• 전자공학회논문지S
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• 제35S권4호
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• pp.58-66
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• 1998

In many situations, it is very important to identify a certain unkown system, it from its input-output data. For this purpose, several system modeling algorithms have been suggested heretofore, and studies regarding the fuzzy modeling based on its nonlinearity get underway as well. Generatlly, fuzzy models have the capability of dividing input space into several subspaces, compared to linear ones. But hitherto subggested fuzzy modeling algorithms do not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem, this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently that conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space, the method of principal component is ued. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• Journal of Power Electronics
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• 제12권6호
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• pp.912-924
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• 2012

In hybrid renewable power systems, the use of a multiple-input dc/dc converter (MIC) leads to simpler circuit and lower cost, when compared to the conventional use of several single-input converters. This paper proposed a novel three-input buck/boost/buck-boost converter, which can be used in applications with various values of input voltage. The energy sources in this converter can deliver power to the load either simultaneously or individually in one switching period. The steady relationship, the power management strategy and the small-signal circuit model of this converter have been derived. With decoupling technology, modeling and regulator design can be obtained under multi-loop control modes. Finally, three generating methods of a multiple-input buck/boost/buck-boost converter is given, and this method can be extended to the other multiple-input dc/dc converters.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.494-498
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• 1996

Three fuzzy input space partitoining methods, which are grid, tree, and scatter method, are mainly used until now. These partition methods represent good performance in the modeling of the linear system and nonlinear system with independent modeling variables. But in the case of the nonlinear system with the coupled modeling variables, there should be many fuzzy rules for acquiring the exact fuzzy model. In this paper, it shows that the fuzzy model is acquired using transformed modeling vector by linear transformation of the modeling vector.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제12권3호
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• pp.171-190
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• 2008

With growing applications of wireless video streaming, an efficient video traffic model featuring modern high-compression techniques is more desirable than ever, because the wireless channel bandwidths are ever limited and time-varying. We propose a modeling and analysis method for video traffic by a class of stochastic processes, which we call '$GEO^Y/G/{\infty}$ input processes'. We model video traffic by $GEO^Y/G/{\infty}$ input process with gamma-distributed batch sizes Y and Weibull-like autocorrelation function. Using four real-encoded, full-length video traces including action movies, a drama, and an animation, we evaluate our modeling performance against existing model, transformed-M/G/${\infty}$ input process, which is one of most recently proposed video modeling methods in the literature. Our proposed $GEO^Y/G/{\infty}$ model is observed to consistently provide conservative performance predictions, in terms of packet loss ratio, within acceptable error at various traffic loads of interest in practical multimedia streaming systems, while the existing transformed-M/G/${\infty}$ fails. For real-time implementation of our model, we analyze G/D/1/K queueing systems with $GEO^Y/G/{\infty}$ input process to upper estimate the packet loss probabilities.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.111-114
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• 1997

Generally, fuzzy models have the capability of dividing input space into several subspaces. compared to liner ones. But hitherto suggested fuzzy modeling algorithms not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem. this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently than conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space. the method of principal component is used. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• 한국정보과학회논문지:소프트웨어및응용
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• 제33권10호
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• pp.868-876
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• 2006

2차원 평면 입력을 통한 모델링에서는 입력 값을 3차원 데이타로 바꾸기 위한 기능과 메뉴들이 필요하지만 가상공간 디자인을 위한 3차원 입력 시스템은 입력 값을 곧바로 3차원 데이타로 변환될 수 있다. 하지만 3차원 입력시스템에서 효율적인 곡면 모델링 방법, 특히 곡면 변형 방법은 제안되지 않고 있다. 본 논문에서는 기존의 변형방법이 3차원 입력시스템에서 적용되었을 때 발생할 수 있는 문제점을 제시한다. 그리고 디자이너가 접근하기 쉬운 목표곡선을 이용한 변형을 제안한다. 이와 같은 3차원 목표곡선을 이용한 변형을 통해 디자이너가 보다 쉽게 3차원 입력시스템에 접근하여 가상공간 스케칭 및 디자인을 구현할 수 있다.

• 한국빅데이터학회지
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• 제7권1호
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• pp.99-111
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• 2022

불확실성과 복잡성이 존재하는 제조시스템의 생산성 분석과 의사결정을 위하여 시뮬레이션 기법이 활용될 수 있다. 이러한 시스템의 시뮬레이션을 위해 대상의 특성을 반영하는 입력 모델링 과정이 필요하다. 하지만 건설기계 조립라인과 같은 수작업 중심과 긴 리드타임을 가지는 복잡한 환경에서는 시뮬레이션에 활용할 데이터의 수집이 제한된다. 본 연구는 입력 데이터의 수집이 어려운 환경에서 동영상 데이터를 이용한 시뮬레이션 입력 모델링 절차를 제안한다. 동영상 데이터 기반 작업분석을 통해 측정시간을 정미시간과 표준시간으로 산정하고, 시뮬레이션의 입력 분포로 활용할 수 있다. 제안하는 절차로 산정된 확률분포를 시뮬레이션에 이용하여 대상 시스템의 주요 생산성 지표를 분석하였다. 본 연구에서 제안하는 절차는 데이터가 적은 상황에서 시뮬레이션을 활용한 생산성 분석으로 의사결정 보조에 도움을 줄 것으로 기대된다.

• 한국경영과학회지
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• 제31권1호
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• pp.15-24
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• 2006

In simulation input modeling, it is important to identify a probability distribution to represent the input process of interest. In this paper, an appropriate sample size is determined for parameter estimation associated with some typical probability distributions frequently encountered in simulation input modeling. For this purpose, a statistical measure is proposed to evaluate the effect of sample size on the precision as well as the accuracy related to the parameter estimation, square rooted mean square error to parameter ratio. Based on this evaluation measure, this sample size effect can be not only analyzed dimensionlessly against parameter's unit but also scaled regardless of parameter's magnitude. In the Monte Carlo simulation experiments, three continuous and one discrete probability distributions are investigated such as ; 1) exponential ; 2) gamma ; 3) normal ; and 4) poisson. The parameter's magnitudes tested are designed in order to represent distinct skewness respectively. Results show that ; 1) the evaluation measure drastically improves until the sample size approaches around 200 ; 2) up to the sample size about 400, the improvement continues but becomes ineffective ; and 3) plots of the evaluation measure have a similar plateau pattern beyond the sample size of 400. A case study with real datasets presents for verifying the experimental results.

• 전기학회논문지
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• 제56권9호
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• pp.1655-1664
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• 2007

This paper presents the problem of controlling asynchronous sequential machines in the presence of input disturbances, which may be also regarded as an adversary in a game theoretic setting. The main objective is to develope a new methodology for including unpredictable behavior of input disturbance into models of asynchronous machines. The input disturbance, representing uncontrollable noise input, is embedded into a new model of asynchronous machines in form of input/state finite state machines. It is shown that the proposed modeling preserves the fundamental model and well-pose of asynchronous machines. The reachability matrix, an important performance index of asynchronous machines, is also adapted according to input disturbance and will be used for constructing corrective controllers in the companion paper.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.493-495
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• 2005

This paper proposes a swarm-based fuzzy system modeling technique for coke oven combustion control diagnosis. The coke plant produces coke for the blast furnace plant in steel making process by charging coal into oven and supplying gas to carbonize it. A conventional mathematical model for coke oven combustion control has been used to control the amount of gas input, but it does not work well because of highly nonlinear feature of coke plant. To solve this problem, swarm-based fuzzy system modeling technique is suggested to construct a diagnosis model of coke oven combustion control. Based on the measured input-output data pairs, the fuzzy rules are generated and the parameters are tuned by the PSO(Particle Swarm Optimizer) to increase the accuracy of the fuzzy system is operated. This system computes the proper amount of gas input taking the operation conditions of coke oven into account, and compares the computed result with the supplied gas input.