• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.402-402
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• 2020

대청호는 1998년 조류경보제 도입 이후 1999년과 2014년을 제외하고 매년 조류 경보가 발령되었으며 2001년 조류 경보 '대발생'이 발령된 후 2017년 가장 높은 조류 발생 수치를 기록하였다. 상시 조류 발생 지역인 대청호 내 녹조를 제어하기 위해 비점오염원 발생원을 기준으로 우선관리지역을 선정하고, 각 지역의 특성을 반영한 관리대책을 제안하였다. 우선관리지역 선정을 위해 대청호 유역 내 오염총량 소유역을 기준으로 각 소유역의 농업 비점오염원의 발생부하량을 산정하고 유출을 고려한 가중치를 추가하였다. 본 연구에서는 농업 비점오염원을 크게 토지계 비점오염원과 축산계 비점오염원으로 분류하였으며, 토지계 농업비점오염원은 논, 밭, 과수원 지역으로 정의하였다. 발생부하량의 산정은 오염총량관리 기술지침(2019, 국립환경과학원)을 기준으로 하였으며, 토지계 발생부하량 산정을 위한 토지계 정보원으로 환경부에서 제작 및 배포하는 세분류 토지피복도를 축산계 발생부하량 산정을 위한 축산 두수는 2017년 기준 전국오염원조사 내 축산두수를 이용하였다. 토지계 비점오염원의 하천까지 유출을 반영하기 위해 각 소유역별 평균 경사도를 가중치로 이용하였으며, 축산계 비점오염원은 오염물질이 발생한 후 하천까지의 평균 유출 정도를 확인하여 가중치로 반영하였다. 실제 하천에 미치는 영향이 높은 지역에 대한 우선적인 관리를 위해 하천수 수질측정망에서 측정한 수질 데이터와의 비교를 통하여 최종 우선관리지역을 보청A03, 보청A05, 금본F14, 금본F22 소유역으로 선정하였다. 각 소유역에 대한 수질 관리목표를 확인하였으며, 지역의 특성을 분석하여 토지계 및 축산계 비점오염원에 대한 적절한 관리대책을 제안하였다. 본 연구에서 사용한 수질 측정망 데이터가 각 소유역보다 적게 분포하여 소유역에서 발생한 비점오염물질이 하천에 미치는 영향을 직접 파악하는데 한계가 있었다. 또한, 축산계의 경우 발생한 비점오염물질이 모두 하천으로 유입되지 않으며, 축산계 비점오염원의 배출경로를 파악하는데 어려움이 있다는 한계를 가진다. 본 연구의 한계를 바탕으로 농림축산식품부 및 축협 등에서 구축하는 사육두수의 데이터를 이용하는 방법론 등의 추가적인 연구가 필요할 것으로 보인다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6C
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• pp.367-377
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• 2008

As part of study to develop LRFD (Load and Resistance Factor Design) codes for foundation structures in Korea, resistance factors for static bearing capacity of driven steel pipe piles were calibrated in the framework of reliability theory. The 57 data sets of static load tests and soil property tests conducted in the whole domestic area were collected and these load test piles were sorted into two cases: SPT N at pile tip less than 50, SPT N at pile tip equal to or more than 50. The static bearing capacity formula and the Meyerhof method using N values were applied to calculate the expected design bearing capacities of the piles. The resistance bias factors were evaluated for the two static design methods by comparing the representative measured bearing capacities with the expected design values. Reliability analysis was performed by two types of advanced methods: the First Order Reliability Method (FORM), and the Monte Carlo Simulation (MCS) method using resistance bias factor statistics. The target reliability indices are selected as 2.0 and 2.33 for group pile case and 2.5 for single pile case, in consideration of the reliability level of the current design practice, redundancy of pile group, acceptable risk level, construction quality control, and significance of individual structure. Resistance factors of driven steel pipe piles were recommended based on the results derived from the First Order Reliability Method and the Monte Carlo Simulation method.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.687-692
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• 2023

We analyze the accuracy of emotion analysis of natural language processing deep learning model and propose to use it for emotional content development. After looking at the outline of the GPT-3 model, about 6,000 pieces of dialogue data provided by Aihub were input to 9 emotion categories: 'joy', 'sadness', 'fear', 'anger', 'disgust', and 'surprise'. ', 'interest', 'boredom', and 'pain'. Performance evaluation was conducted using the evaluation indices of accuracy, precision, recall, and F1-score, which are evaluation methods for natural language processing models. As a result of the emotion analysis, the accuracy was over 91%, and in the case of precision, 'fear' and 'pain' showed low values. In the case of reproducibility, a low value was shown in negative emotions, and in the case of 'disgust' in particular, an error appeared due to the lack of data. In the case of previous studies, emotion analysis was mainly used only for polarity analysis divided into positive, negative, and neutral, and there was a limitation in that it was used only in the feedback stage due to its nature. We expand emotion analysis into 9 categories and suggest its use in the development of emotional content considering it from the planning stage. It is expected that more accurate results can be obtained if emotion analysis is performed by additionally collecting more diverse daily conversations through follow-up research.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.195-200
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• 2023

The purpose of this study was to investigate the effects of a virtual reality rehabilitation program on upper limb function, cognition, and activities of daily living in stroke patients. Among the 25 participants in the program, it was randomly classified 13 experimental group and 12 control group. Rehabilitation training was applied 3 times a week and general occupational therapy 2 times a week using virtual reality rehabilitation training equipment in the experimental group, and general occupational therapy was performed 5 times a week, 30 minutes per session in the control group. As a result of the virtual reality rehabilitation program, cognitive function increased by 3.39 points in the experiment group, The control group who received only general occupational therapy increased by 1.5 points. As for the upper limb function, the average of the experimental group subjected to the virtual reality rehabilitation program increased by 4.84 points The control group who received only general occupational therapy increased by 1.34 points. As for activities of daily living, the average of the experimental group that conducted the virtual reality rehabilitation program increased by 20.38 points, The control group who received only general occupational therapy increased by 7.61 points. This result show that the virtual reality program has an effect on upper limb function, cognition, and activities of daily living of stroke patients.

• Journal of Life Science
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• v.33 no.7
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• pp.593-604
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• 2023

The ocean accounts for over 70% of the Earth's surface and is a space of largely unexplored unknowns and opportunities. Korea is a peninsula surrounded by the sea on three sides, emphasizing the importance of marine research. The ocean has an extremely complex environment with immense biological diversity. In terms of microbiology, the marine environment has varying factors like extreme temperature, pressure, solar radiation, salt concentration, and pH, providing ecologically unique habitats. Due to this variety, marine organisms have very different phylogenetic classifications compared with terrestrial organisms. Although various microorganisms inhabit the ocean, studies on the diversity, isolation, and cultivation of marine microorganisms and the secondary metabolites they produce are still insufficient. Research on bioactive substances from marine microorganisms, which were rarely studied until the 1990s, has accelerated in terms of natural products from marine Actinomycetes since the 2000s. Since then, industries for bioplastic and biofuel production, carbon dioxide capture, probiotics, and pharmaceutical discovery and development of antibacterial, anticancer, antioxidant, and anti-inflammatory drugs using bacteria, archaea, and algae have significantly grown. In this review, we introduce current research findings and the latest trends in life science and biotechnology using marine microorganisms. Through this article, we hope to create consumer awareness of the importance of basic and applied research in various natural product-related discovery fields other than conventional pharmaceutical drug discovery. The article aims to suggest pathways that may boost research on the optimization and application of future marine-derived materials.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.493-502
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• 2023

The development of online content is changing the value of society very diversely and rapidly. In particular, the non-face-to-face e-sports industry is growing significantly in the current situation where the COVID-19 Pandemic has not been completely overcome. The use of NFT in the eSports industry is receiving positive reviews as a field with high potential for future growth that protects digital assets of eSports users, but at the same time, it is raising concerns that cash transactions of digital items could encourage gambling. In this study, the characteristics of the recent eSports industry and NFT were identified and classified through case studies using literature, official sites, and online news articles. Through various cases of eSports and NFT, we discussed the potential for future growth and activation plan of the NFT industry of eSports. The result is as follows. First, it is necessary to use NFT using IP of eSports event itself. Second, it is necessary to combine the functional role of the item with NFT to provide features that users can utilize. Third, it is necessary to provide users with opportunities to engage in economic activities using eSports and NFT. Finally, it is necessary to use NFT to strengthen the digital asset protection of eSports users. Through this study, it is expected to be used as a basis for further discussions on the NFT industry of e-sports and as a material for securing competitiveness.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.199-206
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• 2023

This study attempted to examine the relationship between college students' participation in the learning community according to the characteristics of learners. To this end, the learning community divided the subject-linked learning community into a foundation learning community and an advanced learning community. Learner characteristics were classified by gender, grade, and major. A cross-analysis was conducted to examine the difference between participation in the foundation learning community and the advanced learning community according to the learner characteristics. The results are as follows. First, the participation of female students in the foundation learning community and the advanced learning community was higher than that of male students, but it was not statistically significant. Second, it was found that there was a significant difference in participation in the learning community according to the grade. In the case of the foundation learning community, the participation rate of the first and second year students was relatively high, and in the case of the advanced learning community, the ratio of the third and fourth year students was relatively high. Third, as a result of examining the differences by major, it was found that the participation rate of health and welfare universities was high in both the foundation learning community and the advanced learning community. Based on these results, discussions and suggestions are presented.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.715-720
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• 2023

The military is driving innovative changes such as AI, cloud computing, and drone operation through the Fourth Industrial Revolution. It is expected that such changes will lead to a rapid increase in the demand for information exchange requirements, reaching all lower-ranking soldiers, as networking based on IoT occurs. The flow of such information must ensure efficient information distribution through various infrastructures such as ground networks, stationary satellites, and low-earth orbit small communication satellites, and the demand for information exchange that is distributed through them must be appropriately dispersed. In this study, we redefined the DSCP, which is closely related to QoS (Quality of Service) in information dissemination, into 11 categories and performed research to map each cluster group identified by cluster analysis to the defense "information exchange requirement list" on a one-to-one basis. The purpose of the research is to ensure efficient information dissemination within a multi-layer integrated network (ground network, stationary satellite network, low-earth orbit small communication satellite network) with limited bandwidth by re-establishing QoS policies that prioritize important information exchange requirements so that they are routed in priority. In this paper, we evaluated how well the information exchange requirement lists classified by cluster analysis were assigned to DSCP through M&S, and confirmed that reclassifying DSCP can lead to more efficient information distribution in a network environment with limited bandwidth.

• Journal of the Korean Applied Science and Technology
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• v.39 no.6
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• pp.864-873
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• 2022

The subjective health status of adolescence reflects one's overall socio-emotional function and is an important factor in determining the health-related quality of life during this period. This study was to identify the correlation between subjective health status and health equity of adolescents. Data from the 16th online survey of youth health behavior (2020) was used to analyze 39,987 adolescents. Health equity was used as indicator for residential areas, economic conditions perceived by students, household abundance, family type, and parental education. Subjective health status was classified as a healthy group("very healthy", "healthy") and unhealthy group("normal", "unhealthy", and "very unhealthy") in response to the question "how do you think your health is usually?" The data were analyzed using complex sample analysis by using SPSS/Win 22.0. Significant factors related to the subjective health status of subjects were the area of residence (OR=0.86, p=.031), economic level (OR=1.33-2.09, p<.001), and family type (OR=1.24, p=.033). The economic level perceived by adolescents was the most important variable related to the subjective health status of adolescents, and adolescents from multicultural families often perceived their health as unhealthy compared to adolescents from general families. Therefore, there is a need for continuous interest in adolescents with low economic levels and adolescents from multicultural families and specific strategies to improve their health status.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.1-11
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• 2023

Breast cancer is the disease that affects women the most worldwide. Due to the development of computer technology, the efficiency of machine learning has increased, and thus plays an important role in cancer detection and diagnosis. Deep learning is a field of machine learning technology based on an artificial neural network, and its performance has been rapidly improved in recent years, and its application range is expanding. In this paper, we propose a DNN-SVM hybrid model that combines the structure of a deep neural network (DNN) based on transfer learning and a support vector machine (SVM) for breast cancer classification. The transfer learning-based proposed model is effective for small training data, has a fast learning speed, and can improve model performance by combining all the advantages of a single model, that is, DNN and SVM. To evaluate the performance of the proposed DNN-SVM Hybrid model, the performance test results with WOBC and WDBC breast cancer data provided by the UCI machine learning repository showed that the proposed model is superior to single models such as logistic regression, DNN, and SVM, and ensemble models such as random forest in various performance measures.