• 정보처리학회논문지B
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• 제10B권6호
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• pp.611-618
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• 2003

본 논문에서는 퍼지 추론 방법을 이용하여 비디오 데이터에서 샷(shot)의 경계를 검출하는 방법을 제안한다. 제안된 방법에서는 컷(cut), 페이드(fade), 디졸브(dissolve)와 같은 샷의 경계들을 검출하고, 이들을 그 종류별로 분류하기 위해 기본적으로 퍼지 연상 기억장치를 확장한 퍼지 추론 방법을 이용한다. 본 논문에서는 연속적인 두 영상 사이의 차이를 나타내는 여러 특징들을 입력 퍼지 집합으로 사용하고, 샷 경계들을 출력 퍼지 집합으로 사용한다. 본 논문의 퍼지 추론 시스템은 크게 학습 단계와 추론 단계의 두 단계로 구성된다. 학습 단계에서는 퍼지 소속 함수의 결정을 통해 시스템의 기본 구조를 초기화하고 이를 바탕으로 퍼지 연상 기억장치의 학습 기능을 이용하여 퍼지 규칙을 조건부와 결론부를 연결하는 가중치의 형태로 생성한다. 그리고 추론 단계에서는 구성된 퍼지 추론 모델을 이용하여 실제 추론을 수행한다. 실험에서는 제안된 샷 경계 검출 방법의 성능을 확인하기 위해서 뉴스, 영화, 광고, 다큐멘터리, 뮤직 비디오 등의 비디오 데이터들을 활용하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권4호
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• pp.575-594
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• 2010

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.791-793
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• 2000

The fuzzy inference system is a popular computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantage of fuzzy approach over traditional ones lies on the fact that fuzzy system does not require a detail mathematical description of the system while modeling. As modeling method. the Group Method of Data Handling(GMDH) is introduced by A.G. Ivakhnenko GMDH is an analysis technique for identifying nonlinear relationships between system's inputs and output. We study a Novel Neuro-Fuzzy Network (NNFN) in this paper. NNFN is a network resulting from the combination of a fuzzy inference system and polynomial neural network(PNN) (7) which is advanced structure of GMDH. Simulation involve a series of synthetic as well as experimental data used across various neurofuzzy systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2856-2858
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• 1999

In this paper, the FPNN(Fuzzy Polynomial Neural Networks) algorithm with multi-layer fuzzy inference structure is proposed for the model identification of a complex nonlinear system. The FPNN structure is generated from the mutual combination of PNN (Polynomial Neural Network) structure and fuzzy inference method. The PNN extended from the GMDH(Group Method of Data Handling) uses several types of polynomials such as linear, quadratic and modifled quadratic besides the biquadratic polynomial used in the GMDH. In the fuzzy inference method, simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used Each node of the FPNN is defined as a fuzzy rule and its structure is a kind of fuzzy-neural networks. Gas furnace data used to evaluate the performance of our proposed model.

• 전기학회논문지
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• 제60권11호
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• pp.2157-2164
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• 2011

In this paper, we introduce a new category of fuzzy inference system based on Type-2 fuzzy c-means clustering algorithm (T2FCM-based FIS). The premise part of the rules of the proposed model is realized with the aid of the scatter partition of input space generated by Type-2 FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we can alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with interval sets. To determine the structure and estimate the values of the parameters of Type-2 FCM-based FIS we consider the successive tuning method with generation-based evolution by means of real-coded genetic algorithms. The proposed model is evaluated with the use of numerical experimentation.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
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• pp.193-196
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• 2008

과거 Z. cao는 Relation matrix를 사용한 정밀한 추론이 가능한 NFRM(New fuzzy reasoning method)을 제안하였다. 이는 추론의 규칙 수가 적음에도 불구하고 Mamdani의 퍼지추론방식에 비하여 좋은 성능을 보였다. 그러나 정밀한 추론을 위하여 relation matrix는 시행착오법을 사용하여 구하고, 이는 많은 시간과 노력이 필요하다. 본 연구에서는 이러한 relation matrix를 구하기 위하여 시행착오법에 의해 소요되는 많은 시간과 노력을 줄이고, 더욱 정밀한 추론 성능의 개선을 위하여 경사감소학습법을 사유한 학습기능을 갖는 Z. Cao의 퍼지추론 방식을 제안하고자 한다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.199-202
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• 2001

An elevator system, which is essential equipment for vertical movement of an object, as a property of building, has been driven by various expenditures and purposes. Since developing electrical control technology, control system are highly developed. The elevator system has expanded widely, but a data accuracy acquisition technique and safety predict technique for securing system safety is still at a basic level. So, objective verification for elevator confidence condition requires an absolute accuracy measurement technique. Therefore, this study is executed in order to acquire a method of depending on sense of a manager with simple numeric measurement data, and to construct a logical, analytical foresight system for more efficient elevator management system. As an artificial intelligence for diagnosis, the fuzzy inference algorithm is used for foreseeing the system in this thesis, because the fuzzy algorithm is the most useful method for resolving subjective ideas and a vague judgment of humans. The fuzzy inference algorithm is developed for each sensor signal(i.e. vibration, velocity, current).

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 춘계학술대회 학술발표 논문집 제15권 제1호
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• pp.153-156
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• 2005

In this paper, a framework for implementing UFIS (Unified Fuzzy rule-based knowledge Inference System) is presented. First, fuzzy clustering and fuzzy rules deal with the presence of the knowledge in DB (DataBase) and its value is presented with a value between 0 and 1. Second, RDB (Relational DB) and SQL queries provide more flexible functionality fur knowledge management than the conventional non-fuzzy knowledge management systems. Therefore, the obtained fuzzy rules offer the user additional information to be added to the query with the purpose of guiding the search and improving the retrieval in knowledge base and/ or rule base. The framework can be used as DM (Data Mining) and ES (Expert Systems) development and easily integrated with conventional KMS (Knowledge Management Systems) and ES.

• 제어로봇시스템학회논문지
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• 제9권10호
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• pp.782-788
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• 2003

Making deicing countermeasure plan against snow accretion on power line is a very complicated problem, which should take into account both the possibility of accidents due to snow accretion on power line and the stable operation of power system. As knowledge engineering can be a good solution to this field of problems, a prototype expert system to assist power system operators in forecasting snow accretion on power lines and making a list of all the feasible and effective deicing countermeasures has been developed. The system has been remodelled into a fuzzy expert system by adopting fuzzy rulebase and fuzzy inference method to systematically process the fuzziness included in the heuristic knowledges. Simulation results based on the past snow accretion accident data show that the proposed system is very promising.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1270-1273
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• 1993

Fuzzy optimal control is considered. An optimal sequence of controls is sought best satisfying fuzzy constraints on the controls and fuzzy goals on the states (outputs), with a fuzzy system under control Control over a fixed and specified, implicitly specified, fuzzy, and infinite termination time is discussed. For computational efficiency a small number of reference fuzzy staters and controls is to be assumed by which fuzzy controls and stated are approximated. Optimal control policies reference fuzzy states are determined as a fuzzy relation used, via the compositional rule of inference, to derive an optimal control. Since this requires a large number of overlapping reference fuzzy controls and states implying a low computational efficiency, a small number of nonoverlapping reference fuzzy states and controls is assumed, and then interpolative reasoning is used to infer an optimal fuzzy control for a current fuzzy state.