• Journal of information and communication convergence engineering
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• v.3 no.4
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• pp.209-212
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• 2005

This study proposes hybrid case-based reasoning and genetic algorithms model for customer classification. In this study, vertical and horizontal dimensions of the research data are reduced through integrated feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed model may improve the classification accuracy and outperform various optimization models of typical CBR system.

• Journal of Korean Port Research
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• v.13 no.2
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• pp.279-288
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• 1999

This paper is concerned with developing a standard costing model based on the case study of PECT and GCT in order to improve operation efficiency and design business strategy. In doing so the model can be a useful tool to analyze current calculation system of lease charge at the two terminals and to judge whether the level of lease charge currently applied to them is justifiable for thier profitability.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.159-168
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• 2024

This study was conducted to identify factors influencing clinical performance among nursing students who experienced clinical practice due to COVID-19 and provide basic data to improve clinical performance. This study collected data from October 10 to October 27, 2023, targeting 144 students at two nursing schools in G Province. The collected data were analyzed using descriptive statistics, difference analysis (t-est, on-way ANOVA), correlation, and hierarchical regression analysis. As a result of the study, in Model 1, extrovert and mixed personality types were found to be significant predictive factors explaining clinical performance. The goodness of fit of Model 1 was statistically significant, and the explanatory power was 9.2% (F=8.256, p<.001). In Model 2, interpersonal skills and confidence in nursing skills appeared as significant predictive factors explaining clinical performance ability. Confidence in nursing skills was the best predictor of clinical performance, followed by interpersonal skills. The explanatory power of the model was 50.1%, an increase of 41.3% compared to Model 1. Model fit was also statistically significant. Simulation education that reproduces various situations should be strengthened to increase opportunities to perform direct nursing and improve nursing students' nursing capabilities. If you improve your confidence in nursing skills and interpersonal skills through simulation education, your clinical performance will ultimately improve, and this will be able to be demonstrated as a nursing competency when employed as a nurse.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1879-1882
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• 1991

Kalman Filter application to model predictive control is discussed. Most of refinery and petrochemical processes contain uncertainties in their output. Simplified state estimation algorithm is merged to model predictive control to improve overall control accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.7 no.4
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• pp.11-21
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• 1997

This paper introduces a statistical approach of intrusion detection and tunes an intrusion detection model using fuzzy ste. We describel the method of applying fuzzy set for NIDES intensity measure. By using fuzzy set, we improve the algorithm for evaluating score value of NIDES, and present a possibility of intrusion detection system.

• Journal of The Korean Association of Information Education
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• v.15 no.3
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• pp.345-353
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• 2011

Information and communication brings us some degree of convenient but also serious side effects. Especially, information and communication ethics is an important issue for elementary school children who are early stage in moral value. This paper proposes a triple-connected teaching and learning model. which can be applied to the elementary school children. in order to improve the educational level. The model is formed by selecting three discretionary teaching models, and applying independently them into the different classes with linking the contents for each class. A teacher can choose the teaching models based on what he wants to teach. Caring model, Home-Connected model and Simulation model were linked in our experiment. Experiments were conducted in the same condition but different learning models, Then, an educational effect was compared and analyzed in the educational level improvement point of view, between the Triple-connected teaching and learning model and the single teaching and learning model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.671-686
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• 2020

Due to the long training time and high training cost of traditional surgical training methods, the emerging virtual surgical training method has gradually replaced it as the mainstream. However, the virtual surgical system suffers from poor authenticity and high computational cost problems. For overcoming the deficiency of these problems, we propose an optimized model for the local compression deformation of soft tissue. This model uses a simulated annealing algorithm to optimize the parameters of the soft tissue model to improve the authenticity of the simulation. Meanwhile, although the soft tissue deformation is divided into local deformation region and non-deformation region, our proposed model only needs to calculate and update the deformation region, which can improve the simulation real-time performance. Besides, we define a compensation strategy for the "superelastic" effect which often occurs with the mass-spring model. To verify the validity of the model, we carry out a compression simulation experiment of abdomen and human foot and compare it with other models. The experimental results indicate the proposed model is realistic and effective in soft tissue compression simulation, and it outperforms other models in accuracy and real-time performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.529-534
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• 2009

Climate models, both global and regional, have increased in sophistication and are being run at increasingly higher resolutions. The Land Surface Models (LSMs) coupled to these climate models have evolved from simple bucket models to sophisticated Soil-Vegetation-Atmosphere Transfer (SVAT) schemes needed to support complex linkages and processes. However, some underpinnings of terrestrial hydrologic parameterizations so crucial in the predictions of surface water and energy fluxes cause model errors that often manifest as non-linear drifts in the dynamic response of land surface processes. This requires the improved parameterizations of key processes for the terrestrial hydrologic scheme to improve the model predictability in surface water and energy fluxes. The Common Land Model (CLM), one of state-of-the-art LSMs, is the land component of the Community Climate System Model (CCSM). However, CLM also has energy and water biases resulting from deficiencies in some parameterizations related to hydrological processes. This research presents the implementation of a selected set of parameterizations and their effects on the runoff prediction. The modifications consist of new parameterizations for soil hydraulic conductivity, water table depth, frozen soil, soil water availability, and topographically controlled baseflow. The results from a set of offline simulations are compared with observed data to assess the performance of the new model. It is expected that the advanced terrestrial hydrologic scheme coupled to the current CLM can improve model predictability for better prediction of runoff that has a large impact on the surface water and energy balance crucial to climate variability and change studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2202-2211
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• 2010

The wind power prediction system is composed of a meteorological forecasting module, calculation module of wind power output and HMI(Human Machine Interface) visualization system. The final information from this system is a short-term (6hr ahead) and mid-term (48hr ahead) wind power prediction value. The meteorological forecasting module for wind speed and direction forecasting is a combination of physical and statistical model. In this system, the WRF(Weather Research and Forecasting) model, which is a three-dimensional numerical weather model, is used as the physical model and the GFS(Global Forecasting System) models is used for initial condition forecasting. The 100m resolution terrain data is used to improve the accuracy of this system. In addition, optimization of the physical model carried out using historic weather data in Jeju. The mid-term prediction value from the physical model is used in the statistical method for a short-term prediction. The final power prediction is calculated using an optimal adjustment between the currently observed data and data predicted from the power curve model. The final wind power prediction value is provided to customs using a HMI visualization system. The aim of this study is to further improve the accuracy of this prediction system and develop a practical system for power system operation and the energy market in the Smart-Grid.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.5
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• pp.601-607
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• 2013

This paper presents the development and validation of a wheel loader simulation model. The objective of doing so is to evaluate the performance of the wheel loader and improve its overall performance using Matlab/Simulink. The wheel loader simulation model consists of 4 parts: mechanical/hydraulic powertrain model and vehicle/working dynamic model. An integrated simulation model is required to evaluate and improve the performance of the wheel loader. It is expected that this model will be applied to fuel economizing, improving the pace of operation by using the hybrid system, and the intelligent wheel loader. The performance of the proposed simulation model has been validated by using Matlab/Simulink to compare the driving and the working experimental data.