• Journal of Aquaculture
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• v.20 no.4
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• pp.248-254
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• 2007

Representative cDNA libraries were constructed from various tissue sources of Hemibarbus mylodon, an endangered freshwater fish species in Korea, for the mining of expressed sequence tags (ESTs). Randomized and non-normalized EST analysis was performed with 7 unidirectional cDNA libraries generated from brain, intestine, kidney, liver, muscle, ovary or testis. Of 3,383 ESTs in total, the number of singleton was 2,029, and 333 contigs containing 1,354 ESTs were assembled (percent of unigene = 70.0%). Abundantly expressed gene transcripts and broad clustering of putative gene function were tissue-specific in general, and the redundancy was also variable among those libraries. Over half of H. mylodon ESTs were matched with orthologues from other teleosts among which zebrafish gene sequences were the most frequent in those matches. This initial setting of EST libraries achieved in the present study would be a fundamental basis for the banking of gene resources from this endangered fish species.

• Convergence Security Journal
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• v.7 no.2
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• pp.119-128
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• 2007

Since the internet banking service was introduced to Korea in 1999, the service has placed itself as an indispensable service to most users. The internet banking, which provides convenience for internet users as well as efficiency for banks, is expected to increase its importance more and to play a bigger role as a passage of funds. Meanwhile, numerous accounts as to the misusage of the internet banking service have been reported and the types and size of damages, especially making illegal money transfers and embezzling user information through computer hacking, tend to increase continuously. This paper points out fundamental problems of the current internet banking service by analyzing the all components of the internet banking service and fitting the results of structural analysis of hacking threats in accordance with service flow. This paper also attempts to propose the means to minimize the hacking threats of the internet banking service.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.7-17
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• 2017

Loan consultants assist clients with loan application processing and loan decisions. Their duties may include contacting people to ask if they want a loan, meeting with loan applicants and explaining different loan options. We studied the efficiency of service quality of loan consultants contracted to a bank in Korea. They do not work as a team, but do work independently. Since he/she is not an employee of the bank, the consultant is paid solely in proportion to how much he/she sell loans. In this study, a consultant is considered as a decision making unit (DMU) in the DEA (Data Envelopment Analysis) model. We use a principal component analysis-data envelopment analysis (PCA-DEA) model integrated with Shannon's Entropy to evaluate quality efficiency of the consultants. We adopt a three-stage process to calculate the efficiency of service quality of the consultants. In the first stage, we use PCA to obtain 6 synthetic indicators, including 4 input indicators and 2 output indicators, from survey results in which questionnaire items are constructed on the basis of SERVQUAL model. In the second stage, 3 DEA models allowing negative values are used to calculate the relative efficiency of each DMU. In the third stage, the weight of each result is calculated on the basis of Shannon's Entropy theory, and then we generate a comprehensive efficiency score using it. An example illustrates the proposed process of evaluating the relative quality efficiency of the loan consultants and how to use the efficiency to improve the service quality of the consultants.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.25-30
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• 2015

One of the hottest issues of security is information leakage of financial institution. Financial institutions including commercial banks are frequently threatened by attempts of leakage through hacking and vulnerability, and this information is centered on personal information of their clients. Through this study, I found out that security managers of financial institutions are trying to prevent the leaking of private information, but in fact most of them barely know where their personal information is. Even if they know where it is and trace the data, it is often found in unexpected places. Because there is a lot of waste in time and human resources as search is done manually, we have understood that responding to IT Compliance requires a lot of effort. This study is to improve IT Compliance response and protect information leakage through monitoring PC and servers, the main storage of personal information by automated system, periodically.

• The Journal of Korean Association of Computer Education
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• v.22 no.3
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• pp.55-65
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• 2019

In programming language learning system of flip learning methods, the evaluation of cyber lectures generally proceeds from online to multiple choice questions. In this case, the questions are randomly extracted from the question bank and given to individual learners. In order for these evaluation results to be reflected in the grades, the equity of the examination question is more important than anything else. Especially in the programming language subject, the degree of difficulty that learners think can be different depending on the type of problem. In this paper, we classify the types of multiple-choice problems into two categories, and manage the difficulty level by each type. And we propose a question selection algorithm that considers both difficulty level and type of question. Considering the characteristics of the programming language, experimental results show that the proposed algorithm is more efficient and fair than the conventional method.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.181-189
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• 2022

The purpose of this study is to investigate the innovation of business model and the effectiveness of the data-driven model. the main concepts and policies related to the data economy are reviewed, and implications are drawn through the analysis of data-based convergence service creation cases. This study identified the existing data-driven business model of the creation of MyData service industry in the financial industry and concept of the data economy. According to the empirical analysis result, this study confirmed that t considering the mobile environment and consumer acceptance of data portability, the ripple effect of the implementation of My Data on the financial industry is expected to be significant.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.199-199
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• 2011

ATES(Aquifer Thermal Energy Storage) 열펌프 시스템은 기존의 다양한 열원 적용 시스템 대비 효율이 우수한 것으로 알려져 유럽과 미국에서 건물 냉난방 시스템으로 적용되고 있다. 특히, ATES 시스템은 기존의 냉난방 시스템 대비 경제성이 우수한 것으로 알려져 있으나 국내에서는 이에 대한 연구 결과는 전무한 실정이다. 본 연구에서는 ATES 열펌프 시스템의 실증 성능 결과를 분석하였으며, LNG 보일러와 에어컨을 사용하는 기존의 냉난방 시스템을 비교시스템으로 ATES 열펌프 시스템의 경제성 평가를 수행하였다. ATES 시스템의 연간 실증 성능 실험결과 ATES 시스템은 외기온도와 무관하게 연중 안정적인 성능을 나타내었다. 경제성 평가시에 생애주기법(Life Cycle Cost)을 적용하여 ATES 열펌프 시스템의 설치 및 운전에 필요한 총 소요비용을 산정하고, 이 결과를 바탕으로 투자회수기간법을 통해 ATES 시스템의 투자회수 기간을 산정하였다. 생애주기법 적용 시에 현재가치법을 사용하였으며, 현재가치법은 수명주기에 발생하는 모든 투자비용과 절감액을 일정한 시점을 기준으로 등가환산하는 방법을 의미한다. 현재가치법에 사용되는 현재가치는 초기비용과 현재가치계수의 곱으로 나타나는데, 여기에서 현재가치계수는 임의의 이자율로 일정기간 동안 정기적인 할부금액이 적립될 때의 현재금액을 구하기 위해 사용하는 계수를 의미한다. 전기와 LNG는 각각 2009년 7월의 (주)한국전력공사와 (주)한국가스공사의 고시요금을 적용하였다. 본 시스템은 실증 설비용량인 20RT를 대상 건물로 가정하였고, 초기투자비는 크게 공사비와 냉난방 설비 구입비로 구성되어 있으며, 기본적인 물가지표는 (사)한국물가정보(KPI)의 고시 데이터를 참조하였다. 각 시스템의 초기투자비는 ATES 시스템이 비교대상 기존 냉난방 시스템 대비 5.7배 높게 나타났다. 일일 8시간 사용기준으로 계절별 전력요금을 고려한 연간운전 비용은 ATES 시스템이 기존 시스템 대비 냉난방 시에 각각 77%와 16%를 나타내어 운전비용이 연간 절감되었고, 난방 운전 시 절감 비율이 냉방시보다 크게 나타났다. 두 시스템에 대한 생애주기비용을 산정하기 위하여 에어컨과 보일러의 기존시스템과 ATES 시스템의 가용연수를 모두 20년으로 설정하였고, 유지보수 비용은 초기투자비용의 2%로 설정하고, 할인율은 은행 예금이자를 기준으로 5%로 설정하였다. 전기와 LNG의 요금 상승률은 (사)한국물가정보를 바탕으로 각각 2%와 8%로 가정하였다. 이러한 조건에서 생애주기법을 이용한 경제성평가는 ATES 시스템의 경우 생애운전비용이 초기투자비용보다 작게 나타났으며, 기존 냉난방 시스템은 생애운전비용이 초기투자비용에 비하여 높게 나타났다. 본 연구 대상 ATES 열펌프 시스템의 실증 성능 데이터와 기존 문헌으로부터 얻은 냉난방 시스템의 성능 결과를 이용하여 생애주기 비용을 적용한 결과 ATES 시스템의 기존 시스템 대비 투자회수 기간은 6.62년으로 나타났다. 특히, 본 연구에서는 ATES 시스템이 국내 최초로 적용됨에 따라 스크린 등의 부품을 다소 고가의 제품으로 시스템에 적용하였으므로 ATES 시스템의 신뢰성과 안정성이 확보되면 초기 투자비 감소가 가능할 것으로 예상되며, 기존 시스템 대비 투자회수 기간은 더욱 감소될 수 있을 것으로 예상된다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.2
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• pp.87-94
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• 2017

The utilization of the achievements derived from the national R & D project is a key task of the science and technology industry policy that should lead the national economic growth by enhancing the investment efficiency of the national R&D. Although Korea has implemented various programs supporting technology transfer, commercialization, Performance is not sufficient. One of several causes may include inflexibility of a small or medium-sized company Priority System. This study is exploratory research on the directions for improving the current a small or medium-sized company Priority System. Results: First, Because the current SMEs Priority System contributes positively to enhancing SMEs R&D capability, We have to keep the system in principle. However, it is necessary to improve the direction of giving the strategic flexibility of the system so that the system is not operated formally. First, it is appropriate to make an exceptional contract with a person other than a small or medium-sized company, if a small and medium-sized company is not suitable for a technology execution contract due to the nature of technology. Second, it is desirable to consider the fulfillment of the obligations of this system when "sufficient efforts" are made to find a technical user.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.1
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• pp.1-12
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• 2023

Function of salivary gland and saliva composition can be an indicator of individual's health status. Recently, saliva has been thought to have a high potential for usage in the biomedical field to diagnose, evaluate, and prevent systemic health due to the technological advances in analyzing and detecting small elements such as immunological and metabolic products, viruses, microorganisms, hormones in saliva. As a diagnostic specimen, saliva has some useful advantages compared to serum. Because of simple non-invasive method, saliva sampling is quite comfort for the patient, and it doesn't require specialists to collect samples. The possibility of infection during the collection process is also low. For this reason, proteins, genetic materials, and various biomarkers in saliva are actively being utilized on studying stress, microbiomics, genetics, and epigenetics. For the research on collecting big data related to systemic health, the needs on biobank has been focused. Regeneration of salivary gland based on tissue engineering has been also on advancement. However, there are still many issues to be solved, such as the standardization of sample collection, storage, and usage. This review focuses on the recent trends in the field of saliva research and highlight the future perspectives in biomedical and other applications.