• Journal of the Korea Safety Management & Science
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• v.5 no.4
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• pp.131-145
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• 2003

A strategic plan for an immediate situation must be present in a business enterprise to deal actively and efficiently with the demand environment of new customer. The purpose of this paper is to provide research about the manufacturing strategy situations of the Korean manufacturing company. And this paper is a part of Modeling on Manufacturing Strategy. We emphasizes the importance of dynamism and complexity on the competitive environment, and classify the manufacturing strategy by trade-off model, cumulative model, sandcone model, strategic fit model, and competitiveness model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2352-2360
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• 2011

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.

• Journal of Families and Better Life
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• v.26 no.5
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• pp.91-107
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• 2008

This study investigated the isolation hypothesis and the buffer hypothesis of Demand-Control-Support model in relation to activity satisfaction and psychological well-being. The subjects were 300 elderly women participating in productive activity for example paid work, voluntary activity, and grancdhildren care. This research tested four hypotheses concerning the DCS model. Is there support for the isolation hypothesis, such that the lowest level of activity satisfaction is experienced by the elderly women working in an isolation situation(high demand-low control-low support)? Is there support for the isolation hypothesis, such that the lowest level of psychological well-being is experienced by the elderly women working in an isolation situation(high demand-low control-low support)? Is there support for the buffer hypothesis, i. e. interaction between demand, control, and support, indicating a buffering effect of support on the negative impact of high strain on activity satisfaction? Is there support for the buffer hypothesis, i. e. interaction between demand, control, and support, indicating a buffering effect of support on the negative impact of high strain on psychological well-being? Major results of this study were as follows. and were supported. Activity satisfaction and psychological well-being of the elderly women in isolation situation was the lowest among the sample. was supported that family support level buffered the negative impact of high strain on activity satisfaction. But was not supported. Only main effect of demand level was showed on psychological well-being.

• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1107-1117
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• 2006

In this thesis, we conduct a comprehensive analysis of the current situation and the inherent problems found in modern statistics education in Korea. We investigate the American probability and statistics curriculum currently used in Wisconsin and discuss the overall state of statistics education in The United States. Through comparison of both the Korean and Wisconsin model, we explore the future direction of statistical education.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.385-388
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• 2004

Many environmental destruction factors are accompanied in the mining development work and the secondary environmental disaster and the induction factors are inhered. We aquired digital data using aerial photogrammetry to analyze the terrain current situation according to the development situation of the mining restoration plan. We made the object area to 3D model and conducted terrian change monitoring. Then, we presented the decision-making information to improve rational management according to the original state plan.

• Proceedings of the KGS Conference
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• 2004.05a
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• pp.103-103
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• 2004

With the emergency of geographic information systems (GIS), the traditional layer based data model can only contain the spatial geometry and thematic attributes of phenomena. In real world, geographic phenomena have not only spatial geometry and thematic attribute but the temporal situation and the relationship between each phenomenon. (omitted)

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.37-53
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• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.

• Journal of Navigation and Port Research
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• v.42 no.1
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• pp.25-30
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• 2018

This study was carried out to analyze the risk attitude based on situation awareness of the Vessel Traffic Service Operator (VTSO) on the risk of collision between vessels during the monitoring of vessel traffic through the use of the VTS system. In general, when two vessels are in the close quarters situation, we analyzed the degree of risk of collision from the subjective viewpoint of the VTSOs through an administered survey. Chiefly, we analyzed the risk attitudes of each VTSO in the close quarters situation, by comparing it with the calculated value by the CoRi, which is the ship collision risk model from the VTSO's viewpoint. As a result, it was confirmed that more than 40% of the total VTSO was noted as being in a weak risk aversion type of category. Through a review of the results of analyzing the risk attitude of VTSO according to gender, age, VTS career, VTS center position, accident experience, and boarded career, it was found that there was a significant difference in the VTS career, VTS center position and accident experience. In addition, a regression model that is able to further explain the risk attitude of VTSO was derived as a factor that confirmed the significant difference and applied to CoRi to predict the collision risk according to the individual VTSO to be used as a fundamental information gathering tool for providing more accurate and safe VTS service at sea.

• The Journal of Korean Academic Society of Nursing Education
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• v.17 no.3
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• pp.433-443
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• 2011

Purpose: This study aimed at (a) developing an instructional model of creative thinking education on the problem-based learning method (C-PBL) in undergraduate nursing curriculum at one University, and (b) examining its effect on nursing students' level of creativity and outcomes from problem-based learning. Methods: The C-PBL model was implemented on 43 juniors of the experimental group with a 30 hour-nursing class during one semester. The control group, with 54 seniors, received 4 hours of problem based learning training in an adult nursing class. Pre-and post-tests were done with the Torrance Tests of Creative Thinking for creative thinking ability, the Integrated Creativity Instrument for creative motivation and attitudes, the Problem Solving Competency questionnaire, and the Self-Directed Learning Instrument. Results: The C-PBL model was developed using a caring situation scenario to solve nursing problems with 3 training steps of 'encountering a patient in a caring situation', 'exploring nursing knowledge', and 'designing creative caring beyond given knowledge'. Between the experimental group and the control group, there were significant differences in creativity (p<.010), problem-solving ability (p<.010), and self-directed ability (p<.010). Conclusion: This C-PBL method could contribute in increasing creative competency as well as problem-solving ability for nursing students.