• Journal of Environmental Health Sciences
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• v.28 no.1
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• pp.41-49
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• 2002

Recently, pilot studies showed an evidence of reduction of airborne hexavalent chromium, Cr(VI), on PVC filter during air sampling and storage. However, the information on this in the field was limited. Thus, we studied the reduction behaviors of airborne Cr(VI) on PVC filters during sampling and storage at three field electroplating operations. Regression between sampling time and the reduction (ratio of Cr(VI) to total Cr concentrations) was not statistically significant (p>0.05). However, the reductions in samples collected for 240 ～ 340 minutes were significantly higher than those for 30 - 60 minutes. On the other hand, another experiment showed a good correlation (r=0.96) between sampling time and the reduction without an exceptional value. Storage temperature was not a factor affecting the reduction of Cr(VI) collected on PVC filter. The loss of Cr(VI) samples stored in alkali solution (2% NaOH/3% Na$_2$CO$_3$) was significantly lower than that stored in vial according to NIOSH method (p<0.05). Thus, dipping Cr(VI) samples into alkali solution was a storage method to minimize tile reduction.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.4
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• pp.404-414
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• 2014

Yield prediction is important to manage semiconductor quality. Many researches with machine learning algorithms such as SVM (support vector machine) are conducted to predict yield precisely. However, yield prediction using SVM is hard because extremely imbalanced and big data are generated by final test procedure in semiconductor manufacturing process. Using SVM algorithm with imbalanced data sometimes cause unnecessary support vectors from major class because of unselected support vectors from minor class. So, decision boundary at target class can be overwhelmed by effect of observations in major class. For this reason, we propose a under-sampling method with minor class based SVM (MCSVM) which overcomes the limitations of ordinary SVM algorithm. MCSVM constructs the model that fixes some of data from minor class as support vectors, and they can be good samples representing the nature of target class. Several experimental studies with using the data sets from UCI and real manufacturing process represent that our proposed method performs better than existing sampling methods.

• Korean Journal of Environmental Biology
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• v.41 no.2
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• pp.145-166
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• 2023

Parrots have been threatened by global trade to meet their high demand as pets. Controlling parrot trade is essential because parrots play a vital role in the ecosystem. Accurate species identification is crucial for controlling parrot trade. Parrots have been traded as eggs due to their advantages of lower mortality rates and more accessible transport than live parrots. A molecular method is required to identify parrot eggs because it is difficult to perform identification using morphological features. In this study, DNAs were obtained from 43 unidentified parrot eggs using a non-destructive sampling method. Partial cytochrome b (CYTB) gene was then successfully amplified using polymerase chain reaction (PCR) and sequenced. Sequences newly obtained in the present study were compared to those available in the GenBank by database searching. In addition, phylogenetic analysis was conducted to identify species using available sequences in GenBank along with sequences reported in previous studies. Finally, the 43 parrot eggs were successfully identified as seven species belonging to two families and seven genera. This non-destructive sampling method for obtaining DNA and molecular identification might help control the trade of parrot eggs and prevent their illegal trade.

• Nuclear Engineering and Technology
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• v.41 no.3
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• pp.247-270
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• 2009

This paper reviews studies on information searching behavior in process control systems and discusses some implications learned from previous studies for use in human factors studies on nuclear power plants (NPPs) main control rooms (MCRs). Information searching behavior in NPPs depends on expectancy, value, salience, and effort. The first quantitative scanning model developed by Senders for instrument panel monitoring considered bandwidth (change rate) of instruments as a determining factor in scanning behavior. Senders' model was subsequently elaborated by other researchers to account for value in addition to bandwidth. There is also another type of model based on the operator's situation awareness (SA) which has been developed for NPP application. In these SA-based models, situation-event relations or rules on system dynamics are considered the most significant factor forming expectancy. From the review of previous studies it is recommended that, for NPP application, (1) a set of symptomatic information sources including both changed and unchanged symptoms should be considered along with bandwidth as determining factors governing information searching (or visual sampling) behavior; (2) both data-driven monitoring and knowledge-driven monitoring should be considered and balanced in a systematic way; (3) sound models describing mechanisms of cognitive activities during information searching tasks should be developed so as to bridge studies on information searching behavior and design improvement in HMI; (4) the attention-situation awareness (A-SA) modeling approach should be recognized as a promising approach to be examined further; and (5) information displays should be expected to have totally different characteristics in advanced control rooms. Hence much attention should be devoted to information searching behavior including human-machine interface (HMI) design and human cognitive processes.

• Korean Journal of Health Education and Promotion
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• v.21 no.4
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• pp.15-33
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• 2004

With growing significance of psychological well-being in the worksite, the purpose of this analysis was to overview the empirical studies on worksite stress management and to identity the overall effect of worksite health promotion programs on stress management through meta-analysis. Literature retrieval was conducted on-line first in MEDLINE, EBSCOhost Academic Search Premier, and PSYCHINFO databases in public health, psychology, sociology, and human resource management areas. All studies written in English and published in the peer-reviewed journals during 1990 and 2002 were recruited. Key words used in literature retrieval were 'worksite,' 'intervention,' 'program,' 'work stress,' 'strain,' 'burnout,' 'management,' 'prevention,' 'education,' and 'health promotion.' A total of 18 worksite intervention studies with 48 effect sizes were analyzed and the results were as follows. Approximately 60％ of the studies had quasi-experimental design and were conducted in manufacturing company and public sector. General psychological strains and burnout were frequently used measures of psychological stress. The lecturing and discussion typed intervention and the participatory problem-solving typed intervention were employed more than others in the studies. The average effect (r: pearson's simple correlation coefficient) weighted by sampling error was -0.14 (-0.32 to 0.05). In the conventional category of effects this is a small effect ranging from -0.59 to 0.05. Binomial effect size showed that success rates increased from 43％ without intervention to 57％ after an intervention. Sampling error explained 47.14％ of the observed variance and its effectiveness on stress management were heterogeneous. In regression analysis with suspected moderating factors affecting the worksite interventions, research design was the only significant moderating factor. The studies with quasi-experimental design had greater effects than the studies with experimental design.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.241-254
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• 2017

Drawing a sample can be costly or time consuming in some studies. However, it may be possible to rank the sampling units according to some baseline auxiliary covariates, which are easily obtainable, and/or cost efficient. Ranked set sampling (RSS) is a method to achieve this goal. In this paper, we propose a modified approach of the RSS method to allocate units into an experimental study that compares L groups. Computer simulation estimates the empirical nominal values and the empirical power values for the test procedure of comparing L different groups using modified RSS based on the regression approach in analysis of covariance (ANCOVA) models. A comparison to simple random sampling (SRS) is made to demonstrate efficiency. The results indicate that the required sample sizes for a given precision are smaller under RSS than under SRS. The modified RSS protocol was applied to an experimental study. The experimental study was designed to obtain a better understanding of the pathways by which positive experiences (i.e., goal completion) contribute to higher levels of happiness, well-being, and life satisfaction. The use of the RSS method resulted in a cost reduction associated with smaller sample size without losing the precision of the analysis.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.139-147
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• 2022

This study tried to analyze the relationship among physical activity level, subjective health status, COVID-19 Fear. This study used the 2020 Community Health Survey that includes 229,269 survey data from adults over 19 years old. The complex sampling design was applied including weight, stratification, cluster variables. Through the SPSS statistics program with complex sampling frequency analysis, complex sampling Chi-square and complex sampling regression, this study found followings. First, the group with high level of physical activity showed higher level of subjective health status than the group with low level of physical activity. Second, the group with high level of physical activity showed lower level of COVID-19 fear than the group with low level of physical activity. Third, the group with high level of subjective health status showed lower level of COVID-19 fear than the group with low level of subjective health status. However, this study has the limitation that this study did not check whether participant is diagnosed with Covid-19 or not.

• Smart Media Journal
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• v.13 no.6
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• pp.24-34
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• 2024

If the future can be predicted from network traffic data, which is a time series, it can achieve effects such as efficient resource allocation, prevention of malicious attacks, and energy saving. Many models based on statistical and deep learning techniques have been proposed, and most of these studies have focused on improving model structures and learning algorithms. Another approach to improving the prediction performance of the model is to obtain a good-quality data. With the aim of obtaining a good-quality data, this paper applies a dense sampling technique that augments time series data to the application of network traffic prediction and analyzes the performance improvement. As a dataset, UNSW-NB15, which is widely used for network traffic analysis, is used. Performance is analyzed using RMSE, MAE, and MAPE. To increase the objectivity of performance measurement, experiment is performed independently 10 times and the performance of existing sparse sampling and dense sampling is compared as a box plot. As a result of comparing the performance by changing the window size and the horizon factor, dense sampling consistently showed a better performance.

• Journal of the Korean Geotechnical Society
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• v.16 no.1
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• pp.145-155
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• 2000

Orientation data of discontinuities are of paramount importance for rock slope stability studies because they control the possibility of unstable conditions or excessive deformation. Most orientation data are collected by using linear sampling techniques, such as borehole fracture mapping and the detailed scanline method (outcrop mapping). However, these data, acquired by the above linear sampling techniques, are subjected to bias, owing to the orientation of the sampling line. Even though a weighting factor is applied to orientation data in order to reduce this bias, the bias will not be significantly reduced when certain sampling orientations are involved. That is, if the linear sampling orientation nearly parallels the discontinuity orientation, most discontinuities orientation data which are parallel to sampling line will be excluded from the survey result. This phenomenon can cause serious misinterpretation of discontinuity orientation data because critical information is omitted. In the case study, orientation data collected by using the borehole fracture mapping method (vertical scanline) were compared to those based on orientation data from the detailed scanline method (horizontal scanline). Differences in results for the two procedures revealed a concern that a representative orientation of discontinuities was not accomplished. Equal-area, polar stereo nets were used to determine the distribution of dip angles and to compare the data distribution fur the borehole method versus those for the scanline method.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.349-374
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• 2019

A class imbalance problem arises when one class outnumbers the other class by a large proportion in binary data. Studies such as transforming the learning data have been conducted to solve this imbalance problem. In this study, we compared resampling methods among methods to deal with an imbalance in the classification problem. We sought to find a way to more effectively detect the minority class in the data. Through simulation, a total of 20 methods of over-sampling, under-sampling, and combined method of over- and under-sampling were compared. The logistic regression, support vector machine, and random forest models, which are commonly used in classification problems, were used as classifiers. The simulation results showed that the random under sampling (RUS) method had the highest sensitivity with an accuracy over 0.5. The next most sensitive method was an over-sampling adaptive synthetic sampling approach. This revealed that the RUS method was suitable for finding minority class values. The results of applying to some real data sets were similar to those of the simulation.