• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2225-2227
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• 1998

An efficient stereo matching method with variable matching weights is proposed. The edge segment-based stereo matching has been shown to be efficient method. The method includes 5 matching factor with different weights. The ordinary matching weights are not always adequate for every image. Employing different weight sets depending on the complexity shows better matching performance. In this paper, an evaluation criterion for complexity is suggested and the experimental results with the proposed method is shown.

• 대한수학회논문집
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• 제35권2호
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• pp.517-532
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• 2020

In this paper, we consider a class of nonlocal problems with indefinite weights in Orlicz-Sobolev space. Under some suitable conditions on the nonlinearities, we establish some existence results using variational techniques and Ekeland's variational principle.

• Journal of the Korean Data and Information Science Society
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• 제28권2호
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• pp.443-449
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• 2017

Estimation of population proportion like the distribution rate of LED TV and the prevalence of a disease are often estimated based on survey sample data. Population proportion is generally considered as a special form of population mean. In complex sampling like stratified multistage sampling with unequal probability sampling, the denominator of mean may be random variable and it is estimated like ratio estimator. In this research, we examined the estimation of distribution rate based on stratified multistage sampling, and determined some numerical outcomes using stratified random sample data with about 25% of missing observations. In the data used for this research, the survey weight was determined by deterministic way. So, the weights are not random variable, and the population distribution rate and its variance estimator can be estimated like population mean estimation. When the weights are not random variable, if one estimates the variance of proportion estimator using ratio method, then the variances may be inflated. Therefore, in estimating variance for population proportion, we need to examine the structure of data and survey design before making any decision for estimation methods.

• 전자공학회논문지B
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• 제29B권1호
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• pp.94-101
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• 1992

The multilayer perceptron, trained by the error back-propagation learning rule, has been known as a mapping network which can represent arbitrary functions. However depending on the complexity of a function and the initial weights of the multilayer perceptron, the error back-propagation learning may fall into a local minimum or a flat area which may require a long learning time or lead to unsuccessful learning. To solve such difficulties in training the multilayer perceptron by standard error back-propagation learning rule, the paper proposes a learning method which progressively enlarges the learning domain from a small area to the entire region. The proposed method is devised from the investigation on the roles of hidden nodes and connection weights in the multilayer perceptron which approximates a function of one variable. The validity of the proposed method was illustrated through simulations for a function of one variable and a function of two variable with many extremal points.

• 한국멀티미디어학회논문지
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• 제18권4호
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• pp.524-532
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• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• 한국국방경영분석학회지
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• 제30권1호
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• pp.1-14
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• 2004

In this paper, we propose a new data-mining-based methodology for military occupational specialty assignment. The proposed methodology consists of two phases, feature selection and man-power assignment. In the first phase, the k-means partitioning algorithm and the optimal variable weighting algorithm are used to determine attribute weights. We address limitations of the optimal variable weighting algorithm and suggest a quadratic programming model that can handle categorical variables and non-contributory trivial variables. In the second phase, we present an integer programming model to deal with a man-power assignment problem. In the model, constraints on demand-supply requirements and training capacity are considered. Moreover, the attribute weights obtained in the first phase for each specialty are used to measure dissimilarity. Results of a computational experiment using real-world data are provided along with some analysis.

• 전자공학회논문지B
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• 제32B권3호
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• pp.503-511
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• 1995

A batch-mode algorithm is proposed to increase the speed of learning in the error backpropagation algorithm with variable learning rate and variable momentum parameters in classification problems. The objective function is normalized with respect to the number of patterns and output nodes. Also the gradient of the objective function is normalized in updating the connection weights to increase the effect of its backpropagated error. The learning rate and momentum parameters are determined from a function of the gradient norm and the number of weights. The learning rate depends on the square rott of the gradient norm while the momentum parameters depend on the gradient norm. In the two typical classification problems, simulation results demonstrate the effectiveness of the proposed algorithm.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권3호
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• pp.157-163
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• 2000

In this paper, an Evolving Neural Network Controller(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algoithm(RVEGA) was presented for stabilization of an Inverter Pendulum(IP) system with nonlinearity. This proposed ENNC was described by a simple genetic chromosome. And the deletion of neuron, the determinations of input or output neuron, the deleted neuron and the activation functions types are given according to the various flag types. Therefore, the connection weights, its structure and the neuron types in the given ENNC can be optimized by the proposed evolution strategy. Through the simulations, we showed that the finally acquired optimal ENNC was successfully applied to the stabilization control of an IP system.

• 대한기계학회논문집A
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• 제40권1호
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• pp.103-111
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• 2016

본 논문에서는 차량의 횡방향 안정성을 향상시키기 위해 자세 제어 장치(ESC)와 능동 전륜 조향(AFS)을 이용하는 통합 새시 제어의 적응 가변 가중치 조절 방법을 제안한다. 제어기 설계 방법론을 적용하여 차량을 안정화시키는데 필요한 제어 요 모멘트를 구한 후 이를 가중 역행렬 기반 제어 할당 방법(WPCA)을 이용하여 ESC 의 제동력과 AFS 의 추가 조향각으로 분배한다. 저마찰 노면에서는 차량의 속도가 높다면 횡슬립각이 증가하여 횡방향 안정성이 저하되므로 이를 방지하기 위해 WPCA 의 가변가중치를 상황에 따라 조절하는 방법을 제안한다. 차량 시뮬레이션 패키지인 CarSim 에서 시뮬레이션을 수행하여 제안된 방법이 통합 섀시 제어기의 횡방향 안정성을 향상시킨다는 사실을 검증한다.

• 한국항해항만학회지
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• 제43권1호
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• pp.16-22
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• 2019

본 연구에서는 국내 소형 어선의 재화상태에 따른 중량 및 무게중심 추정식을 제안하였다. 소형 어선에 탑재되는 중량물은 선원, 어구 등의 고정 중량과 연료, 청수, 식량, 미끼, 어획물 등의 가변 중량으로 분류할 수 있다. 다양한 소형 어선들의 중량 데이터를 통계 분석한 후, 각 탑재물의 중량 및 무게중심을 총톤수에 대하여 선형 함수화하였다. 그리고 재화상태를 고려하여 각 가변 중량물에 가중치를 부가하는 방식으로 총 중량 및 무게중심 추정식을 구성하였다. 소형 어선의 길이와 총톤수, 그리고 재화상태 정보만을 활용하여 총 중량 및 무게중심을 상당히 신뢰도 높게 추정할 수 있음을 검증하였다.