• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.1
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• pp.64-77
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• 2022

Objectives: The purpose of this study is to objectify the level of case management performance and the factors influencing performance, to improve the case management performance at the Korea Worker's Compensation & Welfare Service (KWCWS) on the basis of the recognition of the objective realities of case management by job coordinators at the KWCWS, to develop a model of case management fit for the KWCWS, and to provide a basis for establishing guidelines for standardized case management. Methods: A total of 156 questionnaires were distributed to job coordinators at the KWCWS's headquarters, six regional headquarters, and 55 branches. One hundred forty-one questionnaires were collected and 126 were analyzed statistically using SPSS 21.0. Factor analysis and reliability analysis were conducted to verify the validity and reliability of the main measurement items in the research model. Frequency analysis was conducted for general characteristics of survey subjects. Frequency analysis or descriptive statistics were conducted to identify the level of independent variables (case manager's individual variables, job variables, institutional and organizational variables). Dependent variables (case management performance) and the degree of correlation were analyzed through correlation analysis between research variables. Multiple regression analysis and hierarchical regression analysis were conducted to examine the effect of independent variables on case management performance. Results: The results of the study showed that the level of overall performance in the five stages of case management was ordinary, with an average level of 3.45 on a 5-point scale. Levels of performance by step were institutional approach and intake (3.69), assessment (3.63), goal setting and intervention planning (3.46), implementation of intervention plan (3.32), and evaluation and termination (3.20), in that order. The explanatory power of case management performance (overall) by case managers with the KWCWS was case manager's institutional and organizational variables, job variables, and individual variables, in that order. At each stage of case management, the explanatory power of a case manager's institutional and organizational variables was found to be the greatest. The model changes at each stage of case management assume similar aspects statistically. In hierarchical regression analysis, it was institutional support that had a significant effect on case management performance (overall), and institutional support had the greatest effect. The results of multiple regression analysis in which all variables are input simultaneously showed that institutional support and expertise as well as self-efficacy had a positive effect. However, case management work experience, expertise (technology), and autonomy were found to have a negative effect during the stage of case management performance. Conclusions: As a result of the study, it was confirmed that raising the case manager's expertise and support from the institution and organization are important factors to improve the level of case management performance. The research also derived practical ways of reinforcement of case manager capacity, institutional and organizational support, operation of rehabilitation-case management teams, and occupational health-related aspects.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.9-18
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• 2009

This paper introduces the application of DE (Differential Evolutionary) method for the estimation of tensile force of the externally prestressed tendon. The proposed technique, a SI (System Identification) method using the DE algorithm, can make global solution search possible as opposed to classical gradient-based optimization techniques. The numerical tests show that the proposed technique employing DE algorithm is a useful method which can detect the effective nominal diameters as well as estimate the exact tensile forces of the externally prestressed tendon with an estimation error less than 1% although there is no a priori information about the identification variables. In addition, the validity of the proposed technique is experimentally proved using a scale-down model test considering the serviceability state condition without and with the loss of the prestressed force. The test results prove that the technique is a feasible and effective method that can not only estimate the exact tensile forces and detect the effective nominal diameters but also inspect the damping properties of test model irrespective of the loss of the prestressed force. The 2% error of the estimated effective nominal diameter is due to the difference between the real tendon diameter with a wired section and the FE model diameter with a full-section. Finally, The accuracy and superiority of the proposed technique using the DE algorithm are verified through the comparative study with the existing theories.

• Journal of Korean Tunnelling and Underground Space Association
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• v.12 no.3
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• pp.215-222
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• 2010

An experimental study was carried out on a reduced scale model tunnel to investigate the efficiency of disaster prevention at underground and ground ventilation equipments for the fire in road tunnels. Based on Froude modeling, the 1/50 scaled model tunnel (20 m long) was manufactured. The vertical shafts that are used in the analysis of efficiency of disaster prevention are the two models that had considered when the real tunnels are designed and the amounts of smoke exhaust are applied the miniature of the real tunnels' smoke exhaust, 560 and $280\;m^3/s$. As the result of analysis, it is the possible the emissions of the entire quantity of CO gas through the vertical shafts. In the ground ventilation equipments, the concentration of CO is discharged 2.23~2,73 ppm smaller than the underground ventilation equipments. And the temperature rise in the ground ventilation equipments is $0.53{\sim}0.94^{\circ}C$ lower than in the underground ventilation equipments because of a cooling effect of the surface of the tunnel wall. As a result of analysis of CO concentration and the temperature rise in the modeling ventilation equipment, the position of ground ventilation equipment is more effective than the underground ventilation equipment in disaster prevention measures.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.77-84
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• 2024

In the fourth industrial-revolution era, the construction industry is transitioning from traditional methods to digital processes. This shift has been challenging owing to the industry's employment of diverse processes and extensive human resources, leading to a gradual adoption of digital technologies through trial and error. One critical area of focus is the safety management at construction sites, which is undergoing significant research and efforts towards digitization and automation. Despite these initiatives, recent statistics indicate a persistent occurrence of accidents and fatalities in construction sites. To address this issue, this study utilizes large-scale language-model artificial intelligence to analyze big data from a construction safety-management information network. The findings are integrated into on-site models, which incorporate real-time updates from detailed design models and are enriched with location information and spatial characteristics, for enhanced safety management. This research aims to develop a big-data-driven safety-management platform to bolster facility and worker safety by digitizing construction-site safety data. This platform can help prevent construction accidents and provide effective education for safety practices.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.135-144
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• 2024

In this study, we propose an automatic classification model for quantitative multidimensional analysis based on facet theory to understand public opinions and demands on major issues through big data analysis. Civil complaints, as a form of public feedback, are generated by various individuals on multiple topics repeatedly and continuously in real-time, which can be challenging for officials to read and analyze efficiently. Specifically, our research introduces a new classification framework that utilizes facet theory and political analysis models to analyze the characteristics of citizen complaints and apply them to the policy-making process. Furthermore, to reduce administrative tasks related to complaint analysis and processing and to facilitate citizen policy participation, we employ deep learning to automatically extract and classify attributes based on the facet analysis framework. The results of this study are expected to provide important insights into understanding and analyzing the characteristics of big data related to citizen complaints, which can pave the way for future research in various fields beyond the public sector, such as education, industry, and healthcare, for quantifying unstructured data and utilizing multidimensional analysis. In practical terms, improving the processing system for large-scale electronic complaints and automation through deep learning can enhance the efficiency and responsiveness of complaint handling, and this approach can also be applied to text data processing in other fields.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.199-216
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• 2024

This research examines the factors influencing consumer purchase intentions in e-commerce live streaming, set against the backdrop of the internet celebrity economy. The investigation serves as a pivotal inquiry into the dynamics of this economy, striving to uncover the extent of internet celebrities' influence, particularly in terms of their economic impact. Employing the Emotional Behavioral Cognitive (ABC) attitude theory and the Stimulus Organism Response (S-O-R) theory as foundational frameworks, this study scrutinizes internet celebrity live streaming sales. It incorporates direct observations and leverages existing scholarly work to devise a tailored measurement scale and questionnaire. From this, a research model and hypotheses are developed, leading to the establishment of an empirical model. This empirical model is instrumental in statistically analyzing how e-commerce live streaming, within the internet celebrity economy context, shapes consumer purchase intentions. By integrating theoretical insights and empirical findings, the research elucidates the strategic dimensions and consumer behavior aspects in digital commerce. It enhances understanding of how internet celebrity influence intersects with consumer purchasing processes. Overall, this study contributes to the academic discourse on digital marketing and consumer behavior, providing a nuanced perspective on the mechanisms through which internet celebrities affect e-commerce. It offers valuable implications for marketers, strategists, and policymakers aiming to navigate the complex landscape of the internet celebrity economy.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1307-1307
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• 2024

Recent research on heavy equipment has been conducted for the purposes of enhanced safety, productivity improvement, and carbon neutrality at construction sites. A sensor-based approach is being explored to monitor the location and movements of heavy equipment in real time. However, it poses significant challenges in terms of time and cost as multiple sensors should be installed on numerous heavy equipment at construction sites. In addition, there is a limitation in identifying the collaboration or interference between two or more heavy equipment. In light of this, a vision-based deep learning approach is being actively conducted to effectively respond to various working conditions and dynamic environments. To enhance the performance of a vision-based activity recognition model, it is essential to secure a sufficient amount of training datasets (i.e., video datasets collected from actual construction sites). However, due to safety and security issues at construction sites, there are limitations in adequately collecting training dataset under various situations and environmental conditions. In addition, the videos feature a sequence of multiple activities of heavy equipment, making it challenging to clearly distinguish the boundaries between preceding and subsequent activities. To address these challenges, this study proposed a domain adaptation in vision-based transfer learning for automated excavator activity recognition utilizing 3D ResNet (residual deep neural network). Particularly, this study aimed to identify the optimal depth of 3D ResNet (i.e., the number of layers of the feature extractor) suitable for domain adaptation via fine-tuning process. To achieve this, this study sought to evaluate the activity recognition performance of five 3D ResNet models with 18, 34, 50, 101, and 152 layers, which used two consecutive videos with multiple activities (5 mins, 33 secs and 10 mins, 6 secs) collected from actual construction sites. First, pretrained weights from large-scale datasets (i.e., Kinetic-700 and Moment in Time (MiT)) in other domains (e.g., humans, animals, natural phenomena) were utilized. Second, five 3D ResNet models were fine-tuned using a customized dataset (14,185 clips, 60,606 secs). As an evaluation index for activity recognition model, the F1 score showed 0.881, 0.689, 0.74, 0.684, and 0.569 for the five 3D ResNet models, with the 18-layer model performing the best. This result indicated that the activity recognition models with fewer layers could be advantageous in deriving the optimal weights for the target domain (i.e., excavator activities) when fine-tuning with a limited dataset. Consequently, this study identified the optimal depth of 3D ResNet that can maintain a reliable performance in dynamic and complex construction sites, even with a limited dataset. The proposed approach is expected to contribute to the development of decision-support systems capable of systematically managing enhanced safety, productivity improvement, and carbon neutrality in the construction industry.

• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.12 s.138
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• pp.1583-1595
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• 2004

Simon and Sullivan(l993) estimated that clothing and textile related brand equity had the highest magnitude comparing any other industry category. It reflects that fashion brands reinforce the symbolic, social values and emotional characteristics being different from generic brands. Recently, Kim and Lim(2002) developed a fashion brand equity scale to measure a brand's psychometric properties. However, they suggested that additional psychometric tests were needed to compare the relative magnitude of each brand's equity. The purpose of this study was to recognize the psychometric constructs of fashion brand equity and validate Kim and Lim's fashion brand equity scale using the measurement invariance test of cross-group comparison. First, we identified the constructs of fashion brand equity using confirmatory factor analysis through structural equation modeling. Second, we compared the relative magnitude of two brands' equity using the measurement invariance test of multi-group simultaneous factor analysis. Data were collected at six major universities in Seoul, Korea. There were 696 usable surveys for data analysis. The results showed that fashion brand equity was comprised of 16 items representing six dimensions: customer-brand resonance, customer feeling, customer judgment, brand imagery, brand performance and brand awareness. Also, we could support the measurement invariance of two brands' equities by configural and metric invariance tests. There were significant differences in five constructs' mean values. The greatest difference was in customer feeling; the smallest, in customer judgment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.3
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• pp.171-177
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• 2013

The purpose of this study is to find a scheme to scale down the KMA (Korea Meteorological Administration) digital precipitation maps to the grid cell resolution comparable to the rural landscape scale in Korea. As a result, we suggest two steps procedure called RATER (Radar Assisted Topography and Elevation Revision) based on both radar echo data and a mountain precipitation model. In this scheme, the radar reflection intensity at the constant altitude of 1.5 km is applied first to the KMA local analysis and prediction system (KLAPS) 5 km grid cell to obtain 1 km resolution. For the second step the elevation and topography effect on the basis of 270 m digital elevation model (DEM) which represented by the Parameter-elevation Regressions on Independent Slopes Model (PRISM) is applied to the 1 km resolution data to produce the 270 m precipitation map. An experimental watershed with about $50km^2$ catchment area was selected for evaluating this scheme and automated rain gauges were deployed to 13 locations with the various elevations and slope aspects. 19 cases with 1 mm or more precipitation per day were collected from January to May in 2013 and the corresponding KLAPS daily precipitation data were treated with the second step procedure. For the first step, the 24-hour integrated radar echo data were applied to the KLAPS daily precipitation to produce the 1 km resolution data across the watershed. Estimated precipitation at each 1 km grid cell was then regarded as the real world precipitation observed at the center location of the grid cell in order to derive the elevation regressions in the PRISM step. We produced the digital precipitation maps for all the 19 cases by using RATER and extracted the grid cell values corresponding to 13 points from the maps to compare with the observed data. For the cases of 10 mm or more observed precipitation, significant improvement was found in the estimated precipitation at all 13 sites with RATER, compared with the untreated KLAPS 5 km data. Especially, reduction in RMSE was 35% on 30 mm or more observed precipitation.