• 전기학회논문지P
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• 제65권2호
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• pp.130-135
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• 2016

Gait recognition has advantage of non-contact type recognition. But It has disadvantage of low recognition rate when the pedestrian silhouette is changed due to bag or coat. In this paper, we proposed new method using combination of gait energy image feature and thermal face image feature. First, we extracted a face image which has optimal focusing value using human body rate and Tenengrad algorithm. Second step, we extracted features from gait energy image and thermal face image using linear discriminant analysis. Third, calculate euclidean distance between train data and test data, and optimize weights using genetic algorithm. Finally, we compute classification using nearest neighbor classification algorithm. So the proposed method shows a better result than the conventional method.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 추계학술대회논문집A
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• pp.492-497
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• 2000

This paper focuses on the weight minimization of radial gate, as an extention of the previous work. Radial gates are commonly used to regulate the flow-rate of general purpose dams, due to its simplicity in manufacture and control. The present study identifies the optimum position of support point for 2 and 3 arm type radial gate, which guarantees the minimum weight satisfying strength constraint condition. These optimum designs are then compared with previously constructed radial gates. The results indicate that the weights of the optimized radial gates reduce by about 20%, compared to those of the conventionally designed radial gates.

• Journal of the Korean Statistical Society
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• 제33권3호
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• pp.339-351
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• 2004

In this paper we generalize and improve the refined Pickands estimator of Drees (1995) for the extreme value index. The finite-sample performance of the refined Pickands estimator is not good particularly when the sample size n is small. For each fixed k = 1,2,..., a new estimator is defined by a convex combination of k different generalized Pickands estimators and its asymptotic normality is established. Optimal weights defining the estimator are also determined to minimize the asymptotic variance of the estimator. Finally, letting k depend upon n, we see that the resulting estimator has a better finite-sample behavior as well as a better asymptotic efficiency than the refined Pickands estimator.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.1285-1289
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• 2004

Elman Discrete Recurrent Neural Networks Model(EDRNNM) was used to be a suitable short-term hydrological forecasting tool yielding a very high degree of flood stage forecasting accuracy at Musung station of Wi-stream one of IHP representative basins in South Korea. A relative new approach method has recurrent feedback nodes and virtual small memory in the structure. EDRNNM was trained by using two algorithms, namely, LMBP and RBP The model parameters, optimal connection weights and biases, were estimated during training procedure. They were applied to evaluate model validation. Sensitivity analysis test was also performed to account for the uncertainty of input nodes information. The sensitivity analysis approach could suggest a reduction of one from five initially chosen input nodes. Because the uncertainty of input nodes information always result in uncertainty in model results, it can help to reduce the uncertainty of EDRNNM application and management in small catchment.

• 한국정밀공학회지
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• 제15권10호
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• pp.41-50
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• 1998

In this study, to investigate a characteristics of slip ratio control of H.C.U for ABS, half car model tester were developed and a new H.C.U. was compactly designed comparing to the commercical H.C.U. for ABS. In half car model tester, variable inertia wheel has been used to load the car weights and braking forces according to the road surface conditions which were realized by pneumatic cylinder. And solenoid valves using P.W.M. (Pulse Width Modulation) method were installed in the new H.C.U The slip ratio characteristics of tire had been measured using half car model tester and the results were used in the control simulation for a new H.C.U.

• 산업경영시스템학회지
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• 제23권55호
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• pp.1-11
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• 2000

Taguchi's robust design methodology has focus only a single characteristic or response, but the quality of most products is seldom defined by a characteristics, and is rather the composite of a family of characteristics which are often interrelated and nearly always measured in a variety of units. The multiple characteristics problem is how to compromise the conflicts among the selected levels of the design parameters for each individual characteristic. In this paper, Methodology using SN ratio optimized by univariate technique is proposed and a parameter design procedure to achieve the optimal compromise among several different response variables is developed. Also, We propose a decision model to incorporates the values assigned by a group of experts on different factors in weighting decision. Using this model, SN ratio of taguchi method for each of subjective factors as well as values of weights are used in this comprehensive method for weighting decision.

• 대한조선학회지
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• 제20권2호
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• pp.21-26
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• 1983

In this light of economical engineering, the optimal configurations of ship structure that can save weights, production costs and operation costs should be investigated. This paper presents the general method of optimization based on non-linear programming and its application to weight and/or cost minimization of ship structure. Oil tanker is chosen as a ship type because of simple layout and easy calculation of stress. With the data of 16,200 DWT oil tanker built by KSEC 1980, this paper shows the procedure mentioned above by means of SUMT combined with two selected search methods. Then the differences between original and redesigned tanker structures are discussed.

• 전자공학회논문지S
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• 제34S권8호
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• pp.84-91
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• 1997

The range image is divided into surface regions which are homogeneous in their intrinsic properties. In this paper, we use cooperative relaxational algorithm between curvatures to escape local minima and choose optimal possibility to reserve edge. Cooperative relaxational algorithm between curvatures is relaxation process in which weights of center pixel;s and neighbor pixel's possiblility are determined adaptively by using deviation of curvatures. Experimental resutls show that the proposed method segments and classifies the range images more accurately compared to the other relational algorithms.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.19-20
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• 2006

In this paper, to reduce uplink feedback information for the beam weight and simultaneously maintaining the performance, we propose a MIMO-OFDM (Multi Input Multi Output-Orthogonal Frequency Division Multiplexing) system based on beamforming with antenna selection. In the proposed system, to perform the beamforming with more useful transmit antennas, the optimal combination of transmit antennas with maximum MRT (Maximum Ratio Transmission) beamforming gain is selected. Simulation results reveal that the proposed MIMO-OFDM system adopting the beamforming with antenna selection can reduce the feedback information for the beam weights as compared to the system using all the transmit antennas without serious degradation of system performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.1100-1102
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• 1998

This paper presents the Two-Phase Neural Network(TPNN) to slove the Optimal Economic Environmental Dispatch problem of thermal generating units in electric power system. The TPNN, Compared with other Neural Networks, is very accurate and it takes smaller computer time for a optimization problem to converge. In this work, in order to provide useful information to the system operator, we are used the total environmental weight and relative weighting of individual insults(e.g., $SO_2$, $NO_X$ and $CO_2$) also, presented the simulation results of the dispatch changes according to the weights. The Two-Phase Neural Network is tested on a 11-unit 3-pollutant system to prove of effectiveness and applicability.