• 한국사회정책
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• v.24 no.1
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• pp.33-66
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• 2017

Based on the premise that the systematic career process of workers in the general labor market was one of core elements of successful achievements and their establishment both at the individual and organizational level, this study set out to conduct empirical analysis of factors influencing the subjective career success of disabled workers in competitive employment at the multi-dimensional levels of individuals and organizations(corporations) and thus provide practical implications for the career management directionality of their successful vocational life with data based on practical and statistical accuracy. For those purposes, the investigator administered a structured questionnaire to 126 disabled workers at 48 companies in Seoul, Gyeonggi, Chungcheong, and Gangwon and collected data about the individual and organizational characteristics. Then the influential factors were analyzed with the multilevel analysis technique by taking into consideration the organizational effects. The analysis results show that organizational characteristics explained 32.1% of total variance of subjective career success, which confirms practical implications for the importance of organizational variables and the legitimacy of applying the multilevel model. The significant influential factors include the degree of disability, desire for growth, self-initiating career attitude and value-oriented career attitude at the individual level and the provision of disability-related convenience, career support, personnel support, and interpersonal support at the organizational level. The latter turned out to have significant moderating effects on the influences of subjective career success on the characteristic variables at the individual level. Those findings call for plans to increase subjective career success through the activation of individual factors based on organizational effects. The study thus proposed and discussed integrated individual-corporate practice strategies including setting up a convenience support system by reflecting the disability characteristics, applying a worker support program, establishing a frontier career development support system, and providing assistance for a human network.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.558-569
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• 2020

This study examined the frequency of a broiler's stay in each area as stock density using an ICT-based image analysis technique from the perspective of precision livestock farming (PLF) according to the increase in the domestic broiler farms to understand the normal behavior patterns of broilers by age. The broiler was used in the experimental box (3.3×2.7 m) in a poultry house in Gyeonggi province. The stock densities were 9.5 birds/㎡ (n=85) and 19 birds/㎡ (n=170), respectively, and the frequency of stay by feeding, water, and rest area was monitored using a top-view camera. The image data of three-colored-specific broilers identified as the stock density were acquired by age (12, 16, 22, 27, and 29 days) for six hours. In the collected image data, the object tracking technique was used to record the cumulative movement path by connecting approximately 640,000 frames at 30 fps to quantify the frequency of stay in each area. In each stock density, it was significant in the order of the rest area, feeding, and water area (p<0.001). In 9.5 birds/㎡, it was at 57.9, 24.2, and 17.9 %, and 73.2, 16.8, and 10 % in 19 birds/㎡. The frequency of a broiler's stay could be evaluated in each area as the stock density using an ICT-based image analysis technique that minimizes stress. This method is expected to be used to provide basic material for developing an ICT-based management system through real-time monitoring.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1653-1667
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• 2020

In this study, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. We investigated the characteristics of fine particulate matter (PM2.5) distributions in a building-congested district. To analyze the effects of road emission on the PM2.5 concentrations, we calculated road emissions based on the monthly, daily, and hourly emission factors and the total amount of PM2.5 emissions established from the Clean Air Policy Support System (CAPSS) of the Ministry of Environment. We validated the simulated PM2.5 concentrations against those measured at the PKNU-AQ Sensor stations. In the cases of no road emission, the LDAPS-CFD model underestimated the PM2.5 concentrations measured at the PKNU-AQ Sensor stations. The LDAPS-CFD model improved the PM2.5 concentration predictions by considering road emission. At 07 and 19 LST on 22 June 2020, the southerly wind was dominant at the target area. The PM2.5 distribution at 07 LST were similar to that at 19 LST. The simulated PM2.5 concentrations were significantly affected by the road emissions at the roadside but not significantly at the building roof. In the road-emission case, the PM2.5 concentration was high at the north (wind speeds were weak) and west roads (a long street canyon). The PM2.5 concentration was low in the east road where the building density was relatively low.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Tunnel and Underground Space
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• v.32 no.1
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• pp.30-58
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• 2022

The deep geological repository for high-level radioactive waste disposal is a multi barrier system comprised of engineered barriers and a natural barrier. The long-term integrity of the deep geological repository is affected by the coupled interactions between the individual barrier components. Erosion and piping phenomena in the compacted bentonite buffer due to buffer-rock interactions results in the removal of bentonite particles via groundwater flow and can negatively impact the integrity and performance of the buffer. Rapid groundwater inflow at the early stages of disposal can lead to piping in the bentonite buffer due to the buildup of pore water pressure. The physiochemical processes between the bentonite buffer and groundwater lead to bentonite swelling and gelation, resulting in bentonite erosion from the buffer surface. Hence, the evaluation of erosion and piping occurrence and its effects on the integrity of the bentonite buffer is crucial in determining the long-term integrity of the deep geological repository. Previous studies on bentonite erosion and piping failed to consider the complex coupled thermo-hydro-mechanical-chemical behavior of bentonite-groundwater interactions and lacked a comprehensive model that can consider the complex phenomena observed from the experimental tests. In this technical note, previous studies on the mechanisms, lab-scale experiments and numerical modeling of bentonite buffer erosion and piping are introduced, and the future expected challenges in the investigation of bentonite buffer erosion and piping are summarized.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.41-52
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• 2022

As a cultivar of Korean wheat, 'Keumgang' wheat variety has a fast growth period and can be grown stably. Hexaploid wheat (Triticum aestivum) has moderately high salt tolerance compared to tetraploid wheat (Triticum turgidum L.). However, the molecular mechanisms related to salt tolerance of hexaploid wheat have not been elucidated yet. In this study, the candidate genes related to salt tolerance were identified by investigating the genes that are differently expressed in Keumgang variety and examining salt tolerant mutation '2020-s1340.'. A total of 85,771,537 reads were obtained after quality filtering using NextSeq 500 Illumina sequencing technology. A total of 23,634,438 reads were aligned with the NCBI Campala Lr22a pseudomolecule v5 reference genome (Triticum aestivum). A total of 282 differentially expressed genes (DEGs) were identified in the two Triticum aestivum materials. These DEGs have functions, including salt tolerance related traits such as 'wall-associated receptor kinase-like 8', 'cytochrome P450', '6-phosphofructokinase 2'. In addition, the identified DEGs were classified into three categories, including biological process, molecular function, cellular component using gene ontology analysis. These DEGs were enriched significantly for terms such as the 'copper ion transport', 'oxidation-reduction process', 'alternative oxidase activity'. These results, which were obtained using RNA-seq analysis, will improve our understanding of salt tolerance of wheat. Moreover, this study will be a useful resource for breeding wheat varieties with improved salt tolerance using molecular breeding technology.

• Ecology and Resilient Infrastructure
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• v.9 no.3
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• pp.194-205
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• 2022

Sandbars formed by sediment transportation and sedimentation are some of the most important habitats for specific wildlife and they provide an aesthetic landscape in streams. The purpose of this study was to understand the successional process of the colonization and development of early vegetation over time on sandbars exposed by the opening of a gate at a downstream weir. We selected the following four study sites in the Geumgang River, South Korea: three weir-upstream sites with different gate-opening times and a control site that was not affected by weir operation. Changes in the structural characteristics and spatial distribution of the riparian vegetation on the sandbars exposed after opening the gate at the weir were surveyed according to the different exposure periods of the sandbars at the study sites. The newly formed sandbars accounted for more than 33% of the area of the existing floodplain in the three weir-upstream sites of the Geumgang River after opening the gate at the weir. Nine main plant communities were distributed on the exposed sandbars. These communities were classified as annual mesophytic, perennial hydrophytic, perennial hygrophytic, subtree, and tree vegetation based on their species traits. As the duration of exposure of the sandbar increased, the area of the bare sandbar and the annual herbaceous and perennial hydrophytic communities decreased, and the areas occupied by perennial hygrophytic, subtree, and tree communities increased. Changes in vegetation on the sandbar were classified into three types of succession according to the condition of the aquatic habitat before the gate-opening and the degree of physical disturbance caused by the water flow after the gate-opening. The types of succession were: 1) succession starting from hydrophytes in the lentic aquatic zone, 2) succession starting from annual herbaceous hygrophytes in the lotic aquatic zone, and 3) willow-dominated succession in the disturbed channel side. Our results suggested that the dynamics of successional changes in vegetation should be considered during weir operation to ecologically manage the habitats and landscape of the fluvial landforms, including sandbars in streams.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.103-113
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• 2023

The river confluence is a section in which two rivers with different topographical and hyrodynamic characteristics are combined into one, and it is a section in which rapid flow, inflow of sediments, and hydrological topographic changes occur. In the confluence section, the flow of fluid occurs due to the difference in density due to the type of material or temperature difference, which is called a density flow. It is necessary to accurately measure and observe the confluence section including a certain section of the main stream and tributaries in order to understand the mixing behavior of the water body caused by the density difference. A comprehensive analysis of this water mixture can be obtained by obtaining flow field and flow rate information, but there is a limit to understanding the mixing of water bodies with different physical properties and water quality characteristics of rivers flowing with stratigraphic flow. Therefore, this study attempts to grasp the density flow through the water temperature distribution in the confluence section. Among the extensive data of the river, vertical data and water surface data were acquired, and through this, the stratification phenomenon of the confluence was to be confirmed. It was intended to analyze the mixed pattern of the confluence by analyzing the water mixing pattern according to the water temperature difference using the vertical data obtained by measuring the repair volume by installing the ADCP on the side of the boat and measuring the real-time concentration using YSI. This study can supplement the analysis results of the existing water quality measurement in two dimensions. Based on the comparative analysis, it will be used to investigate the current status of stratified sections in the water layer and identify the mixing characteristics of the downstream section of the river.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.421-433
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• 2023

The final disposal of spent nuclear fuel(SNF) from nuclear power plants takes place in a deep geological repository. The metal canister encasing the SNF is made of cast iron and copper, and is engineered to effectively isolate radioactive isotopes for a long period of time. The SNF is further shielded by a multi-barrier disposal system comprising both engineering and natural barriers. The deep disposal environment gradually changes to an anaerobic reducing environment. In this environment, sulfide is one of the most probable substances to induce corrosion of copper canister. Stress-corrosion cracking(SCC) triggered by sulfide can carry substantial implications for the integrity of the copper canister, potentially posing a significant threat to the long-term safety of the deep disposal repository. Sulfate can exist in various forms within the deep disposal environment or be introduced from the geosphere. Sulfate has the potential to be transformed into sulfide by sulfate-reducing bacteria(SRB), and this converted sulfide can contribute to the corrosion of the copper canister. Bentonite, which is considered as a potential material for buffering and backfilling, contains oxidized sulfate minerals such as gypsum(CaSO4). If there is sufficient space for microorganisms to thrive in the deep disposal environment and if electron donors such as organic carbon are adequately supplied, sulfate can be converted to sulfide through microbial activity. However, the majority of the sulfides generated in the deep disposal system or introduced from the geosphere will be intercepted by the buffer, with only a small amount reaching the metal canister. Pyrite, one of the potential sulfide minerals present in the deep disposal environment, can generate sulfates during the dissolution process, thereby contributing to the corrosion of the copper canister. However, the quantity of oxidation byproducts from pyrite is anticipated to be minimal due to its extremely low solubility. Moreover, the migration of these oxidized byproducts to the metal canister will be restricted by the low hydraulic conductivity of saturated bentonite. We have comprehensively analyzed and summarized key research cases related to the presence of sulfates, reduction processes, and the formation and behavior characteristics of sulfides and pyrite in the deep disposal environment. Our objective was to gain an understanding of the impact of sulfates and sulfides on the long-term safety of high-level radioactive waste disposal repository.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.69-88
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• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.