Real-time Safety Research Articles

Design of Real-time Safety Accident Prevention Solution for Socially Vulnerable using Object Recognition and Tracking Technology

All countries around the world want to create a safe society and a safe nation. However, crime targeting the socially vulnerable groups continues to increase. This is because the vulnerable groups have physical and physical weaknesses. Therefore, in order to pursue a safe society, it is necessary to protect the socially vulnerable. In this paper, we utilize the latest trend, drones, to protect against vulnerable groups. That is, we designed a real-time accident prevention solution for the elderly, the disabled, children and women using the drones. Drones' autonomous flight technology, obstacle detection technology, collision avoidance technology, wireless communication linkage technology, and real time image transmission technology. Through this, we have studied to enable safe activities in the blind spot of CCTV. In this paper, we have identified the problem of crime prevention system using existing CCTV system. To solve this problem, we have developed a safety accident prevention solution using drones. To this end, we designed the drones to enable real-time tracking by applying autonomous flight technology, obstacle avoidance technique, and object tracking technology. In addition, wireless communication between the object and the drone was applied to prevent flight deviation. And, it is designed to link emergency notification service through real-time video transmission during flight.

Data Acquisition and Transmission Scheme for Large Projects Based on LoRa Internet of Things Using Improved Linear Integer Programming Model

Aiming at the problem that some security factors in large scale construction projects are difficult to be discovered in time and easily cause accidents, a low-power long-distance data acquisition and transmission scheme using LoRa wireless sensor network is proposed. Firstly, the improved linear integer programming (IILP) model is used to deploy sensor nodes, which is used to collect security risk data. Then, an ARM processor with a high-performance Cortex-M3 architecture is used by the LoRa node to interconnect the LoRa module via the SPI bus for data communication. Finally, the LoRa node sends the data collected by the sensor to the LoRa gateway through the wireless signal, and the data is stored in the cloud database through the TCP/IP protocol. Experimental tests are carried out in actual subway construction projects. The temperature and humidity sensors, deep horizontal displacement sensors, supporting shaft force sensors and groundwater level sensors are installed on the nodes to monitor real-time safety risk factors in the construction process. It is found that under the premise of ensuring more than 90% of the data transmission success rate, the proposed communication distance can be as far as 700 m. Compared with several other schemes, the proposed scheme can show better performance in terms of throughput and network delay. The simulation results of sensor deployment show that the improved ILP model can improve the performance of sensor deployment and save hardware cost to a certain extent. In addition, the proposed scheme combines the advantages of LoRa technology with the characteristics of low maintenance cost and long service life, and has a good reference significance for the application of Internet of Things technology in large-scale construction projects.

Drivers in Expressway Superlong Tunnels: The Change Patterns of Visual Features and the Discriminant Model of Driving Safety

A real-vehicle experiment was carried out in the superlong highway tunnel environment to study the change patterns of driver’s visual features, tracked by eye tracking devices, and the discriminant model of driver’s safety status. On the basis of statistical analysis, a single index and a comprehensive index discriminant model, both based on a C4.5 decision tree, were established. The results showed that compared with the non-tunnel highway sections, the driver’s pupil size was larger, and the gaze duration was longer in the tunnel section. Driver’s pupil size was larger in mid-tunnel section than in the entrance section and exit section. Gazes at the exit section were mainly short gazes. Compared to the exit section, driver’s pupil size changed more dramatically in the entrance section, and the gaze duration was longer. The single visual parameter indicator could clearly discriminate the driver’s safety status in the mid-tunnel section and the non-tunnel sections, while the dual-index-based identification model could clearly discriminate the safety status in each highway sections. The study deepens the research on the driver information perception model in superlong highway tunnels. Also, the study provides a theoretical basis for establishing a visual-feature-based real-time safety status discriminant.

Real-time safety inspection and planning: A first application of the Fagan nomogram

The Fagan nomogram is a graphical tool used for clinical diagnosis and treatment. Considering the similarities between safety inspections and disease diagnosis, this study set out to apply the Fagan nomogram to construction sites with a goal of improving real-time safety inspection and planning. First, the basic principle of the Fagan nomogram was analyzed to determine its applicability. Then, the quantitative relationships between related risks were calculated using the Fagan nomogram. Subsequently, the results were compared with those obtained by Bayesian parameter estimation. A case study was then undertaken to enhance the understanding of its usage and the results were validated through a focus group interview. As a result, the Fagan nomogram was proven to be an effective method for quantifying the causal relationships and risk probability for safety risks, making it a new, simple, and accurate graphical approach for future real-time safety inspections and planning on a construction site.

Transferability of real-time safety performance functions for signalized intersections

Optimizing traffic signals in real-time for safety performance can be executable in the era of Connected Vehicles (CVs) when real-time information on vehicle positions and trajectories is available. To achieve this, real-time safety models are needed to understand how changes in signal controllers affect safety in real-time. Recently, several real-time safety models were developed for signalized intersections that relate various dynamic traffic parameters to the number of rear-end traffic conflicts at the signal cycle level. The traffic parameters included: traffic volume, maximum queue length, shock wave speed and area, and platoon ratio. For wider application of these models to other jurisdictions, the transferability of these models needs to be examined. Therefore, this paper aims to investigate the transferability of several signalized intersections real-time safety models to new jurisdictions. Two corridors of signalized intersections in California and Atlanta were used in the analysis as destination jurisdictions. Detailed vehicle trajectories for these corridors were obtained from the Next Generation Simulation (NGSIM) data. Various transferability analysis approaches were applied. The transferability of the real-time safety models was evaluated with and without a local calibration for the model parameters at the new jurisdictions. Several goodness-of-fit measures were examined to assess the ability of the developed models to predict traffic conflicts. Overall, the results showed that the real-time safety models are transferable, which confirms the validity of using them for real-time safety evaluation of signalized intersections.

Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support.

BackgroundThe continued digitization and maturation of health care information technology has made access to real-time data easier and feasible for more health care organizations. With this increased availability, the promise of using data to algorithmically detect health care–related events in real-time has become more of a reality. However, as more researchers and clinicians utilize real-time data delivery capabilities, it has become apparent that simply gaining access to the data is not a panacea, and some unique data challenges have emerged to the forefront in the process.ObjectiveThe aim of this viewpoint was to highlight some of the challenges that are germane to real-time processing of health care system–generated data and the accurate interpretation of the results.MethodsDistinct challenges related to the use and processing of real-time data for safety event detection were compiled and reported by several informatics and clinical experts at a quaternary pediatric academic institution. The challenges were collated from the experiences of the researchers implementing real-time event detection on more than half a dozen distinct projects. The challenges have been presented in a challenge category-specific challenge-example format.ResultsIn total, 8 major types of challenge categories were reported, with 13 specific challenges and 9 specific examples detailed to provide a context for the challenges. The examples reported are anchored to a specific project using medication order, medication administration record, and smart infusion pump data to detect discrepancies and errors between the 3 datasets.ConclusionsThe use of real-time data to drive safety event detection and clinical decision support is extremely powerful, but it presents its own set of challenges that include data quality and technical complexity. These challenges must be recognized and accommodated for if the full promise of accurate, real-time safety event clinical decision support is to be realized.

Secure Intelligent Vehicular Network Using Fog Computing

VANET (vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. The intermittent exchange of real-time safety message delivery in VANET has become an urgent concern due to DoS (denial of service) and smart and normal intrusions (SNI) attacks. The intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication needs. In this research, fog computing (FC) integrates with hybrid optimization algorithms (OAs) including the Cuckoo search algorithm (CSA), firefly algorithm (FA), firefly neural network, and the key distribution establishment (KDE) for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). A feedforward back propagation neural network (FFBP-NN), also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The SIVNFC scheme is compared with the Cuckoo, the FA, and the firefly neural network to evaluate the quality of services (QoS) parameters such as jitter and throughput.

Quasi-vehicle-trajectory-based real-time safety analysis for expressways

There have been plenty of segment-based real-time safety analyses for expressways or freeways. In these analyses, it was considered that the occurrence of a crash is because of segment conditions, including traffic and geometric. However, the movement of a vehicle is continuous from upstream to downstream. Thus, the crash occurrence of a vehicle might also be because of the traffic conditions along its trajectory. Under this idea, the study on the quasi-vehicle-trajectory-based real-time safety for expressways is proposed. It is hard to directly obtain the trajectory data of the vehicle involved in a crash. Thus, the crash-related vehicle’s trajectory is derived from other traffic data sources, and it is called quasi-trajectory. To find vehicles’ quasi-trajectories, the exact crash time was first identified by observing significant speed drops, and then, the vehicle locations for every one minute was distinguished by using the data from HERE, which provides space-mean speed information. The traffic information along the quasi-trajectories is from the Microwave Vehicle Detection System. A Bayesian matched-case-control logistic regression model was built to explore the impact of traffic parameters along the vehicle quasi-trajectory on real-time crash risk. The results showed that the segment speed difference and segment volume difference along the vehicle quasi-trajectory in the 0–4 min interval before crash occurrence all had a significant positive impact on crash risk. Meanwhile, the truck volume difference and average segment speed in the 0–1 min interval before crashes also have a positive impact on real-time crash likelihood. This study is new in real-time safety analysis and the findings have the potential to be applied to improve the safety of Connected and Autonomous Vehicles (CAV). A CAV continuously collects the traffic information along with its trajectory and could calculate the crash risk. When the hazardous traffic condition happens, actions can be taken by CAV to prevent the crash occurrence.

Participant centred safety surveillance of health care workers receiving influenza vaccination

Following the introduction of mandatory influenza vaccination for staff working in high risk clinical areas in 2018, we conducted active surveillance for adverse events following immunisation utilising an automated online survey to vaccine recipients at three and 42 days post immunisation. Most participants 2285 (92%) agreed to participate; 515 (32%) staff reported any symptom and eight (1.6%) sought medical attention. The odds of having a reaction decreased with age by approximately 2% per year. The system was acceptable to staff, and the data demonstrated rates of reported symptoms within expected rates for influenza vaccines from clinical trials. Rates of medical attendance were similar to previous surveillance. Participant centred real-time safety surveillance proved useful in this staff influenza vaccination context, providing reassurance with expected rates and profile of common adverse events following staff influenza vaccination.

Systems engineering and human factors support of a system of novel EHR-integrated tools to prevent harm in the hospital.

We established a Patient Safety Learning Laboratory comprising 2 core and 3 individual project teams to introduce a suite of digital health tools integrated with our electronic health record to identify, assess, and mitigate threats to patient safety in real time. One of the core teams employed systems engineering (SE) and human factors (HF) methods to analyze problems, design and develop improvements to intervention components, support implementation, and evaluate the system of systems as an integrated whole. Of the 29 participants, 19 and 16 participated in surveys and focus groups, respectively, about their perception of SE and HF. We identified 7 themes regarding use of the 12 SE and HF methods over the 4-year project. Qualitative methods (interviews, focus, groups, observations, usability testing) were most frequently used, typically by individual project teams, and generated the most insight. Quantitative methods (failure mode and effects analysis, simulation modeling) typically were used by the SE and HF core team but generated variable insight. A decentralized project structure led to challenges using these SE and HF methods at the project and systems level. We offer recommendations and insights for using SE and HF to support digital health patient safety initiatives.

Real-Time Safety Risk Identification Model during Metro Construction Adjacent to Buildings

AbstractComprehensive and effective safety risk identification in metro construction has a significant impact on risk management. Although previous studies attempted to obtain a final ranking of th...

Creation of in-sky safety system using ADS-B radar receiver based on RTL-SDR device for SLR station Golosiiv

The purpose of this work is to develop and assemble the real-time in-sky safety system for SLR (Satellite Laser Ranging) system. It is based on receiving ADS-B protocol aircraft data via RTL-SDR device. This solution represents an asynchronous service in the SLR laser control system. It instantly inhibits SLR laser pulses in order to prevent illumination of an aircraft. The service is ready at the SLR station ‘Golosiiv’ (Ukraine) (EUROLAS Data Center ID – 1824).

A practical guide to IT OT convergence – getting value from your business analytics

A lot of time, effort and money has been and is being spent by operating companies and service companies in collecting data from many different sources, agglomerating that data, carrying out analytics on that data with the intention of turning that data into actionable insights that positively impact safety, environment, operations and profitability. This has been done with varying levels of success by approaching the problem from either an information technology (IT) or an operational technology (OT) point of view. What we are finding is that the best outcomes are achieved by having IT and OT domain experience with operational industry expertise within the same team. This has proven to be the case in other industries. There is also a need to understand edge and cloud actionable insights delivery in order to determine the optimal balance of edge and cloud delivery in a hybrid solution model. There is a continuum of analytics from high speed analytics at the edge, for such things as assisting regulatory control and real-time safety system diagnostics, to data lake analysis in the cloud, for planning, supply chain and business prioritisation and optimisation. Some analytics are best done at the edge; some are better done in the cloud. It’s a ‘horses for courses’ approach. The nature of the desired outcome, the required actionable insight and the nature of the data gathering are critical in determining the most effective approach. This paper discusses several examples of how this integrated IT OT approach took a desired outcome and turned it into an analytics platform that delivered significant value to the business. This paper cites examples of edge and cloud analytics that are enabled by integrating clever and sufficient industrial internet of things (IIoT) connectivity, which lead to actionable insights. These, in turn, ensured the correct action was effectively applied to the correct part(s) of the business operation, resulting in effective achievement of the desired outcome.

Development of Modular Cable-Driven Parallel Robotic Systems

Task-oriented modular design and safety monitoring are demonstrated to be outstanding challenges for robots in practical application. In order to obtain better flexibility and better obstacles avoidance capability, design and analysis of a modular cable-driven parallel robot (MCDPR) are presented in this paper, which can be reconfigured to several configurations in engineering applications by changing the number of the mobile modules and the connection mode of the end effector. With regard to the motion control and safety monitoring system, the design and implementation of hardware and software are presented. An experimental prototype for the MCDPR is developed, followed by a brief illustration of the connection mode between the end effector and cables, and the installation of monitoring nodes. The performance of the MCDPR is discussed experimentally, including real-time pose monitoring and obstacle avoidance. The results verify the feasibility and efficiency of kinematics model and obstacle avoidance method, and the results also indicate that the designed motion control and safety monitoring system can realize trajectory tracking control, real-time safety monitoring, and obstacle avoidance for the MCDPR.

SYSTEM ARCHITECTURE TO BRING SMART PERSONAL PROTECTIVE EQUIPMENT WEARABLES AND SENSORS TO TRANSFORM SAFETY AT WORK IN THE UNDERGROUND MINING INDUSTRY

As safety at work has become a number one priority in every industry, it is no wonder that new technology and ideas are being tested to improve safety in a variety of workplaces, including, surface and underground mining, construction sites, power plants, factories, etc. Sensor technologies embedded in personal protective equipment (PPE), can be employed to monitor workers’ health, exposure to harmful elements, their proximity to danger zones, etc. This technology connected to smartphones and smartwatches can also heighten workers’ awareness by collecting data on the workplace itself, detecting environmental and health hazards. In this paper, we will present a prototype system that can be employed in underground mining which uses sensors attached to regular PPE clothing, including hard hats and safety glasses which are connected to smartphones and smartwatches via energy-efficient Bluetooth sensors, to provide real-time safety situation awareness and predict health and safety incidents before they occur.

실시간 철도안전 통합 감시제어시스템의 데이터 분산 서비스 품질 적합성 분석

본 논문에서는 길시간 통합 철도 안전 감시 시스템의 데이터 전송 품질 요구 사항을 만족시키기 위해 OMG DDS 표준에서 제공하는 네트워크 전송 품질을 제어 할 수 있는 DDS(Data Distribution Service) QoS(Quality of Service)에 관한 실험을 통한 분석을 진행하였다. ‘실시간 통합 철도 안전 감시제어 시스템’ 은 철도 분야에서 다양한 센서의 데이터를 수집 및 분석하여 잠재적인 철도사고 위험을 예측하고 방지하는 시스템이다. 이 시스템에서 수집된 데이터를 정확하고 안정적으로 실시간으로 전송하려면 데이터 전송 품질을 보장해야 한다. 실험 결과에 따르면 DDS QoS를 사용하여 철도 안전을 모니터링하고 제어하기 위해 실시간으로 정확하고 안정적인 데이터 전송을 보장할 수 있다.

An Electronic Health Record-Based Real-Time Analytics Program For Patient Safety Surveillance And Improvement.

Twenty years after publication of the report To Err Is Human, studies demonstrate persisting high levels of patient harm. Most patient safety measurement remains highly retrospective, relying on voluntary reporting and post discharge administrative coding. Progress has been limited by the lack of advances in measurement accuracy, detection sensitivity, and timely actionability. The broad adoption of electronic health records (EHRs) offers a significant opportunity to leverage digital information to improve safety measurement and management using real-time data. We developed a novel method to extract safety indicators from EHRs to identify harm and its precursors by implementing a patient safety active management system (PSAM) in hospitals within a national Patient Safety Organization (PSO). The PSAM generated validated adverse event outcomes and leveraged EHR data to develop a real-time safety predictive model. This study describes the PSAM's pilot at two large community hospitals in 2014-17. We found that the PSAM could detect harm in real time, at higher rates than current levels are detected, and that such harm could be predicted. In addition to outlining future opportunities and challenges with this EHR-enabled PSAM approach, we discuss implications and next steps for policy and practice.

Architectural model driven dependability analysis of computer based safety system in nuclear power plant

Abstract The most important non-functional requirements for dependability of any Embedded Real-Time Safety Systems are safety, availability and reliability requirements. System architecture plays the primary role in achieving these requirements. Compliance with these non-functional requirements should be ensured early in the development cycle with appropriate considerations during architectural design. In this paper, we present an application of system architecture modeling for quantitative assessment of system dependability. We use probabilistic model checker (PRISM), for dependability analysis of the DTMC model derived from system architecture model. In general, the model checking techniques do not scale well for analyzing large systems, because of prohibitively large state space. It limits the use of model checking techniques in analyzing the systems of practical interest. We propose abstraction based compositional analysis methodology to circumvent this limitation. The effectiveness of the proposed methodology has been demonstrated using the case study involving the dependability analysis of safety system of a large Pressurized Water Reactor (PWR).

Utilizing bluetooth and adaptive signal control data for real-time safety analysis on urban arterials

Real-time safety analysis has been widely adopted to reveal the relationship between real-time traffic characteristics and crash occurrence, and these results could be applied to improve active traffic management systems and enhance safety performance. Most of the previous studies have been applied to freeways and seldom to arterials. This study attempts to examine the relationships between crash occurrence and real-time traffic and signal timing characteristics based on four urban arterials in Central Florida. Bayesian conditional logistic models (BCL) were developed by incorporating the Bluetooth, adaptive signal control, and weather data, which were extracted for a period of 20 min (four 5-minute intervals) before the time of crash occurrence. Model comparison results indicated that the model based on 5–10 min interval dataset performed the best. It revealed that the average speed, upstream left-turn volume, downstream green ratio, and rainy indicator were found to have significant effects on crash occurrence. Furthermore, Bayesian random parameters conditional logistic model (BRPCL) outperformed Bayesian random parameters logistic (BRPL) and Bayesian conditional logistic models (BCL) in terms of the area under the receiver operating characteristics curve (AUC) and Deviance Information Criterion (DIC) values. These results are important in real-time safety applications in the context of Integrated Active Traffic Management (IATM).

A comprehensive method for safety management of a complex pump injection system used for shale-gas well fracturing

Abstract A pump injection system used in the shale-gas well fracturing process is subjected to various adverse factors during its service, such as high pressures of up to 105 MPa and a large displacement, leading to a high failure rate and a rapid degradation in system performance. To ensure the safety and reliability of such a system, a comprehensive safety management method based on a dynamic object-oriented Bayesian network (DOOBN) is proposed in this article. The approach provides a framework that integrates a system function model, causal model, system behaviour model, and online fault diagnosis model with a remaining life prediction model, to characterise the behaviours in a complex system, such as fault propagation and system degradation. This method could achieve fault diagnosis and also predict the degradation trend of critical components and system performance in the long term, starting from the current system state. The application of the integrated safety management approach to the specific example of the pump injection system demonstrates how each phase of the presented method contributes to the achievement of fault diagnosis and residual life prediction in a systematic and holistic way. It is shown that the proposed model is a reasonable starting point for forecasting the remaining life of pump injection systems. This approach could be integrated into a real-time safety warning device for field application.