• Atmosphere
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• v.25 no.1
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• pp.19-30
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• 2015

The Ka-band cloud radar (KCR) has been operated by the National Institute of Meteorological Research (NIMR) of Korea Meteorological Administration (KMA) at Boseong National Center for Intensive Observation of severe weather since 2013. Evaluation of data quality is an essential process to further analyze cloud information. In this study, we estimate the measurement error and the sampling uncertainty to evaluate data quality. By using vertically pointing data, the statistical uncertainty is obtained by calculating the standard deviation of each radar parameter. The statistical uncertainties decrease as functions of sampling number. The statistical uncertainties of horizontal and vertical reflectivities are identical (0.28 dB). On the other hand, the statistical uncertainties of Doppler velocity (spectrum width) are 2.2 times (1.6 times) larger at the vertical channel. The reflectivity calibration of KCR is also performed using X-band vertically pointing radar (VertiX) and 2-dimensional video disdrometer (2DVD). Since the monitoring of calibration values is useful to evaluate radar condition, the variation of calibration is monitored for five rain events. The average of calibration bias is 10.77 dBZ and standard deviation is 3.69 dB. Finally, the statistical characteristics of cloud properties have been investigated during two months in autumn using calibrated reflectivity. The percentage of clouds is about 26% and 16% on September to October. However, further analyses are required to derive general characteristics of autumn cloud in Korea.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.618-628
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• 2015

The purpose of this study was to seek out the structural relation among Relationship Benefits, Customer Satisfactions, and Customer Preference of members to Personal Training. For the subject of this study, we selected 227 persons among men of more than 20 years old age who used Personal Training Center more than 1 month through the Convenient Sampling Method. For the analysis, we used SPSS 15.0 Statistics Package and AMOS 7.0 program as a research tool and have carried out Frequency Analysis, Confirmatory Factor Analysis, Reliability Analysis, and Structural Equation Model Analysis. The results were as follows: First, the Customization Benefits among Relational Benefits have a positive effect on the Customer Satisfaction. Second, the Psychological Benefits and Social Benefits among Relational Benefits have a positive effect on the Customer Preference. Third, the Customer Satisfaction has a positive effect on the Customer Preference.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2837-2845
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• 2009

This study certified that the mentality training that utilized ICT learning has been working as an important base having much effect on learner's basic attitude on physical education class, improvement of bodily exercise function, and class satisfaction, and that the exercise ability was improved in the scope of speed, form(posture), accuracy(shooting success rate), and adaptability(performance ability). It means it is a much more step-forwarded educational method that the advantages of ICT learning and mentality training at the existing learning method were applied to the reality. Regarding the object of this study, it is a little bit unreasonable to generalize its study results in that it wasn't intended for national unit sampling. Therefore, in the future study, it is necessary to continue to advance the study that its representative-ness was supplemented through the balanced sampling between area and area, and between grade and grade.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.44-54
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• 2019

Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.995-1004
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• 2020

This study aims to analyze the effect of transformational leadership (TL), procedural justice (PJ), and training (T) on employee performance (EP) mediated by self-efficacy (SE). The object of this research is Rumah Sakit Umum Daerah (RSUD) M.Th. Djaman, a hospital in Sanggau Regency, while the subjects are the institution's staff. Data collection search uses purposive sampling with a total of 120 samples. Data are obtained through questionnaires distributed directly to respondents using the Google Form application. Data analysis techniques used in this study include standard error of mean (SEM) with AMOS software version 24.00. Methods use to test validity and reliability of data include Confirmatory Factor Analysis (CFA), Construct Reliability (CR) and VE. The results of the analysis show that only training has a significant effect on self-efficacy, and self-efficacy has a significant effect on employee performance. Also, self-efficacy is proven to mediate the role of training on employee performance; the other hypotheses are not significant. Training is the most prominent positive factor affecting self-efficacy and self-efficacy has a significant effect on employee performance at RSUD M.Th. Djaman. The results of this study can be used as a reference by management in determining what policy priorities should take precedence.

• Journal of Life Science
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• v.14 no.4
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• pp.573-576
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• 2004

The purpose of the present study is to investigate the lipid peroxidation, creatine kinase activity and cortisol hormone levels following the training intensity in elite judo players. Six elite Judo players participated in the experiments (3h repetition judo program), which include stretching, judo skill practice and cool down without recess. Blood sampling were taken at the judo gymnasium at the time of resting, 1h training, 2h training, 3h training, 2h recovery, and 24h recovery time and this were analyzed for CK, MDA and Cortisol levels. The results obtained were analyzed via repeated measures of ANOVA using SPSS package program (ver.10.0) and a value of p<.05 was considered statistically significant. The results from this study were as follows. In the CK levels, which reflect the contribution of creatine phosphate and muscle damage degree, there was a significant difference (p<.05) after judo training in every period. Recovery 24h showed the highest level. In the MDA levels, which reflect lipid peroxidation, there was a significant difference (p<.05) after judo training. Recovery 2h showed the lowest level. In the cortisol hormone levels, which reflect stress status, there was a significant difference (p<.05). In this study, we can conclude that For the trained athletes, MDA level was lower at the time of exercise compare to the other period, this is caused by the increased antioxidant defence mechanism.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4028-4042
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• 2021

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm. First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance. Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples. Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model. The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems. Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.2
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• pp.255-260
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• 2022

SME sector's success also depends on its employees' job satisfaction as satisfied employees are likely to be more productive at the workplace and positively enhance SME business performance. Small and medium firms are the heart of the economy, and employees are the main and valuable asset for the SME firms. If SME business managers can increase employee satisfaction, then SMEs' performance will also increase in the future. Hence, the current study aims to determine the job satisfaction of SME employees by analyzing the impact of job training (JT) and promotion (PRO) opportunities on employee job satisfaction. Purposive sampling is applied in the study, and 202 SME employees have participated as sample respondents. The final sample size is n = 202. SPSS 26.0 version is used to analyze the hypotheses. The study findings show that both job training (JT) and promotion (PRO) have a positive effect on SME employee job satisfaction. It does indicate that SME managers need to provide necessary training programs and timely promotion to their current working employees to keep them satisfied with their job. Promotion and effective job training will certainly enhance employees' job satisfaction. The study has also offered a few strategic implications for SME business managers.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.171-177
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• 2011

Accuracy and computing time are considerable issues in machine learning. In general, the computing time for data analysis is increased in proportion to the size of given data. So, we need a sampling approach to reduce the size of training data. But, the accuracy of constructed model is decreased by going down the data size simultaneously. To solve this problem, we propose a new statistical sampling method having similar performance to the total data. We suggest a rule to select optimal sampling techniques according to given data structure. This paper shows a sampling method for reducing computing time with keeping the most of accuracy using cluster sampling, stratified sampling, and systematic sampling. We verify improved performance of proposed method by accuracy and computing time between sample data and total data using objective machine learning data sets.

• Journal of Korean Society of Forest Science
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• v.109 no.3
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• pp.291-299
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• 2020

The purpose of this study was to propose the most cost-effective sampling method, by analyzing the cost of forest resource investigation per sampling method for the planned harvesting area of in Chunyang-myeon, Byeonghwa-gun, Gyeongsangbuk-do, Korea. For this study, three sampling methods were selected: random sampling method, systematic sampling method, and line transect method. For each method, sample size, hourly wage, number of sample points, survey time, travel time, the sample error rate of the estimated average volume, and the desired sampling error rate were used to calculate the cost of forest resource inventories. Thus, 10 sampling points were extracted for each sampling method, and the factors required for cost analysis were calculated via a field survey. As a result, the field survey cost per ha using the random sampling method was found to be have the lowest cost, regardless of the desired sampling error rate, followed by the systematic sampling method, and the line transect method.