• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.34 no.3
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• pp.279-291
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• 2024

Objective: This was to develop headforms for the labor population, based on a three-dimensional(3D) face dimensions data base(DB) and a principal component analysis(PCA) fit test panel, in selection, use and maintenance of respirators in Korea. Methods: This study was part of a two-year-project initiated in 2021. The study was designed and conducted in line with ISO 16976-2 while subjects were those employed in the development of the PCA fit test panel. The approaches included literature review; examination on conformity of the 3D face dimensions DB; and development of headforms representing the labor population. The mean data were used in order to construct each model of the headforms through a way of 3D modeling and 3D printing technology. Results: A total of 2,752 subjects were determined. Five models of headforms(small, medium, large, long-narrow, short-wide) were completely constructed for the labor population. For example, means of the 10 face dimensions for medium headform model were: minimum frontal breadth 106 mm, face width 136 mm, jaw width 127 mm, face length 111 mm, interpupillary distance 69 mm, head breadth 164 mm, nose protrusion 12 mm, nose breadth 34 mm, nasal root breadth 35 mm, and nose length 50 mm. Conclusions: Five models of headforms were newly constructed using the study data. It is likely desirable that the constructed headforms, together with the 3D face dimensions DB as well as the PCA fit test panel, can be utilized more effectively in selection, use and maintenance of respirators for users including the labor population.

• Analytical Science and Technology
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• v.30 no.5
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• pp.234-239
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• 2017

Identifying the components of residues that are not completely burned at the sites of fires site can provide valuable information for tracing the causes of fires. In order to clarify the types of plastic combustion residues found at the scenes of fires, we studied the residue formed after the combustion of polyethylene (PE) and acrylonitrile butadiene styrene (ABS). Plastic samples were burned at 200, 300, 350, 400, and $500^{\circ}C$ for 3 min using a cone calorimeter, and the changes in weight and combustion products were observed. The powder products obtained by lyophilization and pulverization of the combustion products obtained at each temperature were analyzed by a Fourier transform-near infrared (FT-NIR) spectrometer. When the PE samples were burned, the weight did not change up to $350^{\circ}C$, however a significant change in the weight could be measured above $400^{\circ}C$. The principal component analysis (PCA) of the FT-NIR spectra of the PE and ABS samples obtained at each temperature confirmed that the combustion residues at each temperature were PE and ABS, respectively. Therefore, the types of unburned plastics found at the sites of fires can be confirmed rapidly by near infrared spectroscopy.

• 한국체육학회지인문사회과학편
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• v.57 no.4
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• pp.387-394
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• 2018

The purpose of this study is to provide information regarding the strategies by identifying the main variables that determines the winning team based on the records of all games of the 2017 IIHF World Championship Top league. 64 matches were analyzed for the study. 6 variables were analyzed which included ratio of saves, shots on goal, penalties in minutes, time for power play, power play goals, and face off wins. Logistic regression analysis (LRA), multiple regression analysis (MRA), and principal component analysis (PCA) were implemented to examine the relationship between win and loss. In case of LRA, shots on goal (p<.001), face-off wins (p<.001) had significantly positive relation to winning of game whereas, penalties in minutes (p<.01) and time on power play (p<.01) had significantly negative. Using MRA, win percentage was calculated which had significant positive correlation to ratio of saves (p<.01) and face-off wins (p<.001) whereas, a significant negative with penalties in minutes (p<.001). For PCA, the winning team consisted of penalty, attack, and defense factors whereas, losing teams consisted only the attack and defense factors.

• Journal of the Korea Computer Graphics Society
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• v.24 no.2
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• pp.11-20
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• 2018

We propose a new method particle-based method that explicitly preserves thin liquid sheets for animating liquids on CPU-GPU heterogeneous computing framework. Our primary contribution is a particle-based framework that splits at thin points and collapses at dense points to prevent the breakup of liquid on GPU. In contrast to existing surface tracking methods, the our method does not suffer from numerical diffusion or tangles, and robustly handles topology changes on CPU-GPU framework. The thin features are detected by examining stretches of distributions of neighboring particles by performing PCA(Principle component analysis), which is used to reconstruct thin surfaces with anisotropic kernels. The efficiency of the candidate position extraction process to calculate the position of the fluid particle was rapidly improved based on the CPU-GPU heterogeneous computing techniques. Proposed algorithm is intuitively implemented, easy to parallelize and capable of producing quickly detailed thin liquid animations.

• The Korean Journal of Applied Statistics
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• v.28 no.1
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• pp.103-114
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• 2015

The role of small and medium enterprises as an economic growth factor has been accentuated; consequently, the need to develop a business forecast model and indexes that accurately examine business situation of small and medium enterprises has increased. Most current business model and indexes concerning small and medium enterprises, released by public and private institutions, are based on Business Survey Index (BSI) and depend on subjective (business model and) indexes; therefore, the business model and indexes lack a capacity to grasp an accurate business situation of these enterprises. The business forecast model and indexes suggested in the study have been newly developed with Principal Component Analysis(PCA) and weight method to accurately measure a business situation based on reference dates addressed by the National Statistical Office(NSO). Empirical studies will be presented to prove that the newly proposed business model and indexes have their basis in statistical theory and their trend that resembles the existing Composite Index.

• Korean Journal of Optics and Photonics
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• v.19 no.4
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• pp.270-275
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• 2008

This paper addresses occluded object reconstruction and recognition with computational integral imaging (II). Integral imaging acquires and reconstructs target information in the three-dimensional (3D) space. The reconstruction is performed by averaging the intensities of the corresponding pixels. The distance to the object is estimated by minimizing the sum of the standard deviation of the pixels. We adopt principal component analysis (PCA) to classify occluded objects in the reconstruction space. The Euclidean distance is employed as a metric for decision making. Experimental and simulation results show that occluded targets are successfully classified by the proposed method.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.117-124
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• 2014

Cluttered background is a major obstacle in developing salient object detection and tracking system for mobile device captured natural scene video frames. In this paper we propose a context aware feature vector selection model to provide an efficient noise filtering by machine learning based classifiers. Since the context awareness for feature selection is achieved by searching nearest neighborhoods, known as NP hard problem, we apply a fast approximation method with complexity analysis in details. Separability enhancement in feature vector space by adding the context aware feature subsets is studied rigorously using principal component analysis (PCA). Overall performance enhancement is quantified by the statistical measures in terms of the various machine learning models including MLP, SVM, Naïve Bayesian, CART. Summary of computational costs and performance enhancement is also presented.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.719-725
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• 2011

To apply TMAH-based Py-MS to a field biological detection system for real-time classification of cell-type, reproducible patterns of the TMAH-based Py-MS spectra was known as a critical factor for classification but was seriously disturbed by quantity of cells injected into pyro-tube. This factor is an exterior variable that could not be complemented by improving the performance of the TMAH-based Py-MS instrument. One of idea to solve the knotty problem has been flashed from "Top-down proteomics for identification of intact microoganisms". That is, biomarker peaks are selected from complicate Py-MS spectra for intact microoganisms by tracing out their origins, based on Py-MS spectra for the featured components of different cell-types, in Top-down approach. This idea has been tested in classification of different Gram-type microoganisms. Through the analyses of spectra for the featured components - peptidoglycan and lipoteichoic acid for Gram-positive cells and lipopolysaccharide and lipid A for Gram-negative cells - with comparing to the spectra the corresponding Gram-type cells in the Top-down approach, biomarker peaks were selected to carry out PCA(Principal Component Analysis) in order to see classification of different Gram-types, resulting in significant improvement of their classification. Furthermore, weighting biomarker peaks on intact cell's spectra, based on the data for the featured components of the Gram-types, contributed to elevate classification performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1131-1140
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• 2017

This study is concerned with a design methodology of optimized fuzzy clustering-based neural network classifier for classifying black plastic. Since the amount of waste plastic is increased every year, the technique for recycling waste plastic is getting more attention. The proposed classifier is on a basis of architecture of radial basis function neural network. The hidden layer of the proposed classifier is composed to FCM clustering instead of activation functions, while connection weights are formed as the linear functions and their coefficients are estimated by the local least squares estimator (LLSE)-based learning. Because the raw dataset collected from Raman spectroscopy include high-dimensional variables over about three thousands, principal component analysis(PCA) is applied for the dimensional reduction. In addition, artificial bee colony(ABC), which is one of the evolutionary algorithm, is used in order to identify the architecture and parameters of the proposed network. In experiment, the proposed classifier sorts the three kinds of plastics which is the most largely discharged in the real world. The effectiveness of the proposed classifier is proved through a comparison of performance between dataset obtained from chemical analysis and entire dataset extracted directly from Raman spectroscopy.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.957-968
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• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.