Safety Warning System Research Articles

A Novel Visual System for Conducting Safety Evaluations of Operational Tunnel Linings

Based on the lining structure of an operational tunnel, the AHP and Fuzzy mathematical models were used to determine the weight of the evaluation index and solve the membership matrix. The weighted-average Fuzzy comprehensive function was used to combine the two, and the Fuzzy–AHP evaluation model was built and programmed. According to the self-developed Fuzzy–AHP evaluation-programmed model, a visualized structure safety evaluation system for operational tunnels was developed by using MATLAB. The system’s functional design, program development, and computational visualized interface were implemented, and key codes were provided. The system can be divided into four modules: data management, fuzzy computation, predictive analysis and key disease indexes to focus on. In addition, the system can easily edit and modify the evaluation function, which includes not only the Fuzzy evaluation but also other types of evaluation functions applicable to other practical engineering projects, improving the applicability of the system. After that, the system was applied to the structure safety evaluation of a mountain tunnel, which provided the evaluation results and key indexes to focus on in the tunnel. Finally, the rationality of the system design was verified by constructing the corresponding BP–RBF combined neural network. This study provides a reference for the establishment of an intelligent structure safety warning system for operational tunnels.

Real-time safety distance detection and warning for elevator inspection based on adaptive monocular depth estimation

During elevator inspection, workers are prone to collisions with hazards in narrow elevator machine rooms. Because of the numerous elevator machine rooms with diverse scenes in cities, it is difficult to effectively monitor the distance between workers and hazards. We propose a real-time safety distance detection and warning system consisting of four parts: object detection, depth estimation, safety distance detection, and safety warning. By introducing two adaptive error correction terms, the proposed adaptive monocular depth estimation algorithm effectively reduces the distortion of object pixel size with changes in depth and shooting angle. Then the calculation formula has been improved to accurately measure the distance between objects. The system has been successfully applied to safety supervision at the elevator inspection site in Nanchang City, Jiangxi Province, China. The results, with an average relative error of 3.34% and 84 frames detected per second, show the system exhibits excellent accuracy and real-time performance.

Zinc tungstate integrated onto zinc oxide h-MDs-based flexible piezoelectric power device: A new energy technology for real-time indoor laboratory safety warning system

With the rapid development of research world, the number of researchers and research laboratories is increasing rapidly. Simultaneously, accidents in laboratories are on the rise, drawing significant attention from everyone. To address this issue, the current study demonstrates a real-time safety early warning alarm system aimed at preventing accidents and enhancing safety management in laboratories. We developed a zinc tungstate nanoparticles (NPs) integrated on zinc oxide hexagonal microdiscks (h-MDs) (ZnO–ZnWO4@PDMS) composite based on a novel flexible piezoelectric nanogenerator (FPENG). The observed piezoelectric response was 1.32 V, which is ∼4 times better than that of pure ZnWO4, owing to the synergistic effect of ZnO–ZnWO4@PDMS. When subjected to finger-tapping motion, the FPENG can charge a 1 μF capacitor, and it reached 81 mV within 56 s. Subsequently, the light-emitting diodes (LEDs) are powered with amplification. The FPENG system was demonstrated and validated through real-time application in the laboratory setting, marked as a “caution/restricted” line. The application of the system demonstrated its capability to simultaneously illuminate LEDs and sound alarms when people touch or cross the caution line. Furthermore, the proposed FPENG exhibited ∼660 mV even ∼90 % humidity, with no noticeable electrical changes under different solvents, confirming that the proposed FPENG is not affected by varying humidity and various indoor conditions. These outstanding results and real-time demonstrations unequivocally establish the feasibility of this system in contexts such as bank lockers, metro systems, hilly areas, and more.

Construction of a Climate Early Warning System: Predicting Future Temperatures and Climate Security Using BiLSTM

In light of the worsening global climate, providing predictive models for surface temperature and energy consumption is crucial for formulating effective climate action strategies. Initially, a Bi-directional Long Short-Term Memory (BiLSTM) network model is established to predict the maximum surface temperatures over the next century, with the Seasonal AutoRegressive Integrated Moving Average (SARIMA) model serving as a benchmark. To assess the risk levels of climate security, the k-means clustering algorithm is utilized to classify the growth rates of carbon dioxide emissions, enabling the construction of a three-tier climate security early warning index. Subsequently, a hybrid classification model based on Support Vector Machine (SVM) and Random Forest (RF) takes the energy consumption growth rates as inputs and the warning indices as outputs to construct a climate security early warning system. The BiLSTM model is employed to predict the energy consumption growth rates for the upcoming decade, and these rates are input into the SVM-RF model to forecast future warning levels. The study demonstrates that the model can effectively predict the maximum surface temperatures and provide a three-tier safety warning system for future climate risk management. The intent of this research is to offer a novel tool for global climate prevention and to deliver practical application value to policymakers in finance, energy, and environmental sectors.

On the Unforgeability of “Privacy-Preserving Aggregation-Authentication Scheme for Safety Warning System in Fog-Cloud Based VANET”

On the Unforgeability of “Privacy-Preserving Aggregation-Authentication Scheme for Safety Warning System in Fog-Cloud Based VANET”

Research on hand-held power frequency electric field measurement safety warning system

Research on hand-held power frequency electric field measurement safety warning system

Rebuttal to ‘On the Unforgeability of “Privacy-Preserving Aggregation-Authentication Scheme for Safety Warning System in Fog-Cloud Based VANET” ’

Rebuttal to ‘On the Unforgeability of “Privacy-Preserving Aggregation-Authentication Scheme for Safety Warning System in Fog-Cloud Based VANET” ’

Thermal runaway chain reaction determination and mechanism model establishment of NCA-graphite battery based on the internal temperature

The measurement of battery internal temperature directly affects the determination of the sequence of thermal runaway chain reaction and the choice of model heat sources, and the optimal combination identification of kinetic parameters corresponding to each heat source is the premise of establishing model. This paper focuses on the Lix(Ni0.8Co0.15Al0.05)O2-graphite pouch battery with built-in thermocouple. Based on the stage division of thermal runaway behavior and the decoupling of irreversible exothermic reaction between battery components, the thermal runaway mechanism is determined. Then, the optimal combination of kinetic parameters is determined by combining the DSC tests under variant heating rates, the Kissinger's method and the genetic algorithm. Furthermore, a thermal runaway model considering hierarchical properties is established, and the thermal contribution of six exothermic reactions at each stage of thermal runaway is quantified. Anode+electrolyte and cathode+anode contribute 53.8% and 18.7% respectively to the heat accumulation before thermal runaway. Internal short circuit accounts for 27.5%, and the heat is released instantaneously, directly triggering thermal runaway. This work has important reference significance for the design of battery safety warning system and the battery modification strategy.

Efficient Conditional Privacy-Preserving Authentication Scheme for Safety Warning System in Edge-Assisted Internet of Things

With the advent of smart cities, the significance of the Internet of Things (IoT) is gaining greater prominence. At the same time, the safety early warning system in the IoT has a significant impact on real-time monitoring and the response to potential risks. Despite the advancements made in edge-assisted IoT deployments, several challenges and constraints persist. Given the potential threat to life posed by safety-related messages, ensuring the authenticity of messages in the edge-assisted IoT safety warning system is crucial. However, considering the identity privacy of devices participating in the edge-assisted Internet of Things system, directly verifying the identity of the sending device is undesirable. To address this issue, in this work, we design a linkable group signature scheme that allows devices to anonymously send safety-related messages to edge nodes, defending against Sybil attacks while ensuring the traceability of malicious device identities. Then, we present a high-efficiency conditional privacy-preserving authentication (CPPA) scheme based on the designed group signatures for the safety warning system in edge-assisted IoT. This scheme effectively protects device identity privacy while providing a reliable authentication mechanism to ensure the credibility and traceability of alert messages. The proposed scheme contributes to the field of safety warning systems in the context of edge-assisted IoT, providing a robust solution for privacy preservation and authentication.

Electron radiation belt safety indices based on the SafeSpace modelling pipeline and dedicated to the internal charging risk

Abstract. In this paper, we present the SafeSpace prototype for a safety warning system, dedicated to the electron radiation-belt-induced internal charging hazard aboard spacecraft. The space weather tool relies on a synergy of physical models associated in a chain that covers the whole Sun–interplanetary-space–Earth's inner magnetosphere medium. With the propagation of uncertainties along the modelling pipeline, the safety prototype provides a global nowcast and forecast (within a 4 d lead time) of the electron radiation belt dynamic as well as tailored indicators for space industry operators. They are meant to inform the users about the severity of the electron space environment via a three-coloured alarm system, which sorts the index intensity according to a representative historical distribution of in situ data. The system was tested during the challenging 2015 St Patrick's Day storm in order to assess its performance. It showed overall good nowcasting and forecasting capabilities due to its broad physics-driven pipeline.

Pedestrian Safety and Early Warning System Based on Green Energy

In Taiwan, pedestrians are run over by vehicles often in many intersectionsdue to poor lighting or lack of traffic lights. Thus, we developed a green energy-driven automatic warning system to ensure the safety of drivers and pedestrians. A variety of sensing modules, such as Hall sensors and coil loops, were used in the system to detect approachingvehicles. When an approaching vehicle was detected, the LED warning light warned pedestrians and drivers. In addition, according to data released by the government, sensors or detectors were set up at a sufficient distance from the crosswalk to ensure that the vehicle could stop before it reached the sidewalk. In the system, green energy was used to ensure that the entire system was operated for a long time and reduced energy consumption. It was verified that the proposed system could be used in various environments such as suburbs or remote intersections to improve safety.

Vision-Based Work Zone Safety Alert System in a Connected Vehicle Environment

Work zones, being a critical component of roadway transportation systems, can benefit greatly from computer vision-enabled roadway infrastructures, specifically in connected vehicle (CV) environments. Connected infrastructures, such as roadside units (RSU) and on-board units (OBU), can greatly improve the environmental awareness and safety of CVs driving through a work zone. The contribution of this paper lies in developing a vision-based approach to generate work zone safety messages in real time, utilizing video streams from roadside monocular traffic cameras that can be used by CV work zone safety apps on mobile devices to reliably navigate through a work zone. A monocular traffic camera calibration method is proposed to establish the accurate mapping between the image plane and Global Position System (GPS) space. Real test scenarios show that our algorithm can precisely and effectively locate work zone boundaries from a monocular traffic camera in real time. We demonstrate the capabilities and features of our system through real-world experiments where the driver cannot see the work zone. End-to-end latency analysis reveals that the vision-based work zone safety warning system satisfies the active safety latency requirements. This vision-based work zone safety alert system ensures the safety of both the worker and the driver in a CV environment.

Industrial Boiler Intelligent Monitoring System Based on LabVIEW

As a kind of traditional heating and power equipment, the industrial boiler is more and more applied in industrial production and life. However, due to the uneven heating of the boiler, the process of using the boiler would result in boiler leakage accidents and cause burns to workers. Given the above situation, the intelligent control system of the industrial boiler is studied in this paper to achieve safe and stable operation of the boiler. This design uses the distributed thermocouple temperature measurement method to design the boiler safety warning system based on the thermocouple sensor collaborative monitoring. Multiple sensors are used to cooperatively monitor and collect boiler operating signals. COMSOL model is used to realize 3D temperature field simulation and reconstruction inside the furnace lining, and the results are presented as early warning results through LabVIEW upper computer interface, which solves the shortcomings of low acquisition accuracy, narrow measurement accuracy, and single measurement method of the traditional contact measurement method. The practice shows that the system has the advantages of low cost, convenient installation and maintenance, easy expansion, high reliability, and strong anti-interference.

Implementation of a Digital-Based Early Safety Warning System Program on a Branch Logistics Company in Jakarta

Lack of involvement in interventions to carry out observations and analysis that causes the conditions and behavior of employees at one of the logistics companies at the Jakarta branch. The research aims to reduce potential unsafe of conditions & unsafe actions in the workplace environment. This process involves employees recognizing potential problems in the workplace within the procedures of observation, feedback, and employee behavior interventions. The methodology used in this study is a descriptive method to describe present or ongoing issues. This method allows for assessing a condition and program implementation to make further improvements in the future. There are still several obstacles in implementing the K3 Program (Safety, Health, and Environment), where the program is still being performed manually. BBS STAR program does not cover unsafe conditions and unsafe actions, so reporting workers to management regarding conditions at work still not optimal yet. Employees also have some difficulty reporting unsafe conditions and unsafe actions in the workplace. SEWS program can be a preventive step in the occurrence of potential danger. The quality of employees could increase with the habit of analyzing dangerous aspects of the workplace environment. Employees' involvement in the SEWS contribution program can also help establish moral consistency. Keywords: K3, Unsafe conditions and unsafe actions, SEWS Program

Research on Electric Vehicle Charging Safety Warning Based on A-LSTM Algorithm

Accidents involving electric vehicle fires have increased as the number of electric vehicles has grown recently. The issue of charging safety is a key barrier to the growth of the electric vehicle sector because these accidents have resulted in large financial losses for car owners and charging facility operators. The approach for resolving the issue of electric car charging safety through an electric vehicle charging safety warning system is suggested in this research. The suggested solution uses an adaptive optimization of long short-term memory neural network (A-LSTM) to forecast voltage changes throughout the whole charging process by using the vehicle’s daily historical charging data. The warning threshold adjustment method is established by the difference between the predicted voltage data and the actual voltage data, which is dynamically adjusted as the charging process progresses. Finally, a real-time warning model for vehicle charging alert is developed. The daily charging data of electric vehicles is used in the paper to verify the precision of data prediction and the accuracy and timeliness of the model. The study’s findings demonstrate that the early warning model suggested in this paper can quickly send out early warning signals to safeguard the safety of car charging and can identify aberrant charging data.

Indoor Safety Monitoring for Falls or Restricted Areas Using Wi-Fi Channel State Information and Deep Learning Methods in Mega Building Construction Projects

With the trend of sustainable development growing worldwide, both the numbers of new mega building construction projects and renovations to existing high-rise buildings are increasing. At such construction sites, most construction workers can be described as performing various activities in indoor spaces. The literature shows that the indoor safety protection measures in such construction sites are often imperfect, resulting in an endless stream of incidents such as falls. Thus, this research aims at developing a flexible indoor safety warning system, based on Wi-Fi-generated channel state information (CSI), for monitoring the construction workers approaching restricted areas or floor openings. In the proposed approach, construction workers do not have to carry any sensors, and each indoor space only needs to have the specified Wi-Fi devices installed. Since deep learning methods are employed to analyze the CSI data collected, the total deployment time, including setting up the Wi-Fi devices and performing data collection and training work, has been measured. Efficiency and effectiveness of the developed system, along with further developments, have been evaluated and discussed by 12 construction safety experts. It is expected that the proposed approach can be enhanced to accommodate other types of safety hazards and be implemented in all mega building construction projects so that the construction workers can have safer working environments.

Predicting real-time deformation of structure in fire using machine learning with CFD and FEM

Real-time prediction of structural safety conditions is critical to firefighting teams during building fire rescue operations. This paper presents a numerical data-based machine learning (ML) framework for structural fire response prediction by integrating Computational Fluid Dynamics (CFD) and Finite Element Methods (FEM). Multiple ML models are developed to predict the real-time vertical displacement of the structure based on corresponding temperature field data with a numerical case of an 8 m × 8 m × 0.6 m virtual steel roof structure under 1200 virtual fire scenarios. The models are comparatively evaluated in terms of predictive accuracy, computational efficiency, and model robustness. The results show the effectiveness of Random Forest and Gradient Boosting models in real-time displacement prediction, indicating the feasibility of the framework in providing timely and reliable predictions of structural safety conditions for firefighters. The CFD/FEM-based framework could be embedded in a structural fire safety warning system to facilitate smart firefighting in the future.

Crosswalk Safety Warning System for Pedestrians to Cross the Street Intelligently

During signal transitions at road sections and intersections, pedestrians and vehicles often clash and cause traffic accidents due to unclear right-of-way. To solve this problem, a vehicle safety braking distance model considering human–vehicle characteristics is established and applied to the designed crosswalk safety warning system to enable pedestrians to cross the street intelligently. The model developed to consider human–vehicle characteristics improves the parking sight distance and pedestrian crossing safety psychological distance models by adding consideration of the effect of vehicle size and type on pedestrian psychology. The established model considering human–vehicle characteristics was improved for the stopping sight distance and pedestrian crossing safety psychological distance models. The effects of vehicle size and type on pedestrian psychology were taken into account. The designed warning system can be divided into a detection module, control module, warning module, and wireless communication module. The system detects the position and speed of pedestrians and vehicles and discriminates the conflict situation, executing the corresponding warning plan for three different types of situations. The system provides warning to pedestrians and vehicles through the different color displays of the intelligent crosswalk. The results show that the proposed model, which synergistically couples vehicle speed, driver reaction time, road characteristic correlation coefficients, and the psychological impact of vehicle size and type on pedestrians, is safe and effective. The designed system solves the problem of pedestrian crossing safety from both theoretical and technical aspects.

Privacy-Preserving Aggregation-Authentication Scheme for Safety Warning System in Fog-Cloud Based VANET

As cities become smarter, the importance of vehicular ad hoc networks (VANETs) will be increasingly pronounced. To support latency- and time-sensitive applications, there have been attempts to utilize fog-cloud computing in VANETs. There are, however, a number of limitations in existing fog-cloud based VANET deployments, ranging from computation and communication bottlenecks to privacy leakage to costly certificate/ pseudonym management to key escrow, and so on. Therefore, in this paper we propose a privacy-preserving aggregation authentication scheme (PPAAS). The scheme is designed for deployment in a safety warning system for fog-cloud based VANETs. Specifically, the PPAAS scheme is realized using a novel efficient anonymous certificateless aggregation signcryption scheme (CASS) proposed in this paper, and allows a fog node to aggregate signcrypted traffic-related messages from surrounding vehicles into an aggregated ciphertext and unsigncrypt them in a batch. We then evaluate the security of PPAAS and demonstrate that it supports confidentiality, authentication, and (efficient) conditional privacy, and key escrow freeness. In particular, our scheme is the first in the literature to achieve efficient conditional privacy, which avoids the need for costly pseudonym management. We also demonstrate that the scheme is practical, based on our simulation results.

5G-V2X Based Traffic Safety Warning System through Mobile Sensor and Wireless Communication

This project designs a traffic safety early warning system based on 5G-V2X for the current situation of increasing traffic accidents in China, which concentrates on two modules of V2V (vehicle-to-vehicle), V2I (vehicle-to-road) for early warning system design, with OBU (vehicle communication unit) and RSU (roadside communication unit) based on 5G-V2X communication technology to establish vehicle-to-vehicle and vehicle-to-road interactive communication, and realize V2V collision warning and V2I traffic light emergency event warning at intersections through collision risk warning algorithm and intersection passage assistance algorithm, thus alerting drivers to avoid dangerous situations and reducing the incidence of traffic accidents.