• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.2
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• pp.115-125
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• 2023

Objectives: This study was conducted to measure the level of radon in the air at a highway tunnel construction site in a gneiss area using the New Austrian Tunneling Method (NATM) and to evaluate exposure levels by occupation. Methods: Radon concentrations in the air were measured using E-PERM at points 300 m, 600 m, and 900 m from the tunnel entrance during the excavation and waterproofing work inside the tunnel. In addition, radon concentrations were measured during external excavation to compare with the inside of the tunnel. Personal exposure levels for major occupations including tunnel workers, construction equipment operators, waterproofers, shotcrete workers, and safety and health managers who participated in the construction were estimated using radon concentration measured in the work process area and working hours by occupation. Results: As a result of a total of 77 radon measurements, the geometric mean (GM) concentration was 71.1 Bq/m³, and the maximum concentration was 127.3 Bq/m³, which was below the indoor air quality criteria. Radon concentration by process decreased in the order of the tunnel excavation process (GM= Bq/m³, GSD=1.2), waterproofing process (GM=73.35 Bq/m³, GSD=1.2), and outside excavating process (GM=45.28 Bq/m³, GSD=1.2). Processes inside the tunnel were significantly higher than outside excavating processes (p<0.05). There was no statistically significant difference in radon concentration measured inside by distance from the tunnel entrance, but the innermost point of the tunnel, 900 m (GM=79.24 Bq/m³, GSD=1.27), measured the highest. Conclusions: The occupation with the highest individual exposure to radon was tunnel worker (64.16 Bq/m³), followed by construction equipment driver (64.04 Bq/m³) and waterproofer (63.13 Bq/m³).

• Industry Promotion Research
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• v.8 no.1
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• pp.1-10
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• 2023

In this study, a formula (EJ-F101) was prepared to develop a raw material for acne-prone skin improvement using wheat germ extract, and a clinical trial cream was prepared and clinical trials were conducted. As a result of the analysis, when comparing before and after using the product, both the test group and the control group showed significant improvement effects in terms of open comedones, occluded comedones, papules, sebum and oil content in the facial region at 4 weeks after product use, compared to the control group in the test group which showed a more significant improvement effect. As a result of the survey on the efficacy of the product, most items showed higher positive answers in the test product compared to the control product four weeks after the use of the product, and about 43-81% of the study subjects answered positively in the test product, except for the "open surface" item. In addition, for all items related to the usability of the product, about 14-86% of the test group and 38-90% of the control group answered positively at the time point 4 weeks after using the product. As a result of skin safety evaluation, no adverse skin reactions were observed in all subjects of this study. Based on the above results, it is considered that the cream using wheat germ extract is suitable for use on acne-prone skin(non comedogenicity).

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.33 no.3
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• pp.375-383
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• 2023

Objectives: Coal-fired power plants feature diverse working conditions, including multi-layered employment structures and irregular work cycles due to outsourcing and non-standardized tasks. The current uniform occupational environment measurement systems have limitations in accurately assessing and evaluating these varied conditions. This study aims to propose alternative measurement and assessment strategies to supplement existing methods. Methods: Major domestic coal-fired power plants were selected as the study targets. To prepare for the study and establish strategies, work processes were identified and existing occupational environment measurement results were compared and analyzed. The study proceeded by employing three strategies: specific exposure groups (SEGs) measurement, continuous monitoring, and supplementary measurements, which were then compared and discussed. Results: Previous exposure index evaluations (5,268 cases) indicated that crystalline silica, a type of respirable particulate matter, had detection limits below the threshold (non-detectable) in 82.6% (4,349 cases) of instances. Exposures below 10% of the exposure limit were observed at a very low concentration of 96.1%. Similar exposure group measurements yielded results where detection limits were below the threshold in 38.2% of cases, and exposures below 10% of the limit were observed in 70.6%. Continuous monitoring indicated detection limits below the threshold in 12.6% of cases, and exposures below 10% of the limit were observed in 75.6%. Instances requiring active workplace management accounted for more than 30% of cases, with SEGs at 11.8% (four cases), showing a higher proportion compared to 3.0% (four cases) in continuous monitoring. For coal dust, exposures below 10% of the limit were highest in legal measurements at 90.2% (113 cases), followed by 74.0% (91 cases) in continuous monitoring, and 47.0% (16 cases) in SEGs. Instances exceeding 30% were most prevalent in SEGs at 14.7% (five cases), followed by legal measurements at 5.0% (eight cases), and continuous monitoring at 2.4% (three cases). When examining exposure levels through arithmetic means, crystalline silica was found to be 104.7% higher in SEGs at 0.0088 mg/m³ compared to 0.0043 mg/m³ in continuous monitoring. Coal dust measurements were highest in SEGs at 0.1247 mg/m³, followed by 0.1224 mg/m³ in legal measurements, and 0.0935 mg/m³ in continuous monitoring. Conclusions: Strategies involving SEGs measurement and continuous monitoring can enhance measurement reliability in environments with irregular work processes and frequent fluctuations in working conditions, as observed in coal-fired power plants. These strategies reduce the likelihood of omitting or underestimating processes and enhance measurement accuracy. In particular, a significant reduction in detection limits below the threshold for crystalline silica was observed. Supplementary measurements can identify worker exposure characteristics, uncover potential risks in blind spots of management, and provide a complementary method for legal measurements.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.496-504
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• 2023

Countries worldwide are striving to find new sources of sustainable energy without carbon emission due to the increasing impact of global warming. With the advancement of the fourth industrial revolution on a global scale, there has been a substantial rise in energy demand. Simultaneously, there is a growing emphasis on utilizing energy sources with minimal or zero carbon content to ensure a stable power supply while reducing greenhouse gas emissions. In this comprehensive overview, a comparative analysis of carbon reduction policies of government was conducted. Based on international carbon neutrality scenarios and the presence of remaining thermal power generation, it can be categorized into two types: "Rapid" and "Safety". For the domestic scenario, the projected power demand and current greenhouse gas emissions in alignment with "The 10th Basic Plan for Electricity Supply and Demand" was examined. Considering all these factors, an overview of the current status of carbon neutrality technologies by focusing on the energy sector, encompassing transitions, hydrogen, transportation and carbon capture, utilization, and storage (CCUS) was offered followed by summarization of key technological trends and government-driven policies. Furthermore, the central aspects of the domestic carbon reduction strategy were proposed by taking account of current mega trends in the energy sector which are highlighted in international scenario analyses.

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1124-1150
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• 2023

This study aimed to analyze the leading research materials and research trends related to livestock food in Asia in recent years and propose future research agendas to ultimately contribute to the development of related livestock species. On analyzing more than 200 relevant articles, a high frequency of studies on livestock species and products with large breeding scales and vast markets was observed. Asia possesses the largest pig population and most extensive pork market, followed by that of beef, chicken, and milk; moreover, blood and egg markets have also been studied. Regarding research keywords, "meat quality" and "probiotics" were the most common, followed by "antioxidants", which have been extensively studied in the past, and "cultured meat", which has recently gained traction. The future research agenda for meat products is expected to be dominated by alternative livestock products, such as cultured and plant-derived meats; improved meat product functionality and safety; the environmental impacts of livestock farming; and animal welfare research. The future research agenda for dairy products is anticipated to include animal welfare, dairy production, probiotic-based development of high-quality functional dairy products, the development of alternative dairy products, and the advancement of lactose-free or personalized dairy products. However, determining the extent to which the various research articles' findings have been applied in real-world industry proved challenging, and research related to animal food laws and policies and consumer surveys was lacking. In addition, studies on alternatives for sustainable livestock development could not be identified. Therefore, future research may augment industrial application, and multidisciplinary research related to animal food laws and policies as well as eco-friendly livestock production should be strengthened.

• Journal of Venture Innovation
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• v.5 no.2
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• pp.85-99
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• 2022

Dryers becoming commercially available for experimental and industrial use are classified to general drying oven, hot-air dryer, vacuum dryer, freezing dryer, etc. and kinds of them are various from the function, size and volume, etc. But the moisture measurement is not applied although it is important factor for the quality control and the performance improvement of products, and then now is very passive because the weight is weighed arbitrarily after dry-end. Generally the method for measuring moisture is divided by a direct measurement method and a indirect measurement method, and the former such as the change of weight or volume on the front and rear of separation of moisture, etc. is mainly used. Relatively a indirect measurement is very limited to apply due to utilize measurement apparatuses using temperature conductivity and micro-wave etc. In this research, we easily designed the moisture measurement system using the open-source based Arduino, and monitored moisture fluctuations and weight profiles in the real-time without the effect of external environment. Concretely the temperature-humidity and load cell sensors were packaged into a drying oven and the various change values were measured, and their sensors capable to operate 60℃ and 80℃ were selected to suitable for the moisture sensitive materials and the food dry. And also the performance safety using the organic samples of banana, pear, sawdust could be secured because the changes of evaporation rate as the dry time and temperature, and the measurement values of load cell appeared stable response characteristics through repeated experiments. Hereafter we judge that the reliability can be improved increasingly through the expansion of temperature-humidity range and the comparative analysis with CFD(Computational Fluid Dynamics) program.

• Explosives and Blasting
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• v.41 no.3
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• pp.62-72
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• 2023

The aging of plant structures due to industrialization in the 1970s has increased the demand for blast demolition. While blasting can reduce exposure to environmental pollution by shortening the demolition period, improper blasting design and construction plans pose significant safety risks. Thus, it is vital to consider optimal blasting demolition conditions and other factors through collapse behavior simulation. This study utilizes a 3-D combined finite-discrete element method (FDEM) code-based 3-D DFPA to simulate the collapse of a chimney structure in a thermal power plant in Seocheon, South Korea. The collapse behavior from the numerical simulation is compared to the actual structure collapse, and the numerical simulation result presents good agreement with the actual building demolition. Additionally, various numerical simulations have been conducted on the chimney models to analyze the impact of the duct size in the pre-weakening area. The no-duct, duct, and double-area duct models were compared in terms of crack pattern and history of Z-axis displacement. The findings show that the elapse-time for demolition decreases as the area of the duct increases, causing collapse to occur quickly by increasing the load-bearing area.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.31-41
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• 2024

Deep learning models show excellent performance in tasks such as image classification and object detection in the field of computer vision, and are used in various ways in actual industrial sites. Recently, research on improving robustness has been actively conducted, along with pointing out that this deep learning model is vulnerable to hostile examples. A hostile example is an image in which small noise is added to induce misclassification, and can pose a significant threat when applying a deep learning model to a real environment. In this paper, we tried to confirm the robustness of the edge-learning classification model and the performance of the adversarial example detection model using it for adversarial examples of various algorithms. As a result of robustness experiments, the basic classification model showed about 17% accuracy for the FGSM algorithm, while the edge-learning models maintained accuracy in the 60-70% range, and the basic classification model showed accuracy in the 0-1% range for the PGD/DeepFool/CW algorithm, while the edge-learning models maintained accuracy in 80-90%. As a result of the adversarial example detection experiment, a high detection rate of 91-95% was confirmed for all algorithms of FGSM/PGD/DeepFool/CW. By presenting the possibility of defending against various hostile algorithms through this study, it is expected to improve the safety and reliability of deep learning models in various industries using computer vision.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.147-155
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• 2024

Currently, the 4th Industrial Revolution, like other revolutions, is bringing great change and new life to humanity, and in particular, the demand for and use of drones, which can be applied by combining various technologies such as big data, artificial intelligence, and information and communications technology, is increasing. Recently, it has been widely used to carry out dangerous military operations and missions, such as the Russia-Ukraine war and North Korea's reconnaissance against South Korea, and as the demand for and use of drones increases, concerns about the safety and security of drones are growing. Currently, a variety of research is being conducted, such as detection of wireless communication abnormalities and sensor data abnormalities related to drones, but research on real-time detection of threats using radio frequency characteristic data is insufficient. Therefore, in this paper, we conduct a study to determine whether the characteristic data is normal or abnormal signal data by collecting radio frequency signal characteristic data generated while the drone communicates with the ground control system while performing a mission in a HITL(Hardware In The Loop) simulation environment similar to the real environment. proceeded. In addition, we propose an unsupervised learning-based threat detection system and optimal threshold that can detect threat signals in real time while a drone is performing a mission.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.23-29
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• 2024

In this scholarly investigation, the focus is placed on the transformative potential of edge computing in enhancing Intelligent Transportation Systems (ITS) for the facilitation of autonomous driving. The intrinsic capability of edge computing to process voluminous datasets locally and in a real-time manner is identified as paramount in meeting the exigent requirements of autonomous vehicles, encompassing expedited decision-making processes and the bolstering of safety protocols. This inquiry delves into the synergy between edge computing and extant ITS infrastructures, elucidating the manner in which localized data processing can substantially diminish latency, thereby augmenting the responsiveness of autonomous vehicles. Further, the study scrutinizes the deployment of edge servers, an array of sensors, and Vehicle-to-Everything (V2X) communication technologies, positing these elements as constituents of a robust framework designed to support instantaneous traffic management, collision avoidance mechanisms, and the dynamic optimization of vehicular routes. Moreover, this research addresses the principal challenges encountered in the incorporation of edge computing within ITS, including issues related to security, the integration of data, and the scalability of systems. It proffers insights into viable solutions and delineates directions for future scholarly inquiry.