• Korean Journal of Ichthyology
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• v.35 no.1
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• pp.39-43
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• 2023

The water temperature and salinity have an important effect on the growth and survival of rearing fish. This study investigates the effect of water temperature and salinity on larvae and juveniles of Platycephalus indicus. The experimental water temperature was set to 13, 16, 19, 22, and 25℃, respectively, and the salinity was set to 7, 14, 21, 28, and 32 psu, respectively. Ten individuals were randomly collected daily and measured the total length using a stereo microscope. The growth rate was the highest at 25℃ (21.62±0.14 mm), 28 psu (15.02±0.05 mm) and the lowest at 13℃ (7.04±0.05 mm), 7 psu. The survival rate was the highest at 22℃ (69.2%), 32 psu (84.1%) and the lowest at 13℃ (15.1%), 7 psu. This study demonstrates that the water temperature and salinity affected the survival and growth of Platycephalus indicus larvae and the juvenile.

• Journal of Industrial Convergence
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• v.21 no.8
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• pp.69-74
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• 2023

This paper was conducted to examine the effect of Brussels Sprouts Extract on the inhibition of pro-inflammatory cytokines. The inflammatory response is manifested by mediators such as reactive oxygen species and inflammatory cytokines such as TNF-α, IL-1β, and IL-8. Therefore, this paper examined the toxicity to cells using the MTS assay, stimulated RAW264.7 macrophages with lipopolysaccharide (LPS), and stimulated reactive oxygen species such as NO and TNF-α, IL-1β, and IL-8. Inhibition of inflammatory cytokines after treatment with 10 mg/mL, 100 mg/mL, and 1000 mg/mL of Brussels Sprouts Extract was investigated. As a result of the experiment, Brussels Sprouts Extract inhibited NO production, TNF-α and IL-8 in a concentration-dependent manner without cytotoxicity, and showed significant inhibition especially at a concentration of 1000 mg/mL. Brussels Sprouts Extract, which inhibits the production of inflammatory cytokines, suggests the possibility of reducing inflammatory response and controlling inflammation, and can be seen as providing potential as a health functional food or prevention and treatment of inflammation.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.278-281
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• 2023

연구목적: 본 연구는 요양병원 등 재난취약시설에 재난이 발생할 경우 잔류한 요구조자를 정확하게 파악하여 소방 등 대응기관에 제공하는 APC(Auto People Counting)의 인식률 개선에 목적이 있다. 현재 재난 발생 시 건물 내 요구조자의 현황 파악을 위해 대응기관이 재난 현장에 도착하여 건물관계자에게 직접 물어보고 있다. 이는 요구조자에 대한 부정확한 정보일 가능성이 있어 대응기관의 업무범위가 확대되고 이로인해 구조자의 안전에도 위험이 될 수 있다. APC는 건물내 출입하는 인원을 자동으로 집계하여 실시간 잔류인원 정보를 제공함으로써 재난 시 요구조자 현황을 정확히 파악할 수 있다. 본 연구에서는 APC가 보다 정확하게 출입 인원을 집계할 수 있도록 최적의 인공지능 알고리즘을 선정하는데 목적이 있다. 연구방법: 본 연구에서는 실제 재난취약시설에 설치되어 운영 중인 APC를 대상으로 카메라를 통해 출입 인원의 이미지를 인식하는 알고리즘을 개선하기 위해 CNN모델을 활용하여 베이스라인 모델링을 하였다. 다양한 알고리즘의 성능을 분석하여 상위 7개의 후보군을 선정하고 전이학습 모델을 활용하여 성능이 가장 우수한 최적의 알고리즘을 선정하는 방법으로 연구를 수행하였다. 연구결과: 실험결과 시간과 성능이 가장 좋은 Densenet201, Resnet152v2 모델의 정밀도와 재현율을 확인한 결과 모든 라벨에 대해서 정확도 100%를 나타내는 것을 확인할 수 있었다. 이 중 Densenet201 모델이 더 높은 성능을 보여주었다. 결론: 다양한 인공지능 알고리즘 중 APC에 적용할 수 있는 최적의 알고리즘을 선정하였고 이는 APC의 인식률을 개선하여 재난시 요구조자의 정보를 정확하게 파악하여 신속하고 안전한 구조작업이 가능할 것이다. 이는 요구조자의 안전한 구조뿐만 아니라 구조작업을 수행하는 구조자의 안전을 확보하는 데 기여할 것으로 기대된다. 향후 연무 등 다양한 재난상황에서 재난취약시설 내 출입인원을 정확하게 파악할 수 있도록 알고리즘 분석 및 학습에 대한 추가 연구가 요구된다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.557-561
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• 2023

This study aims to assess how effective environmental management can be accomplished through monitoring of environmental conditions in patient discharge rooms within healthcare facilities through direct observation and the use of fluorescent markers. From March to July 2013, this study evaluated 448 check-out beds in wards and intensive care units before and 494 after intensive environmental monitoring activities. The collected data were analyzed using the SPSS 21.0 program. According to the study's findings, direct observation increased from 95.2% prior to the implementation of intensive environmental monitoring activities to 98.9% following the implementation, which was statistically significant. The non-detection rate of fluorescent markers exhibited an increase from 96.1% prior to the commencement of intensive environmental monitoring activities to 98.0% following their implementation. However, it should be noted that this observed increase was not deemed statistically significant. In light of the results of this research, it is imperative to evaluate the effectiveness of environmental management by employing a variety of assessment methods, including direct observation and fluorescent markers.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.

• Journal of Industrial Convergence
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• v.22 no.2
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• pp.135-141
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• 2024

This study aimed to analyze the characteristics of health check-up recipients in their 20s and 30s in Korea and identify factors influencing the participation rate in order to enhance the rate of health check-ups. The study population and methods utilized data from the 8th year of the Korea National Health and Nutrition Examination Survey, specifically the 2nd year (2020), and targeted 1,453 Korean residents aged between 20 and 30. The factors affecting health check-up participation were divided into sociodemographic factors, health behavior factors, mental health factors, and medical utilization factors, and both simple logistic regression analysis and multiple logistic regression analysis were conducted. The analysis results showed that educational level, marital status, type of health insurance, employment status, and subjective level of health were the factors influencing health check-up participation among Korean individuals in their 20s and 30s. These research findings can serve as foundational data for improving the health check-up participation rate among individuals in their 20s and 30s.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.17-22
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• 2024

Recently, as the ICT field has been used in various environments, it has become possible to analyze pests by crops, use robots when harvesting crops, and predict by big data by utilizing ICT technologies in a sustainable agricultural environment. However, in a sustainable agricultural environment, efforts to solve resource depletion, agricultural population decline, poverty increase, and environmental destruction are constantly being demanded. This paper proposes an artificial intelligence-based big data processing analysis method to reduce the production cost and increase the efficiency of crops based on a sustainable agricultural environment. The proposed technique strengthens the security and reliability of data by processing big data of crops combined with AI, and enables better decision-making and business value extraction. It can lead to innovative changes in various industries and fields and promote the development of data-oriented business models. During the experiment, the proposed technique gave an accurate answer to only a small amount of data, and at a farm site where it is difficult to tag the correct answer one by one, the performance similar to that of learning with a large amount of correct answer data (with an error rate within 0.05) was found.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.168-181
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• 2023

This study analyzed the effects of traffic enforcement on accident reduction. The results revealed a significant reduction in both overall accidents (28.53%) and fatal accidents (39.44%). Notably, enforcement equipment targeting speed limits of 30 km/h and 50 km/h demonstrated similar accident reduction rates of 42.23% and 25.85%, respectively. However, variations were observed based on accident types and types of traffic violations. Therefore, it is evident that enforcement equipment yields distinct accident reduction effects depending on speed limits and types of traffic accidents. This finding underscores the potential for making informed policy decisions to enhance traffic safety measures.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.181-185
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• 2024

The purpose of this research is to investigate how purchase cycle of a product and bundle frame influence consumers' responses to the bundle promotion. In order to test hypotheses of this study, we conducted an experimental study that was a 2(Purchase cyle: Long vs. Short) X 2(Bundle frame: 1+1 vs. Buy2 and 50% off) between-subjects design. The reseults of this study showed the interaction effects of purchase cycle of product and bundle frame on perceived level of discount and product attitude. Based on the results, we provide theoretical implcations to extent the existing research regarding bundling promotion. Moreover, the results of this study suggest some practical implications and a new aspect about bunle promotions.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.459-463
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• 2024

Frequent closures among self-employed individuals lead to national economic losses. Given the high closure rates in the restaurant industry, predicting changes in this sector is crucial for business survival. While research on factors affecting restaurant industry survival is active, studies predicting commercial district changes are lacking. Thus, this study focuses on forecasting such alterations, designing a deep learning model for Seoul's administrative district commercial district changes. It collects 2023 and 2022 second-quarter variables related to these changes, converting yearly fluctuations into percentages for augmentation. The proposed deep learning model aims to predict commercial district changes. Future policies, considering this study, could support restaurant industry growth and economic development.