• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1467-1474
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• 2014

In this paper, we consider the two computer intensive methods for extended Euclidean algdrithm. Two methods we propose are C-programming based approach and Microsoft excel based method, respectively. Thses methods are applied to the derivation of greatest commnon devisor, multiplicative inverse for modular operation and the solution of diophantine equation. Concrete investigation for extended Euclidean algorithm with the computer intensive process is given. For the application of extended Euclidean algorithm, we consider the RSA encrytion method which is still popular recently.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.43-49
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• 2016

This paper presents a workflow validation method for data-intensive graphical workflow models using real-time workflow tracing mode on data-intensive workflow designer. In order to model and validate workflows, we try to divide as modes have editable mode and tracing mode on data-intensive workflow designer. We could design data-intensive workflow using drag and drop in editable-mode, otherwise we could not design but view and trace workflow model in tracing mode. We would like to focus on tracing-mode for workflow validation, and describe how to use workflow tracing on data-intensive workflow model designer. Especially, it is support data centered operation about control logics and exchange variables on workflow runtime for workflow tracing.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.189-198
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• 2023

The phenomenon of climate change is deteriorating which increased heatwaves, typhoons and heavy snowfalls in recent years. Followed by the 25^th United nations framework convention on climate change(COP25), the world countries have achieved a consensus on achieving carbon neutrality. City plays a crucial role in achieving carbon mitigation as well as economic development. Considering economic and environmental factors, we selected 63 cities in South Korea to analyze carbon emission situation by Elbow method and K-means clustering algorithm. The results reflected that cities in South Korea can be categorized into 6 clusters, which are technology-intensive cities, light-manufacturing intensive cities, central-innovation intensive cities, heavy-manufacturing intensive cities, service-intensive cities, rural and household-intensive cities. Specific suggestions are provided to improve city-level carbon mitigation development.

• The Korean Journal of Applied Statistics
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• v.3 no.1
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• pp.121-141
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• 1990

Computer intensive bootstrap methods are studied as a tool of statistics. Practical calculation and theoretical justification problem of the methods in estimating the sampling distribution and construction confidence region of parameters are discussed through several examples. Statistical meaning of the methods are also considered.

• International Journal of Contents
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• v.10 no.2
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• pp.47-58
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• 2014

Physiological signals provide important clues in the diagnosis and prediction of disease. Analyzing these signals is important in health and medicine. In particular, data preprocessing for physiological signal analysis is a vital issue because missing values, noise, and outliers may degrade the analysis performance. In this paper, we propose PhysioCover, a system that can recover missing values of physiological signals that were monitored in real time. PhysioCover integrates a gradual method and EM-based Principle Component Analysis (PCA). This approach can (1) more readily recover long- and short-term missing data than existing methods, such as traditional EM-based PCA, linear interpolation, 5-average and Missing Value Singular Value Decomposition (MSVD), (2) more effectively detect hidden variables than PCA and Independent component analysis (ICA), and (3) offer fast computation time through real-time processing. Experimental results with the physiological data of an intensive care unit show that the proposed method assigns more accurate missing values than previous methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.969-973
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• 2005

This paper presents a survey of software reliability modeling and it's application to pre-built software system combined with hardware such as numerical controller based on personal computer systems. Many a systems in these days are much more becoming software intensive and many software intensive systems are safety critical. For this reason, the technique well developed to measure of software reliability is very important for whom to assess such a system. This paper provides a brief idea of method to evaluate such a system's reliability based on hardware performance.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.61-68
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• 2020

In the era of the fourth industrial revolution, many production plants are gradually evolving into smart factories that apply information and communication technology to manufacturing, distribution, production, and quality management. The conversion from conventional factories to smart factories has resulted in the automation of production sites using the internet and the internet of things (IoT) technology. Thus, labor-intensive production can easily collect necessary information. However, implementing a smart factory required a significant amount of time, effort, and money. In particular, labor-intensive production industries are not automated, and productivity is determined by human skill. A representative industry of such industries is sewing the industry. In the sewing industry, wherein productivity is determined by the operator's skills. This study suggests that production performance, inventory management and product delivery of the sewing industries can be managed efficiently with existing production method by using smart buttons incorporating IoT functions, without using automated machinery.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.69-76
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• 2022

This paper proposes a method of refining and processing time-series data using Medical Information Mart for Intensive Care (MIMIC-IV) v2.0 data. In addition, the significance of the processing method was validated through a machine learning-based pressure ulcer early warning system using a dataset processed based on the proposed method. The implemented system alerts medical staff in advance 12 and 24 hours before a lesion occurs. In conjunction with the Electronic Medical Record (EMR) system, it informs the medical staff of the risk of a patient's pressure ulcer development in real-time to support a clinical decision, and further, it enables the efficient allocation of medical resources. Among several machine learning models, the GRU model showed the best performance with AUROC of 0.831 for 12 hours and 0.822 for 24 hours.

• Journal of the Korean Data and Information Science Society
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• v.5 no.1
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• pp.1-10
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• 1994

Projection pursuit(PP) is a computer-intensive method which seeks out interesting linear projections of multivariate data onto a lower dimension space by machine. By working with lower dimensional projections, projection pursuit avoids the sparseness of high dimensional data. We show through simulation that two projection pursuit discriminant mothods proposed by Chen(1989) and Huber(1985) do not improve very much the error rate than the existing methods and compare several classification procedures.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.628-631
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• 1992

The position control for DC motor under the unpredictable load variations is presented. Neural network controller trained to deal with this problem provide the estimates of system parameters. Pole placement is also performed in accordance with them. The proposed method is validated through computer simulation.