This study analyzes the aerodynamic and structural characteristics of an H-Darrieus vertical-axis wind turbine (VAWT) under varying inlet velocities using transient analysis. The k-ε turbulence model and six-DOF were applied to simulate urban environments in the flow analysis, while the structural analysis considered blade momentum of inertia and RPM conditions. The numerical results showed that the drag and lift forces increased by 60% and 53% respectively from the nominal wind speed to the cut-off wind speed conditions. Structural analysis indicated that the maximum Von-Mises stress in the blade did not exceed the yield strength of 69 MPa of PC-ABS, ensuring structural stability. However, the connecting rod exceeded the yield strength of SPCC 270 MPa, suggesting potential failure due to repeated rotational loads. This study confirms that materials with a yield strength of more than 1,100 MPa required for connecting rods to ensure reliable operation at high wind speed. These findings provide important insights for the design of robust VAWTs suitable for extreme environments.

교통안전시설물의 관리는 도로교통의 안전과 직결되는 문제이다. 운전자는 신호등, 표지판, 노면표시 등을 통해 운전에 필요한 정보 를 얻는다. 노후된 표지판과 노면표시는 운전자에게 잘못된 정보를 제공할 수 있으므로 주기적인 시설물의 관리가 필요하다. 본 연구 는 딥 러닝 기술을 활용해 운전자 시각의 영상 자료에서 교통안전표지를 자동으로 탐지하고자 하며, 교통안전표지의 공통된 색상 특 징을 기반으로 클래스를 그룹으로 묶어 데이터셋을 구축하는 방법을 제안한다. 객체탐지의 성능지표로 널리 활용되는 mAP를 사용해 클래스 묶음 여부에 따른 탐지 성능을 비교한 결과, 색상 기반 클래스 묶음을 적용한 모델의 탐지 성능이 비교군에 비해 약 36% 상승 함을 확인하였다.

Recently marine accidents involving floating objects have been continuously increasing due to domestic coastal traffic conditions, and as a result cases of secondary-linked reduction gear damage have also occurred one after another. This research aims to evaluate the ship propulsion system safety through the analysis the effect of the torsional stress generated on the propeller shaft system when a rope or net is wrapped around a propeller at sea through theoretical analysis, simulation analysis, and ship empirical test.

This study analyzes the importance-performance analysis (IPA) of the 10 dimensions of the smart construction safety management system, and analyzes which dimensions are important and which dimensions are performing to determine key improvement tasks, incremental improvement tasks, Maintenance and reinforcement tasks and continuous maintenance tasks were derived. Among the 10 dimensions of the smart construction safety management system, the dimensions that are recognized as important by all field managers and field workers and have high performance are the automatic risk displacement measurement system, smart environmental sensor system, and heavy equipment seizure prevention system. However, areas that were perceived as having high importance but low performance were worker location tracking systems, smart safety helmet chin muscles, and smart safety ring fastening. Among the smart construction safety management systems perceived by field managers, areas for key improvement with high importance and low performance included worker location tracking system and smart safety ring fastening. Among the smart construction safety management systems perceived by field workers, the area for key improvement with high importance and low performance was the automatic risk displacement measurement system.

This study aims to develop a Commercial Vehicle Integrated Traffic Safety System utilizing Connected Intelligent Transportation Systems (C-ITS) technology. This system provides functionalities for accident prevention and efficient traffic management through vehicle-to-vehicle and vehicle-to-infrastructure communications. The key findings suggest that the integrated system using C-ITS can offer functions for traffic safety and preliminary stages of autonomous driving. It is anticipated that by applying vehicle and Information and Communication Technology (ICT) technologies, traffic safety issues and driver convenience can be enhanced.

In response to the global transition towards carbon neutrality, there's an increasing emphasis on sustainable energy solutions, with offshore wind power playing a crucial role, especially in South Korea. This study presents an AI-based safety management system specifically designed for offshore wind operators. At the heart of this system is a machine learning algorithm that processes sensor data to automatically recognize human behavior and improve the accuracy of predicting worker actions and conditions. Such predictive analytics not only refines the analysis of behavioral patterns, but also increases the effectiveness of accident prevention. The results of this research are expected to significantly improve safety measures in offshore wind facilities and further sustainable energy initiatives.

Considering the difficulties of the manufacturing industry by improving production efficiency in the era of high wages and aging in domestic automation facilities, automation facilities are considered an irreversible trend, but many serious related disasters are occurring due to the problems of increasing automation facilities due to the enlargement of manufacturing processes, line-up, and automation. The purpose of this study is to review the usage conditions and safety measures for industrial robots that are experiencing serious industrial accidents and are expected to continue to increase in facilities among automation facilities at the automation industrial site and propose ways to ensure the fundamental safety of the facilities at all times The suggestions are as follows. The purpose is to prevent safety accidents in advance by applying safety door aids to industrial sites and installing additional safety devices in safety slide door lock systems applied to safety fence doors of new and already installed facilities to detach safety keys and ensure that workers carry them at all times.

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

As the importance of artificial intelligence grows rapidly and emerges as a leader in technology, it is becoming an important variable in the next-generation industrial system along with the robot industry. In this study, a safety system was developed using deep learning technology to provide worker safety in a robot workplace environment. The implemented safety system has multiple cameras installed with various viewing directions to avoid blind spots caused by interference. Workers in various scenario situations were detected, and appropriate robot response scenarios were implemented according to the worker's risk level through IO communication. For human detection, the YOLO algorithm, which is widely used in object detection, was used, and a separate robot class was added and learned to compensate for the problem of misrecognizing the robot as a human. The performance of the implemented system was evaluated by operator detection performance by applying various operator scenarios, and it was confirmed that the safety system operated stably.

원자력발전소 지진 확률론적 안전성 평가인 PSA(Probabilistic Safety Assessment)는 오랜 기간에 걸쳐 확고히 구축되어 왔다. 반면 에 다양한 공정 기반의 산업시설물의 경우 화재, 폭발, 확산(유출) 재난에 대해 주로 연구되어 왔으며, 지진에 대해서는 상대적으로 연 구가 미미하였다. 하지만, 플랜트 설계 당시와 달리 해당 부지가 지진 영향권에 들어갈 경우 지진 PSA 수행은 필수적이다. 지진 PSA 를 수행하기 위해서는 확률론적 지진 재해도 해석(Probabilistic Seismic Hazard Analysis), 사건수목 해석(Event Tree Analysis), 고장수 목 해석(Fault Tree Analysis), 취약도 곡선 등을 필요로 한다. 원자력 발전소의 경우 노심 손상 방지라는 최우선 목표에 따라 많은 사고 시나리오 분석을 통해 사건수목이 구축되었지만, 산업시설물의 경우 공정의 다양성과 최우선 손상 방지 핵심설비의 부재로 인해 일 반적인 사건수목 구축이 어렵다. 따라서, 본 연구에서는 산업시설물 지진 PSA를 수행하기 위해 고장수목을 바탕으로 확률론적 시각 도구인 베이지안 네트워크(Bayesian Network, BN)로 변환하여 리스크를 평가하는 방법을 제안한다. 제안된 방법을 이용하여 임의로 생성된 가스플랜트 Plot Plan에 대해 최종 BN을 구축하고, 다양한 사건 경우에 대한 효용성있는 의사결정과정을 보임으로써 그 우수 성을 확인하였다.

This study attempted to analyze the comparative advantage in terms of disaster safety costs in verifying the effectiveness and economic feasibility of the high-performance water-bulwark system in the pole tunnel, which was recently promoted as a part of the acceleration of vehicles. The tunnel to be analyzed was divided into a short tunnel(Anyang, Cheonggye) and a long tunnel(Suraksan, Sapaesan). As a result, it was analyzed that 25% of the improvement effect would occur if one lane was secured by applying the Water-Bulwark System. It was analyzed that this is because the time value cost, which accounts for a large proportion of the traffic congestion cost of short tunnels and pole tunnels, differs depending on the congestion time and traffic volume, not the length of the tunnel.

The need for an intelligent information-based ship accident prevention and control system for various marine accidents is very clearly emerging. As it is a variety of marine accidents, the causes are diverse. Therefore, it can be said that it is very important to prevent these marine accidents and their causes in advance. Therefore, a study was conducted on an intelligent information-based ship safety management decision support system that provides information necessary for decision-making at sea and land through an integrated management device for ships that informs safety-related risks in real-time ship operation. In the future, we intend to pursue the development of a system that can aim for safer and more economical ship operation by linking it to navigation instruments through the evaluation and analysis of AI, IoT, and big data.