• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권3호
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• pp.263-267
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• 2005

Building classifiers for financial real-world classification problems is often plagued by severely overlapping and highly skewed class distribution. New performance measures such as receiver operating characteristic (ROC) curve and area under ROC curve (AUC) have been recently introduced in evaluating and building classifiers for those kind of problems. They are, however, in-effective to evaluation of classifier's discrimination performance in a particular class of the classification problems that interests lie in only a local operating range of the classifier, In this paper, a new method is proposed that enables us to directly improve classifier's discrimination performance at a desired local operating range by defining and optimizing a partial area under ROC curve or domain-specific curve, which is difficult to achieve with conventional classification accuracy based learning methods. The effectiveness of the proposed approach is demonstrated in terms of fraud detection capability in a real-world fraud detection problem compared with the MSE-based approach.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권1호
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• pp.8-16
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• 2014

We consider the visual barcode recognition problem in a noisy video data setup. Unlike most existing single-frame recognizers that require considerable user effort to acquire clean, motionless and blur-free barcode signals, we eliminate such extra human efforts by proposing a robust video-based barcode recognition algorithm. We deal with a sequence of noisy blurred barcode image frames by posing it as an online filtering problem. In the proposed dynamic recognition model, at each frame we infer the blur level of the frame as well as the digit class label. In contrast to a frame-by-frame based approach with heuristic majority voting scheme, the class labels and frame-wise noise levels are propagated along the frame sequences in our model, and hence we exploit all cues from noisy frames that are potentially useful for predicting the barcode label in a probabilistically reasonable sense. We also suggest a visual barcode tracking approach that efficiently localizes barcode areas in video frames. The effectiveness of the proposed approaches is demonstrated empirically on both synthetic and real data setup.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권2호
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• pp.121-125
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• 2008

It is essential to maintain hierarchical information properly for efficient XML query processing. Well known approach to represent hierarchical information of XML tree is assigning a specific node number to each node of XML tree. Insertion and deletion of XML node can occur at any position in a dynamic XML tree. A dynamic numbering scheme allows us to add nodes to or delete nodes from an XML tree without relabeling or with relabeling only a few existing nodes of XML tree while executing XML query efficiently. According to W3C XQuery update facility specifications a node can be added as first or last child of the existing node in XML tree. Generating new number for last child requires referencing the number of previous last child. Getting the number of last child is very costly with previous approaches. We have developed a new dynamic numbering scheme PSN which is very effective for insertion of a node as last child. Our approach reduces the time to find last child dramatically by removing sorting of children.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권3호
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• pp.153-158
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• 2015

Nowadays, automatic keyphrase extraction is considered to be an important task. Most of the previous studies focused only on selecting keyphrases within the body of input documents. These studies overlooked latent keyphrases that did not appear in documents. In addition, a small number of studies on latent keyphrase extraction methods had some structural limitations. Although latent keyphrases do not appear in documents, they can still undertake an important role in text mining because they link meaningful concepts or contents of documents and can be utilized in short articles such as social network service, which rarely have explicit keyphrases. In this paper, we propose a new approach that selects qualified latent keyphrases from input documents and overcomes some structural limitations by using deep belief networks in a supervised manner. The main idea of this approach is to capture the intrinsic representations of documents and extract eligible latent keyphrases by using them. Our experimental results showed that latent keyphrases were successfully extracted using our proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권2호
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• pp.91-99
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• 2013

An autonomous unmanned underwater vehicle is a type of marine self-propelled robot that executes some specific mission and returns to base on completion of the task. In order to successfully execute the requested operations, the vehicle must be guided by an effective navigation algorithm that enables it to avoid obstacles and follow the best path. Architectures and principles for intelligent dynamic systems are being developed, not only in the underwater arena but also in related areas where the work does not fully justify the name. The problem of increasing the capacity of systems management is highly relevant based on the development of new methods for dynamic analysis, pattern recognition, artificial intelligence, and adaptation. Among the large variety of navigation methods that presently exist, the dynamic window approach is worth noting. It was originally presented by Fox et al. and has been implemented in indoor office robots. In this paper, the dynamic window approach is applied to the marine world by developing and extending it to manipulate vehicles in 3D marine environments. This algorithm is provided to enable efficient avoidance of obstacles and attainment of targets. Experiments conducted using the algorithm in MATLAB indicate that it is an effective obstacle avoidance approach for marine vehicles.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권1호
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• pp.1-6
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• 2007

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. Recently, with the objective of generalizing the SVDD which treats all training data with equal importance, the so-called D-SVDD (density-induced support vector data description) was proposed incorporating the idea that the data in a higher density region are more significant than those in a lower density region. In this paper, we consider the problem of further improving the D-SVDD toward the use of a partial reference set for testing, and propose an LMI (linear matrix inequality)-based optimization approach to solve the improved version of the D-SVDD problems. Our approach utilizes a new class of density-induced distance measures based on the RSDE (reduced set density estimator) along with the LMI-based mathematical formulation in the form of the SDP (semi-definite programming) problems, which can be efficiently solved by interior point methods. The validity of the proposed approach is illustrated via numerical experiments using real data sets.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.240-250
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• 2015

The development of the control algorithms for under-actuated systems is important. Decoupled sliding mode control has been successfully employed to control under-actuated systems in a decoupling manner with the use of sliding mode control. However, in such a control scheme, the system functions must be known. If there are uncertainties in those functions, the control performance may not be satisfactory.In this paper, the direct adaptive fuzzy sliding mode control is employed to control a class of under-actuated uncertain systems which can be regarded as a combination of several subsystems with one same control input. By using the hierarchical sliding control approach, a sliding control law is derived so as to make every subsystem stabilized at the same time. But, since the system considered is assumed to be uncertain, the sliding control law cannot be readily facilitated. Therefore, in the study, based on Lyapunov stable theory a fuzzy compensator is proposed to approximate the uncertain part of the sliding control law. From those simulations, it can be concluded that the proposed compensator can indeed cope with system uncertainties. Besides, it can be found that the proposed compensator also provide good robustness properties.

• 전자공학회논문지S
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• 제36S권9호
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• pp.49-57
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• 1999

퍼지 집합 이론과 신경망 이론의 융합은 퍼지논리 시스템의 최적 규칙 베이스를 얻기 위해 신경망을 적용하는 방향으로 주된 연구가 진행되고 있다. 그러나 이러한 접근 방법은 신경망의 제한된 학습능력으로 인해 최적성의 한계는 여전히 극복되지 못하고 있는 실정이다. 따라서 본 논문에서는 이러한 어려움을 극복하기 위해 입출력 데이터로부터 최적의 규칙을 얻을 수 있는 Rough Set 이론과 뉴로-퍼지의 새로운 융합기법을 제안한 알고리즘을 생성된 규칙 베이스가 중첩되지 않기 때문에 기존의 FNN과 비교하여 더욱더 우수함을 냉장고의 온도추론 시스템에 적요하여 검증하였다.

• 전기전자학회논문지
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• 제23권2호
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• pp.529-533
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• 2019

오늘날 복합 화력 발전소는 전력 생산을 위해 많이 사용되고 있고, 최근에는 운전 매개 변수를 기반으로 발전 출력을 예측하는 것이 주요 관심사이다. 본 논문에서는 복합 화력 발전소의 출력을 예측하기 위해 컴퓨터 지능 기법을 이용하는 방법을 제시한다. 컴퓨터 지능 기술은 지속적으로 발전되어 많은 실제 문제에 적용되어 왔다. 본 논문에서는 트리 구조의 퍼지 뉴럴 네트워크를 이용하여 발전 출력을 예측하고자 한다. 트리 구조의 퍼지 뉴럴 네트워크는 퍼지 뉴런을 노드로 선택하고 관련 입력을 최적으로 선택하여 규칙 수를 줄이는 장점이 있다. 네트워크의 최적화를 위해 2 단계 최적화 방법이 사용된다. 유전 알고리즘은 최적의 노드와 리프를 선택하여 네트워크의 이진 구조를 최적화 한 다음 랜덤 신호 기반 학습을 수행하여 최적화 된 이진 연결을 단위 구간에서 미세 학습한다. 제안 된 방법의 유용성을 검증하기 위해 UCI Machine Learning Repository Database에서 얻은 복합 화력 발전소 데이터를 사용한다.

• 지능정보연구
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• 제12권3호
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• pp.15-32
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• 2006

본 논문에서는 각 대안의 속성 평가와 속성 자체의 중요도에 대한 평가에 있어 불명료한 선호정보 형태로 주어진 경우, linguistic quantifier를 통한 퍼지논리를 활용하여 그룹의사결정을 지원하는 방법을 제시하였다. 불명료한 선호정보는 의사결정 관련 문헌에서 의사결정자에게 요구되는 선호정보 명시의 부담을 줄여주고, 판단의 모호성을 받아들이고자 하는 시각으로서 다뤄져 왔다. 그러나 불명료한 유형의 선호정보를 허용할 경우 의사결정그룹이 원하는 대안의 명확한 선택이 보다 어려워진다. 따라서 추가적인 정보획득을 위한 의사결정자들과의 상호작용이 요구되지만, 이는 불명료한 선호정보를 허용하였던 초기의 취지를 반감시킬 뿐더러, 반드시 최적의 대안을 보장하는 것도 아니다. 이러한 상황을 타계하기 위하여, fuzzy majority의 의미를 반영하고 있는 linguistic quantifier를 활용함으로써 satisfying solution을 구하는 절차를 제시하였다. 이는 mathematical programming을 활용한 의사결정 기법과 다수의 객체를 집성하기 위한 개략적 해법을 결합한 접근방식이다.