• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.23-28
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• 2004

This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent on the current data sets. As the results, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations. Finally, the new space and time sensor fusion(STSF) scheme is applied to the control of a mobile robot in an unstructured environment as well as structured environment.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.104.2-104
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• 2002

This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent on the current data sets. As the results, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system , the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is il lustrated by examples and...

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.1056-1060
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• 2002

Model-based tuning rules of the PID controller are proposed incorporating with real-coded genetic algorithms. The optimal parameter sets of the PID controller for step set-point tracking are obtained based on the first-order time delay model and a real-coded genetic algorithm as an optimization tool. As for assessing the performance of the controllers, performance indices(ISE, IAE and ITAE) are adopted. Then tuning rules are derived using the tuned parameter sets, potential rule models and another real-coded genetic algorithm A set of simulation works is carried out to verify the effectiveness of the proposed rules.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.6
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• pp.753-759
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• 2006

Integrating processes are frequently encountered in process industries. In this paper, new tuning formulae of the PID controllers for set-point tracking and load disturbance rejection are presented for integrating processes involving time delay. First, the controller parameter sets are tuned using a real-coded genetic algorithm (RCGA) such that performance criterion(IAE, ISE or ITSE) is minimized. Then, tuning rules are addressed using tuned PID parameter sets. tuning model and another RCGA. The performances of the proposed rules are tested on two processes.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.102-107
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• 2009

The Possibilistic C-means(PCM) was proposed to overcome some of the drawbacks associated with the Fuzzy C-means(FCM) such as improved performance for noise data. However, PCM possesses some drawbacks such as sensitivity in initial parameter values and to patterns that have relatively short distances between the prototypes. To overcome these drawbacks, we propose an interval type 2 fuzzy approach to PCM by considering uncertainty in the fuzzy parameter m in the PCM algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1007-1013
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• 2012

Modeling of photovoltaic power systems is significant to analytically predict its dynamics in practical applications. This paper presents a novel modeling algorithm of such system by using probability and statistic theories. We first establish a linear model basically composed of Fourier parameter sets for mapping the input/output variable of photovoltaic systems. The proposed model includes solar irradiation and ambient temperature of photovoltaic modules as an input vector and the inverter power output is estimated sequentially. We deal with these measurements as random variables and derive a parameter learning algorithm of the model in terms of statistics. Our learning algorithm requires computation of an expectation and joint expectation against solar irradiation and ambient temperature, which are analytically solved from the integral calculus. For testing the proposed modeling algorithm, we utilize realistic measurement data sets obtained from the Seokwang Solar power plant in Youngcheon, Korea. We demonstrate reliability and superiority of the proposed photovoltaic system model by observing error signals between a practical system output and its estimation.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.1-19
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• 2021

Heavy tailed distributions are useful for modeling actuarial and financial risk management problems. Actuaries often search for finding distributions that provide the best fit to heavy tailed data sets. In the present work, we introduce a new class of heavy tailed distributions of a special sub-model of the proposed family, called a new extended alpha power transformed Weibull distribution, useful for modeling heavy tailed data sets. Mathematical properties along with certain characterizations of the proposed distribution are presented. Maximum likelihood estimates of the model parameters are obtained. A simulation study is provided to evaluate the performance of the maximum likelihood estimators. Actuarial measures such as Value at Risk and Tail Value at Risk are also calculated. Further, a simulation study based on the actuarial measures is done. Finally, an application of the proposed model to a heavy tailed data set is presented. The proposed distribution is compared with some well-known (i) two-parameter models, (ii) three-parameter models and (iii) four-parameter models.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2167-2170
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• 2002

In this paper, model-based tuning rules of the PID controller are proposed incorporating with genetic algorithms. Three sets of optimal PID parameters for set-point tracking are obtained based on the first-order time delay model and a genetic algorithm as a optimization tool which minimizes performance indices(IAE, ISE and ITAE). Then tuning rules are derived using the tuned parameter sets, potential rule models and a genetic algorithm. Simulation is carried out to verify the effectiveness of the proposed rules.

• Communications of the Korean Mathematical Society
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• v.28 no.4
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• pp.833-850
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• 2013

We present an algorithm for computing the Hausdorff distance between two parametric curves in $\mathbb{R}^n$, or more generally between two sets of parametric curves in $\mathbb{R}^n$. During repeated subdivision of the parameter space, we prune subintervals that cannot contain an optimal point. Typically, our algorithm costs O(logM) operations, compared with O(M) operations for a direct, brute-force method, to achieve an accuracy of $O(M^{-1})$.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.4 s.38
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• pp.27-36
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• 2006

Conventionally, in order to get accurate geolocation of satellite images we need a set of ground control points with respect to individual scenes. In this paper, we tested the possibilities of modeling satellite orbits from individual scenes by establishing a sensor model for one scene and by applying the model, which was derived from the same orbital segment, to other scenes that has been acquired from the same orbital segment. We investigated orbit-attitude models with several interpolation methods and with various parameter sets to be adjusted. We used 7 satellite images of SPOT-3 with a length of 420km and ground control points acquired from GPS surveying. Results of the conventional individual scene modeling hardly introduced differences among different interpolation methods and different adjustment parameter sets. As the results of orbit modeling, the best model was the one with Lagrange interpolation for position/velocity and linear interpolation for attitude and with position/angle bias as parameter sets. The best model showed that it is possible to model orbital segments of 420km with ground control points measured within one scene (60km).