• Aerospace Engineering and Technology
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• v.5 no.2
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• pp.227-239
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• 2006

Reliability prediction provides a rational basis for design decisions such as the choice between alternative concepts, choice of part quality levels, derating factors to be applied, use of proven versus state-of-the-art techniques, and other factors. For this reasons, reliability prediction is essential functions in developing space systems. The worth of the quantitative expression lies in the information conveyed with the numerical value and the use which is made of that information and reliability prediction should be initiated early in the configuration definition stage to aid in the evaluation of the design and to provide a basis for item reliability allocation (apportionment) and establishing corrective action priorities. Reliability models and predictions are updated when there is a significant change in the item design availability of design details, environmental requirements, stress data, failure rate data, or service use profile. In this paper, the procedure, selection of reliability data and methods for space system reliability prediction is presented.

• Journal of the Korean Wood Science and Technology
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• v.44 no.6
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• pp.864-871
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• 2016

This study was conducted to compare and analyze gross calorific values from measurement methods of lignocellulosic biomass and calculation data from calorific value prediction models based on the elemental content. The deviation of Liriodendron tulipifera (LT) and Populus euramericana (PE) was shown 7.7 cal/g and 7.4 cal/g respectively in palletization method, which are within repeatability limit 28.8 cal/g of ISO FDIS 18125. In the case of Thailand charcoal (TC), nontreatment method and palletization method was satisfied with repeatability limit as 22.8 cal/g and 8.8 cal/g respectively. Seowon charcoal (SC) was shown deviation of 11.4 cal/g in nontreatment method, because the density and chemical affinity of sample increases as the carbon content increases from heat treatment at high temperature in the case of TC and SC. In addition, after applying the elemental content of each of these samples to the calorific value prediction models, the study found that Model Equation (3) was relatively consistent with measured calorific values of all these lignocellulosic biomass. Thus, study about the correlation between the density and size of particle should be conducted in order to select the measurement method for a wide range of solid biofuels in the future.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.251-257
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• 2018

As the use of big data is necessary for increasing business value, the size of the big data market is getting bigger. Accordingly, it is important to apply competitive patents in order to gain the big data market. In this study, we conducted the patent analysis based keyword network to analyze the trend of big data patents. The analysis procedure consists of big data collection and preprocessing, network construction, and network analysis. The results of the study are as follows. Most of big data patents are related to data processing and analysis, and the keywords with high degree centrality and between centrality are "analysis", "process", "information", "data", "prediction", "server", "service", and "construction". we expect that the results of this study will offer useful information in applying big data patent.

• Journal of East-West Nursing Research
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• v.12 no.1
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• pp.5-13
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• 2006

Purpose: This research has been conducted in order to confirm the major factors that prediction organizational commitment in registered nurses. Method: The subjects were 350 registered nurses from 3 hospitals in Seoul. The sample for data collection consisted of 329 useable questionnaires (94% overall return rate) for 2 weeks. The Instrument tools utilized in this study were organizational commitment scale, empowerment scale, job stress scale and job satisfaction scale and thoroughly modified to verify validity and reliability. The collected data have been analyzed using SPSS 11.0 program. Three outliers which were bigger than 3 in absolute value were found, so after taking them off, Multiple Regression was used for further analysis. Result: The major factors that prediction organizational commitment in registered nurses were job satisfaction, empowerment, age and unit experience, which explained 51.9% of organizational commitment. Conclusion: It has been confirmed that the regression equation model of this research may serve as a organizational commitment prediction factors in Registered Nurses.

• Aerospace Engineering and Technology
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• v.7 no.1
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• pp.223-228
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• 2008

In order to acquire the images requested by users, it is very important to calculate mission schedule parameters such as imaging execution time and attitude tilt angle accurately. These parameters are based on orbit prediction. This paper describes the accuracy of orbit propagation for image planning. The orbit prediction data from PSS and MAPS has a certain discrepancy due to different orbit propagator. It is necessary for mission planner to confirm this value during mission planning phase. The pointing error which means the difference between target center and real image received is calculated and analyzed using KOMPSAT-2 image data.

• Journal of the Korean Society of Safety
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• v.24 no.3
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• pp.88-95
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• 2009

Most of subway stations are located underground and the number of passengers is far more than that of designed value, therefore the risk of accident is growing bigger and serious damage is expected in case of disaster. In Korea the period of evacuation study is short and numerical and experimental data of evacuation phenomena in subway station is rare. Many egress evaluation depend on foreign commercial S/Ws which are not yet proven its availability in special case such as subway station. In this paper outflow coefficients which are essential in egress evaluation are calculated at train door, stairway and turnstile at 3 most crowed subway stations. This numerical data can be used in prediction of egress evaluation and the result of other prediction methods can be verified with these experimental data.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.257-268
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• 2022

When there is a missing value in the raw data, if ignore the missing values and proceed with the analysis, the accuracy decrease due to the decrease in the number of sample. The method of imputation and analyzing patterns and significant values can compensate for the problem of lower analysis quality and analysis accuracy as a result of bias rather than simply removing missing values. In this study, we proposed to study irregular data patterns and missing processing methods of data using machine learning techniques for the study of correction of missing values. we would like to propose a plan to replace the missing with data from a similar past point in time by finding the situation at the time when the missing data occurred. Unlike previous studies, data correction techniques present new algorithms using DNN and KNN-MLE techniques. As a result of the performance evaluation, the ANAE measurement value compared to the existing missing section correction algorithm confirmed a performance improvement of about 0.041 to 0.321.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1608-1614
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• 2014

Power demand forecasting is an important factor of the peak management. This paper deals with the 15 minutes ahead load forecasting problem in a DC urban railway system. Since supplied power lines to trains are connected with parallel, the load characteristics are too complex and highly non-linear. The main idea of the proposed method for the 15 minutes ahead prediction is to use the daily load similarity accounting for the load nonlinearity. An Euclidean norm with weighted factors including loads of the neighbor substation is used for the similar load selection. The prediction value is determinated by the sum of the similar load and the correction value. The correction has applied the neural network model. The feasibility of the proposed method is exemplified through some simulations applied to the actual load data of Incheon subway system.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.605-618
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• 2018

Risk management has been a crucial part of the daily operations of the financial industry over the past two decades. Value at Risk (VaR), a quantitative measure introduced by JP Morgan in 1995, is the most popular and simplest quantitative measure of risk. VaR has been widely applied to the risk evaluation over all types of financial activities, including portfolio management and asset allocation. This paper uses the implementations of multivariate GARCH models and copula methods to illustrate the performance of a one-day-ahead VaR prediction modeling process for high-dimensional portfolios. Many factors, such as the interaction among included assets, are included in the modeling process. Additionally, empirical data analyses and backtesting results are demonstrated through a rolling analysis, which help capture the instability of parameter estimates. We find that our way of modeling is relatively robust and flexible.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.703-710
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• 2018

Although various efforts have been made every year to reduce electric fire accidents such as accident analysis and inspection for electric fire accidents, there is no effective countermeasure due to lack of effective decision support system and existing cumulative data utilization method. The purpose of this study is to develop an algorithm for predicting electric fire based on data such as electric safety inspection data, electric fire accident information, building information, and weather information. Through the pre-processing of collected data for each institution such as Korea Electrical Safety Corporation, Meteorological Administration, Ministry of Land, Infrastructure, and Transport, Fire Defense Headquarters, convergence, analysis, modeling, and verification process, we derive the factors influencing electric fire and develop prediction models. The results showed insulation resistance value, humidity, wind speed, building deterioration(aging), floor space ratio, building coverage ratio and building use. The accuracy of prediction model using random forest algorithm was 74.7%.