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.

In modern society, the delivery service market has grown explosively due to rapid changes in social structure and the recent COVID-19 pandemic. Therefore, various problems such as injury to workers and an increase in human accidents are occurring due to the loading and unloading of parcels. In order to solve this problem, domestic company n is developing a “robot-based cargo loading and unloading system”. In developing a new technology system, quantitative reliability targets should be set for efficient operation and development. In this paper, reliability analysis was conducted through field data for the pneumatic gripper of the “robot-based cargo loading system”. The reliability of the failure data was analyzed to estimate the distribution parameters and MTTF. Random data was derived for the probability of occurrence of a failure with the estimated value. By repeating the simulation to predict the number and year of failures according to the estimated parameters of the probability distribution, it was proposed as a method that reflects realistic probabilities rather than calculating with simple arithmetic using the average MTTF previously used in the field.

This paper is the result of a comparative analysis of crew members' workloads using the EMG and OWAS methods according to the fishing process before and after the improvement of the operating system of the experimental vessel, with the aim of improving operational efficiency and safety work of coastal improved stow net fishing vessels. The target crew members were three people with at least five years of experience on board the same fishing vessel as the experimental vessel. After improving the operating system, such as installing a power block crane and two capstans, change the location of the ball-roller, the time required for setting and hauling work decreased (p < .01), and the evaluation results by OWAS showed that the overall workload for setting and hauling net work decreased. The results of muscle activity analysis showed a decrease in lashing anchor work (p < .01) in the case of hauling net and in anchor dropping work in the case of setting net (p < .001). It is judged that the use of ball rollers in net handling has been reduced; consequently, work safety has been improved.

The development of technology related to the Fourth Industrial Revolution and the growth of the online market due to pandemic are continuing the growth of the logistics market for product delivery. If it is difficult to deliver the product directly to the customer during delivery, storage and delivery using the unmanned courier box are being carried out. However, existing storage boxes are not actively used due to lack of usability even though they have the advantage of storing goods and delivering non-face-to-face. In addition, existing courier boxes are not prepared for cold chain transportation. The unmanned delivery storage device with ICT cold chain technology should be developed to prepare for the transition to non-face-to-face society, to improve logistics efficiency and meet user's requirements. Also, it is necessary to consider the measures to reduce the safety problems that may occur during the use and maintenance of the automatic system.This study conducted a model-based analysis for the development of unmanned delivery storage devices with ICT cold chain technology, and conducted a study to derive the system development specifications that meet the requirements and secure safety and apply them to the development process.

This study examined the certification effects of safety and health management system (SHMS) on the establishment level of SHMS and accident statistics in construction industry. This study obtained the establishment level of SHMS for 106 construction companies surveyed from our previous study. In addition, three major accident statistics (mortality rate, accidental mortality rate, and injury rate) for the companies were collected from the database in Korean Occupational Safety and Health Agency. The statistical analysis results revealed that the establishment level for SHMS certified companies was significantly higher than those for uncertified or certification preparing companies. Furthermore, SHMS certified companies showed significantly smaller accident statistics compared to uncertified or certification preparing companies. The results of this study support the positive effects of SHMS on reducing major industrial accidents in construction companies.

The growth of the online market is accelerating due to the development of technology and the pandemic era. The delivery service through the courier must be used to deliver the ordered goods to the customer through the online market. With the growth of the online market, the logistics market for delivery is also growing. The traffic and environmental problems are emerging as social issues. Urban logistics technology using underground space based on the urban railway developed to improve logistics efficiency in a metropolitan area and a new alternative to environmental problems. This study proposed a plan to secure system safety through safety analysis based on operational concept definition and scenario analysis by applying model-based perspective analysis to the system under development.

In this study, the Euro NCAP-based AEB system evaluation simulation was conducted by applying the calculated corrected TTC by road condition estimation of scenarios (CPFA, CPNC, CPLA) of the V2P situation scenario using PC-Crash, a program used for traffic accident analysis. The scenario was evaluated in consideration of the two road conditions. the low-speed conditions among every scenarios avoided collision, but in the medium and high-speed conditions has been collided with pedestrian. It was confirmed that the time point at recognizing pedestrian was lower than set TTC at which AEB system was operated, even though the AEB was operated immediately, a collision occurred due to insufficient braking distance. As in this study, if studies such as V2V, V2P, and V2B considering road friction are actively conducted, it is expected to be useful data for automobile accident prevention and accident analysis.