• Korean System Dynamics Review
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• v.14 no.3
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• pp.105-134
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• 2013

Electrical construction business has public and professional characters. It may require appropriate interventions of the government because these business activities stand for not only profit-seeking competition, but also supplies of one of the key functions in our society. In other words, public benefit and private benefit are still in existence. The government therefore considers such an aspect of public importance of the business sector and needs to plan to adjust technical and engineering manpower of this market. This study focuses on the imbalance for labor supply and demand of technical engineer in electrical construction business. A system dynamics analysis is applied to understand and simulate the imbalance as a soft approach. It has the merit of causal loop diagram to alleviate the limitation of data lack problem. We find that excess demand is expected from 2010 to 2011, and excess supply is predicted from 2012 to 2021 about the manpower of technical engineer. It shows considerable disagreement between the supply and demand of human resource. So we suggest that it is strong necessity to construct statistics infrastructure for a manpower supply and demand plan.

• Safety and Health at Work
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• v.13 no.4
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• pp.469-474
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• 2022

Background: Nowadays, counter-productive work behaviors (CWBs) have turned into a common and costly position for many organizations and especially health centers. Therefore, the study was carried out to examine and compare the demand-control-support (DCS) and effort-reward imbalance (ERI) models as predictors of CWBs. Methods: The study was cross-sectional. The population was all nurses working in public hospitals in Hamadan, Iran of whom 320 were selected as the sample based on simple random sampling method. The instruments used were Job Content Questionnaire, Effort-Reward Imbalance Questionnaire, and Counterproductivity Work Behavior Questionnaire. Data were analyzed using correlation and regression analysis in SPSS18. Results: The findings indicated that both ERI and DCS models could predict CWB (p ≤ 0.05); however, the DCS model variables can explain the variance of CWB-I and CWB-O approximately 8% more than the ERI model variables and have more power in predicting these behaviors in the nursing community. Conclusion: According to the results, job stress is a key factor in the incidence of CWBs among nurses. Considering the importance and impact of each component of ERI and DCS models in the occurrence of CWBs, corrective actions can be taken to reduce their incidence in nurses.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1310-1319
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• 2013

Resolvers are transducers that are used to sense the angular position of rotational machines. The analog resolver is necessary to use resolver to digital converter. Among the RDC software method, angle tracking observer (ATO) is the most popular method. In an actual resolver-based position sensing system, amplitude imbalance dominantly distorts the estimate position information of ATO. Minority papers have reported position error compensation of resolver's output signal with amplitude imbalance. This paper proposes new ATO algorithm in order to compensate position errors caused by the amplitude imbalance. There is no need premeasured off line data. This is easy, simple, cost-effective, and able to work on line compensation. To verify feasibility of the proposed algorithm, simulation and experiments are carried out.

• Journal of the Korean Society of Safety
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• v.37 no.5
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• pp.62-73
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• 2022

This study investigates the working conditions of shift workers according to age group by analyzing the sixth Korean Working Conditions Survey's data. A total of 1,323 shift workers were extracted from the dataset. Three age groups (A: 20s-30s, B: 40s-50s, C: 60s and above) were statistically compared in terms of health problems, emotional hazard exposure, work-life imbalance, and satisfaction with working conditions. Elderly shift workers (those in their 60s and above) had significantly more severe health problems and work-life imbalance, greater exposure to emotional hazards, and lower satisfaction with working conditions than young shift workers (those in their 20s-50s). The study's findings reveal the characteristics of working conditions for elderly shift workers and would be useful for improving shift workers' quality of life, as well as safety and productivity in the workplace.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.177-183
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• 2024

The study investigates the impact of data quality on the performance of artificial intelligence (AI). To this end, the impact of labeling error levels on the performance of artificial intelligence was compared and analyzed through simulation, taking into account the similarity of data features and the imbalance of class composition. As a result, data with high similarity between characteristic variables were found to be more sensitive to labeling accuracy than data with low similarity between characteristic variables. It was observed that artificial intelligence accuracy tended to decrease rapidly as class imbalance increased. This will serve as the fundamental data for evaluating the quality criteria and conducting related research on artificial intelligence learning data.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.808-813
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• 2022

This study presents the problems of data imbalance, varying difficulties across target objects, and small objects in construction object segmentation for far-field monitoring and utilize compound loss functions to address it. Construction site scenes of assembling scaffolds were analyzed to test the effectiveness of compound loss functions for five construction object classes---workers, hardhats, harnesses, straps, hooks. The challenging problem was mitigated by employing a focal and Jaccard loss terms in the original loss function of LinkNet segmentation model. The findings indicates the importance of the loss function design for model performance on construction site scenes for far-field monitoring.

• Journal of Chest Surgery
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• v.49 no.sup1
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• pp.14-19
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• 2016

Background: This study uses the relevance index to understand the condition of regional medical service use for cardiovascular surgery and to identify the medical service use imbalance between regions. Methods: This study calculated the relevance index of 16 metropolitan cities and provinces using resident registration address data from the Ministry of Government Administration and Home Affairs and the 2010-2014 health insurance, medical care assistance, and medical benefits claims data from the Health Insurance Review and Assessment Service. We identified developments over the 5-year time period and analyzed the level of regional imbalance regarding cardiovascular surgery through the relative comparison of relevance indexes between cardiovascular and other types of surgery. Results: The relevance index was high in large cities such as Seoul, Daegu, and Gwangju, but low in regions that were geographically far from the capital area, such as the Gangwon and Jeju areas. Relevance indexes also fell as the years passed. Cardiovascular surgery has a relatively low relevance index compared to key types of surgery of other fields, such as neurosurgery and colorectal surgery. Conclusion: This study identified medical service use imbalance between regions for cardiovascular surgery. Results of this study demonstrate the need for political intervention to enhance the accessibility of necessary special treatment, such as cardiovascular surgery.

• Smart Media Journal
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• v.8 no.2
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• pp.46-57
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• 2019

In recent research, deep learning-based methods have achieved state-of-the-art performance in various computer vision tasks. However, these methods are commonly supervised, and require huge amounts of annotated data to train. Acquisition of data demands an additional costly effort, particularly for the tasks where it becomes challenging to obtain large amounts of data considering the time constraints and the requirement of professional human diligence. In this paper, we present a data level synthetic sampling solution to learn from small and imbalanced data sets using Generative Adversarial Networks (GANs). The reason for using GANs are the challenges posed in various fields to manage with the small datasets and fluctuating amounts of samples per class. As a result, we present an approach that can improve learning with respect to data distributions, reducing the partiality introduced by class imbalance and hence shifting the classification decision boundary towards more accurate results. Our novel method is demonstrated on a small dataset of 2789 tomato plant disease images, highly corrupted with class imbalance in 9 disease categories. Moreover, we evaluate our results in terms of different metrics and compare the quality of these results for distinct classes.

• Safety and Health at Work
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• v.10 no.2
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• pp.205-212
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• 2019

Background: Workforce health is one of the primary and most challenging issues, particularly in industrialized countries. This article aims at modeling the major factors affecting accidents in the workplace, including general health, work-family conflict, effort-reward imbalance, and internal and external locus of control. Methods: A cross-sectional study was conducted in Esfahan Steel Company in Iran. A total of 450 participants were divided into two groups-control and case-and the questionnaires were distributed among them. Data were collected through a 7-part questionnaire. Finally, the results were analyzed using SPSS 22.0 and Amos software. Results: All the studied variables had a significant relationship with the accident proneness. In the case group, general health with a coefficient of -0.37, worke-family conflict with 0.10, effort-reward imbalance with 0.10, internal locus of control with -0.07, and external locus of control with 0.40 had a direct effect on occupational stress. Occupational stress also had a positive direct effect on accident proneness with a coefficient of 0.47. In addition, fitness indices of control group showed general health (-0.35), worke-family conflict (0.36), effort-reward imbalance (0.13), internal locus of control (-0.15), and external locus of control (0.12) have a direct effect on occupational stress. Besides, occupational stress with a coefficient of 0.09 had a direct effect on accident proneness. Conclusion: It can be concluded that although previous studies and the present study showed the effect of stress on accident and accident proneness, some hidden and external factors such as work-family conflict, effort-reward imbalance, and external locus of control that affect stress should also be considered. It helps industries face less occupational stress and, consequently, less occurrence rates of accidents.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.525-535
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• 2018

If the frequency of a particular class is excessively higher than the frequency of other classes in the classification problem, data imbalance problems occur, which make machine learning distorted. Corporate bankruptcy prediction often suffers from data imbalance problems since the ratio of insolvent companies is generally very low, whereas the ratio of solvent companies is very high. To mitigate these problems, it is required to apply a proper sampling technique. Until now, oversampling techniques which adjust the class distribution of a data set by sampling minor class with replacement have popularly been used. However, they are a risk of overfitting. Under this background, this study proposes ROSE(Random Over Sampling Examples) technique which is proposed by Menardi and Torelli in 2014 for the effective corporate bankruptcy prediction. The ROSE technique creates new learning samples by synthesizing the samples for learning, so it leads to better prediction accuracy of the classifiers while avoiding the risk of overfitting. Specifically, our study proposes to combine the ROSE method with SVM(support vector machine), which is known as the best binary classifier. We applied the proposed method to a real-world bankruptcy prediction case of a Korean major bank, and compared its performance with other sampling techniques. Experimental results showed that ROSE contributed to the improvement of the prediction accuracy of SVM in bankruptcy prediction compared to other techniques, with statistical significance. These results shed a light on the fact that ROSE can be a good alternative for resolving data imbalance problems of the prediction problems in social science area other than bankruptcy prediction.