• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2007년도 춘계학술대회
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• pp.309-322
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• 2007

This paper shows application guide of dependability data from the field, life cycle costing, and maintainability. Moreover, this study introduces mathematical expressions and predictions for reliability, availability and maintainability. This paper also shows compliance test procedures for steady-state availability, and application of Markov techniques.

• 대한인간공학회지
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• 제30권2호
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• pp.319-330
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• 2011

In computer forensics investigation, the investigators collect, protect, analyze and interpret massive amount of data which were used in cyber crime. However, due to its huge amount of information, it takes a great deal of time and errors often result even when they use forensics investigation tool in the process. The information visualization techniques will greatly help to improve the information processing ability of human when they deal with the overwhelming amount of data and have to find out significant information in it. The importance of Intrusion Detection System(IDS) among network forensics is being emphasized in computer forensics. In this study, we apply the information visualization techniques which are proposed to be a great help to IDS and carry out the usability test to find out the most effective information visualization techniques for IDS.

• 품질경영학회지
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• 제13권2호
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• pp.21-28
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• 1985

The purpose of this study is to analyze the direct relationship between doctors and number of patients to be treated by applying many kinds of IE techniques. Generally, doctors in this research work both at Out Patient Data & In Patient Data. Under the hospital management system that they are applying in, doctor's daily working schedules are instable because the number of OPD patients daily. Therefore, the amount of time they spend for inward patients are variable too. So the numbers of patients have great influence to the whole hospital system management.

• 한국주조공학회지
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• 제42권1호
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• pp.35-41
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• 2022

In the present paper, the data management of casting processes has been discussed. In order to construct a smart factory in the foundry industry, understanding of the whole casting processes has to be in the first place. Casting process data can be obtained at the kiosk operated by casting engineers and data acquired by sensors in the foundry facility. However, preprocessing of the casting process data must be carried out in order to analyze the casting process by the data. Techniques and some examples for data preprocessing in the foundry was introduced.

• Computers and Concrete
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• 제33권4호
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• pp.425-443
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• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.105-108
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• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• 한국경영과학회지
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• 제41권3호
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• pp.45-58
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• 2016

Data are important in an organization because they are used in making decisions and obtaining insights. Furthermore, given the increasing importance of data in modern society, data governance should be requested to increase an organization's competitive power. However, data governance concepts have caused confusion because of the myriad of guidelines proposed by related institutions and researchers. In this study, we re-established the concept of ambiguous data governance and derived the top-level components by analyzing previous research. This study identified the components of data governance and quantitatively analyzed the relation between these components by using DEMATEL and context analysis techniques that are often used to solve complex problems. Three higher components (data compliance management, data quality management, and data organization management) and 13 lower components are derived as data governance components. Furthermore, importance analysis shows that data quality management, data compliance management, and data organization management are the top components of data governance in order of priority. This study can be used as a basis for presenting standards or establishing concepts of data governance.

• 여성건강간호학회지
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• 제23권3호
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• pp.155-165
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• 2017

Purpose: The aim of this study was to understand the hand-on breast massage techniques used by well-known experts in breastfeeding clinics. Methods: A qualitative multiple-case design was applied that involved a feasibility test. Four experts sampling qualitative data collected by observing participants and in individual interviews were analyzed by content analysis, linking data to the propositions, and cross-case pattern matching. This study explored differences within and between cases, and the possibilities of replicating findings across cases. Thirty-nine postpartum women participated voluntarily in the feasibility test, which investigated the usability of four massage techniques. Results: The four techniques showed considerable similarities in terms of the application of stimulation to the breast base and increased flexibility of the wired flexible body, which was the core mechanism underlying the techniques. The breast management strategies were consistent with existing practice guidelines with the exception of using cold cabbage to control engorgement pain. There was insufficient scientific evidence for supporting the massage techniques used by the experts. All of the techniques showed 100% education completeness, but application rates were higher for self-control-oriented techniques. Conclusion: The massage techniques applied by experts in breastfeeding were based on hypotheses and self-control techniques are feasible to apply in practice.

• 품질경영학회지
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• 제51권2호
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• pp.223-245
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• 2023

Purpose: This study aim to identify the trends in AI-based PHM technology that can enhance reliability and minimize costs. Furthermore, this research provides valuable guidelines for future studies in various industries Methods: In this study, I collected and selected AI-based PHM studies, established classification criteria, and analyzed research trends based on classified fields and techniques. Results: Analysis of 125 domestic studies revealed a greater emphasis on machinery in both diagnosis and prognosis, with more papers dedicated to diagnosis. various algorithms were employed, including CNN for image diagnosis and frequency analysis for signal data. LSTM was commonly used in prognosis for predicting failures and remaining life. Different industries, data types, and objectives required diverse AI techniques, with GAN used for data augmentation and GA for feature extraction. Conclusion: As studies on AI-based PHM continue to grow, selecting appropriate algorithms for data types and analysis purposes is essential. Thus, analyzing research trends in AI-based PHM is crucial for its rapid development.

• Journal of Computing Science and Engineering
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• 제10권1호
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• pp.21-31
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• 2016

With the application and development of biomedical techniques such as next-generation sequencing, mass spectrometry, and medical imaging, the amount of biomedical data have been growing explosively. In terms of processing such data, we face the problems surrounding big data, highly intensive computation, and high dimensionality data. Fortunately, cloud computing represents significant advantages of resource allocation, data storage, computation, and sharing and offers a solution to solve big data problems of biomedical research. In order to improve the efficiency of resource management in cloud computing, this paper proposes a clustering method and adopts Radial Basis Function in order to compress comprehensive data sets found in biology and medicine in high quality, and stores these data with resource management in cloud computing. Experiments have validated that with such a data-compression-based resource management in cloud computing, one can store large data sets from biology and medicine in fewer capacities. Furthermore, with reverse operation of the Radial Basis Function, these compressed data can be reconstructed with high accuracy.