• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3C
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• pp.157-162
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• 2011

For blind equalization of communication channels, concurrent equalization is useful to improve convergence characteristics. However, the concurrent equalization will result in limited performance enhancement by continuing concurrent adaptation with two algorithms after the equalizer converges to steady-state. In this paper, to improve the convergence characteristics and steady-state performance of the concurrent equalization, proposed is a new concurrent equalization technique with variable step-size parameter and weight-based tap coefficients update. The proposed concurrent vsCMA+DD equalization calculates weight factors using error signals of the variable step-size CMA (vsCMA) and DD (decision-directed) algorithm, and then updates the two equalizers based on the weights respectively. The proposed method, first, improves the error performance of the CMA by the vsCMA, and enhances the steady-state performance as well as the convergence speed further by the weight-based tap coefficients update. The performance improvement by the proposed scheme is verified through simulations.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.103-113
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• 2009

Weighting is a common form of unit nonresponse adjustment in sample surveys where entire questionnaires are missing due to noncontact or refusal to participate. A common approach computes the response weight as the inverse of the response rate within adjustment cells based on covariate information. In this paper, we consider the efficiency and robustness of nonresponse weight adjustment bated on the response propensity and predictive mean. In the simulation study based on 2000 Fishry Census in Korea, the root mean squared errors for assessing the various ways of forming nonresponse adjustment cell s are investigated. The simulation result suggest that the most important feature of variables for inclusion in weighting adjustment is that they are predictive of survey outcomes. Though useful, prediction of the propensity to response is a secondary. Also the result suggest that adjustment cells based on joint classification by the response propensity and predictor of the outcomes is productive.

• Journal of Internet Computing and Services
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• v.4 no.1
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• pp.39-52
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• 2003

As software is developed for many applications and software defects have caused serious problem sin those applications, the concern of software quality evaluation increases rapidly. Although there has been many efforts for establishing standards for software evaluation, such as ISO/IEC 9126, they provide only a framework for defining quality characteristics and evaluation process. They, however, do not provide practical guidances for deriving resonable weight value criteria for software evaluation. This paper presents a method to draw quantitative weight values from evaluator's subjective data in the process of software evaluation as observing the ISO/IEC 9126 standard. To eliminate the evaluators' subjectiveness and the uncertainty of weight value during evaluation, the Dempster-Shafer (D-S) theory is adopted and utilized. In this paper, the D-S theory is supplemented with an improved merge rule to reduce the bias of weight value when they are merged with other evaluator's weight value. The proposed merge rule has been tested and proved with actual evaluation data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.1-11
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• 2018

This paper consider the MBTI match type in order to meet the customer's dating in internet and online. The MBTI match type is confirmed through appropriate surveys and identifies the appropriate type of preference for each type. The preference type is different according to each MBTI type, and even though the same preference type is used, the preference type of each of men and women is different. In order to solve these problems, the preference type of each man and woman is weighted, and the weighted weights are used to calculate the fitness between the two. The system that weights the MBTI match type is highly likely to match in an online dating system because it uses the social human nature of people, and can be used in interpersonal relationship systems and teaching and learning systems and calibration systems.

• Journal of KIISE:Software and Applications
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• v.32 no.1
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• pp.1-9
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• 2005

In this paper we propose a modified fuzzy min-max neural network model for pattern classification and discuss the usefulness of the model. We define a new hypercube membership function which has a weight factor to each of the feature within a hyperbox. The weight factor makes it possible to consider the degree of relevance of each feature to a class during the classification process. Based on the proposed model, a knowledge extraction method is presented. In this method, a list of relevant features for a given class is extracted from the trained network using the hyperbox membership functions and connection weights. Ft)r this purpose we define a Relevance Factor that represents a degree of relevance of a feature to the given class and a similarity measure between fuzzy membership functions of the hyperboxes. Experimental results for the proposed methods and discussions are presented for the evaluation of the effectiveness and feasibility of the proposed methods.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.350-356
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• 2008

In this paper, we propose shading functions to the appropriate focused beamforming for near-field target estimation. This near field shading functions are based on Chebychev and Manning windows. In order to obtain the optimum sensor weighting values with the help of the proposed shading technique, we assume that the sensor positions associated to the non-uniformly distributed array are precisely known. We calculate a series of sensor weighting values from the FFT operation of given shading functions in time domain. By applying the shading weights on the sensor array, we can see that the level of sidelobe becomes diminished and the performance of estimating range and azimuth gets improved. In addition, we propose a non-uniform structure in terms of frequency bands, which may minimize the attenuation of incoming signals.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• KDI Journal of Economic Policy
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• v.19 no.3
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• pp.195-246
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• 1997

우리나라에서 전기요금은 공공요금으로서 정부의 정책의지에 의하여 크게 영향을 받아왔다. 또한 전기요금수준 조정시 규제당국의 주된 관심은 요금인상이 국민경제에 미치는 영향, 특히 물가 및 무역수지에 미치는 영향에 있었다고 할 수 있다. 이러한 상황에서 전기요금변동의 국민경제적 영향에 대한 신뢰할 수 있는 분석결과는 올바른 정책수립에 필수적이라고 할 수 있다. 본고는 계산가능한 일반균형모형(Computable General Equilibrium model)을 이용하여 1993년도의 산업연관표를 토대로 전기요금의 인상이 물가, 수출입 등 거시변수에 미치는 효과 및 산업부문별 효과를 살펴본 것이다. 전기요금인상이 물가에 미치는 영향은 간단히 '전기요금인상률${\times}$물가가중치'라는 공식으로 계산해볼 수 있다. 이에 따르면 전기의 소비자물가 가중치가 14/1,000이므로 전기요금인상률이 4%일 때 소비자물가상승률은 약 0.056%가 된다. 그러나 전기가 타산업의 중간투입물로 사용되므로 전기요금인상은 타산업 산출물의 가격상승을 유발하고 다시 투입-산출관계에 의하여 추가적인 물가상승을 불러일으키게 된다. 이러한 일반균형적 효과를 모두 고려하여, 본 연구에서 계산한 소비자물가상승률은 0.083%로서 위 수치의 약 1.5배이다. 또한 본고에서는 전기요금인상에 따라 수출과 수입 모두 감소하되, 수출감소율이 수입감소율보다 크게 나타났다. 이러한 결과는 전기요금인상에 따라 전기수요가 감소하여 에너지수입이 감소하고, 그로 인해 무역수지가 개선되리라는 일부의 주장과는 매우 대조적이다. 산업별로는 전기요금인상에 따라 서비스업의 가격상승이 두드러지는 것으로 나타났는데, 이는 서비스업부문의 국내재와 수입재간의 대체가능성이 타부문에 비하여 크게 낮은 데 기인한 것으로 보인다. 본고의 결과를 전기요금이 인상되어서는 안 된다고 해석하는 것은 오류일 수 있다. 전기요금인상의 타당성은 전력산업에 대한 종합적인 미시적 분석에 기초하여야 한다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.6 no.2
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• pp.133-147
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• 2011

This study presents the credit evaluation model for medical venture business which has been growing within the recent decade. We develop the model with two steps. At the first step, the evaluation indexes for each of the financial and non-financial factors of a firm are listed. At the second step, the weight for each index is measured by using the Analytic Hierarchy Process of Saaty(1980). The financial factors consists of 5 upper level indexes and 10 lower level indexes. The upper level indexes of the financial sector are profitability, safety, utilization, growth, and productivity. And the non-financial factors consists of 5 upper level indexes and 17 lower lever indexes. The upper level indexes in this sector are manager's competence, technical capability, marketability, business validity, and reliability. In order to get the empirical results for our model, we conduct the questionnaire survey targeting the credit assessment officers, who are practicing at the financial institutions or the credit guarantee company located within the Wonju Medical Devices Cluster.

• Journal of Korea Port Economic Association
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• v.28 no.2
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• pp.179-194
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• 2012

In this study, we compared the efficiency of container terminals using DEA. To do this, we designed an AHP/DEA hybrid model using AHP and DEA, and evaluated the efficiency by comparing the container terminal operation company in Gwangyang(KEC, KIT, GICT) and Busan(HBCT, DPCT, KBCT, UPT, Gamman, PNC, PNIT, HJNC, HPNT). The proposed model can control the number of selected promising container terminal by applying DEA-AR model. This model can also improve the credibility of analysis by using objective weights through the AHP application to efficiency evaluation data and normalizing the evaluation data to apply AHP and DEA. The model assumes inputs to be container crane, transfer crane, yard tractor, and reach stacker and output as container traffic. The result shows that DPCT was an efficient DMU.