• Safety and Health at Work
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• 제13권1호
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• pp.9-16
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• 2022

Background: The global shift toward greener societies demands new technologies and work operations in the waste-management sector. However, progressive industrial methods do not necessarily consider workers' health. This study characterized workers' exposure to bioaerosols and investigated the bioaerosols' potential to engage the immune system in vitro. Methods: Full shift personal aerosol sampling was conducted over three consecutive days. Dust load was analyzed by gravimetry, fungal and actinobacterial spores were analyzed by scanning electron microscopy, and endotoxin by limulus amebocyte lysate (LAL) assay. In vitro exposure of HEK cells to airborne dust samples was used to investigate the potential of inducing an inflammatory reaction. Results: The total dust exposure level exceeded the recommended occupational exposure limit (OEL) of 5.0 mg/m³ in 3 out of 15 samples. The inhalable endotoxin level exceeded the recommended exposure level by a 7-fold, whereas the fungal spore level exceeded the recommended exposure level by an 11-fold. Actinobacterial spores were identified in 8 out of 14 samples. In vitro experiments revealed significant TLR2 activation in 9 out of 14 samples vs. significant TLR4 activation in all samples. Conclusion: The present study showed that the dust samples contained potentially health-impairing endotoxin, fungi, and actinobacterial levels. Furthermore, the sampled dust contained microbial components capable of inducing TLR activation and thus have the potential to evoke an inflammatory response in exposed individuals.

• 정보보호학회논문지
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• 제32권5호
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• pp.891-898
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• 2022

세계적 관심과 함께 드론은 물품 수송, 산림 보호, 안전 관리 등 활용 분야의 저변을 확대해 나가고 있으며 드론은 활용 분야 확대 및 규모 확장에 따라 군사 작전, 환경 감시 등 다양한 분야에서 군집 비행이 응용되고 있다. 현재 국내에서는 특정 산업 분야의 서비스를 위한 이음 5G와 같은 특화망을 구축해 나가고 있다. 이와 관련하여 드론 시스템 또한 AI와 자율비행 등과 융합된 서비스를 제공하기 위해 특화망을 구축하려는 움직임이 보이고 있다. 드론이 여러 서비스와 융합됨에 따라 다양한 환경에서의 다양한 보안 위협 또한 종속되고 있으며, 이에 대응하여 국내에서는 드론 보안에 대한 요구사항과 가이드라인을 마련하고 있는 추세이다. 본 논문에서는 드론 시스템 중 드론의 군집 비행 시스템과 이를 위한 이음 5G와 같은 이동통신 특화망에서 무선랜과 양자 엔트로피 기반 난수 발생기를 탑재한 암호모듈을 활용하여 군집 비행 드론 간 피아식별 및 안전한 정보 제공 기술 방법을 제안하고, 구현에 참고할 수 있는 테스트 벡터를 제공한다.

• 한국방사선학회논문지
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• 제16권2호
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• pp.185-194
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• 2022

국제적으로 과거 원자력 및 방사선 관련 활동으로 인해 부지 및 토양 등이 오염된 레거시 부지에 대한 해결과 관리가 관심의 대상이 되고 있다. 국내에서도 연구로 해체 등의 사례가 있었으며 북한은 최신화된 안전규제가 적용되고 있지 않을 가능성이 있으며 관련하여 운영 기록 등의 관리되지 않고 있을 가능성이 있어 레거시 부지가 될 가능성이 크다. 따라서 본 연구에서는 레거시 부지의 해결에 대한 국제 사례를 검토하고 이로부터 레거시 부지의 특성과 문제점을 파악하였다. 이에 국제 관리 기준에 따른 레거시 부지 대응 절차와 규제 체계를 분석하고 규제 체계 개발 시 고려되어야 할 사항을 도출 하였다. 레거시 부지 대응 방안의 개발은 향후 레거시 부지 발생시 대응과 레거시 부지의 발생을 예방하는데 사용될 수 있다.

• 한국기계가공학회지
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• 제21권4호
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• pp.114-130
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• 2022

The purpose of this study was to investigate the possibility of increasing the efficiency of disaster relief rescue operations through collaboration among multiple aerial and ground robots. The robots create 2.5D maps, which are merged into a 2.5D map. The 2.5D map can be handled by a low-specification controller of an aerial robot and is suitable for ground robot navigation. For localization of the aerial robot, a six-degree-of-freedom pose recognition method using VIO was applied. To build a 2.5D map, an image conversion technique was employed. In addition, to merge 2.5D maps, an image similarity calculation technique based on the features on a wall was used. Localization and navigation were performed using a ground robot to evaluate the reliability of the 2.5D map. As a result, it was possible to estimate the location with an average and standard error of less than 0.3 m for the place where the 2.5D map was normally built, and there were only four collisions for the obstacle with the smallest volume. Based on the 2.5D map building and map merging system for the aerial robot used in this study, it is expected that disaster response work efficiency can be improved by combining the advantages of heterogeneous robots.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.369-390
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• 2023

Technologies for disaster management are highly sought areas for research and commercial deployment. Landslides, Flood, cyclones, earthquakes, forest fires and road/train accidents are some causes of disasters. Capturing video and accessing data in real time from the disaster site can help first responders make split second decisions which may save human lives and valuable resource destructions. In this context the communication technologies performing the task should have high bandwidth and low latency which only 5G can deliver. But unfortunately in India, deployment of the 5G mobile communication systems is yet to give a shape and again in remote areas unavailability of 4G signals is still severe. In this situation the authors have proposed, simulated and experimented a 4G-5G communication scheme where from the disaster site the signals will be transmitted by a 5G terminal to a nearby 4G-5G gateway installed in a mobile vehicle. The received 5G signal will be further relayed by the 4G-5G gateway to the fixed 4G base station for onward transmission towards the disaster management station for decision making, deployment and relief monitoring. The 4G-5G gateway acts as a relay and converter of 5G signal to 4G signal and vice versa. This relayed system can be further mounted on a vehicle mounted relay (VMR) as proposed by 3GPP in Release 18. The scheme is also in the same line of context with Verizon's, "Tactical Humanitarian Operations Response" (THOR) vehicle concept. The performance of the link is studied in different channel conditions, the throughput achieved is superb. The authors have implemented the above mentioned system towards smart campus networking and monitoring landslides activities which are common in their regions.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• 전자통신동향분석
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• 제38권1호
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• pp.55-64
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• 2023

Recently, preparations for 6G have led to the increasing interest in integrated or hybrid communication networks considering low-orbit satellite communication networks with terrestrial mobile communication networks. In addition, the demand for frequency allocation for new mobile services from low-orbit small satellites to provide global internet of things (IoT) services is increasing. The operation of such satellites and terrestrial mobile communication networks may inevitably cause interference in adjacent bands and the same band frequency between satellites and terrestrial systems. Focusing on the results of the recent ITU-R WP4C meeting, this study introduces the current status of frequency sharing and interference issues between satellites and terrestrial systems, and frequency allocation issues for new mobile satellite operations. Coexistence and compatibility studies with terrestrial IMT in L band and 2.6 GHz band, operated by Inmassat and India, respectively, and a new frequency allocation study (WRC-23 AI 1.18) are carried out to reflect satellite IoT demand. For the L band, technical requirements have been developed for emission from IMT devices at 1,492 MHz to 1,518 MHz to bands above 1,518 MHz. Related studies in the 2 GHz and 2.6 GHz bands are not discussed due to lack of contributions at the recent meeting. In particular, concerning the WRC-23 agenda 1.18 study on the new frequency allocation method of narrowband mobile satellite work in the Region 1 candidate band 2,010 MHz to 2,025 MHz, Region 2 candidate bands 1,695 MHz to 1,710 MHz, 3,300 MHz to 3,315 MHz, and 3,385 MHz to 3,400 MHz, ITU-R results show no new frequency allocation to narrow mobile satellite services. Given the expected various collaborations between satellites and the terrestrial component are in the future, interference issues between terrestrial IMT and mobile satellite services are similarly expected to continuously increase. Therefore, participation in related studies at ITU-R WP4C and active response to protect terrestrial IMT are necessary to protect domestic radio resources and secure additional frequencies reflecting satellite service use plans.

• 임상간호연구
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• 제15권2호
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• pp.115-126
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• 2009

Purpose: The purpose of this study was to assess the frequency and characteristics of occupational blood and body fluid exposure for operating room personnel during surgical procedures and identify risk factors. Methods: A self-report questionnaire was used to survey 519 operating room personnel who participated in 132 surgeries in an acute care hospital in Seoul between April 12 and 20, 2006. Results: The response rate was 82% (530/646) and analyzed total 519 except for improper 11 questionnaires. Exposure rate was 45.5% (60/132) during operations and 20.4% (106/519) for operating room personnel. By job categories, the exposure rate was as follows; anesthesiologists and nurses 31.2% (44/141), operators and assistants 22.1% (32/145), circulating nurses 12.0% (11/92), scrub nurses 11.4% (14/123). The mucous membrane-eye exposure with a high risk of transmitting infection was 6.6% (7/106) and the most frequent route of exposure was blood and body fluid touching unprotected skin. In multivariate analysis, the risk factors of exposure were services, duration of operation, a patient who had a peripheral arterial catheter line, and job category. Conclusion: Educational programs and appropriate use of personal protective equipment are necessary to prevent operating room personnel from being exposed to blood and body fluid.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.339-343
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• 2015

To rapidly recover ceased functionality of a facility after a catastrophic seismic event, critical decisions on facility repair works are made within a limited period of time. However, prolonged damage assessment of facilities, due to massive damage in the surrounding region and the complicated damage judgment procedures, may impede restoration planning. To assist reliable structural damage estimation without a deep knowledge and rapid interactive analysis among facility damage and restoration operations during the approximate restoration project planning phase, we developed a prototype of distributed facility restoration simulations through the use of high-level architecture (HLA) (IEEE 1516). The simulation prototype, in which three different simulations (including a seismic data retrieval technique, a structural response simulator, and a restoration simulation module) interact with each other, enables immediate damage estimation by promptly detecting earthquake intensity and the restoration operation analysis according to estimated damage. By conducting case simulations and experiments, research outcomes provide key insights into post-disaster restoration planning, including the extent to which facility damage varies according to disaster severity, facility location, and structures. Additional insights arise regarding the extent to which different facility damage patterns impact a project's performance, especially when facility damage is hard to estimate by observation. In particular, an understanding of required type and amount of repair activities (e.g., demolition works, structural reinforcement, frame installation, or finishing works) is expected to support project managers in approximate work scheduling or resource procurement plans.