• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.969-973
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• 2013

To simulate the effect of high dose-rate radiation on semiconductor devices, device modeling work has been performed especially in the area of photo-current generation by a PIN diode. The resultant analytical values were compared with experimental ones that were specially designed and performed to benchmark the simulation results. Initial results showed 27.85% error between the simulation and the experiment. The error can be further reduced by improvement both in simulation and in related experiments. The developed technique from the study can be applicable to radiation dosimetry and to analysis on the radiation effects in electronics.

• International Journal of Safety
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• v.2 no.1
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• pp.50-56
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• 2003

There are a lot of uncertainties in the severe accident phenomena and scenarios in nuclear power plants (NPPs) and one of the major issues for severe accident management is the reduction of these uncertainties. The severe accident management aid system using Probabilistic Safety Assessments (PSA) technology is developed for the management staff in order to reduce the uncertainties. The developed system includes the graphical display for plant and equipment status, previous research results by a knowledge-base technique, and the expected plant behavior using PSA. The plant model used in this paper is oriented to identify plant response and vulnerabilities via analyzing the quantified results, and to set up a framework for an accident management program based on these analysis results. Therefore the developed system may playa central role of information source for decision-making for severe accident management, and will be used as a training tool for severe accident management.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.217-227
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• 2005

The data mining is a new approach to extract useful information through effective analysis of huge data in numerous fields. We utilized this data mining technique to analyze medical record of 35,671 people. Whole data were assorted by BMI score and divided into two groups. We tried to find out BMI risk factor from overweight group by analyzing the raw data with data mining approach. The result extracted by C5.0 decision tree method showed that important risk factors for BMI score are triglyceride, gender, age and HDL cholesterol. Odds ratio of major risk factors were calculated to show individual effect of each factors.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.655-660
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• 2000

Animation technique from the PIV database is particularly emphasized to give macroscopic and quantitative description of complex flow fields. This paper shows animation construction and application example for the post-processing of PIV data. As examples, first case is a pitching airfoil immersed in tree surface water circulating tunnel. Second case is a wake of a model-ship. Third case of PIV data is a large scale surface flow field. Obtained images are processed in time sequence by PIV exclusive routines where an efficient and reliable cross correlation algorithm is included for vector identification. All. animation Jobs are implemented completely on single personal computer environment. Compressed digital images are obtained initially by Motion-JPEG board and various An files are finally obtained through graphic processes.

• Korean Journal of Computational Design and Engineering
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• v.12 no.6
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• pp.413-421
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• 2007

3D rendering is becoming a common feature of mobile application programs with the rapid advance of mobile devices. Since the existing rendering engines do not provide triangulation functions, mobile 3D programs have focused on an efficient handling with pre-tessellated geometry. In addition, triangulation is comparatively expensive in computation, so it seems that the triangulation cannot be easily implemented on mobile devices with limited resources. Triangulation of 3D geometry is the essential process of visualization of 3D model data and many different triangulation methods have been reported. We developed a light and fast visualization process that involves constrained triangulation based on Voronoi diagram and applied it to a mobile computer application. In this paper, we applied kd-tree to the original incremental construction algorithm and produced new O(nlogn) incremental construction algorithm. And we show a simple and efficient constrained triangulation method based on Voronoi diagram. This paper also describes an implementation of mobile STEP data viewer as an application of our proposed algorithms.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.395-398
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• 2004

It is effective and desirable for a proper customer relational management(CRM) to send an email of product sales' advertisement bills for the prospective customers rather than to send spam mails for non specific customers. This study identifies the prospective customers with high probability to buy the specific products using Artificial Neural Network(ANN) and Induction Rule(IR) technique. We suggest an integrated model, IRANN of ANN and IR of decision tree program C5.0 and, also compare and analyze the accuracy of ANN, IR, and IRANN each other.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.253-259
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• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.

• The Journal of Society for e-Business Studies
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• v.13 no.3
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• pp.51-66
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• 2008

The Business Process Management System(BPMS) has received more attentions as companies increasingly realize the importance of business processes. However, traditional BPMS has focused mainly on correct modeling and exact automation of process flow, and paid little attention to the achievement of final goals of improving process efficiency and innovating processes. BPMS usually generates enormous amounts of log data during and after execution of processes, where numerous meaningful rules and patterns are hidden. In the present study we employ the data mining technique to find out useful knowledge from the complicated process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and the framework.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.91-100
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• 2003

In this paper we investigate the role that warfare played In the formation of the network of alliances between sites that are associated with the formation of the state in the Valley of Oaxaca, Mexico. A model of state formation proposed by Marcos and Flannery (1996) is used as the basis for an agent-based simulation model. Agents reside in sites and their actions are constrained by knowledge extracted from the Oaxaca Surface Archaeological Survey (Kowalewski 1989). The simulation is run with two different sets of constraint rules for the agents. The first set is based upon the raw data collected in the surface survey. This represents a total of 79 sites and constitutes a minimal level of warfare (raiding) in the Valley. The other site represents the generalization of these constraints to sites with similar locational characteristics. This set corresponds to 987 sites and represents a much more active role for warfare in the Valley. The rules were produced by a data mining technique, Decision Trees, guided by Genetic Algorithms. Simulations were run using the two different rule sets and compared with each other and the archaeological data for the Valley. The results strongly suggest that warfare was a necessary process in the aggregations of resources needed to support the emergence of the state in the Valley.