• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.77-83
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• 2011

In this paper, an active backstepping design is proposed to achieve control and synchronization of a new hyperchaotic system. The proposed method is a systematic design approach and exists in a recursive procedure that interlaces the choice of a Lyapunov function with the design of the active control. The proposed controller enables stabilization of chaotic motion to the origin as well as synchronization of the two identical new hyperchaotic systems. Numerical simulations illustrate the validity of the proposed control technique.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.256-262
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• 2012

Direct teaching of the industrial robot is a novel technique to easily teach manipulators. However, teaching data by human hand cannot help having large noise errors ranging from low to high frequency. To use teaching data, post-processing to correct the teaching trajectory is required. Here, a novel shape-based trajectory correction method to rebuild teaching data with the feature information of curvature and velocity is proposed. The proposed method is tested on square and circular objects.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.285-288
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• 2013

의료영상에서의 노이즈는 환자 진단에 있어서 막대한 영향을 미치는 영상의 화질을 떨어트림으로써, 진단에 대한 유효성을 낮추게 된다. 특히, 현재 이슈화 되고 있는 저선량 의료영상은 기존의 고선량 의료영상보다 노이즈 레벨이 높으며, 이에 따라서 의료영상에서의 노이즈 제거 기술은 매우 중요한 사안으로 부각되고 있다. 본 논문에서 제시하는 노이즈 제거 기술은 각각의 투영 영상을 여러개의 부대역(sub-band)으로 분해하는 것으로부터 시작한다. 분해된 각각의 부대역 영상은 엣지 검출기를 통하여 엣지 부분과 평탄한 영역으로 구별되어 진다. 검출된 엣지는 0 ~ 1 사이의 값으로 정규화 되며, 퍼지기반의 연산을 통하여 엣지의 확실성을 나타내는 엣지맵으로 변환하게 된다. 이 엣지맵을 통하여 각 부대역 영상의 필터링 정도를 제어하고, 분해된 각 부대역을 결합하는 방식을 취함으로써 영상의 엣지 부분을 최대한 보존하면서 노이즈는 효과적으로 제거하도록 하였다.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1671-1679
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• 2004

This study has proposed visual-feature extraction methods for each band in wavelet domain with both spatial frequency features and multi resolution features. In addition, it has brought forward similarity measurement method using fuzzy theory and new color feature expression method taking advantage of the frequency of the same color after color quantization for reducing quantization error, a disadvantage of the existing color histogram intersection method. Experiments are performed on a database containing 1,000 color images. The proposed method gives better performance than the conventional method in both objective and subjective performance evaluation.

• Fire Science and Engineering
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• v.26 no.6
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• pp.24-30
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• 2012

This study is on the method of the detection for flame and series arc which can be happened at poor contact point added a vibration in part of contact point of low voltage line. In general, the causes of electric fire are over current, short circuit, poor contact, ect. The over-current or short circuit among those causes is detected by measuring a instant current value, but poor contact is difficult to detect by measuring a excessive value of the voltage and current and a distortion of waveforms. And therefore, in this paper, it is studied on the optimal technique of the arc judgement using fuzzy logic and MDET (Multi Dimension Estimation Technique). And it carries out the simulation for arc detection and the experiment for controller and load test. In result, the controller and detection algoristhm, is classified with normal wave and abnormal arc wave without relation with each loads and so the controller can detect a series arc successfully.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.146-153
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• 2010

For the indoor location, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor location which does not require any special equipments dedicated for positioning. As fingerprinting method,k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighborsk and positions of reference points(RPs). So possibilistic fuzzy c-means(PFCM) clustering algorithm is applied to improve KNN, which is the KNN/PFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN,k RPs are firstly chosen as the data samples of PFCM based on signal to noise ratio(SNR). Then, thek RPs are classified into different clusters through PFCM based on SNR. Experimental results indicate that the proposed KNN/PFCM hybrid algorithm generally outperforms KNN and KNN/FCM algorithm when the locations error is less than 2m.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.299-305
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• 2015

Global environmental concerns and the ever increasing need of energy, coupled with steady progress in renewable energy technologies, are opening up new opportunities for utilization of renewable energy resources. Distributed electricity generation is a suitable option for sustainable development thanks to the load management benefits and the opportunity to provide electricity to remote areas. Solar energy being easy to harness, non-polluting and never ending is one of the best renewable energy sources for electricity generation in present and future time. Due to the random and intermittent nature of solar source, PV plants require the adoption of an energy storage and management system to compensate fluctuations and to meet the energy demand during night hours. This paper presents an efficient, economic and technical model for the design of a MPPT based grid connected PV with battery storage and management system. This system satisfies the energy demand through the PV based battery energy storage system. The aim is to present PV-BES system design and management strategy to maximize the system performance and economic profitability. PV-BES (photovoltaic based battery energy storage) system is operated in different modes to verify the system feasibility. In case of excess energy (mode 1), Li-ion batteries are charged using CC-CV mechanism effectively controlled by fuzzy logic based PID control system whereas during the time of insufficient power from PV system (mode 2), batteries are used as backup to compensate the power shortage at load and likewise other modes for different scenarios. This operational mode change in PV-BES system is implemented by State flow chart technique based on SOC, DC bus voltages and solar Irradiance. Performance of the proposed PV-BES system is verified by some simulations study. Simulation results showed that proposed system can overcome the disturbance of external environmental changes, and controls the energy flow in efficient and economical way.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.801-804
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• 2003

The autopilot system of vessel is proposed to take service safety sorority, to elevate service efficiency, to decrease labor and to improve working environment. Ultimate purpose of it is to minimize the number of crew by guaranteeing economical efficiency of shipping service. Recently, the research is being achieving to compensate various nonlinear parameters of vessel and apply it is course keeping control, track keeping control, roll-rudder stabilization, dynamic ship positioning and automatic mooring control etc. using optimizing control technique. Relation between rudder angle controlled by steering machine of vessel and ship-heading angle, and load condition of ship are nonlinear, which affect various parameters of shipping service. The speed and direction of waves, velocity and quantity of wind, which also cause the non-linearity of it. Therefore the autopilot system of ship requires the robust control algorithm can overcome various non-linearity. On this paper, we design the autopilot system of ship, which overcome nonlinear parameters and disturbance of it using Fuzzy Algorithm, evaluate the proposed algorithm and its excellence through simulation

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1401-1408
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• 2000

Critical prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development cost because the problems in early phases largely affected the quality of the late products. Real-time systems such as telecommunication system are so large that criticality prediction is more important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing cause of the prediction results and low extendability. In this paper, we propose a criticality prediction model using fuzzy rulebase constructed by genetic algorithm. This model makes it easy to analyze the cause of the result and also provides high extendability, high applicability, and no limit on the number of rules to be found.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.