• International Journal of Fuzzy Logic and Intelligent Systems
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• 제3권1호
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• pp.1-6
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• 2003

The Delayed Feedback Control method (DFC) proposed by Pyragas applies an input based on the difference between the current state of the system, which is generating chaos orbits, and the $\tau$-time delayed state, and stabilizes the chaos orbit into a target. In DFC, the information about a position in the state space is unnecessary if the period of the unstable periodic orbit to stabilize is known. There exists the fault that DFC cannot stabilize the unstable periodic orbit when a linearlized system around the periodic point has an odd number property. There is the chaos control method using the prediction of the $\tau$-time future state (PDFC) proposed by Ushio et al. as the method to compensate this fault. Then, we propose a method such as improving the fault of the DFC. Namely, we combine DFC and PDFC with parameter W, which indicates the balance of both methods, not to lose each advantage. Therefore, we stabilize the state into the $\tau$ periodic orbit, and ask for the ranges of Wand gain K using Jury' method, and determine the quasi-optimum pair of (W, K) using a genetic algorithm. Finally, we apply the proposed method to a discrete-time chaotic system, and show the efficiency through some examples of numerical experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권3호
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• pp.191-197
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• 2009

Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fatal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input-output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.

• 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.

• 대한임베디드공학회논문지
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• 제9권3호
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• pp.157-163
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• 2014

This paper demonstrates the benefit of using MOOS-IvP in the development of control system for Unmanned Underwater Vehicles(UUV). The demand for autonomy in UUVs has significantly increased due to the complexity in missions to be performed. Furthermore, the increased number of sensors and actuators that are interconnected through a network has introduced a need for a middleware platform for UUVs. In this context, MOOS-IvP, which is an open source software architecture, has been developed by several researchers from MIT, Oxford University, and NUWC. The MOOS software is a communication middleware based on the publish-subscribe architecture allowing each application to communicate through a MOOS database. The IvP Helm, which is one of the MOOS modules, publishes vehicle commands using multi-objective optimization in order to implement autonomous decision making. This paper explores the benefit of MOOS-IvP in the development of control software for UUVs by using a case study with an auto depth control system based on self-organizing fuzzy logic control. The simulation results show that the design and verification of UUV control software based on MOOS-IvP can be carried out quickly and efficiently thanks to the reuse of source codes, modular-based architecture, and the high level of scalability.

• 대한전기학회논문지:전력기술부문A
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• 제52권8호
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• pp.471-480
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• 2003

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. Among the reasons for its complexity are the system size, conflicting requirements of estimator accuracy, reliability in the presence of transducer noise and data communication failures, adaptability to change in the network topology and cost minimization. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs) which is widely used in areas such as: optimization of the objective function, learning of neural networks, tuning of fuzzy membership functions, machine learning, system identification and control. This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권1호
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• pp.52-58
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• 2005

We have developed a Group Method of Data Handling (GMDH) type algorithm for designing multi-layered neural networks. The algorithm is general enough that it will accept any number of inputs and any sized training set. Each neuron of the resulting network is a function of two of the inputs to the layer. The equation for each of the neurons is a quadratic polynomial. Several forms of the equation are tested for each neuron to make sure that only the best equation of two inputs is kept. All possible combinations of two inputs to each layer are also tested. By carefully testing each resulting neuron, we have developed an algorithm to keep only the best neurons at each level. The algorithm's goal is to create as accurate a network as possible while minimizing the size of the network. Software was developed to train and simulate networks using our algorithm. Several applications were modeled using our software, and the result was that our algorithm succeeded in developing small, accurate, multi-layer networks.

• 한국지능시스템학회논문지
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• 제23권6호
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• pp.533-538
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• 2013

최근 빈번히 일어나는 국지성 집중호우로 인해 피해가 급격히 증가하고 있다. 인구가 밀집한 수도권과 같은 경우 산사태와 토석류 및 홍수로 인해 인명 및 재산피해가 심각하다. 따라서 집중호우에 대한 예측의 중요성이 증가하고 있다. 우리나라 악천후 강수의 특징으로는 태풍과 집중호우로 구분된다. 이는 지속시간과 지역에 따라 차이를 보인다. 또한, 지역적인 강수는 계절에 따라 변동성이 크고 비선형적이기 때문에 강수를 예측하는데 어려움이 따른다. 본 논문에서는 기상청에서 현업으로 사용하는 초단기 기상 분석 및 예측시스템 (Korea Local Analysis and Prediction System; KLAPS)의 기상 관측 자료를 이용하여 초단기 호우 예측 패턴 모델을 구현한다. 그리고 악천후 시 피해가 큰 수도권을 중심으로 여름철 호우 특보를 예측한다. 유전자 알고리즘(Genetic Algorithm; GA) 기반 다항식 방사형 기저함수 신경회로망(Radial Basis Function Neural Networks; RBFNNs)을 이용하여 초단기 강수 예측 패턴 모델을 설계한다. 최적화된 분류기를 설계하기 위하여 유전자 알고리즘을 이용하여 주요 파라미터인 입력변수의 수, 다항식 차수, 퍼지화 계수, FCM(Fuzzy C-mean) 클러스터 수를 동조한다.

• 디지털산업정보학회논문지
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• 제13권3호
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• pp.19-25
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• 2017

Nowadays, networked computer systems play an increasingly important role in our society and its economy. They have become the targets of a wide array of malicious attacks that invariably turn into actual intrusions. This is the reason computer security has become an essential concern for network administrators. Recently, a number of Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems, are useful only for the existing patterns of intrusion. Therefore, probe detection has become a major security protection technology to detection potential attacks. Probe detection needs to take into account a variety of factors ant the relationship between the various factors to reduce false negative & positive error. It is necessary to develop new technology of probe detection that can find new pattern of probe. In this paper, we propose an hybrid probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) in dynamic environment such as Cloud and IoT. Also, in order to verify the proposed method, experiments about measuring detection rate in dynamic environments and possibility of countermeasure against intrusion were performed. From experimental results, decrease of false detection and the possibilities of countermeasures against intrusions were confirmed.

• 한국지능시스템학회논문지
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• 제18권1호
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• pp.86-93
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• 2008

복잡한 의미관계를 정의하는 온톨로지를 구축하는 일은 매우 정밀하고 전문적인 작업이다. 잘 구축된 온톨로지를 응용 시스템에 활용하기 위해서는 온톨로지 클래스에 대한 많은 인스턴스 정보를 구축해야 한다. 본 논문은 온톨로지 인스턴스 정보 추출을 위하여 방대한 양의 웹 문서로부터 주어진 주제에 적합한 문서만을 추출하는 주제 중심 웹 문서 수집 알고리즘을 제안하고, 이 알고리즘을 바탕으로 문서 수집 시스템을 개발한다. 제안하는 문서 수집 알고리즘은 URL의 패턴을 이용하여 주제에 적합한 링크만을 추출함으로써 빠른 속도의 문서 수집을 가능하게 한다. 또한 링크 블록 텍스트에 대한 퍼지집합으로 표현된 주제 적합도는 문서의 주제 관련성을 지능적으로 판단하여 주제 중심 문서 수집의 정확도를 향상시킨다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권12호
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• pp.4776-4798
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• 2015

The location selection of virtual machines in distributed cloud is difficult because of the physical resource distribution, allocation of multi-dimensional resources, and resource unit cost. In this study, we propose a multi-object virtual machine location selection algorithm (MOVMLSA) based on group information, doubly linked list structure and genetic algorithm. On the basis of the collaboration of multi-dimensional resources, a fitness function is designed using fuzzy logic control parameters, which can be used to optimize search space solutions. In the location selection process, an orderly information code based on group and resource information can be generated by adopting the memory mechanism of biological immune systems. This approach, along with the dominant elite strategy, enables the updating of the population. The tournament selection method is used to optimize the operator mechanisms of the single-point crossover and X-point mutation during the population selection. Such a method can be used to obtain an optimal solution for the rapid location selection of virtual machines. Experimental results show that the proposed algorithm is effective in reducing the number of used physical machines and in improving the resource utilization of physical machines. The algorithm improves the utilization degree of multi-dimensional resource synergy and reduces the comprehensive unit cost of resources.