Real-time Safety Research Articles

A high-resolution trajectory data driven method for real-time evaluation of traffic safety

Real-time safety evaluation is essential for developing proactive safety management strategy and improving the overall traffic safety. This paper proposes a method for real-time evaluation of road safety, in which traffic states and conflicts are combined to explore the internal relationship based on high-resolution trajectory data. In order to assess the real-time traffic safety at a lane level, the trajectory data of the HighD dataset from Germany are utilized to collect lane-based dataset. A surrogate safety measure, time-to-collision (TTC) index, is used for the conflict identification. A binary logistic regression model is employed to quantify the relationship between traffic states and conflicts. Moreover, machine learning methods, including support vector machine, decision tree, random forest, and gradient boosting decision tree, are applied for real-time evaluation. A total of 24 models are trained using the selected four classifier algorithms, and random forest achieves the best performance with 0.85 of the overall accuracy. The results show that the conflict risk can be well estimated by the proposed method. The findings of this study contribute to the high-precision evaluation of real-time traffic safety and the development of proactive safety management.

Development of Advanced Advisory System for Anomalies (AAA) to Predict and Detect the Abnormal Operation in Fired Heaters for Real Time Process Safety and Optimization

As the technology is emerging, the process industries are actively migrating to Industry 4.0 to optimize energy, production, profit, and the quality of products. It should be noted that real-time process monitoring is the area where most of the energies are being placed for the sake of optimization and safety. Big data and knowledge-based platforms are receiving much attention to provide a comprehensive decision support system. In this study, the Advanced Advisory system for Anomalies (AAA) is developed to predict and detect the abnormal operation in fired heaters for real-time process safety and optimization in a petrochemical plant. This system predicts and raises an alarm for future problems and detects and diagnoses abnormal conditions using root cause analysis (RCA), using the combination of FMEA (failure mode and effects analysis) and FTA (fault tree analysis) techniques. The developed AAA system has been integrated with databases in a petrochemical plant, and the results have been validated well by testing the application over an extensive period. This AAA online system provides a flexible architecture, and it can also be integrated into other systems or databases available at different levels in a plant. This automated AAA platform continuously monitors the operation, checks the dynamic conditions configured in it, and raises an alarm if the statistics exceed their control thresholds. Moreover, the effect of heaters’ abnormal conditions on efficiency and other KPIs (key performance indicators) is studied to explore the scope of improvement in heaters’ operation.

Research on Full Link Intelligent Control Platform for Substation Maintenance through Edge-Aware

In view of the imperfect process control, safety control and data control in substation maintenance operation, this paper proposes a full link intelligent control platform for substation maintenance operation based on edge-aware. The platform is composed of on-site edge-aware system and big data decision service system. The on-site edge-aware system uses various intelligent tools to collect data, carries out real-time safety behavior early warning, maintenance quality control and maintenance process supervision through edge computing technology, and uploads the analyzed and processed information to the big data decision service system. The big data decision service system provides support for online decision-making of substation equipment maintenance according to the panoramic multi-source information of the maintenance site provided by the site edge perception system. The full link intelligent control platform for substation maintenance operation has built a control system for real-time control of on-site maintenance operation process, on-site maintenance information and on-site maintenance safety information, which improves the management efficiency of substation maintenance on-site operation and enhances the safety guarantee of maintenance operation.

Digital Twin Empowered Mobile Edge Computing for Intelligent Vehicular Lane-Changing

With automated driving forthcoming, lane-changing for Connected and Automated Vehicles (CAVs) has received wide attention. The main challenge is that lane-changing requires not only local CAV control but also interactions with the surrounding traffic. Nevertheless, the Line-of-Sight (LoS) sensing range of the CAVs imposes severe limitations on lane-changing safety, and the lane-changing decision that is made based only on self-interest ignores its impact on the traffic flow efficiency. To overcome these difficulties, this article proposes a Digital Twin (DT) empowered mobile edge computing (MEC) architecture. With MEC, the sensing and computing capabilities of the CAVs can be strengthened to guarantee real-time safety. The virtualization and offline learning capabilities of the DT can be leveraged to enable the CAVs to learn from the experience of the physical MEC network and make lane-changing decisions via a ‘foresight intelligent’ approach. A case study of lane-changing is provided where the DT is constituted by a cellular automata based road traffic simulator coupled with a LTE-V based MEC network simulator. Deep reinforcement learning is adopted to train the lane-changing strategy and results validate the effectiveness of our proposed architecture.

New Green-Ampt model based on fractional derivative and its application in 3D slope stability analysis

The infiltration process in slopes is an essential issue in geotechnical engineering. A new Green-Ampt model for describing the two-stage infiltration in slopes based on fractional derivatives was proposed in this paper. The Green and Ampt (1911)’s model, and Chen and Young (2006)’s model are two special cases of the proposed model. A 1D loess soil column test in the literature as well as a laboratory rainfall test of loess slope were used to validate the proposed model. The travel curve of wetting front obtained by the proposed model was obviously closer to the actual test value than that obtained by the Chen and Young’s model, indicating that the performance of the proposed model is superior to the Chen and Young’s model due to the use of fractional derivatives. The proposed Green-Ampt model was combined with a rigorous 3D limit equilibrium method to obtain the real-time safety factor of the slope during rainfall. The calculated failure time of the loess slope by the proposed method is close to the actual failure time of the laboratory test, while the corresponding of Chen and Young’s model is obviously shorter than the actual status. Six parameters are included in the proposed model, and sensitivity analysis shows that the fractional derivative has the most significant impact on the wetting front depth, followed by the permeability coefficient, moisture content difference, suction head, rainfall intensity, and slope angle.

Synergistic Integration of Chemo-Resistive and SERS Sensing for Label-Free Multiplex Gas Detection.

Practical sensing applications such as real-time safety alerts and clinical diagnoses require sensor devices to differentiate between various target molecules with high sensitivity and selectivity, yet conventional devices such as oxide-based chemo-resistive sensors and metal-based surface-enhanced Raman spectroscopy (SERS) sensors usually do not satisfy such requirements. Here, a label-free, chemo-resistive/SERS multimodal sensor based on a systematically assembled 3D cross-point multifunctional nanoarchitecture (3D-CMA), which has unusually strong enhancements in both "chemo-resistive" and "SERS" sensing characteristics is introduced. 3D-CMA combines several sensing mechanisms and sensing elements via 3D integration of semiconducting SnO2 nanowire frameworks and dual-functioning Au metallic nanoparticles. It is shown that the multimodal sensor can successfully estimate mixed-gas compositions selectively and quantitatively at the sub-100 ppm level, even for mixtures of gaseous aromatic compounds (nitrobenzene and toluene) with very similar molecular structures. This is enabled by combined chemo-resistive and SERS multimodal sensing providing complementary information.

Real-time signal-vehicle coupled control: An application of connected vehicle data to improve intersection safety

The proliferation of Connected Vehicles and their ability to collect a large amount of data presents an opportunity for the real-time safety optimization of traffic networks. At intersections, Adaptive Traffic Signal Control (ATSC) systems and dynamic speed advisories are among the proactive real-time safety interventions that can assist in preventing rear-end collisions. This study proposes a Signal-Vehicle Coupled Control (SVCC) system incorporating ATSC and speed advisories to optimize safety in real-time. By applying a rule-based approach in conjunction with a Soft-Actor Critic RL framework, the system assigns speed advisories to platoons of vehicles on each approach and extends the current signal time accordingly. Dynamic traffic parameters are collected in real-time and are used to estimate the current conflict rate at the intersection, which is then used both as an input to the model and to evaluate performance. The system was tested on two different intersections modeled using real-world data through the simulation platform VISSIM. Traffic conflicts were reduced by 41–55%, and vehicle delay was reduced by 21–24%. The results also show that the system functions at lower levels of market penetration, with diminishing returns beyond 50% MPR. The proposed system presents an SVCC framework that is both effective and low in computational intensity to optimize safety at signalized intersections.

Autonomous navigation of a magnetic colonoscope using force sensing and a heuristic search algorithm

This paper presents an autonomous navigation system for cost-effective magnetic-assisted colonoscopy, employing force-based sensors, an actuator, a proportional–integrator controller and a real-time heuristic searching method. The force sensing system uses load cells installed between the robotic arm and external permanent magnets to derive attractive force data as the basis for real-time surgical safety monitoring and tracking information to navigate the disposable magnetic colonoscope. The average tracking accuracy on magnetic field navigator (MFN) platform in x-axis and y-axis are 1.14 ± 0.59 mm and 1.61 ± 0.45 mm, respectively, presented in mean error ± standard deviation. The average detectable radius of the tracking system is 15 cm. Three simulations of path planning algorithms are presented and the learning real-time A* (LRTA*) algorithm with our proposed directional heuristic evaluation design has the best performance. It takes 75 steps to complete the traveling in unknown synthetic colon map. By integrating the force-based sensing technology and LRTA* path planning algorithm, the average time required to complete autonomous navigation of a highly realistic colonoscopy training model on the MFN platform is 15 min 38 s and the intubation rate is 83.33%. All autonomous navigation experiments are completed without intervention by the operator.

Real-Time Safety Monitoring of Charging Pile Based on 5G and Cloud Computing

How to ensure the safety of charging pile including the protection of people, electric vehicles and batteries, has become the focus of social attention. This paper proposes a real-time safety monitoring scheme for the charging process based on 5G low latency and massively parallel cloud processing. The scheme is a second line of big data safety to ensure the safety of charging process. By accessing massive Internet of Things data in real time, it calculates in real time in the cloud to predict accidents, and gives early warning to the control system of the charging pile and the on-site management personnel of the charging station in time, so that they can timely intervene and control in advance, so as to avoid and reduce the occurrence of accidents.

ClipAssistNet: bringing real-time safety feedback to operating rooms

PurposeCholecystectomy is one of the most common laparoscopic procedures. A critical phase of laparoscopic cholecystectomy consists in clipping the cystic duct and artery before cutting them. Surgeons can improve the clipping safety by ensuring full visibility of the clipper, while enclosing the artery or the duct with the clip applier jaws. This can prevent unintentional interaction with neighboring tissues or clip misplacement. In this article, we present a novel real-time feedback to ensure safe visibility of the instrument during this critical phase. This feedback incites surgeons to keep the tip of their clip applier visible while operating.MethodsWe present a new dataset of 300 laparoscopic cholecystectomy videos with frame-wise annotation of clipper tip visibility. We further present ClipAssistNet, a neural network-based image classifier which detects the clipper tip visibility in single frames. ClipAssistNet ensembles predictions from 5 neural networks trained on different subsets of the dataset.ResultsOur model learns to classify the clipper tip visibility by detecting its presence in the image. Measured on a separate test set, ClipAssistNet classifies the clipper tip visibility with an AUROC of 0.9107, and 66.15% specificity at 95% sensitivity. Additionally, it can perform real-time inference (16 FPS) on an embedded computing board; this enables its deployment in operating room settings.ConclusionThis work presents a new application of computer-assisted surgery for laparoscopic cholecystectomy, namely real-time feedback on adequate visibility of the clip applier. We believe this feedback can increase surgeons’ attentiveness when departing from safe visibility during the critical clipping of the cystic duct and artery.

Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression

Various engineering systems such as naval and aerial vehicles, offshore structures, and mechanical components of motorized systems, are exposed to fatigue failures due to stochastic loadings. Methods for early failure prediction are essential for engineering, military, and civil applications. In addition to the prediction of time to failure (TtF), uncertainty quantification (UQ) is of major importance for real-time decision-making purposes. Usually, time domain or frequency domain methods are used for fatigue prediction, such as rainflow counting and Miner’s rule or Dirlik’s method. However, those methods suffer from over-simplistic modeling and inaccurate failure predictions under stochastic loadings. During the last years, several data-driven models were suggested for offline fatigue failure. However, most of them are not capable of both accurate real-time fatigue prediction and UQ. In the current work, a probabilistic data-driven model is introduced. A hybrid architecture of a fully connected artificial neural network (FC-ANN) and Gaussian process regression (GPR) is proposed to ensure enhanced predictive abilities and simultaneous UQ of the predicted TtF. The real-time prediction and UQ performances of the suggested model are validated using both synthetic and experimental data. This novel hybrid method is fully data-driven and extends the forecasting capabilities of existing time-domain and machine learning-based methods for fatigue prediction. It paves the way towards the development of a preventive system that provides real-time safety and operational instructions and insights for structural health monitoring (SHM) purposes, allowing prevention of environmental damage, and loss of human lives.

Safety evaluation of leak in a storage tank using fault tree analysis and risk matrix analysis

The work presented in this paper used a quantitative analysis of relevant risks through the development of fault tree analysis and risk analysis methods to aid real time risk prediction and safety evaluation of leak in a storage tank. Criticality of risk elements and their attributes can be used with real time data to predict potential failures likely to occur. As an example, a risk matrix was used to rank risk of events that could lead to a leak in a storage tank and to make decisions on risks to be allowed based on past statistical data. An intelligent system that recognizes increasing level(s) and draws awareness to the possibility of additional increase before unsafe levels are attained was used to analyse and make critical decisions. After a visual depiction of relationships between hazards and controls had been actualized, dynamic risk modelling was used to quantify the effect controls can potentially have on hazards by applying historical and real-time data into a probabilistic model. The output of a dynamic risk model is near real-time quantitative predictions of risk likelihood. Results from the risk matrix analysis method mixed with RTD and FTA were analyzed, evaluated, and compared.

A Target Detection and Evaluation Method for Safety Protection Equipment

The existing safety detection methods are not sufficient to achieve real-time accurate detection, and cannot perform real-time safety assessment. Taking the construction site as an example, this paper proposes a safety equipment wear detection method based on the joint constraint of linear distance and angle, and proposes the idea of establishing a knowledge database of safety protection equipment wear behavior, and proposes a safety factor evaluation model. In this paper, convolutional neural networks are used to identify the construction personnel and safety equipment in two basic poses, and the linear distance and deflection angle of the regional center point coordinates are used to jointly determine the wearing status of the safety equipment. The generated result matches the safety information knowledge base to obtain the final test results. We obtained the real-time safety factor of the construction personnel by correlating the detection results with the actual scene. Finally, the method used in the article is summarized and prospected.

Design of intelligent real-time safety supervision system for pension institutions based on LBS

Abstract With the gradual ageing of China's population and the single-child family system becoming the backbone of society, the traditional pension way of relying on children at home not only does not guarantee the quality of life of the elderly, but also brings great pressure to the work and life of young people. The way of relying on pension institutions to provide for the aged is becoming gradually accepted by society. In elderly care institutions, safety supervision systems provide an important link to protect the safety of the elderly, thereby ensuring accurate analysis and supervision of the elderly persons’ activity routes and activity rules, and thus allowing an early warning to be given in a dangerous situation. The system realises the real-time positioning and monitoring of the elderly activity track, and can give early warning and timely rescue to the elderly who enter into any dangerous area. The system includes a server monitoring program and mobile APP; ASP.NET Technology to develop the monitoring program; SQL Server 2012 for data storage; and model view controller (MVC) architecture for the APP to improve the efficiency of program development. The supervision program comprises the functions of personnel information management, fence management, positioning service, activity track record, danger warning and rapid rescue. The APP deploys the location-based service (LBS) technology of Android, uses Baidu map API to obtain location information and draw activity track, and stores the information in the mobile phone SQLite database. The APP is installed on mobile phones, bracelets and other devices carried by the elderly and staff, which can record the current position of the person in real-time and simultaneously transmit it to the computer. When the elderly enter into a dangerous area, the system can quickly select the nearest staff and inform them for rescue, so as to ensure the safety of the elderly. After simulation test, the system has now reached the preset function.

Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning.

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations.

Analysis of high-resolution inertial sensor and global navigation satellite system (GNSS) data collected by mobile and wearable devices is a relatively new methodology in forestry and safety research that provides opportunities for modeling work activities in greater detail than traditional time study analysis. The objective of this study was to evaluate whether smartwatch-based activity recognition models could quantify the activities of rigging crew workers setting and disconnecting log chokers on cable logging operations. Four productive cycle elements (travel to log, set choker, travel away, clear) were timed for choker setters and four productive cycle elements (travel to log, unhook, travel away, clear) were timed for chasers working at five logging sites in North Idaho. Each worker wore a smartwatch that recorded accelerometer data at 25 Hz. Random forest machine learning was used to develop predictive models that classified the different cycle elements based on features extracted from the smartwatch acceleration data using 15 sliding window sizes (1 to 15 s) and five window overlap levels (0%, 25%, 50%, 75%, and 90%). Models were compared using multiclass area under the Receiver Operating Characteristic (ROC) curve, or AUC. The best choker setter model was created using a 3-s window with 90% overlap and had sensitivity values ranging from 76.95% to 83.59% and precision values ranging from 41.42% to 97.08%. The best chaser model was created using a 1-s window with 90% overlap and had sensitivity values ranging from 71.95% to 82.75% and precision values ranging from 14.74% to 99.16%. These results have demonstrated the feasibility of quantifying forestry work activities using smartwatch-based activity recognition models, a basic step needed to develop real-time safety notifications associated with high-risk job functions and to advance subsequent, comparative analysis of health and safety metrics across stand, site, and work conditions.

French organization for the pharmacovigilance of COVID-19 vaccines: A major challenge

In this special issue, we present the main highlights of the first weeks of pharmacovigilance monitoring of coronavirus disease 2019 (COVID-19) vaccines in this unprecedented situation in France: the deployment of a vaccination during an epidemic period with the aim of vaccinating the entire population and the intense pharmacovigilance and surveillance of these vaccines still under conditional marketing authorizations. In this unprecedented situation, the cross approach and interaction between the French pharmacovigilance network and French National Agency for the Safety of Medicines and Health Products (ANSM) has been optimized to provide a real-time safety related to COVID-19 vaccines. Every week, pair of regional pharmacovigilance centers gathered safety data from the French pharmacovigilance network, to acutely expertise all the adverse drug reactions (ADRs) reported with each COVID-19 vaccine within a direct circuit with ANSM. Results of this expertise are presented and discussed with ANSM in order to raise safety signals and take appropriate measures if necessary. These reports are then published online. At the 25th of March 2021, more than 9 815 000 doses were injected and 20,265 ADRs were reported, mostly non-serious (76%). Several potential or confirmed signals were raised at the european level for those vaccines and others ADRs are under special attentions. This underlines the adaptiveness of the French pharmacovigilance system to both the identification of new patient profiles experiencing ADRs and the evolution of the vaccine strategy. Such an efficiency is necessary to manage a careful and acute surveillance of these new COVID-19 vaccines for and to face the pandemic at the same time.

Novel Real-time Safety Algorithm for Predicting Danger-ous Targets on the Driving region in the Unstructured Road

A certain number of people die every year from traffic accidents on unstructured farmland roads. Detection and prediction of dangerous targets could help to reduce accidents. A novel real-time algorithm based on YOLACT, a combination of GPM and RCM, was proposed to achieve the goal. On this basis, a self-collected data set of 5000 test samples is used for testing. In addition, we have carried out experiments on actual roads. The results showed that, targets on the road could be detected, tracked and predicted. The detect accuracy of algorithm in the data set could reach up to 90%, the processing speed to 30.4 fps. This study can be used for automatic driving and assisted driving.

Real-time monitoring system for scour around monopile foundation of offshore wind turbine

The monopile foundation, as the main form of offshore wind turbine foundations, may be unstable owing to scouring under reciprocating currents; therefore, inspection and maintenance are needed. In contrast to manual inspection, with a real-time monitoring system, the staff can be informed of the safety condition of the wind turbine foundation in real time and maintain it immediately after a system early warning is received. According to the monopile-supported wind turbine of Jiangsu Rudong Offshore Wind Farm Demonstration Project, a blade–tower–monopile–soil coupling integrated numerical model considering scour was established in this study, and scour warning indicators and warning limits related to two aspects—dynamic characteristics and dynamic response—were obtained through calculation and analysis. For the latter, the effect of the wind speed can be excluded by considering the control system. Furthermore, according to the results of the numerical analysis, a real-time safety monitoring system for scouring around a monopile foundation was established in this study, which is composed of data acquisition, data transmission, and data application layers. An inclinometer and accelerometer were selected as sensors. Time- and frequency-domain analyses of the long-term monitoring data revealed a strong annual periodicity (particularly in the east–west direction) in the tower-bottom inclination. According to the proposed early warning indicators, a safety warning was not triggered in this wind farm, and the two early warning indicators exhibited consistent results. The proposed real-time monitoring system can be easily applied to the monitoring of the foundation scouring of other offshore wind farms.

Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China

The traditional way to evaluate pedestrian safety is a reactive approach using the data at an aggregate level. The objective of this study is to develop real-time safety models for pedestrian red-light running using the signal cycle level traffic data. Traffic data for 464 signal cycles during 16 h were collected at eight crosswalks on two intersections in the city of Nanjing, China. Various real-time safety models of pedestrian red-light running were developed based on the different combination of explanatory variables using the Bayesian Poisson-lognormal (PLN) model. The Bayesian estimation approach based on Markov chain Monte Carlo simulation is utilized for the real-time safety models estimates. The models’ comparison results show that the model incorporated exposure, pedestrians’ characteristics and crossing maneuver, and traffic control and crosswalk design outperforms the model incorporated exposure and the model incorporated exposure, pedestrians’ characteristics, and crossing maneuver. The result indicates that including more variables in the real-time safety model could improve the model fit. The model estimation results show that pedestrian volume, ratio of males, ratio of pedestrians on phone talking, pedestrian waiting time, green ratio, signal type, and length of crosswalk are statistically significantly associated with the pedestrians’ red-light running. The findings from this study could be useful in real-time pedestrian safety evaluation as well as in crosswalk design and pedestrian signal optimization.