• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.210-216
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• 2006

In the rule based modeling, data partitioning plays crucial role be cause partitioned sub data set implies particular information of the given data set or system. In this paper, we present an empirical study result of the data pattern estimation to find underlying data patterns of the given data. Presented method performs crisp type clustering with given n number of data samples by means of the sequential agglomerative hierarchical nested model (SAHN). In each sequence, the average value of the sum of all inter-distance between centroid and data point. In the sequel, compute the derivation of the weighted average distance to observe a pattern distribution. For the final step, after overall clustering process is completed, weighted average distance value is applied to estimate range of the number of clusters in given dataset. The proposed estimation method and its result are considered with the use of FCM demo data set in MATLAB fuzzy logic toolbox and Box and Jenkins's gas furnace data.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.534-540
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• 1993

In this paper, we present an iterative learning method of compensating for position sensor error. The previously known compensation algrithms need a special perfect position sensor or a priori information about error sources, while ours does not. To our best knowledge, any iterative learning approach has not been taken for sensor error compensation. Furthermore, our iterative learning algorithm does not have the drawbacks of the existing iterative learning control theories. To be more specific, our algorithm learns a uncertain function inself rather than its special time-trajectory and does not request the derivatives of measurement signals. Moreover, it does not require the learning system to start with the same initial condition for all iterations. To illuminate the generality and practical use of our algorithm, we give the rigorous proof for its convergence and some experimental results.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.300-305
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• 2004

This paper concerns controller design for the Takagi-Sugeno (TS) fuzzy systems. The design method proposed in this paper is derived in the framework of the optimal control theory utilizing the piecewise quadratic optimal value functions. The major part of the proposed design procedure consists of solving linear matrix inequalities (LMIs). Since LMIs can be solved efficiently within a given tolerance by the recently developed interior point methods, the design procedure of this paper is useful in practice. A design example is given to illustrate the applicability of the proposed method.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.155-164
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• 2006

This paper focuses on a new optimization technique of a fuzzy logic based proportional integral (FLPI) load frequency controller by the multiple tabu search (MTS) algorithm. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and error method or experiences of designers. To overcome this problem, the MTS algorithm is proposed to simultaneously tune proportional integral gains, the membership functions and control rules of a FLPI load frequency controller in order to minimize the frequency deviations of the interconnected power system against load disturbances. The MTS algorithm introduces additional techniques for improvement of the search process such as initialization, adaptive search, multiple searches, crossover and restart process. Simulation results explicitly show that the performance of the proposed FLPI controller is superior to conventional PI and FLPI controllers in terms of overshoot and settling time. Furthermore, the robustness of the proposed FLPI controller under variation of system parameters and load change are higher than that of conventional PI and FLPI controllers.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.959-962
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• 2005

Efficient video data storage and search techniques are essential for DVR-based security systems. We have designed appropriate data structures and search techniques for efficient image storage and search, in this study. The date and time can be saved and searched as a folder form. The overall system is designed for MPEG4 CODEC. It can handle variable sizes of frames (100bytes $^{\sim}$ 6Kbytes) produced by MPEG4 CODEC without errors. We also have developed image transmission techniques through inter-net networking.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.227-232
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• 2003

Recently, support vector learning attracts an enormous amount of interest in the areas of function approximation, pattern classification, and novelty detection. One of the main reasons for the success of the support vector machines(SVMs) seems to be the availability of global and sparse solutions. Among the approaches sharing the same reasons for success and exhibiting a similarly good performance, we have KFD(kernel Fisher discriminant) approach. In this paper, we consider the problem of function approximation utilizing both predetermined basis functions and the KFD approach for regression. After reviewing support vector regression, semi-parametric approach for including predetermined basis functions, and the KFD regression, this paper presents an extension of the conventional KFD approach for regression toward the direction that can utilize predetermined basis functions. The applicability of the presented method is illustrated via a regression example.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.445-447
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• 1998

This paper shows simulation results for stability and performance of two INS/GPS integration systems. First, the code tracking error of GPS receiver is analyzed by spectrum analysis and simulated for the tight and loose INS/GPS integrations. Next, stability of the integrated systems are simulated using root locus method. As loop filter in the GPS receiver, passive filter and active filter are used and compared.

• Smart Structures and Systems
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• v.16 no.4
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• pp.623-640
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• 2015

This paper presents techniques to harvest higher voltage from piezoelectric cantilever energy harvester by structural alteration. Three different energy harvesting structures are considered namely, stepped cantilever beam, stepped cantilever beam with rectangular and trapezoidal cavity. The analytical model of three energy harvesting structures are developed using Euler-Bernoulli beam theory. The thickness, position of the rectangular cavity and the taper angle of the trapezoidal cavity is found to shift the neutral axis away from the surface of the piezoelectric element which in turn increases the generated voltage. The performance of the energy harvesters is evaluated experimentally and is compared with regular piezoelectric cantilever energy harvester. The analytical and experimental investigations reveal that, the proposed energy harvesting structures generate higher output voltage as compared to the regular piezoelectric cantilever energy harvesting structure. This work suggests that through simple structural modifications higher energy can be harvested from the widely reported piezoelectric cantilever energy harvester.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.2B no.3
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• pp.90-94
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• 2002

The line start permanent magnet (LSPM) motor has been developed facilitate to the design of the synchronous motor. The rotor of this motor is composed of interior permanent magnets and aluminum bars instead of rotor windings. It is difficult to predict the performance characteristics accurately, because many characteristics are produced by the aluminum rotor bars and the permanent magnets. Therefore, in this paper the dynamic characteristics of the LSPM motor are described and compared via the time-stepped finite element method with those of the cage-type induction motor to find the characteristics of the permanent magnets and the rotor bars in the LSPM motor.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.83-86
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• 1998

In this paper, a fuzzy model based on the Polynomial Neural Network(PNN) structure is proposed to estimate the emission pattern for air pollutant in power plants. the new algorithm uses PNN algorithm based on Group Mehtod of Data Handling (GMDH) algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NOx emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy model. The simulation results show that the proposed technique can produce the optimal fuzzy model with higher accuracy and feasibility than other works achieved previously.