are introduced to improve the optimization efficiency. Approximation models are established based on the Ellipsoidal Basis Function (EBF) neural network to replace the finite element analysis in layout optimizers. On the basis of a two-level optimization, the simultaneous structure material collaborative optimization for the pressure vessel is implemented. The optimal configuration of metal liner and frames and composite material is obtained with the comprehensive consideration of structure and material performances. The weight of the composite pressure hull decreases by 30.3% after optimization and the validation is carried out. Collaborative optimization based on the lamination parameters can optimize the composite pressure hull effectively, as well as provide a solution for low efficiency and non-convergence of direct optimization with design variables.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

• 대한기계학회논문집A
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• 제26권6호
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• pp.1092-1098
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• 2002

This paper discusses the composition of the theory of reinforcement teaming, which is applied in real-time teaming, and evolutionary strategy, which proves its the superiority in the finding of the optimal solution at the off-line teaming method. The individuals are reduced in order to team the evolutionary strategy in real-time, and new method that guarantee the convergence of evolutionary mutations are proposed. It is possible to control the control object varied as time changes. As the state value of the control object is generated, applied evolutionary strategy each sampling time because of the teaming process of an estimation, selection, mutation in real-time. These algorithms can be applied, the people who do not have knowledge about the technical tuning of dynamic systems could design the controller or problems in which the characteristics of the system dynamics are slightly varied as time changes. In the future, studies are needed on the proof of the theory through experiments and the characteristic considerations of the robustness against the outside disturbances.

• 한국소음진동공학회논문집
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• 제14권4호
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• pp.267-275
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• 2004

This research is concerned with the development of a real-time adaptive PPF controller for the active vibration suppression of smart structure. In general, the tuning of the PPF controller is carried out off-line. In this research, the real-time learning algorithm is developed to find the optimal filter frequency of the PPF controller in real time and the efficacy of the algorithm is proved by implementing it in real time. To this end, the adaptive algorithm is developed by applying the gradient descent method to the predefined performance index, which is similar to the method used popularly in the optimization and neural network controller design. The experiment was carried out to verify the validity of the adaptive PPF controller developed in this research. The experimental results showed that adaptive PPF controller is effective for active vibration control of the structure which is excited by either impact or harmonic disturbance. The filter frequency of the PPF controller is tuned in a very short period of time thus proving the efficiency of the adaptive PPF controller.

• 한국통신학회논문지
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• 제18권3호
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• pp.410-424
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• 1993

본 논문에서 디지털 신호처리를 위한 시스톨릭 배열과 비동기 제어 설계를 새로운 기법에 대하여 작성하였다. 특히, 배열안에 사전에 데이터를 입력하는 방법이나, 전체적인 제어와 같은 예전 방법과 다르게 최적의 성능을 성취할 수 있는 간단하고 내부적인 상호 연결을 갖는 시스톨릭 배열을 제안하고 있다. 이와 같은 디지털 신호처리에 대한 비동기 시스톨릭 배열은 일정하게 설계된 배열에서 비교한 경우 전체 계산 시간을 상당히 줄일 수 있음을 비교를 통해서 다루었다. 비동기 시스톨릭 배열의 중요한 요소는 입력 데이터를 적절하고 효율적으로 제어할 수 있는 통신 프로토콜 설계이다. 마지막으로, Transputer의 병렬 컴퓨터 언어인 OCCAM를 이용하여 배열의 효율성을 비교 분석하고 시뮬레이션의 결과를 보였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 춘계학술대회논문집
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• pp.302-307
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• 2001

This research is concerned with the development of a real-time adaptive PPF controller for the active vibration suppression of smart structure. In general, the tuning of the PPF controller is carried out off-line. In this research, the real-time learning algorithm is developed to find the optimal filter frequency of the PPF controller in real time and the efficacy of the algorithm is proved by implementing it in real time. To this end, the adaptive algorithm is developed by applying the gradient descent method to the predefined performance index, which is similar to the method used popularly in the optimization and neural network controller design. The experiment was carried out to verify the validity of the adaptive PPF controller developed in this research. The experimental results showed that adaptive PPF controller is effective for active vibration control of the structure which is excited by either impact or harmonic disturbance. The filter frequency of the PPF controller can be tuned in a very short period of time thus proving the efficiency of the adaptive PPF controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2821-2823
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• 2002

In this paper we propose the Multi-FNN (Fuzzy-Neural Networks) for optimal identification modeling of complex system. The proposed Multi-FNNs is based on a concept of FNNs and exploit linear inference being treated as generic inference mechanisms. In the networks learning, backpropagation(BP) algorithm of neural networks is used to updata the parameters of the network in order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM(Hard C-Means)clustering algorithm which carry out the input-output dat a preprocessing function and Genetic Algorithm which carry out optimization of model The HCM clustering method is utilized to determine the structure of Multi-FNNs. The parameters of Multi-FNN model such as apexes of membership function, learning rates, and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization abilities of the model. NOx emission process data of gas turbine power plant is simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

• 대한안전경영과학회지
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• 제8권4호
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• pp.151-166
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• 2006

One of The standard issue in riding on the wave of the information system(IS) is records of performance, grow unprecedently fast, and has highly uncertain future. Within the framework of IS Evaluation, The main purpose of this study is to suggest performance quality evaluation system as follows, The Integrated Quality Evaluation Model Design(IQEMD) of Information System helps corporations to improve productivity, customer service and corporate competitive power. To respond flexibly this trend, we have to realize that the information system has to keep the most effective optimal information network at the proper time. The existing evaluation method has been concentrated on the evaluation of a degree of the information system maturity focused on static viewpoints. The evaluation of potential factors to affect the future-oriented performance, that can analyze both unit business being able to apply an information system base was used efficiently well and what performance evaluation item was obtained actually, was insufficient relatively. In this paper, An integrated quality evaluation method will analyze a degree of information system maturity in dynamic evaluation viewpoints rather than in static evaluation viewpoints. finally we estimated the relative degree of importance of each determinant using the AHP methodology The validity of the proposed model was also partially proved using two different methods, holistic and historical approach.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented 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 and the numerical data of nonlinear function.

• 제어로봇시스템학회논문지
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• 제3권6호
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• pp.639-643
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• 1997

This paper presents how nonlinear PID control algorithms can be applied on chemical processes for a more stable operation and perfect automation. A pass balance controller is designed to balance the exiting temperatures of a heater and a heat exchange network. The proposed controller has gain-varying integral action and deals with the operational constraints in an efficient manner. Also, the use of a PID gap controller is proposed to maximize energy saving and operation stability and to minimize operator intervention in operation of air fan coolers. The proposed controller adjusts the opening of a louver automatically in such a way that it keeps the air fan pitch position within the desired range. All these nonlinear PID controllers have been implemented on the distributed control system (DCS) for good reliability and operability. Operator acceptance was very high and the implemented controllers have shown good performance and high service factor still now on. The proposed methodology can be directly applied to similar processes without any modification.