• Communications for Statistical Applications and Methods
• /
• v.28 no.5
• /
• pp.521-536
• /
• 2021

As life expectancies increase continuously over the world, the accuracy of forecasting mortality is more and more important to maintain social systems in the aging era. Currently, the most popular model used is the Lee-Carter model but various studies have been conducted to improve this model with one of them being 6-parametric factor model (6-PFM) which is introduced in this paper. To this new model, long short-term memory (LSTM) and regularized LSTM are applied in addition to vector autoregression (VAR), which is a traditional time-series method. Forecasting accuracies of several models, including the LC model, 4-PFM, 5-PFM, and 3 6-PFM's, are compared by using the U.S. and Korea life-tables. The results show that 6-PFM forecasts better than the other models (LC model, 4-PFM, and 5-PFM). Among the three 6-PFMs studied, regularized LSTM performs better than the other two methods for most of the tests.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.109.2-109
• /
• 2001

In the PCB assembly lines, as components become more complex and smaller, the conventional inspection method using traditional ICT and function test show their limitations in application. The automatic optical inspection(AOI) gradually becomes the alternative in the PCB assembly line. In Particular, the PCB inspection machines need more reliable and flexible object recognition algorithms for high inspection accuracy. The conventional AOI machines use the algorithmic approaches such as template matching, Fourier analysis, edge analysis, geometric feature recognition or optical character recognition (OCR), which mostly require much of teaching time and expertise of human operators. To solve this problem, in this paper, a statistical learning based part recognition method is proposed. The performance of the ...

• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.11a
• /
• pp.347-350
• /
• 2011

This paper propose a statistical color model of background extraction base on Hue-Saturation-Value(HSV) color space, instead of the traditional RGB space, and shows that it provides a better use of the color information. HSV color space corresponds closely to the human perception of color and it has revealed more accuracy to distinguish shadows [3] [4]. The key feature of this segmentation method is based on processing hue component of color in HSV color space on image area. The HSV color model is used, its color components are efficiently analyzed and treated separately so that the proposed algorithm can adapt to different environmental illumination condition and shadows. Polar and linear statistical operations are used to calculate the background from the video frames. The experimental results show that the proposed background subtraction method can automatically segment video objects robustly and accurately in various illuminating and shadow environments.

• Communications for Statistical Applications and Methods
• /
• v.30 no.3
• /
• pp.273-289
• /
• 2023

In this paper, we develop a new time series model for predicting IPO (initial public offering) data with non-negative integer value. The proposed model is based on integer-valued autoregressive (INAR) model with a Poisson thinning operator. Just as the heterogeneous autoregressive (HAR) model with daily, weekly and monthly averages in a form of cascade, the integer-valued heterogeneous autoregressive (INHAR) model is considered to reflect efficiently the long memory. The parameters of the INHAR model are estimated using the conditional least squares estimate and Yule-Walker estimate. Through simulations, bias and standard error are calculated to compare the performance of the estimates. Effects of model fitting to the Korea's IPO are evaluated using performance measures such as mean square error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) etc. The results show that INHAR model provides better performance than traditional INAR model. The empirical analysis of the Korea's IPO indicates that our proposed model is efficient in forecasting monthly IPO volumes.

• Communications for Statistical Applications and Methods
• /
• v.31 no.3
• /
• pp.279-289
• /
• 2024

Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is difficult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying meta-classifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques.

• Journal of Microbiology and Biotechnology
• /
• v.25 no.2
• /
• pp.217-226
• /
• 2015

Metabolite profiles of seven commercial vinegars and two traditional vinegars were performed by gas chromatography time-of-flight mass spectrometry with multivariate statistical analysis. During alcohol fermentation, yeast, nuruk, and koji were used as sugars for nutrients and as fermentation substrates. Commercial and traditional vinegars were significantly separated in the principal component analysis and orthogonal partial least square discriminant analysis. Six sugars and sugar alcohols, three organic acids, and two other components were selected as different metabolites. Target analysis by ultra-performance liquid chromatography quadruple-time-of-flight mass spectrometry and liquid chromatography-ion trap-mass spectrometry/mass spectrometry were used to detect several metabolites having antioxidant activity, such as cyanidin-3-xylosylrutinoside, cyanidin-3-rutinoside, and quercetin, which were mainly detected in Rural Korean Black raspberry vinegar (RKB). These metabolites contributed to the highest antioxidant activity measured in RKB among the nine vinegars. This study revealed that MS-based metabolite profiling was useful in helping to understand the metabolite differences between commercial and traditional vinegars and to evaluate the association between active compounds of vinegar and antioxidant activity.

• Journal of Society of Preventive Korean Medicine
• /
• v.13 no.2
• /
• pp.129-146
• /
• 2009

Background and Objectives: The purpose of the study was to explore the attitude of workers toward a system of collaborative hospital practice between western and traditional Korean medicine, to identify factors influencing this attitude, and discuss the reasons socioeconomic groups' differences. Method: The data were collected with a questionnaire for this study from 14 April 2009 to 1 May 2009. Data were analyzed mainly via non-parametric statistics and logistic regressions utilising SPSS 17.0 (Statistical Package for the Social Sciences) to determine the workers' attitude about the hospital system and to predict factors contributing to positive attitudes. Results: A total of 1,260 workers working for large factories in Gwangju Metropolitan City. Findings confirmed that more than 40% of the workers show interest in the system and about 44% of the workers also express positive attitudes. Factors found to influence the workers' response included marital status, income level, health status, experience in complementary medicine services, the number of health care facilities' visit. Conclusions: The prospects to establish the system of collaborative hospital practice as reflected by the workers' view about the Korean health care service delivery system. Their attitudes toward the system differed among socioeconomic groups. Government and health care providers should identify the socioeconomic subgroups' demands and opinions in order to find and develop measures of integrating western and traditional Korean medicine in health care facilities.

• International Journal of Highway Engineering
• /
• v.15 no.3
• /
• pp.93-101
• /
• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• Korean Institute of Interior Design Journal
• /
• v.24 no.2
• /
• pp.142-150
• /
• 2015

The purpose of this study is to analysis the change of rural house type and house plans in Bonghwa province. According to definition of rural area, the scopes of the research of rural houses limited the Bonghwa rural area(1 eup, 9 myeon). The method of study is to compare and analyze about housing situation, structure of house, housing type and construction of house space etc. through the statistical data of Bongwha statistical yearbook, space syntax(convex analysis) and other various data etc. during these 10 years. As a results of the analysis 1) According to Change of family member the supply ratio of detached house is steadily decreasing and changing from a detached house to multi-household house in Bongwha areas. 2) Most of houses structure were using lightweight steel construction because of cost-cutting of construction and easy way to construct etc.. 3) The highest Integration space is living space in rural house plans 4) The highest segregation space is bathroom space of master bed room in rural house plans. Some of bed rooms are classed as segregation space regardless of Integration space 5) Traditional front yard's function is changing from the place with the various functions to the place with the specific functions.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.27 no.3
• /
• pp.406-415
• /
• 2024

As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.