• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.2
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• pp.112-120
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• 2001

Genetic diversity of 47 East-Asian vegetable soybean was characterized by means of agro-morphological traits and RAPD markers. A field trial was conducted to evaluate 14 agro-morphological traits. To study RAPD-based DNA analysis, a total of sixty 10-mer random primers were screened. Of these, 23 polymorphic markers in 16 varieties used for screening. Among 207 markers amplified, 48 were polymorphic for at least one pairwise comparison within the 47 varieties. A higher differentiation level between varieties was observed by using RAPD markers compared to morphological markers. Correspondence analysis using both types of marker showed that RAPD data could fully discriminate between all varieties, whereas morphological markers could not achieve a complete discrimination. Genetic distances between the varieties were estimated from simple matching coefficients, ranged from 0.0 to 0.640 with an average of 0.295$\pm$0.131 for morphological traits and 0.042 to 0.625 with an average of 0.336$\pm$0.099 for RAPD data, respectively. Cluster analysis based on genetic dissimilarity of these varieties gave rise to 4 distinct groups. The clustering results based on RAPDs did not match with those based on morphological traits. Geographical distribution of most varieties in each of the groups were not well defined. The results suggested that the level of genetic diversity within this group of East-Asian vegetable soybean varieties was sufficient for a breeding program and can be used to establish genetic relationships among them with unknown or unrelated pedigrees.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.64-70
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• 2018

The development of weapon systems (or components) is hindered by the number of tests due to the limited development period and cost, which reduces the scale of accumulated data related to failures. Nevertheless, because a large amount of failure data and maintenance details during the operational period are managed by computerized data, the cause of failure of weapon systems (or components) can be analyzed using the data. On the other hand, analyzing the failure and maintenance details of various weapon systems is difficult because of the variation among groups and companies, and details of the cause of failure are described as unstructured text data. Fortunately, the recent developments of big data processing technology, machine learning algorithm, and improved HW computation ability have supported major research into various methods for processing the above unstructured data. In this paper, unstructured data related to the failure / maintenance of defense weapon systems (or components) is presented by applying doc2vec, a machine learning technique, to analyze the failure cases.

• Safety and Health at Work
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• v.7 no.3
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• pp.201-207
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• 2016

Background: Korean Working Conditions Surveys (KWCS), referencing European Working Conditions Surveys, have been conducted three times in order to survey working condition and develop work-related policies. However, we found three limitations for managing the collected KWCS data: (1) there was no computerized system for managing data; (2) statistical KWCS data were provided by limited one-way communication; and (3) the concept of a one-time provision of information was pursued. We suggest a web-based public service system that enables ordinary people to make greater use of the KWCS data, which can be managed constantly in the future. Methods: After considering data characteristics, we designed a database, which was able to have the result of all pairwise combinations with two extracted data to construct an analysis system. Using the data of the social network for each user, the tailored analysis system was developed. This system was developed with three methods: clustering and classification for building a social network, and an infographic method for improving readability through a friendly user interface. Results: We developed a database including one input entity consisting of the sociodemographic characteristics and one output entity consisting of working condition characteristics, such as working pattern and work satisfaction. A web-based public service system to provide tailored contents was completed. Conclusion: This study aimed to present a customized analysis system to use the KWCS data efficiently, provide a large amount of data in a form that can give users a better understanding, and lay the ground for helping researchers and policy makers understand the characteristics.

• The Journal of Society for e-Business Studies
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• v.17 no.4
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• pp.39-56
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• 2012

Technological advancements in information technology including RFID and mobile technologies have made it feasible to track the customers travel path in a store. The customer travel paths provide valuable implications to understanding the customer behaviors in a store. In our research, we develop a shopping path analysis system to track and analyze the customer travel path. The proposed system consists of RFID systems for collecting the customer paths and analysis system. The analysis system conducts clustering for identifying the distinctive shopping patterns, and analyzes the profile of a grocery, such as congestion rate, visiting rate, and staying time, etc. We show the applicability of our proposed system using the actual data obtained at a grocery in Seoul as a case study.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.333-342
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• 2005

This paper proposes a method, which recognizes student's identification card by using image processing and recognition technology and can manage student information on the web. The presented scheme sets up an average brightness as a threshold, based on the brightest Pixel and the least bright one for the source image of the ID card. It is converting to binary image, applies a horizontal histogram, and extracts student number through its location. And, it removes the noise of the student number region by the mode smoothing with 3$\times$3 mask. After removing noise from the student number region, each number is extracted using vertical histogram and normalized. Using the enhanced ART1 algorithm recognized the extracted student number region. In this study, we propose the enhanced ART1 algorithm different from the conventional ART1 algorithm by the dynamical establishment of the vigilance parameter. which shows a tolerance limit of unbalance between voluntary and stored patterns for clustering. The Experiment results showed that the recognition rate of the proposed ART1 algorithm was improved much more than that of the conventional ART1 algorithm. So, we develop an educational matters administration system by using the proposed recognition method of the student's identification card.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.141-150
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• 2005

Wireless Sensor Networks have been rapidly developed due to the advances of sensor technology and are expected to be applied to various applications in many fields. In Wireless Sensor Networks, schemes for managing the network energy-efficiently are most important. For this purpose, there have been a variety of researches to suggest routing protocols. However, existing researches have ideal assumption that all sensor nodes have sensing data to transmit. In this paper, we designed and implemented a sensing-aware cluster selection algorithm based on LEACH-C for the sensor network in which part of sensors have sensing data. We also simulated proposed algorithm on several network situation and analyzed which situation is suitable for the algorithm. By the simulation result, selecting cluster head among the sensing nodes is most energy-efficient and the result shows application of sensing-awareness in cluster head selection when not all sensors have sensing data.

• Journal of Information Technology Applications and Management
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• v.22 no.1
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• pp.77-93
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• 2015

As the range of the customer choice becomes more diverse, the average life span of companies' products and services is becoming shorter. Most companies are striving to maximize the revenue by understanding the customer's needs and providing customized products and services. However, companies had to bear a significant burden, in terms of the time and cost involved in the process of determining each individual customer's needs. Therefore, an alternative method is employed that involves grouping the customers into different categories based on certain criteria and establishing a marketing strategy tailored for each group. In this way, customer segmentation and customer clustering are performed using demographic information and behavioral information. Demographic information included sex, age, income level, and etc., while behavioral information was usually identified indirectly through customers' purchase history and search history. However, there is a limitation regarding companies' customer behavioral information, because the information is usually obtained through the limited data provided by a customer on a company's website. This is because the pattern indicated when a customer accesses a particular site might not be representative of the general tendency of that customer. Therefore, in this study, rather than the pattern indicated through a particular site, a customer's interest is identified using that customer's access record pertaining to external news. Hence, by utilizing this method, we proposed a methodology to perform customer segmentation. In addition, by extracting the main issues through a topic analysis covering approximately 3,000 Internet news articles, the actual experiment applying customer segmentation is performed and the applicability of the proposed methodology is analyzed.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.89-99
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• 2015

The World Economic Forum and the Korean Ministry of Knowledge Economy have selected big data as one of the top 10 in core information technology. The key of big data is to analyze effectively the properties that do have data. Clustering analysis method of big data techniques is a method of assigning a set of objects into the clusters so that the objects in the same cluster are more similar to each other clusters. Similarity measures being used in the cluster analysis may be classified into various types depending on the nature of the data. In this paper, we studied upper and lower bounds for binary similarity measures with negative matches such as Russel and Rao measure, simple matching measure by Sokal and Michener, Rogers and Tanimoto measure, Sokal and Sneath measure, Hamann measure, and Baroni-Urbani and Buser mesures I, II. And the comparative studies with these measures were shown by real data and simulated experiment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12B
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• pp.1349-1358
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• 2009

Because of the limitation of supporting power, the current WSN(Wireless Sensor Network) Technologies whose one of the core attributes is low power consumption are the best solution for shipping container networking in stack environment such as on vessel. So it is effective to use the Wireless Sensor Network Technology. In this case, many nodes join in the network through a sink node because there are difficulties to get big money and efforts to set up a lot of sink node. It needs clustering-based proactive protocol to manage many nodes. But it shows low reliability because they have effect on radio frequency in metal obstacle environments(interference, distortion, reflection, and etc) like the intelligent container. In this paper, we proposed an improved Modified LEACH Protocol for stableness radio frequency environment. In the proposed protocol, we tried to join the network and derived stable topology composition after the measuring of link quality. Finally, we verified that the proposed protocol is composing more stable topology than previously protocol in metal obstacle environment.

• Convergence Security Journal
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• v.12 no.6
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• pp.25-30
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• 2012

As the network composed of numerous sensor nodes, sensor network conducts the function of sensing the surrounding information by sensor and of the sensed information. Our military has also developed ICT(Information and Communication Technology) along with the methods for effective war by sharing smooth information of battlefield resources through network with each object. In this paper, a sensor network is clustered in advance and a cluster header (CH) is elected for clusters. Before deployment, a certificate is provided between the BS and the sensor nodes, and after clustering, authentication is done between the BS and the sensor nodes. Moreover, inter-CH authentication technique is used to allow active response to destruction or replacement of sensor nodes. Also, because authentication is done twice, higher level of security can be provided.