• IEIE Transactions on Smart Processing and Computing
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• v.4 no.2
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• pp.83-88
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• 2015

Core-A is 32-bit synthesizable processor core with a unique instruction set architecture (ISA). In this paper, the Core-A ISA is introduced with discussion of useful features and the development environment, including the software tool chain and hardware on-chip debugger. Core-A is described using Verilog-HDL and can be customized for a given application and synthesized for an application-specific integrated circuit or field-programmable gate array target. Also, the GNU Compiler Collection has been ported to support Core-A, and various predesigned platforms are well equipped with the established design flow to speed up the hardware/software co-design for a Core-A-based system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.2
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• pp.9-18
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• 2011

Vender prioritization process is one of the most critical tasks of production and logistics management for many companies. Determining the most critical criteria for vender prioritization process is a vital means for a purchasing company to improve its supply chain productivity. This study discuss the use of a Fuzzy analytic network process (Fuzzy ANP) model which is an efficient tool to handle the fuzziness of the data involved in deciding the preferences of different criteria which are not independent. Also, the comparison of classical ANP and Fuzzy ANP is described using simulation with triangular distribution random number generation. It is shown that Fuzzy ANP model possesses some attractive properties and could be used as an alternative to the known vender prioritization methods.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1053-1063
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• 2017

Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

• Annual Conference of KIPS
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• 2019.10a
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• pp.704-707
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• 2019

오늘날 객체지향 코드 내부 복잡도가 지속적으로 증가하는 데에 반해 IT 벤처/중소기업에서는 요구사항 및 설계문서 미비의 코드 개발과 테스트 중심의 경우가 빈번하다. 이는 시스템의 코드를 이해하고 수정, 유지보수를 하는데 많은 시간과 비용이 투자되고 있다. 본 연구는 객체지향 코드의 내부 구조 시각화를 위해 Tool-Chain방법을 이용한 정적 분석기 구축 및 가시화를 제안 한다. 이를 통해, 역공학 도구, 테스트 프로세스 등을 도입이 어려운 중소기업의 소프트웨어 품질 향상에 도움을 줄 수 있을 것으로 기대된다.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• International Commerce and Information Review
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• v.15 no.4
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• pp.267-286
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• 2013

The WCO Time Release Study (TRS) is a unique tool and method for measuring the actual performance of Customs activities. The ultimate aim of TRS is to improve the performance of the function being measured. To be more specific, TRS is used for identifying bottlenecks in the international supply chain and/or constraints affecting Customs release, assessing newly introduced and modified techniques, procedures, technologies and infrastructure, or administrative changes, establishing baseline trade facilitation performance measurement, identifying opportunities for trade facilitation improvements and estimating the country's approximate comparative position as a benchmark tool. The effective utilization methods of TRS for trade facilitation in Korea Customs Services are as follows ; Firstly, it is necessary to make every efforts to identify bottlenecks in border-related procedures and improve their procedures for continuous and more improved trade facilitation. Secondly, it is necessary to optimize and simplify export-related procedures using the TRS in exportation for efficiency of total international supply chain. Thirdly, it is necessary to make coordinated border management with main trading partners. Lastly, it is necessary to enhance Korea's international status to support underdeveloped countries in the field of trade procedures.

• The Journal of Bigdata
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• v.1 no.2
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• pp.65-78
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• 2016

In the rapid change of business environment, it is crucial that several companies with core competence cooperate together in order to deliver competitive products to the market faster. Thus a lot of companies are participating in supply chains and SCM (Supply Chain Management) become more important. To efficiently manage supply chains, the analysis of data from SCM systems is required. In this paper, we explain how to analyze SCM related data with process mining techniques. After discussing the data requirement for process mining, several process mining techniques for the data analysis are explained. To show the applicability of the techniques, we have performed a case study with a company in South Korea. The case study shows that process mining is useful tool to analyze SCM data. On specifically, an overall process, several performance measures, and social networks can be easily discovered and analyzed with the techniques.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.12
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• pp.1686-1695
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• 2015

Molecular marker selection has been an acceptable tool in the acceleration of the genetic response of desired traits to improve production performance in chickens. The crossbreds from commercial parent stock (PS) broilers with four Thai synthetic breeds; Kaen Thong (KT), Khai Mook Esarn (KM), Soi Nin (SN), and Soi Pet (SP) were used to study the association among chicken growth hormones (cGH) and the insulin-like growth factor (IGF-I) genes for growth and carcass traits; for the purpose of developing a suitable terminal breeding program for Thai broilers. A total of 408 chickens of four Thai broiler lines were genotyped, using polymerase chain reaction-restriction fragment length polymorphism methods. The cGH gene was significantly associated with body weight at hatching; at 4, 6, 8, 10 weeks of age and with average daily gain (ADG); during 2 to 4, 4 to 6, 0 to 6, 0 to 8, and 0 to 10 weeks of age in $PS{\times}KM$ chickens. For $PS{\times}KT$ populations, cGH gene showed significant association with body weight at hatching, and ADG; during 8 to 10 weeks of age. The single nucleotide polymorphism variant confirmed that allele G has positive effects for body weight and ADG. Within carcass traits, cGH revealed a tentative association within the dressing percentage. For the IGF-I gene polymorphism, there were significant associations with body weight at hatching; at 2, 4, and 6 weeks of age and ADG; during 0 to 2, 4 to 6, and 0 to 6 weeks of age; in all of four Thai broiler populations. There were tentative associations of the IGF-I gene within the percentages of breast muscles and wings. Thus, cGH gene may be used as a candidate gene, to improve growth traits of Thai broilers.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.4
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• pp.323-337
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• 2006

Samsung, recognized as a global leading company, has huge and complex supply chain structures and has been improving them continuously for its fast-growing global businesses. SCM 6 Sigma is the state-of-the-art methodology developed through a combination of SCM innovation concepts accumulated from SCM Business Group in Samsung SDS and 6 Sigma which has successfully settled down as the management innovation tool for many companies in Samsung. The ultimate goal of SCM 6 Sigma is to train and develop future supply chain leaders who are more capable of leading SCM innovations. By leveraging the established 6 Sigma Belt System, Samsung aims to alleviate a shortage of SCM talents that has been a bottleneck in improving SCM performances at its group companies. This explains why SCM 6 Sigma is created. SCM 6 Sigma is the unique and critical component for Samsung to implement its various strategies for continuous improvement of its operations at a higher level of effectiveness and systematically as well. In return on these efforts, many SCM innovation projects have been successfully executed through SCM 6 Sigma up to today. In this paper, we introduce the methodology and explain the business rationale behind it together with its deployment case.

• Information Systems Review
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• v.10 no.2
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• pp.195-209
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• 2008

Customers' needs change every moment. Profitability of stores can't be increased anymore with an existing standardized chain store management. Accordingly, a personalized store management tool needs through prediction of customers' preference. In this study, we propose a recommending procedure using dynamic customers' preference by analyzing the transaction database. We utilize self-organizing map algorithm and association rule mining which are applied to cluster the chain stores and explore purchase sequence of customers. We demonstrate that the proposed methodology makes an effect on recommendation of products in the market which is characterized by a fast fashion and a short product life cycle.