• Journal of the Economic Geographical Society of Korea
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• v.27 no.1
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• pp.38-52
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• 2024

Digital transformation industry contributes to the improvement of productivity in overall industrial production, the smart green industry for carbon neutrality and sustainable growth is growing as a future industry. The purpose of this paper is to explore the status and role of the industry in the future industry innovation ecosystem through the analysis of the growth drivers and location pattern changes of the smart green industry. The industry is on the rise in both metropolitan and non-metropolitan areas, and the growth of the industry can be seen in non-metropolitan and non-urban areas. In particular, due to the smart green industrial complex pilot project, the creation of Gwangju Jeonnam Innovation City, and the promotion of new and renewable energy policies, the emergence of core aggregation areas (HH type) in the coastal areas of Honam and Chungcheongnam-do, and the formation of isolated centers (HL type) in the Gyeongsang region, new and renewable energy production companies are being accumulated in non-metropolitan areas. Therefore, the smart green industry is expected to promote the formation of various specialized spokes in non-urban areas in the future industrial innovation ecosystem that forms a multipolar hub-spoke network structure, where policy factors are the triggers for growth.

• Microbiology and Biotechnology Letters
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• v.51 no.4
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• pp.500-506
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• 2023

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is emerging as a worldwide public health threat. Recently, Klebsiella pneumoniae carbapenemase-2 (KPC-2)-producing sequence type (ST) 307 was identified main clone of CRKP, and dissemination of ST307 was reported in South Korea. This study examined the molecular characteristic and antimicrobial resistance pattern of 50 CRKP isolated from a tertiary hospital in Daejeon, from March 2020 to December 2021. Epidemiological relationship was analyzed by Multilocus sequence typing (MLST) and antimicrobial susceptibility test was determined using disk-diffusion method. PCR and DNA sequence analysis were performed to identify carbapenemase genes. CRKP infections were significantly more frequent in males and the patients aged ≥ 60 years. Among the 50 CRKP isolates, 46 isolates (92.0%) were multidrug-resistant (MDR), and 44 isolates (88.0%) were carbapenemase-producing K. pneumoniae (CPKP). The major carbapenemase type was KPC-2 (36 isolates, 72.0%) and New Delhi metalloenzyme-1 (NDM-1) and NDM-5 were identified in 7 isolates (14.0%) and 1 isolate (2.0%), respectively. In particular, 88.9% (32/36) of KPC-2-producing K. pneumoniae belonged to ST307, whereas 87.5% (7/8) of NDM-1,-5-producing K. pneumoniae belonged to non-ST307. These results suggest that proper infection control and effective surveillance network need to prevent not olny the spread of ST307, but also the development of non-ST307.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.73-85
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• 2024

Predicting water quality of rivers and reservoirs is necessary for the management of water resources. Artificial Neural Networks (ANNs) have been used in many studies to predict water quality with high accuracy. Previous studies have used Gradient Descent (GD)-based optimizers as an optimizer, an operator of ANN that searches parameters. However, GD-based optimizers have the disadvantages of the possibility of local optimal convergence and absence of a solution storage and comparison structure. This study developed improved optimizers to overcome the disadvantages of GD-based optimizers. Proposed optimizers are optimizers that combine adaptive moments (Adam) and Nesterov-accelerated adaptive moments (Nadam), which have low learning errors among GD-based optimizers, with Harmony Search (HS) or Novel Self-adaptive Harmony Search (NSHS). To evaluate the performance of Long Short-Term Memory (LSTM) using improved optimizers, the water quality data from the Dasan water quality monitoring station were used for training and prediction. Comparing the learning results, Mean Squared Error (MSE) of LSTM using Nadam combined with NSHS (NadamNSHS) was the lowest at 0.002921. In addition, the prediction rankings according to MSE and R² for the four water quality indices for each optimizer were compared. Comparing the average of ranking for each optimizer, it was confirmed that LSTM using NadamNSHS was the highest at 2.25.

• The Journal of Korean Society for School & Community Health Education
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• v.25 no.2
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• pp.71-81
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• 2024

Purpose: This study aims to identify research trends in papers related to the elderly and the disabled published in the journal of Korean society for school & community health education from 2000 to 2023 and seek the direction of the academic development of this journal in the future. Method: A total of 26 articles related to the elderly and the disabled, who are vulnerable groups, were analyzed by year by analyzing the specific subjects, research themes, research design, data collection methods, and keywords of papers published from 2000 to 2023. Results: Looking at the research subjects, studies on the elderly (18 studies) accounted for a larger proportion than studies on the disabled (8 studies). Research themes in the field of healthy living practices for the elderly (44.4%) and research in the field of mental health management (37.5%) for the disabled accounted for a high proportion. The design of research were mostly quantitative and cross-sectional studies. Data collection is mostly based on secondary data. In studies targeting the elderly, keywords appeared in the following order: 'Health' and 'Elderly'. And research targeting the disabled appeared in the following order: 'Disabilities', 'Health', and 'COVID-19'. Additionally, research on the elderly and the disabled has recently shown an increasing trend. Conclusion: Research on the elderly and the disabled has been conducted in line with the purpose of the Korean society for school & community health education, However, In terms of quantitative expansion and qualitative research, research themes, research designs, and data collection methods must be diversified. Methods, public perception. Additionally, research on vulnerable groups that fit the public health promotion and health education paradigm is needed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.3
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• pp.281-301
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• 2024

The power cable tunnels which are part of the underground transmission line project, are constructed using the shield TBM method. The disc cutter among the shield TBM components plays an important role in breaking rock mass. Efficient tunnel construction is possible only when appropriate replacement occurs as the wear limit is reached or damage such as uneven wear occurs. A study was conducted to determine the wear conditions of disc cutter using a deep learning algorithm based on real-time measurement data of wear and rotation speed. Based on the results of full-scaled tunnelling tests, it was confirmed that measurement data was obtained differently depending on the wear conditions of disc cutter. Using real-time measurement data, an algorithm was developed to determine disc cutter wear characteristics based on a convolutional neural network model. Distributional patterns of data can be learned through CNN filters, and the performance of the model that can classify uniform wear and uneven wear through these pattern features.

• Journal of Adhesion and Interface
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• v.25 no.2
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• pp.69-74
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• 2024

Soft actuators can be applied to various fields such as the medical industry and manufacturing industry due to the flexibility and smooth movement resulted from their constituent materials. Stimuli-responsive hydrogels are a class of materials that can show large volume changes due to various surrounding stimuli and thus is suitable as a soft actuator material. However, because the change in volume of the stimuli-responsive hydrogel depends on the rate of temperature change and the rate at which the solvent diffuses into the polymer network, in most typical operating conditions, the response time of the actuator is slow due to inefficient heat transfer and diffusion process. In this study, a conjugated polymer was introduced into polydiethylacrylamide, a thermoresponsive hydrogel, to implement a soft actuator driven by light, and the improvement in response time by the photothermal effect of the conjugated polymer was investigated. It was confirmed that the response time was improved by 41% by the introduction of the conjugated polymer, due to the improvement in heat transfer efficiency. Finally, a soft gripper using the hydrogel with improved response time was fabricated and the response time of the gripper was investigated.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.3
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• pp.99-114
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• 2024

Recently, the concept of neighborhood unit plan has been receiving attention due to expectations of balanced development and sustainable development through resolving regional gaps and reflecting regional characteristics. Accessibility to essential living facilities that can support daily life is considered an important factor in neighborhood unit plan. Therefore, this study analyzed accessibility from facilities based on the living facilities and access range set in the neighborhood unit plan, and analyzed spatial accessibility inequality in connection with the neighborhood unit plan and spatial clustering. As a result of analyzing accessibility in Busan Metropolitan City, various accessibility ranges were found depending on the facility. In addition, as a result of analyzing in connection with spatial clustering, regional inequality was found, such as hotspot areas in Gangdong, old downtown, Dongrae, and Haeundae, and coldspot areas in Gangseo and Gijang, and spatial inequality was found in which hotspots and coldspots exist simultaneously within the same neighborhood unit. Considering these spatial characteristics, detailed planning and policy establishment are necessary for facilities lacking in small-size neighborhood units, and the results of the analysis are expected to be meaningful in realizing the urban policy of balanced development that has been recently promoted.

• Journal of the Society of Disaster Information
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• v.20 no.3
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• pp.484-499
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• 2024

Purpose: Recently, due to the aging of safety facilities in national industrial complexes, there has been an increase in the frequency and scale of safety accidents, highlighting the need for a shift toward a prevention-centered disaster management paradigm and the establishment of a digital safety network. In response, this study aims to provide an information system that supports more rapid and precise decision-making during disasters by utilizing digital twin-based integrated control technology to predict the spread of hazardous substances, trace the origin of accidents, and offer safe evacuation routes. Method: We considered various simulation results, such as surface diffusion, upper-level diffusion, and combined diffusion, based on the actual characteristics of hazardous substances and weather conditions, addressing the limitations of previous studies. Additionally, we designed an integrated management system to minimize the limitations of spatiotemporal monitoring by utilizing an IoT sensor-based backtracking model to predict leakage points of hazardous substances in spatiotemporal blind spots. Results: We selected two pilot companies in the Gumi Industrial Complex and installed IoT sensors. Then, we operated a living lab by establishing an integrated management system that provides services such as prediction of hazardous substance dispersion, traceback, AI-based leakage prediction, and evacuation information guidance, all based on digital twin technology within the industrial complex. Conclusion: Taking into account the limitations of previous research, we used digital twin-based AI analysis to predict hazardous chemical leaks, detect leakage accidents, and forecast three-dimensional compound dispersion and traceback diffusion.

• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.69-79
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• 2024

Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.

• Journal of Platform Technology
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• v.12 no.1
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• pp.91-105
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• 2024

As the number of cases of applying IT systems to the existing isolated ICS (Industrial Control System) network environment continues to increase, security threats in the ICS environment have rapidly increased. Security threat scenarios help to design security strategies in cybersecurity training, including analysis, prediction, and response to cyberattacks. For successful cybersecurity training, research is needed to develop valid and reliable security threat scenarios for meaningful training. Therefore, this paper proposes a case-based security threat scenario development methodology for cybersecurity training in the ICS environment. To this end, we develop a methodology consisting of five steps based on analyzing actual cybersecurity incident cases targeting ICS. Threat techniques are standardized in the same form using objective data based on the MITER ATT&CK framework, and then a list of CVEs and CWEs corresponding to the threat technique is identified. Additionally, it analyzes and identifies vulnerable functions in programming used in CWE and ICS assets. Based on the data generated up to the previous stage, develop security threat scenarios for cybersecurity training for new ICS. As a result of verification through a comparative analysis between the proposed methodology and existing research confirmed that the proposed method was more effective than the existing method regarding scenario validity, appropriateness of evidence, and development of various scenarios.