• 산업기술연구
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• 제29권B호
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• pp.9-15
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• 2009

In the case of traditional binary encoding technique, it takes long time to converge the optimal solutions and brings about complexity of the systems due to encoding and decoding procedures. However, the ROGAs (real-coded genetic algorithms) do not require these procedures, and the k-means clustering algorithm can avoid global searching space. Thus, this paper proposes a new approach by using their advantages. The proposed method constructs the multiple predictors using the optimal differences that can reveal the patterns better and properties concealed in non-stationary time series where the k-means clustering algorithm is used for data classification to each predictor, then selects the best predictor. After selecting the best predictor, the cluster centers of the predictor are tuned finely via RCGKA in secondary tuning procedure. Therefore, performance of the predictor can be more enhanced. Finally, we verifies the prediction performance of the proposed system via simulating typical time series examples.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.308-317
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• 2016

Multiple evidences based decision making is an important functionality for computers and robots. To combine multiple evidences, mathematical theory of evidence has been developed, and it involves the most vital part called Dempster's rule of combination. The rule is used for combining multiple evidences. However, the combined result gives a counterintuitive conclusion when highly conflicting evidences exist. In particular, when we obtain two different sources of evidence for a single hypothesis, only one of the sources may contain evidence. In this paper, we introduce a modified combination rule based on the partial conflict measurement by using an absolute difference between two evidences' basic probability numbers. The basic probability number is described in details in Section 2 "Mathematical Theory of Evidence". As a result, the proposed combination rule outperforms Dempster's rule of combination. More precisely, the modified combination rule provides a reasonable conclusion when combining highly conflicting evidences and shows similar results with Dempster's rule of combination in the case of the both sources of evidence are not conflicting. In addition, when obtained evidences contain multiple hypotheses, our proposed combination rule shows more logically acceptable results in compared with the results of Dempster's rule.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권1호
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• pp.39-46
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• 2006

We propose an global color model based method for tracking motions of multiple human using a networked multiple-camera system in intelligent space as a human-robot coexistent system. An intelligent space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of intelligent space as well. One of the main goals of intelligent space is to assist humans and to do different services for them. In order to be capable of doing that, intelligent space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and intelligent space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권2호
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• pp.118-123
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• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.5023-5038
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• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

• 지능정보연구
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• 제12권1호
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• pp.57-73
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• 2006

제조 라인의 설계에 있어서 물류설비의 선정은 매우 중요한 부분이다. 생산라인의 특성을 충분히 고려하여 물류설비를 선정하기 위해서는 다양한 요소들이 고려되어야 하며 그 요소들 가운데는 정량적인 요소(예, 자재 부피, 무게)들 뿐만 아니라 정성적인 요소(예, 유지 보수, 통합성)들도 포함된다. 정량적인 요소는 해당 물류설비의 사양 등을 통해 보다 쉽게 평가가 가능하지만 정성적인 요소는 객관적인 분석이 매우 어려운 부분이다. 실제 사례에서도 물류설비선정 시 정량적인 요소들만 검증되고 정성적인 요소들은 대부분 배제되는 것으로 나타나고 있다. 본 연구에서는 물류설비의 보다 효율적인 평가 및 선정을 위해 정량적인 요소뿐만 아니라 정성적인 요소들을 반영할 수 있는 방안을 제시하고자 한다. 이를 위해 전문가 지식 기반의 룰 (Rule) 및 퍼지 로직을 연계한 통합 방안을 개발하였다. 우선 전문가 지식 기반의 룰을 통해 해당 공정간 적절한 물류설비 유형 및 가능한 대안 유형들을 찾아내고 이들 중 정성적인 요소들까지를 반영하여 최적의 물류설비를 선정하기 위해 퍼지이론이 적용되었다. 본 연구를 통해 퍼지 이론의 제조 물류부분 적용 가능성을 제시하였다.

• 한국컴퓨터산업학회논문지
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• 제5권2호
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• pp.251-260
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• 2004

네트워크 보안의 중요성과 필요성이 증대됨에 따라 많은 조직들이 다양한 보안 시스템을 네트워크에 적용하고 있다. 침입 차단 시스템, 침입 탐지 시스템, 취약점 스캐너와 같은 보안 시스템들이 취약성 정보를 공유하게 되면 일관된 통합 보안 환경을 구축할 수 있다. 본 논문에서는 침입탐지 에이전트와 방화벽 에이전트가 계약망 프로토콜(Contract Net Protocol)에 의해서 서로 연동할 수 있는 구조를 디자인하고 구축하려다 계약망 프로토콜은 분산 시스템과 같은 이 기종의 컴퓨터 시스템의 효과적인 연통을 위한 방법으로서 여러 에이전트들이 모여 서로 협력하며 하나의 문제를 해결하게 된다 계약망 프로토콜의 선택 알고리즘과 퍼지 제어기를 사용하였을 경우 성능을 비교함으로써 에이전트 선택에 있어서 보다 효과적인 방법을 제시할 것이다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권1호
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• pp.53-59
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• 2015

This paper proposes a novel method for acoustically and wirelessly transmitting data underwater with a high transmission rate. The method uses the most promising physical layer and multiple access technique (i.e., the code division multiple channel access technique) to divide the channel into subchannels. Data is transmitted through these subchannels. The codes are pseudo-random noise (PN) sequences. In the spread-spectrum technique, a signal such as electrical, electromagnetic, acoustic signal generated in a particular bandwidth is deliberately spread in the frequency domain, which results in a signal with a wider bandwidth. This paper reviews the possibility of application of the direct-sequence code division multiple access (DS-CDMA) technique in an underwater system using MATLAB. As the result of our review, we recognize that the DS-CDMA technique can be applied to underwater environments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.

• KIEE International Transaction on Systems and Control
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• 제2D권2호
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• pp.125-134
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• 2002

This paper proposes a method for identifying temporal pattern clusters to predict events in time series. Instead of predicting future values of the time series, the proposed method forecasts specific events that may be arbitrarily defined by the user. The prediction is defined by an event characterization function, which is the target of prediction. The events are predicted when the time series belong to temporal pattern clusters. To identify the optimal temporal pattern clusters, fuzzy goal programming is formulated to combine multiple objectives and solved by an adaptive differential evolution technique that can overcome the sensitivity problem of control parameters in conventional differential evolution. To evaluate the prediction method, five test examples are considered. The adaptive differential evolution is also tested for twelve optimization problems.