• 대한전자공학회논문지SP
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• 제45권5호
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• pp.95-102
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• 2008

이 논문은 고강도매질 CR(Computed Radiography) 영상의 잡음을 모델링하는 적절한 접근법을 제시한다. 잡음 유형의 통계적이고 비선형적 특성이 구체적으로 고안되었다. CR영상은 컴퓨터 처리에 의해 코드화되기 이전 이미 훼손된다. 다양한 형태의 잡음은 비록 디지털화된 상태로 검출된다 하더라도 통상 방사선 영상을 오염시킨다. 양자 방출시의 포아송 분포는 CR 영상판에서의 광자 분포에서 포아송 잡음 분포를 항상 유지하지 않는다. 그 통계적 특성은 재질 특성에 의해 상대적이며 경우의존적이다. 통계적 잡음모델링 과정에서 통상적인 포아송, 이항 내지는 가우스 통계분포의 가정이 고려되었으며 아울러 비선형 효과 또한 포함시켰다. 이는 잡음 영역의 고저 전 방사선량에 걸쳐 추정하는 해석적 모델을 구현한다. 그리고 이 분석적 접근은 고강도 강판튜브 스텝웨지의 방사선측정실험을 통해 관측한 CR 영상데이터에서 구현되었다. 그 결과는 매질의 두께변화에 따른 잡음의 일관성, 잡음분포특성, SNR 및 비선형 보간을 측정하는 상호비교의 파라미터연구에 유용하다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1449-1454
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• 2003

For many industrial processes there are good static models used for process design and steady state operation. By using system identification techniques, it is possible to obtain black-box models with reasonable complexity that describe the system well in specific operating conditions [1]. But black-box models using inductive modeling(IM) is not suitable for model based control because they are only valid for specific operating conditions. Thus we need to use deductive modeling(DM) for a wide operating range. Furthermore, deductive modeling is several merits: First, the model is possible to be modularized. Second, we can increase and decrease the model complexity. Finally, we are able to use model for plant design. Power plant must be able to operate well at dramatic load change and consider safety and efficiency. This paper proposes a simplified nonlinear model of an industrial boiler, one of component parts of a power plant, by DM method and applies optimal control to the model.

• 산업경영시스템학회지
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• 제40권4호
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• pp.29-37
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• 2017

This papers focuses on remanufacturing processes in a closed loop supply chain. The remanufacturing processes is considered as one of the effective strategies for enterprises' sustainability. For this reason, a lot of companies have attempted to apply remanufacturing related methods to their manufacturing processes. While many research studies focused on the return rate for remanufacturing parts as a control parameter, the relationship with demand certainties has been studied less comparatively. This paper considers a closed loop supply chain environment with remanufacturing processes, where highly fluctuating demands are embedded. While other research studies capture uncertainties using probability theories, highly fluctuating demands are modeled using a fuzzy logic based ambiguity based modeling framework. The previous studies on the remanufacturing have been limited in solving the actual supply chain management situation and issues by analyzing the various situations and variables constituting the supply chain model in a linear relationship. In order to overcome these limitations, this papers considers that the relationship between price and demand is nonlinear. In order to interpret the relationship between demand and price, a new price elasticity of demand is modeled using a fuzzy based nonlinear function and analyzed. This papers contributes to setup and to provide an effective price strategy reflecting highly demand uncertainties in the closed loop supply chain management with remanufacturing processes. Also, this papers present various procedures and analytical methods for constructing accurate parameter and membership functions that deal with extended uncertainty through fuzzy logic system based modeling rather than existing probability distribution based uncertainty modeling.

• 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.

• Steel and Composite Structures
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• 제18권5호
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• pp.1197-1214
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• 2015

There is a great difference in mechanical behavior between design model one-time loading and step-by-step construction process. This paper presents practical computational methods for simulating the structural behavior of long-span rigid steel structures during construction processes. It introduces the positioning principle of node rectification for installation which is especially suitable for rigid long-span steel structures. Novel improved nonlinear analytical methods, known as element birth and death of node rectification, are introduced based on several calculating methods, as well as a forward iteration of node rectification method. These methods proposed in this paper can solve the problem of element's 'floating' and can be easily incorporated in commercial finite element software. These proposed methods were eventually implemented in the computer simulation and analysis of the main stadium for the Universiade Sports Center during the construction process. The optimum construction scheme of the structure is determined by the improved algorithm and the computational results matched well with the measured values in the project, thus indicating that the novel nonlinear time-varying analysis approach is effective construction simulation of complex rigid long-span steel structures and provides useful reference for future design and construction.

• 한국정보전자통신기술학회논문지
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• 제5권4호
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• pp.224-231
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• 2012

본 논문에서는 비선형 공정을 퍼지 모델링하기 위해 FCM 클러스터링 알고리즘을 기반으로 하는 퍼지 추론 시스템을 소개한다. 비선형 공정에 대한 퍼지 규칙의 생성은 일반적으로 차원이 증가할수록 규칙의 수가 지수적으로 증가하는 문제를 가지고 있다. 이를 해결하기 위해, FCM 클러스터링 알고리즘을 이용하여 입력 공간을 분산 형태로 분할함으로써 퍼지 모델의 규칙을 생성한다. 퍼지 규칙의 전반부 파라미터는 FCM 클러스터링 알고리즘에 의한 소속행렬로 결정된다. 퍼지 규칙의 후반부는 다항식 함수의 형태로 표현되며, 각 규칙의 후반부 파라미터들은 표준 최소자승법에 의해 동정된다. 마지막으로, 비선형 공정의 특성 및 성능을 평가하기 위하여 비선형 공정으로는 널리 이용되는 데이터를 이용한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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• pp.192-196
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• 2004

This paper proposes an alternative observation scheme, T-S fuzzy model based indirect adaptive fuzzy observer. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The adaptive fuzzy scheme estimates the parameters comprising the fuzzy model representing the observation system. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observation method, it is applied to an inverted pendulum on a cart.

• 한국지능시스템학회논문지
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• 제14권7호
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• pp.927-933
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• 2004

This paper proposes an alternative observation scheme, T-S fuzzy model based indirect adaptive fuzzy observer. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The adaptive fuzzy scheme estimates the parameters comprising the fuzzy model representing the observation system. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observation method, it is applied to an inverted pendulum on a cart.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.348-353
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• 2004

This paper proposes an alternative observation scheme, T-S fuzzy model based indirect adaptive fuzzy observer. Nonlinear systems arc represented by fuzzy models since fuzzy logic systems arc universal approximators. In order to estimate the unmeasurable states of a given nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The adaptive fuzzy scheme estimates the parameters comprising the fuzzy model representing the observation system. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observation method, it is applied to an inverted pendulum on a cart.

• 조명전기설비학회논문지
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• 제23권11호
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• pp.9-21
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• 2009

This paper proposes the alternative observer and controller design scheme based on T-S fuzzy model. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given unknown nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. The proposed controller is based on a simple output feedback method. Therefore, it solves the singularity problem, without any additional algorithm, which occurs in the inverse dynamics based on the feedback linearization method. The adaptive fuzzy scheme estimates the parameters and the feedback gain comprising the fuzzy model representing the observation system. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observer and controller, they are applied to an inverted pendulum on a cart.