Secondary Accidents Research Articles

에너지 절감을 위한 전기 자동차용 고효율 독립 다상 IPM BLDC 허브모터 설계에 관한 연구

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함정 재해의 신속 진압을 위한 경로 탐색에 관한 연구

Naval ships could be seriously damaged by enemy attacks in battle. Moreover, this damage could be spread and deteriorated into a secondary accident. Secondary accidents that have adverse effects on naval ship’s survivability, such as fire, flood, smoke extension, and patient occurrence, are defined as casualties. These casualties sharply degrade the survivability of naval ships. Furthermore, naval ships could be burned-out and sunk by casualties in isolated sea. Therefore, damage control and rapid suppression of the casualties in the naval ships is essential. This study was conducted in the establishment of suppression paths according to the characteristics of each casualty so that the developed system can support the rapid suppression in an emergency and even the training situation on a regular state. To establish the suppression paths, the two-dimensional numerical map is designed by converting the three-dimensional features of the naval ships, and the well known algorithms are compared to present the appropriate one for path finding problem on the naval ships. Finally, we devised a specific routing algorithm that fits the characteristics of each casualty in accordance with the Korean Navy’s doctrines and handbooks of casualty suppression.

Study on the influence of barriers in the diffusion process of liquid hydrogen leakage

Based on the computational fluid dynamics method, a numerical model of large-scale liquid hydrogen leakage and diffusion is established. By simulating the diffusion process of hydrogen cloud when the obstacle is located at different positions, we get the motion law of cloud and the important time parameters of Cloud Diffusion reaching the dangerous range (combustible concentration). Compared with the process of liquid hydrogen leakage and diffusion under barrier-free conditions, it was found that setting an obstacle in the wind direction of the leakage port can accelerate the diffusion speed of the hydrogen cloud. The time to reduce the cloud concentration below the flammable range is reduced by 15%, therefor, can decrease the risk of fire; however, setting barrier in the downwind direction will lead to the accumulation of hydrogen cloud near the ground, and the time when the cloud concentration drops below the flammable range is 64% longer than the barrier-free working conditions, the possibility of serious secondary accidents was increased.

Topological Sequence Recognition Mechanism of Dynamic Connected Cars Using the Connected Mobile Virtual Fence (CMVF) System for Connected Car Technology

To prevent traffic accidents, even autonomous vehicles, as well as connected cars, need to know the driving situation of other vehicles in the vicinity. In particular, in emergency situations, messages’ transmission among vehicles can face many problems such as the broadcast storm, message flooding, or message contention. Therefore, this paper proposes a topological sequence recognition mechanism that calculates the driving direction of vehicles, the geographical location and relative position associated with the driving direction, and the relative safety distance for each vehicle in connected subgroups of connected cars using the Connected Mobile Virtual Fence (CMVF) system. Thus, the proposed mechanism can alleviate issues with message dissemination as a vehicle will know the driving situations of other nearby vehicles. In addition, the proposed mechanism is found to be very effective, particularly in preventing secondary accidents due to traffic accidents in front of the vehicle so that emergency messages can be disseminated to the trailing vehicles. Finally, it is expected that the proposed mechanism will be reflected in the technology of connected cars and autonomous vehicles.

Transit-Based Evacuation for Urban Rail Transit Line Emergency

Urban rail systems are the backbone of urban transit networks and are characterized by large passenger volumes, high speeds, punctuality, and low environmental impacts. However, unforeseen events such as rail transit line emergencies can lead to unexpected costs and delays. As a means of disruption management, we divide the decision support system for urban rail transit line emergency situations into two stages—transit-based evacuation and bus bridging management. This paper focuses on the transit-based evacuation under emergency scenarios on a single rail line. The model determines the vehicles and routes within traditional transit systems required to evacuate stranded passengers within a given time window. In addition, the proposed method ensures the reliability of traditional transit systems by considering the operating fleet and reserve fleet in the traditional transit systems. Therefore, the proposed optimization model is established with the objective of maximizing the total number of stranded passengers transferred within the given time window and headway constraint. Herein, we present the optimization model and solution method, and the proposed method is validated. The effectiveness of the proposed control method is evaluated in the Changchun urban transit network. By analyzing stranded passengers at stations under different numbers of vehicles and time periods, the results show that the proposed model can significantly provide routing arrangements to maximize the number of passengers evacuated from stations. The results are useful in the development of emergency evacuation plans to prevent secondary accidents and evacuate stranded passengers during a rail transit line emergency.

Design and Implementation of Traffic Incident Acquisition and Reporting Device Based on LTE Communication

With the development of Internet of Things (IOT) technology, V2X vehicle-road collaboration platform is rapidly applied to intelligent traffic control and road condition service. However, the existing platforms have the problems of inaccurate collection and release of traffic incidents, which can not solve the situation of a large number of secondary accidents and traffic congestion. To meet this demand, a road traffic incident collection and reporting device based on LTE communication is designed. As a roadside intelligent device of LTE-V2X vehicle infrastructure cooperative platform, it realizes the real-time and fast collection and reporting of traffic incidents, and sends them out in real time through V2X internet of vehicle service platform to help drivers adjust their driving routes and avoid congestion points. To help the traffic management department locate the incident quickly and deal with it effectively, so as to eliminate the negative impact of the incident on traffic operation in the shortest time, and further improve the road operation safety and smoothness index and operation efficiency.

Urban expressway parallel pattern recognition based on intelligent IOT data processing for smart city

With the sustained and rapid development of the social economy and the rapid growth of urban vehicles, urban expressway has developed rapidly. As the roadmap of urban traffic, the urban expressway has a relatively high and stable driving speed, and also bears a large amount of urban traffic. However, in recent years, with the expansion of the city scale, the congestion of urban expressways has become increasingly severe. In various areas of the merged area, due to various factors such as mismatched traffic capacity and reduced driving speed, it is easy to cause deterioration of road conditions in a short period of time and cause more secondary accidents. In order to reduce the incidence of expressway traffic accidents and to avoid as much as possible the casualties and property losses caused by accidents, Intelligent Transportation Systems (ITS) supported by information technology, data communication transmission technology, control technology and traffic engineering were introduced. With the rise of artificial intelligence and the continuous development of ITS, the video acquisition methods of real-time traffic flow data and the image recognition ability of video sequences continue to improve, providing theoretical basis and technical support for the research of urban expressway parallel pattern recognition. Aiming at the shortcomings of traditional pattern recognition methods, such as weak anti-interference to complex traffic environment and low correct recognition rate, this paper studies the pattern recognition method based on image processing, and selects the fuzzy C-means clustering in the currently used clustering methods (FCM) algorithm Because the FCM algorithm cannot obtain the global optimal solution and need to determine the number of cluster categories in advance, this paper uses ReliefF algorithm and Particle Swarm Optimization (PSO) to compare the feature weight and number of clusters of traditional FCM algorithm. Make improvements. Through the experimental analysis of the acquired video images, the results show that the improved FCM algorithm based on the proposed method has better real-time and accuracy in the application of urban expressway parallel pattern recognition.

Effect Analysis of Virtual-reality Vestibular Rehabilitation based on Eye-tracking

Vertigo is one of the most common complaints encountered by physicians and the patients are steadily increasing. These patients are exposed to the risk of secondary accidents such as falls due to vertigo. There are two ways to improve this symptom: medication and rehabilitation. Although temporary symptomatic improvement may be expected in patients treated with medication, vertigo may recur and medication can delay central compensation. In contrast vestibular rehabilitation exploits central mechanisms of neuroplasticity to increase postural stability and enhance visual-vestibular interactions in situations that generate conflicting sensory information. However, vestibular rehabilitation may be compromised by incorrect performance of exercises, and there is a need for active effort and interest from the patient during rehabilitation. To solve these problems, we decided to apply FOVE HMD for eye-tracking and Unity3D to create virtual reality. The proposed eye-tracking based algorithm calculates the concentration of users with eye tracking data and calculates the motion width of the patient with nystagmus, thus the severity of the patient according to the score can be determined. According to our experimental test against healty group and patients group, this result showed the meaningful data to use define the contents result.

Longitudinal Influence of Autonomous Vehicles and Vehicular Communication on Post-Accident Traffic

The primary objective of this study is to quantify the role of autonomous vehicles (AVs) and vehicular communication on post-accident traffic flow. We propose an analytical framework embedding car-following models for safety analysis for mixed traffic flow of human-driven vehicles and AVs, especially under vehicular communication. We then calibrate car-following models with real-world data. Through the model-based simulation experiments, we examine and discuss in details traffic flow stability, throughput, and post-accident safety. The results show that the traffic congestion resulting from the primary accident lasted shorter with the growing penetration of AVs. It is appealing that relaxing the safety grade of AVs can increase the throughput and this enables us to balance the safety level of AV operation and traffic throughput. In addition, incorporating vehicular communication can alleviate the risk of relaxing safety requirement and strikingly reduce the number and the severity of the secondary accidents.

Influence mechanism of congestion degree of layout on domino effect in petrochemical plants

The study focused on the establishment of the congestion degree in describing the layout of process plants, and discovering the influence mechanism of congestion degree on the risk of domino effect, in view of the fact that the congestion degree has not been used to represent the characteristics of petrochemical plant layout. A characterization model was built for the congestion degree of process plant layouts applying the theory of fuzzy model that it took safety distance, the amount of materials involved and the hazard of these substances into consideration, and came out with a membership function through logical calculus, which made congestion degree a comparable data. Secondly, a systematic domino effect risk analysis method was generalized on account of domino effect quantitative risk analysis method which include the equipment damage probability, the domino effect occurrence probability, risk analysis based on the consequences, individual risk index and social risk index, the process plant natural risk index and real risk index, and then these indices were compared to select the most suitable ones to illustrate the risk of domino effect and to research about part of the storage tank field of a petrochemical plant. Finally, the influence mechanism of layout congestion degree on domino effect of regional accident was obtained through this quantitative analysis. Simultaneously, a range of the congestion degree was acquired according to the individual risk index which can be used as a reference in the design stage of the process plant. The result proved that high congestion degree in process plant greatly increased the risk of domino effect in most of the quantitative risk analysis index, such as the probability of resulting in secondary accidents, the harm on both human and the equipments caused by the initial accident, and the individual risk and social risk, etc. The characterization models for the congestion degree and the domino risk analysis method established and summarized in the study have been applied in the design stage of process plant layout to optimize the layout, and thus reduce the risk of domino effect caused by accidents.

MAR_Sybil: Cooperative RSU Based Detection and Prevention of Sybil Attacks in Routing Process of VANET

VANET is a next generation communication technology where vehicles create an autonomous network with assistance of RSU (Road Side Units). VANET provides legitimate information to the users on the road, in order to increase the road and user’s safety. It provides useful information to the vehicles about directions, location mapping, premises, etc. The fast propagation of emergency and local warning messages to the approaching vehicles will be helpful for preventing secondary accidents. If security of the network is not guaranteed, several attacks may occur, thereby alert messages may not reach to the RSUs on time or message may get spoof due to attack on it. Major attacks on emergency messages such as timing attack, spoofing attack, DoS attack, Sybil attack, etc. would be considered to mitigate and make the communication and infrastructure more secure. One of the major emergency message attack is Sybil, which is used by selfish message propagators to claim authenticity of their message by propagating with multiple identities. By deceiving other nodes with their false support using multiple identifies, they can achieve various purpose in network like vehicle re-routing, speed shift etc. This work proposes a RSU cooperative detection mechanism involving triangulation and fake propagation by RSU to identify the Sybil attacks in the VANET. The proposed methodology secures fast communication emergency messages to prevent secondary accidents by validating the message sources, nodes will accept only the messages from RSU which are digitally signed and source validated against Sybil nodes. The performance evaluation is made with existing methodology and effectiveness of the proposed methodology results demonstrated in article. The spatial location and trajectory of attacker is learnt effectively with RSU cooperation in the proposed work.

2 차 사고 예방을 위한 IoT 안전 삼각대

한국도로공사 및 국민안전처 통계에 따르면 2018년 4월 기준, 2차 사고 치사율은 54.1%로 일반 사고 치사율(9.3%)에 비해 약 6배 높다. 또한 최근 5년간 고속도로에서 발생한 2차 사고 사망자는 연평균 약 37명, 부상자는 약 1,142명으로 이는 교통사고 전체 사망자 수의 약 15%를 차지한다. 발생한 대부분의 2차 사고는 안전 삼각대를 설치하러 가는 도중에 많이 발생한다고 한다. 따라서 설치 시 안전성과 효과적으로 후행 차량에 사고 사실을 알리는 효율성이 높은 새로운 삼각대의 설계가 필요하다. 본 논문의 연구 목적은 IoT 기술을 안전 삼각대에 결합하여 운전자의 직접적인 설치를 요했던 기존 삼각대와는 달리 사고의 정도에 따라 수동 및 자동 조작이 가능하도록 설계하여 차별화를 둠으로써 안전성과 편의성 증대 효과를 기대할 수 있으며 2차 사고 예방을 통해 인명 또는 물적 피해를 최소화하는 것이다.

Human-Vehicle Collision Detection Algorithm Based on Image Processing

In recent years, with the growth of China’s economy and the development of the automobile manufacturing industry, the number of various vehicles has continuously increased, and the incidence of traffic accidents has also increased. Especially in traffic blind areas, right-turning areas of vehicles, etc., traffic accidents such as vehicle collisions are extremely easy to occur, which poses a serious threat to people’s lives and property, and is extremely harmful. Therefore, related research on collision detection of people and vehicles has been traffic-safe and has received extensive attention from field researchers. At present, the research on human-vehicle collision detection is to detect human-vehicle collision accidents by tracking the track of vehicles and pedestrians, but there are problems such as poor tracking effect, low accuracy of collision discrimination and complex algorithms. Aiming at these problems, this paper studies the human-vehicle collision detection algorithm based on image processing. Through the image processing of traffic monitoring video, the vehicle and pedestrian contour information is extracted. Based on this, a mathematical model for collision detection is constructed to realize human-vehicle collision detection. The results show that the proposed method can effectively distinguish the collision between pedestrians and vehicles, and the algorithm for image processing is simpler than the traditional tracking algorithm, and the time is shorter. The results show that the image-based collision detection algorithm based on image processing can effectively and quickly identify the traffic accidents in which people and vehicles collide, and then can issue alarm signals in time, shortening the accident processing time and reducing the accident time. The possibility of a secondary accident has a high practicability in the detection of traffic accidents in which people and vehicles collide.

Design of Expressway Safety Early Warning System Based on Internet of Things

Aiming at the current situation of frequent accidents and slow rescue in the safety management of expressways in China, this paper integrates electronic, computer and Internet of Things technologies to form a self-organizing network through the routing nodes laid on the expressways, and constitutes a expressway safety warning system. At the same time, the mobile App, Web and PC monitoring platforms were developed and aggregated into the expressway traffic management center to realize real-time transmission and release of cross-platform alarm information. The test results show that the system can effectively reduce the incidence of accidents, especially secondary accidents, shorten the rescue time and improve the smooth flow rate of roads.

Optimal location of lane-changing warning point in a two-lane road considering different traffic flows

The timely lane-changing (LC) warning information should be informed to vehicles upstream when some things (such as accident or vehicle breakdown) happen. To avoid secondary accidents or reduce traffic congestion, this paper investigates the optimal LC warning point in a two-lane road considering the different traffic condition. The Intelligent Driver car-following Model and MOBIL LC model are used to describe the vehicle’s longitudinal and lateral motion rules, and methods based on probability theory and numerical simulations are proposed to investigate the optimal warning point under different traffic scenarios. The numerical results show that: i) the successful LC probability is not monotone changing with velocity. ii) providing LC warning information can reduce the average travel time, especially in moderate or congested traffic conditions. iii) the appropriate LC warning point location range becomes smaller with the increase of flow rate. iv) the effective LC warning​ point is about 30–300 meters far from the event point under different traffic flows.

Analysis method for causal factors in emergency processes of fire accidents for oil-gas storage and transportation based on ISM and MBN

The paper focuses on emergency process safety and presents a novel analysis method for the causal factors in emergency processes of fire accidents for oil-gas storage and transportation based on the interpretative structural modeling (ISM) and modified Bayesian network (MBN) model. Twenty-one causal factors are summarized according to the statistical analysis of actual accident cases. Based on three classes of hazard theory, a classification system of causal factors leading to secondary accidents in emergency processes, which includes twenty-one influencing factors from four aspects of the human-material-environment-management, is established. Using the ISM method, the relationships among causal factors are analyzed, and an 8-layer hierarchical ISM model of the causal factors in emergency processes is established. Moreover, the direct factors, indirect factors and root factors leading to secondary accidents are obtained. The fault tree model of secondary accidents is constructed, and the probability importance of each cause factor is calculated using the fault tree analysis method. However, to more accurately assess the quantitative influence of each cause factor on secondary accidents, the fault tree model is transformed into a Bayesian network (BN) model. Furthermore, the conditional probability tables in the BN are modified using the expert scoring method, and the MBN model is established. The more accurate posterior probability of each causal factor is calculated based on the MBN model, and the sensitivity and influence are analyzed by GeNIe software. The six most probable paths leading to secondary accidents are identified. Finally, the proposed method is applied to analyze the causal factors in the emergency processes of two actual accident cases. The research results have important scientific significance and application value for grasping the evolution law of secondary accidents in emergency processes and preventing secondary accidents in oil-gas storage and transportation systems.

Investigation of the safety evaluation method of piping system

Plumbing is necessary for boilers and heat exchangers and in buildings and power plants, including those that produce green energy. Further, among all the components used in plumbing, piping systems are subjected to high temperatures and pressures. A twisted or cracked pipe adversely affects the efficiency of a power plant and production rate of a factory or causes secondary accidents. This study investigates the safety evaluation of the SUS material, which is mainly used in boiler and heat exchanger piping. SUS is an inelastic material; therefore, the leak-before-break concept of fracture mechanics, for which various safety evaluation methods and specifications have been presented, can be applied during the design stage. However, the test is performed as a substitute for the actual pipe testing because of the high cost and difficulty level. Thus, a method to test compact pipes based on various loading ratios is developed. This study determines the test jig shape to obtain fragility similar to the theoretical value. The proposed test method is only available under certain load conditions and requires a new J-integral when the jig geometry or load ratio is changed. Therefore, the J-integral equation for each load condition is integrated to propose a J-integral equation independent of the load ratio.

Abnormal Gait Recognition Using 3D Joint information of Multiple Kinects System and RNN-LSTM.

Gait is an important indicator for specific diseases. Abnormal gait patterns are caused by various factors such as physical, neurological, and sensory problems. If it is possible to recognize abnormal gait patterns in the early stage of the related disease, patients can receive proper treatment early and prevent secondary accidents such as falls caused by unbalanced gait. In this paper, we propose a gait recognition system that can recognize 5 abnormal gait patterns. Our system using 3D joint information obtained by using multiple Kinect v2 sensors and RNN-LSTM. In particular, abnormal gaits caused by physical problems such as injury, weakness of muscle, and joint problems are targeted for recognition. The purpose of this paper is to find optimal condition for gait recognition when using the multiple Kinect v2 sensors. Experiments were conducted by comparing the test accuracies on 14 combinations of human joint. Through this experiment, we selected optimal joints to show outstanding results so that our gait recognition model performs optimally. Results show that Ankles, Wrists, and the Head are the most influential joints on RNN-LSTM model. We applied 25-joint information of the human body to recognize gait patterns and achieved an accuracy over 97%.

The early warning efficiency analysis of secondary accidents in expressway tunnels

Secondary accidents usually occur after an accident in a freeway tunnel. To this end, this article proposes an integrated safety early warning system for preventing secondary accidents in tunnels. ...

Incident Detection Method of Expressway Based on Traffic Flow Simulation Model**Supported by the National Natural Science Foundation of China under Grant Nos. 71871130, 71471104, 71771019, 71571109; the University Science and Technology Program Funding Projects of Shandong Province under Grant No. J17KA211; and the Project of Public Security Department of Shandong Province under Grant No. GATHT2015-236

The expressway traffic incidents have the characteristics of high harmful, strong destructive and refractory. Incident detection can guarantee smooth operation of the expressway, reduce traffic congestion and avoid secondary accident by informing the accident, detection and treatment timely. In this paper, an incident detection method is proposed using the toll station data that takes into account the traffic ratio at the entrances and crossway in the network. The expressway traffic simulation model is improved and a simulation algorithm is established to describe the movement of the vehicles. A numerical example is experimented on the expressway network of Shandong province. The proposed method can effectively detect the expressway incidents, and dynamically estimate the traffic network states so as to provide advice for the highway management department.