• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.639-652
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• 2015

In today's manufacturing environment, the process data can be easily measured and transferred to a computer for analysis in a real-time mode. As a result, it is possible to monitor several correlated quality variables simultaneously. Various multivariate statistical process control (MSPC) procedures have been presented to detect an out-of-control event. Although the classical MSPC procedures give the out-of-control signal, it is difficult to determine which variable has caused the signal. In order to solve this problem, data mining and machine learning techniques can be considered. In this paper, we applied the technique of decision tree learning to the MSPC, and we did simulation for MSPC procedures to monitor the bivariate normal process means. The results of simulation show that the overall performance of the MSPC procedure using decision tree learning technique is similar for several values of correlation coefficient, and the accurate classification rates for out-of-control are different depending on the values of correlation coefficient and the shift magnitude. The introduced procedure has the advantage that it provides the information about assignable causes, which can be required by practitioners.

• Journal of KIISE
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• v.45 no.3
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• pp.242-250
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• 2018

For high-quality software development, it is necessary to detect and fix the defects inserted. If defect management activities are not properly performed, it will lead to the project delay and project failure due to rework. Therefore, organizations need to establish defect management process and institutionalize it. Process standard models handle defect management in the area of project monitoring and control. However, small organizations experience difficulties in implementing and applying defect management process in a real situation. In this paper, we propose a defect management process for small organization which is designed in accordance with the characteristics of a small projects such as few participants and short development period. The proposed defect management process will be based on a tool chain with open source software such as Redmine, Subversion, Maven, Jenkins that support a defect management process and SW Visualization in systematic way. We also proposed a way of constructing defect database and various methods of analyzing and controlling defect data based on it. In an effort to prove the effectiveness of the proposed process, we applied the process and tool chain to a small organization.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.8
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• pp.507-516
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• 2013

Mobile devices have limited computing power and resources. Since mobile devices are equipped with rich network connectivity, an approach to subscribe cloud services can effectively remedy the problem, which is called Mobile Cloud Computing (MCC). Most works on MCC depend on a method to offload functional components at runtime. However, these works only consider the limited verion of offloading to a pre-defined, designated node. Moveover, there is the limitation of managing services subscribed by applications. To provide a comprehensive and practical solution for MCC, in this paper, we propose a self-stabilizing process and its management-related methods. The proposed process is based on an autonomic computing paradigm and works with diverse quality remedy actions such as migration or replicating services. And, we devise a pratical offloading mechanism which is still in an initial stage of the study. The proposed offloading mechanism is based on our proposed MCC meta-model. By adopting the self-stabilization process for MCC, many of the technical issues are effectively resolved, and mobile cloud environments can maintain consistent levels of quality in autonomous manner.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.4
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• pp.173-193
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• 2022

Dissolved nutrients in seawater are a key variable for understanding the role of the ocean in controlling atmospheric carbon dioxide, which is a major cause of global warming. In order to continuously monitor changes in the marine environment in the waters around the Korean Peninsula, dissolved nutrient data are being measured through regular observations by national institutions and various research projects. To increase the utilization of these data, the Jurisdictional Ocean Information Sharing System (JOISS), which integrates data from each institution, was established. In this study, for the dissolved nutrient data of JOISS, primary quality control was performed using the regional dissolved nutrient concentration range in the waters around the Korean Peninsula, and the correlation between the dissolved nutrient and other oceanographic characteristics or the correlation within dissolved nutrient components. Providing the quality control flags of regional range and primary quality control may increase the reliability of JOISS dissolved nutrient data and promote the utilization of dissolved nutrient data in JOISS. In addition, we proposed a secondary quality control method essential for improving the international comparability of JOISS dissolved nutrients.

• Industry Promotion Research
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• v.8 no.2
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• pp.37-43
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• 2023

Sampling In this study, we examined the problems and their improvement plans associated with the reliability management sector in quality management engineer test conducted in Korea. First of all, there seems to be a problem in that the terminology is not unified and some techniques essential for reliability analysis are not included. We also looked at quality and reliability tests performed in foreign countries (especially USA) that Koreans often acquire. In particular, it can be seen that the CRE test almost overlaps with the contents of the reliability management engineer test in Korea. However, while the USA is an open book test, Korea is not, so the problem is that there are too many formulas to memorize on the part of the test takers. In addition, the analysis of the data is done manually without using computer software. If the test were an open book test like the CRE test in USA, it will be a test that can go beyond fragmentary knowledge and check whether test takers have the essential elements in reliability management. Lastly, if we adopt re-certification system through education and work within a certain period of time, as in USA, it will be a qualification test suitable for modern people living in a flood of information.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.1-7
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• 2013

Big data management is becoming increasingly important in both industry and academia of information science community. One of the important categories of big data generated from software systems is log data. Log data is generally used for better services in various service providers and can also be used as information for qualification. This paper presents a big data management architecture specialized for log data. Specifically, it provides the aggregation of log messages sent from multiple clients and provides intelligent functionalities such as analyzing log data. The proposed architecture supports an asynchronous process in client-server architectures to prevent the potential bottleneck of accessing data. Accordingly, it does not affect the client performance although using remote data store. We implement the proposed architecture and show that it works well for processing big log data. All components are implemented based on open source software and the developed prototypes are now publicly available.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.3
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• pp.143-151
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• 2009

Forest type map is a main forest thematic map which shows forest type, age class, diameter class and density. This map has been used as a base data in various fields such as forest policy decision-making, land suitability assessment and eco map production etc. But the existing map had many kinds of errors and no metadata because there was no standard for digital map production. On this study, we developed standard for digital forest type map production consisting of 33 unit processes including DB schema design, mapping process, quality control and metadata input process. Based on this standard, country-wide digital forest type map can be produced with consistent quality and standard metadata.

• Annual Conference of KIPS
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• 2003.05b
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• pp.763-766
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• 2003

산업 분야에서 자동화 시스템은 제품의 자동설계, 생산 공정의 자동제어, 장애의 발견과 복구, 품질 검사 등 각종 생산과 관련되어 생산성을 향상시킨다. 자동화 시스템을 구성하는 제어 시스템에 사용되는 흐름 제어 언어에는 IL, ST, FBD, SFC, LD의 5종이 있다. 일반적인 소프트웨어에서 동시에 사용할 수 있는 흐름 제어 언어의 종류는 2종 이하로 제한되어 있다. 이에 본 논문에서는 그래픽 기반의 흐름 제어 언어인 FBD, LD, SFC의 3종을 통합하여 처리가 가능한 소프트웨어를 제시한다. 그래픽 기반의 흐름 제어 언어인 FBD, LD, SFC의 형식을 모두 포함하는 데이터 구조를 설계하고, 데이터 구조로부터 읽어 들인 데이터를 출력하는 그래픽 처리 방안을 제시한다. 소프트웨어에서 편집한 FBD LD, SFC는 파일 단위로 관리할 수 있고, 각 파일들은 프로젝트 파일로 통합하여 관리가 가능하다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.441-447
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• 2004

삼면이 바다인 우리나라의 국토환경에서 해양공간정보에 대한 국민들의 수요가 증가하고 다양해짐에 따라, 육상 및 해상의 지리정보는 관련기관, 민간부분 및 일반인에게 확산하여 부가가치를 창출할 수 있는 사회간접자본으로서의 역할이 요구되고 있는 실정이다. 이에 해양수산부는 제2차 국가지리정보체계 기본계획에 입각하여, 21세기 디지털 해양건설의 초석을 마련하기 위하여 해양지리정보 사업을 추진하고 있다. 본 연구에서는 해양지리정보체계(MGIS: Marine Geographic Information System)사업의 일환으로 해양기본지리정보에 관한 연구를 통하여 해양기본지리정보의 구축 시 필요한 필수항목으로 도출되었던 항목을 기준으로 데이터 모델링 및 데이터구축지침서를 작성하였다. 또한, 향후 NGIS의 통합을 고려한 국가차원의 해양기본지리정보 구성(안)을 제시하였으며 데이터의 품질기준, 유지관리를 위한 방안을 마련하였다.