• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.83-89
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• 2004

The object of this study is to survey present conditions, to analyze the development of bio-industry in northern area of Kyonggi province by the decision making method of the SWOT model, to suggest a plan for the prospect of continued development field and the location of industry, and to extract fundamental data for establishment of annual action and investment plan which can develop bio-industry. For the purpose of making a policy decision in national operation policy, business administration policy, and new product design, the research for extracting more objective and standard approach method should be continuously conducted.

• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2000.10a
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• pp.118-124
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• 2000

Zn-resistant Methylosobacterium strains were isolated from several non-polluted soil samples collected in all over Japan. Zn-resistant Methylosobacterium strains were predominantly detected in all soil samples and they were also characterized as a UV-tolerant bacteria. The MIC test revealed that the isolates have high zinc resistance in comparison with that of reference Methylosobacterium strains obtained from culture collections. The 16S rDNA-based phylogenetic analysis showed that all strains were divided into two clusters designated as cluster A and cluster B in the present study. All isolates were distributed only in the cluster A. The phylogenetic clustering also well coincided with the differences in the pattern of carbon source utilization.

• IE interfaces
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• v.16 no.spc
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• pp.123-126
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• 2003

In this paper, we use three cluster algorithms (K-means, Self-Organizing Map, and Fuzzy K-means) to find proper graded stock market brokerage commission rates based on the cumulative transactions on both stock exchange market and HTS (Home Trading System). Stock trading investors for both modes are classified in terms of the total transaction as well as the corresponding mode of investment, respectively. Empirical analysis results indicated that fuzzy K-means cluster analysis is the best fit for the segmentation of customers of both transaction modes in terms of robustness. We then propose the rules for three grouping of customers based on decision tree and apply different brokerage commission to be 0.4%, 0.45%, and 0.5% for exchange market while 0.06%, 0.1%, 0.18% for HTS.

• IE interfaces
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• v.25 no.2
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• pp.229-240
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• 2012

Conventional research on customer relationship management(CRM) in general has focused on the effects of individual customer's satisfaction, retention and profit management. However, corporate customers are more profitable than individual customers because of high volume and frequent transactions between companies. In this article, a prototype for a corporate customer relationship management is developed in the postal service. First, the frequency and amount of customers' usage were examined, and thereby the corporate customer rating scheme was established to provide customized service. Second, five different types of usage patterns were determined using clustering analysis. In addition, we presented the rationales behind the five types of patterns. Third, RFM(recency, frequency, monetary) analysis was performed, and then action plans were developed to increase sales. Finally, the prototype software was developed to automatically perform the above analysis using MS Excel program.

• Annual Conference of KIPS
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• 2004.05a
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• pp.453-456
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• 2004

전자상거래에서 추천 시스템은 일반적으로 협동적 필터링이라는 정보 필터링 기술을 사용한다. 협동적 필터링 기술은 유사한 성향을 갖는 다른 고객들이 상품에 대해서 매긴 평가에 기반한다. 협동적 필터링이 유사 선호도를 갖는 이웃 고객들의 평가에 근거하기 때문에 고객에게 가장 적합한 유사 이웃들을 적절히 선정해 내는 것은 추천 시스템에서 예측의 질 향상을 위해 필요하다. 본 논문에서 우리는 ordered clustering을 이용하여 협동적 필터링을 위한 향상된 이웃선정 방법을 제안한다. 이 방법은 탐색 공간을 줄이기 위해 k-means 클러스터링 방법을 사용한다. 그리고 클러스터링에 의해 구성된 고객들에 대해서 threshold 값에 의해 보다 정제된 고객들을 최종 선정함으로써 고객에게 보다 의미 있는 적합한 고객이 최종적인 이웃으로 선정될 수 있도록 한다. 실험은 Compaq Computer Corporation에 의해 제공된 EachMovie 데이터 셋을 사용하였다. 실험 결과로 우리는 제안한 방법이 다른 방법보다 좋은 예측 정확도를 갖는 것을 확인할 수 있었다.

• Journal of the Korean Academic Society of Industrial Cluster
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• v.2 no.1
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• pp.44-60
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• 2008

This paper analyses the relative efficiency and competitiveness of production structure for the industrial clusters(Ulsan, Changwon, Kumi, Wonjoo, Banwol & Siwha, Kwangwju, Gunsan) which had allocated in 2004 in Korea by the DEA approaches. The results show that except U1san, Wonjoo, the 5 industrial clusters have improved the relative efficiency in terms of input and output since they were allocated. And, the reason of the inefficiency for the 5 industrial clusters were not for the technical relationship but for the production scaling size. That is, by clustering for the industrial production firms, the economic effect came true throughout the production scaling size effects. Also, by the positioning approach for the production factors such as labor, capital, and R&D investment via production growth, the results show that Banwol & Siwha, Goomi clusters have effectively been managed, but Wonjoo, U1san and Gunsan are not.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1419-1429
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• 2010

Industry structure and environment of the domestic bank have been changed by an influx of large foreign-banks and advanced financial products when the currency crisis erupted in Korea. In a competitive environment, accurate forecasts of changes and tendencies are essential for the survival and development. Forecast of whether to approve loan applications for customer or not is an important matter because that is related to profit generation and risk management on the bank. Therefore, this paper proposes the method to improve forecast accuracy of loan underwriting. Processes in experiments are as follows. First, we select the predictor variables which affect significantly to the result of loan underwriting by correlation analysis and feature selection technique, and then cluster the customers by the 2-Step clustering technique based on selected variables. Second, we find the most accurate forecasting model for each clustering by applying LR, NN and SVM. Finally, we compare the forecasting accuracy of the proposed method with the forecasting accuracy of existing application way.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.1-9
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• 2017

Recently, the active research into wireless sensor networks has led to the development of sensor nodes with improved performance, including their mobility and location awareness. One of the most important goals of such sensor networks is to transmit the data generated by mobile sensors nodes. Since these sensor nodes move in the mobile wireless sensor networks (MWSNs), the energy consumption required for them to transmit the sensed data to the fixed sink is increased. In order to solve this problem, the use of mobile sinks to collect the data while moving inside the network is studied herein. The important issues are the mobility and energy consumption in MWSNs. Because of the sensor nodes' limited energy, their energy consumption for data transmission affects the lifetime of the network. In this paper, a mobile-sink based energy-efficient clustering scheme is proposed for use in mobile wireless sensor networks (MECMs). The proposed scheme improves the energy efficiency when selecting a new cluster head according to the mobility of the mobile sensor nodes. In order to take into consideration the mobility problem, this method divides the entire network into several cluster groups based on mobile sinks, thereby decreasing the overall energy consumption. Through both analysis and simulation, it was shown that the proposed MECM is better than previous clustering methods in mobile sensor networks from the viewpoint of the network energy efficiency.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.65-65
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• 2002

In remote sensing, images are acquired over the same area by sensors of different spectral ranges (from the visible to the microwave) and/or with different number, position, and width of spectral bands. These images are generally partially redundant, as they represent the same scene, and partially complementary. For many applications of image classification, the information provided by a single sensor is often incomplete or imprecise resulting in misclassification. Fusion with redundant data can draw more consistent inferences for the interpretation of the scene, and can then improve classification accuracy. The common approach to the classification of multisensor data as a data fusion scheme at pixel level is to concatenate the data into one vector as if they were measurements from a single sensor. The multiband data acquired by a single multispectral sensor or by two or more different sensors are not completely independent, and a certain degree of informative overlap may exist between the observation spaces of the different bands. This dependence may make the data less informative and should be properly modeled in the analysis so that its effect can be eliminated. For modeling and eliminating the effect of such dependence, this study employs a strategy using self and conditional information variation measures. The self information variation reflects the self certainty of the individual bands, while the conditional information variation reflects the degree of dependence of the different bands. One data set might be very less reliable than others in the analysis and even exacerbate the classification results. The unreliable data set should be excluded in the analysis. To account for this, the self information variation is utilized to measure the degrees of reliability. The team of positively dependent bands can gather more information jointly than the team of independent ones. But, when bands are negatively dependent, the combined analysis of these bands may give worse information. Using the conditional information variation measure, the multiband data are split into two or more subsets according the dependence between the bands. Each subsets are classified separately, and a data fusion scheme at decision level is applied to integrate the individual classification results. In this study. a two-level algorithm using hierarchical clustering procedure is used for unsupervised image classification. Hierarchical clustering algorithm is based on similarity measures between all pairs of candidates being considered for merging. In the first level, the image is partitioned as any number of regions which are sets of spatially contiguous pixels so that no union of adjacent regions is statistically uniform. The regions resulted from the low level are clustered into a parsimonious number of groups according to their statistical characteristics. The algorithm has been applied to satellite multispectral data and airbone SAR data.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.