• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.119-124
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• 2001

In this pattern recognition on the large volumes of remote sensing satellite images, the inference time is much increased. In the case of the remote sensing data [5] having 4 wavebands, the 778 training patterns are learned. Each land cover pattern is classified by using 159, 900 patterns including the trained patterns. For the fuzzy classification, the 778 fuzzy rules are generated. Each fuzzy rule has 4 fuzzy variables in the condition part. Therefore, high performance parallel fuzzy inference system is needed. In this paper, we propose a novel parallel fuzzy inference system on T3E parallel computer. In this, fuzzy rules are distributed and executed simultaneously. The ONE_To_ALL algorithm is used to broadcast the fuzzy input to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of the fuzzy rules, the parallel fuzzy inference algorithm extracts match parallelism and achieves a good speed factor. This system can be used in a large expert system that ha many inference variables in the condition and the consequent part.

• 한국지능시스템학회논문지
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• 제7권2호
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• pp.3-15
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• 1997

퍼지논리를 적용하기 위해서는 두가지 과제가 이루어져야 하는데 그것은 퍼지룰의 유도와 맴버쉽함수의 결정이다. 이 과제는 어렵고 또한 시간을 요하게 된다. 본 논문에서는 문제에 적용 가능한 멤버쉽함수와 퍼지룰을 자동으로 유도하기 위한 알고리즘적 방법을 제시하고 있다. 이 알고리즘적 방법은 샘플을 구분하는 엔트로피 최소화의 원리에 입각하고 있다. 멤버쉽함수는 샘플을 연속적으로 구분하여 이루어지며 퍼지룰 또한 엔트로피 최소화 원리에 의하여 이루어진다. 퍼지룰의 유도에서는 룰 비중 또한 같이 계산된다. 결정 문제에 적용을 위한 추론법 및 방법도 논의되었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.525-528
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• 1999

Abstract - In this paper, a segmentation method for brain Magnetic Resonance(MR) image using region clustering technique with statistical distribution of gradient image and fuzzy rules is described. The brain MRI consists of gray matter and white matter, cerebrospinal fluid. But due to noise, overlap, vagueness, and various parameters, segmentation of MR image is a very difficult task. We use gradient information rather than intensity directly from the MR images and find appropriate thresholds for region classification using gradient approximation, rayleigh distribution function, region clustering, and merging techniques. And then, we propose the adaptive fuzzy rules in order to extract anatomical structures and diseases from brain MR image data. The experimental results shows that the proposed segmentation algorithm given better performance than traditional segmentation techniques.

• 대한조선학회 특별논문집
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• 대한조선학회 2007년도 특별논문집
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• pp.135-141
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• 2007

CSR(Common Structure Rules) enter into force on $1^{st}$ April 2006. Generally for double hull tankers of less than 150m in length, the Rules of the individual Classification Society are to be applied. Where high tensile stresses act through an intermediate plate, increased fillet welds or penetration welds are to be used longitudinal/transverse bulkhead primary support member end connections to the double bottom. If workers have begun to make used of established procedures between corrugated BHD and lower stool joint, first to welding on groove of face and then it has to gouging to blow on groove of root. So amount of man-hour increased, productivity secreased.

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.956-958
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• 1995

This paper presents the automatic construction of fuzzy rule-based systems for diagnosing the faults of complex systems. Generally, fuzzy systems work well when we can use expert's experience to articulate fuzzy IF-THEN rules and memberships for fuzzy sets. When we cannot do this, we should generate the fuzzy rules and membership functions for fuzzy sets directly from experimental data. In this paper, we propose a new method on how to extract fuzzy sets and fuzzy rules. We also introduce an efficient fine-tunning algorithm of the parameters of membership functions.

• Journal of Information Processing Systems
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• 제13권6호
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• pp.1554-1574
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• 2017

This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classification depends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that it presents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also consider the relationship of these kinds of PRS's to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FI PRS's can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FI PRS's also have maximum Shannon entropy, while sequences from LFSR's do not, nor are such sequences FI random.

• 한국도서관정보학회지
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• 제19권
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• pp.35-57
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• 1992

This study is an attempt to provide some helpful data for the design and the implementation of the expert system for the book-classification based on the analysis of various cases of the classification-expert system models. Following the introduction, the concepts and some features of an expert system were overviewed in the second chapter, on the basis of which the following concrete cases were introduced and analyzed in the third chapter : (1) ACN System for NC, (2) Expert System for NDC, (3) Expert System for UDC, (4) Herba Medica System, (5) Expert System for IPC, (6) Stratcyclode Project, (7) Expert System for Classification of INIS Database, (8) AutoBC System, and etc. In the conclusion, for the development of the classification-expert system, it was turned out that constructing a new system by using an AI language such as Prolog or LISP is more desirable than employing any one of expert system shells. Together it is necessary for the following requirements to be met : (1) The subject concept of a document elicited should be accurate. (2) Not only a domain knowledge but also the knowledge covering all the subjects should be represented in the knowledge-bases. (3) The knowledge-bases should be organized in such a way that the characteristics of the knowledge about classification should be well defined. (4) rule-base consisting of accurate rules about classification should be made. (5) It should be possible for classification code wanted to be generated immediately.

• 정보처리학회논문지B
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• 제12B권3호
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• pp.349-356
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• 2005

동적이고 비정형적인 환경에서 작업을 수행하기 위해 인공유기체를 이용하는 응용 분야가 빠른 속도로 확대되고 있다. 이러한 분야에서 인공유기체의 행동 지식 표현법으로 일반적인 프로그래밍 또는 전통적인 인공지능 방법을 사용하면, 예측치 못한 상황으로 인한 빈번한 변경과 나쁜 응답성의 문제가 발생한다. 이들 문제들을 기계학습적으로 해결하기 위한 방법으로는 유전자 프로그래밍과 진화 신경망이 대표적이다. 그러나 아직까지도 인공유기체의 학습방법이 문제가 되고 있으며, 같은 환경 속에 서식하는 인공유기체의 종이 같아서 여러생명체를 대표할수 없는 문제점이 있다. 본 논문에서는 학습의 속도와 질을 향상시키기 위해 강화역전파 신경망과 분류규칙을 이용하였으며, 한 환경속에 서식하는 인공유기체의 종을 달리하였다. 제안된 모델을 평가하기 위해서 이종간 인공유기체 집단이 한 가상환경속에서 서로 경쟁하면서 생활하는 시뮬레이터를 설계 및 구현하였고, 그들의 행동진화를 수행결과로 보여주었으며, 타시스템과의 비교분석을 하였다. 결과적으로, 학습의 속도와 질적인 면에서 제안된 모델이 모두 우수한 것을 확인하였다. 본 모델의 특징으로는, 유전자 알고리즘에 의해서 염색체에 표현된 분류 규칙들과 신경망의 학습이 동시에 수행되며, 분류 규칙과 강화역전파 신경망의 2단계의 처리 과정으로 인하여 학습 능력이 강화된다는 점이다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.301-304
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• 2005

For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques or frequent episode mining in order to analyze an audit data. But, since they mainly analyze the average behaviour of user's activities, some anomalies can be detected inaccurately. Therefore, we propose an anomaly detection method that utilizes an associative classification for modelling intrusion detection. Finally, we proof that a prediction model built from associative classification method yields better accuracy than a prediction model built from a traditional methods by experimental results.