Real-time Warning Research Articles

Real-Time Structural Monitoring of the Multi-Point Hoisting of a Long-Span Converter Station Steel Structure.

In the process of using a long-span converter station steel structure, engineering disasters can easily occur. Structural monitoring is an important method to reduce hoisting risk. In previous engineering cases, the structural monitoring of long-span converter station steel structure hoisting is rare. Thus, no relevant hoisting experience can be referenced. Traditional monitoring methods have a small scope of application, making it difficult to coordinate monitoring and construction control. In the monitoring process, many problems arise, such as complicated installation processes, large-scale data processing, and large-scale installation errors. With a real-time structural monitoring system, the mechanical changes in the long-span converter station steel structure during the hoisting process can be monitored in real-time in order to achieve real-time warning of engineering disasters, timely identification of engineering issues, and allow for rapid decision-making, thus avoiding the occurrence of engineering disasters. Based on this concept, automatic monitoring and manual measurement of the mechanical changes in the longest long-span converter station steel structure in the world is carried out, and the monitoring results were compared with the corresponding numerical simulation results in order to develop a real-time structural monitoring system for the whole long-span converter station steel structure’s multi-point lifting process. This approach collects the monitoring data and outputs the deflection, stress, strain, wind force, and temperature of the long-span converter station steel structure in real-time, enabling real-time monitoring to ensure the safety of the lifting process. This research offers a new method and basis for the structural monitoring of the multi-point hoisting of a long-span converter station steel structure.

Development and Testing of a Low-Cost Water Level Sensor and a Mobile Based Real-time Flood Warning System for the Flash Flood Prone North Eastern Part of Bangladesh

Background: The North-Eastern part of Bangladesh suffers from frequent flash flood, causing colossal damage to life and properties, especially the vast croplands. A distributed sensing system can monitor the water level on a continuous basis to warn people near the riverbank beforehand and reduce the damage largely. However, the required communication infrastructure is not available in most of the remote rural areas in a developing country like Bangladesh. Objective: This study intends to develop a low-cost sensor-based warning system, customizing to the Bangladesh context. Method: The system utilizes a low-cost ultrasound based sensor device, a lightweight mobile phone based server, low-cost IoT sensing nodes, and a central server for continuous monitoring of river stage data along with the provision of storage and long-term data analytics. Results: A flash flood warning system developed afterward with the sensors, mobile-based server, and appropriate webbased interfaces. The device was tested for some environmental conditions in the lab and deployed it later in the outdoor conditions for short-term periods. Conclusion: Overall, the warning system performed well in the lab as well as the outdoor environment, with the ability to detect water level at reasonable accuracy and transmit data to the server in real time. Some minor shortcomings still noted with the scope for improvements, which are in the way to improve further.

Safety Monitoring and Warning System for Subway Construction Workers Using Wearable Technology

Objective: This study focused on the application of wearable technology in the safety monitoring and early warning for subway construction workers. Methods: With the help of real-time video surveillance and RFID positioning which was applied in the construction has realized the real-time monitoring and early warning of on-site construction to a certain extent, but there are still some problems. Real-time video surveillance technology relies on monitoring equipment, while the location of the equipment is fixed, so it is difficult to meet the full coverage of the construction site. However, wearable technologies can solve this problem, they have outstanding performance in collecting workers’ information, especially physiological state data and positioning data. Meanwhile, wearable technology has no impact on work and is not subject to the inference of dynamic environment. Results and Conclusion: The first time the system applied to subway construction was a great success. During the construction of the station, the number of occurrences of safety warnings was 43 times, but the number of occurrences of safety accidents was 0, which showed that the safety monitoring and early warning system played a significant role and worked out perfectly.

Research on Early Warning Model of Electric Vehicle Charging Safety

At present, an effective evaluation system of the charging safety of electric vehicles has not been formed and lacks the corresponding safety early warning and protection control measures. It is of great significance to ensure the charging safety of electric vehicles and promote the rapid development of electric vehicles. Through the analysis of the establishment of the electric vehicle charging safety evaluation index system, the establishment method of the index system is studied. Finally, the demand analysis of the electric vehicle charging safety early warning and protection system is carried out. The function of establishing safety evaluation model in the system is clarified. For the comprehensive demonstration operation of electric vehicle, it is verified that the electric vehicle charging safety warning and protection system has real-time warning and protection function for battery, charging equipment and distribution network, which embodies the practical engineering value of the system.

Crowded treading warning system for urban rail transit stations based on video detection technology

Based on the rapid development of rail transit, it have a greater hidden danger of congestion and trampling, this paper proposes the construction of an urban rail transit station congestion and trampling warning system based on video detection technology, which uses video detection technology to collect the passenger flow, velocity and density data of the bottleneck channel in rail transit stations, then build and train neural network model to predict the next time passenger flow density combine with the three-parameter relationship of the traffic flow, and use the SPSS software used to fit and analyze the passenger flow parameters. So the limit value of the flow-density polynomial model is predicted by pedestrian flow mutation model and used to realize the definition of the critical passenger flow density model based on the catastrophe theory. On this basis, this article compares the real-time predicted value of passenger flow density to it and get the analysis result, then set the corresponding early warning coefficient, early warning level and early warning measures, so it can truly realize the real-time warning of congestion and trampling in rail transit stations based on passenger flow density prediction.

SMART Subsea Cables for Observing the Ocean and Earth

Abstract The Joint Task Force (JTF) for Science Monitoring And Reliable Telecommunications (SMART) Subsea Cables will facilitate integration of sensors into commercial submarine telecommunications cables for climate monitoring and disaster warning. Our vision is a planetary scale array monitoring ocean heat and circulation and sea-level rise and revolutionizing real-time warning systems for earthquake and tsunami disaster mitigation. This is enabled by the trans-ocean cable infrastructure linking society together: 1.4 million km of cable, 20,000 repeaters every 70 km hosting the sensors, constantly being refreshed over 10‐25 years, without interfering with telecom. Initial sensors are ocean bottom temperature, pressure and seismic acceleration. System suppliers are on board, the first major SMART project is funded and underway in Portugal (2020), and seven others are in various stages of planning and funding—a perfect example of the Blue Economy in action for the UN Decade.JTF will provide coordination between ocean science, operational oceanography, hazard early warning centers, industry, and relevant government agencies. SMART cables will create profound opportunities for innovation—requiring people with appropriate depth and breadth of expertise. JTF will facilitate SMART cable projects that will catalyze and include education, training, and outreach programs to build necessary capacity

Research on driving proneness in car-following behaviours based on multi-source real driving data

There is little research on the degrees of drivers’ short-term behaviours regarding driving safety. To solve this problem, this paper investigated the concept of driving proneness and evaluated the propensities of different drivers to engage in different operations for the following scenarios of urban traffic. From the real driving data of sixteen drivers on a city road, car-following data fragments were extracted and six key parameters were obtained: throttle percentage, change rate of throttle percentage, brake pressure, change rate of brake pressure, absolute value of steering angle and absolute value of steering angle speed. Symbolic Aggregate Approximation was used to reduce the dimensionality of the parameters. The input of the Hidden Markov Model-Viterbi was obtained by the use of statistical methods. The output of the model is the probability of the three proneness states of introversion, neutrality and extroversion, from which the proneness value of each driver was calculated. The weighted proneness value of each driver was obtained by the use of the entropy weight method to assign weights to each parameter. The operating characteristics of the drivers were also analysed and described. The method presented in this paper can provide accurate and real-time warning in network-driven environments.

Research on intelligent risk early warning of open-pit blasting site based on deep learning

ABSTRACT The safety of blasting site in open-pit mine can be greatly improved by risk early warning. Therefore, an intelligent real-time risk early warning method of blasting site in open-pit mine based on deep learning was proposed in this research. The mobile wireless webcams, H.264 video compression algorithm, and real-time transport protocol were applied to achieve real-time video acquisition and transmission of blasting site in open-pit mine. A single-stage deep neural network DG-YOLOv3 was proposed in this research. DG-YOLOv3 is an improvement of Gaussian YOLOv3, among which Darknet41 is used to improve the model’s detection speed and detection accuracy of small targets. To further improve the performance (i.e., speed and accuracy) of risk early warning, the surveillance videos were first split into pictures by frame. Then, the pictures were processed by weighted average grayscale and contrast limited adaptive histogram equalization. Experiments show that the mean average precision of DG-YOLOv3 proposed in this paper reaches 87.45 and the detection speed reaches 56.82 frames per second, which has better accuracy and speed compared with other algorithms. In addition, DG-YOLOv3 has good robustness in complex scenarios. Based on the detection results, the intelligent real-time risk early warning of the blasting site in open-pit mine is achieved finally.

Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

Body Temperature-Indoor Condition Monitor and Activity Recognition by MEMS Accelerometer Based on IoT-Alert System for People in Quarantine Due to COVID-19.

Coronavirus disease 19 (COVID-19) is a virus that spreads through contact with the respiratory droplets of infected persons, so quarantine is mandatory to break the infection chain. This paper proposes a wearable device with the Internet of Things (IoT) integration for real-time monitoring of body temperature the indoor condition via an alert system to the person in quarantine. The alert is transferred when the body thermal exceeds the allowed threshold temperature. Moreover, an algorithm Repetition Spikes Counter (RSC) based on an accelerometer is employed in the role of human activity recognition to realize whether the quarantined person is doing physical exercise or not, for auto-adjustment of threshold temperature. The real-time warning and stored data analysis support the family members/doctors in following and updating the quarantined people’s body temperature behavior in the tele-distance. The experiment includes an M5stickC wearable device, a Microelectromechanical system (MEMS) accelerometer, an infrared thermometer, and a digital temperature sensor equipped with the user’s wrist. The indoor temperature and humidity are measured to restrict the virus spread and supervise the room condition of the person in quarantine. The information is transferred to the cloud via Wi-Fi with Message Queue Telemetry Transport (MQTT) broker. The Bluetooth is integrated as an option for the data transfer from the self-isolated person to the electronic device of a family member in the case of Wi-Fi failed connection. The tested result was obtained from a student in quarantine for 14 days. The designed system successfully monitored the body temperature, exercise activity, and indoor condition of the quarantined person that handy during the Covid-19 pandemic.

Overview of lane-keeping assist system based on human–machine cooperative control

As the final stage of intelligent vehicle, traffic accidents can be effectively reduced by automatic driving. However, neither the technology nor the regulations are mature for autonomous driving. The lane-keeping assist system is one of the important components of the advanced driver-assistance system. When driver fatigue or inattention is detected, the system can effectively prevent the vehicle departure from the lane. Information such as vehicle status, driver status, and external environment can be used by the lane-keeping assist system based on human–machine dynamic cooperative control, thereby smoothly changing the driving rights between the driver and the automatic controller. The system can keep the vehicle in the lane while complying with the driver's intention, thereby ensuring vehicle safety and driver comfort. The research status and future development suggestions on lane-departure decision models, dynamic allocation of driving rights, and performance evaluation were analyzed in this paper. Regarding lane-departure decision models, different decision models considering the driver's state should be developed. The decision model can be established as an adaptive adjustment model and also should allow the manual adjustment of the preset parameters according to the driver’s preferences and the external driving environment. Concerning the allocation of driving rights, a more reasonable dynamic allocation of driving rights should be explored, and intelligent optimization algorithms or control models should be designed. Regarding performance evaluation indicators, evaluation indicators related to the reduction of human–machine conflict and the amount of control effort should be added. A scientific and complete subjective evaluation system should be developed. Future studies on lane-keeping assist system based on human–machine cooperative control should deeply integrate driver factors, issue real-time warnings and active intervention, and perform complete testing and evaluation of the system.

Real-Time Monitoring System of Automobile Driver Status and Intelligent Fatigue Warning Based on Triboelectric Nanogenerator.

To improve automobile safety, identifying driver fatigue is considerably crucial because it is one of the main causes of traffic accidents. In this research, smart systems based on a triboelectric nanogenerator are designed, which can provide driver status monitoring and fatigue warning in real time. The smart system consists of a self-powered steering-wheel angle sensor (SSAS) and a signal processing unit. The SSAS, which comprises a stator, a rotor, and a sleeve, is mounted on the steering rod, and the electrodes are designed with a phase difference to improve the resolution of the sensor. The turning angle of the steering wheel operated by the driver is recorded by the SSAS; meanwhile, the number of rotations, the average angle, and other parameters in the driver's recorded data are analyzed by the signal processing unit from which a warning threshold for each parameter is determined. The system assesses the status of the driver in real-time by comparing these parameters and threshold values, and experimental results demonstrate that driver status is accurately judged. This work has important potential applications in the fields of traffic safety and intelligent driving.

Reply to “Comments on ‘Flash Flood Verification: Pondering Precipitation Proxies’”

AbstractWe applaud Gourley and Vergara for their thorough investigation of the relationship between precipitation and flash flood reports, as well as their inclusion of information from advanced hydrologic model output. We conducted some additional analysis to identify the reasons for the substantial differences between their findings and ours. The primary reason for the differences was found to be temporal sampling. The high temporal resolution of the MRMS dataset, as well as their use of “rolling” accumulation periods, explains most of the discrepancies. For guidance related to real-time warning decisions for flash flooding, Gourley and Vergara’s analyses provide an important new guide and we recommend the use of their results for this purpose. For other applications, including model postprocessing and for precipitation datasets with lower temporal resolution, our results will continue to prove useful.

GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation

Global Navigation Satellite System (GNSS) is used in seismology to study the ground displacements as well as to monitor the ionospheric total electron content (TEC) perturbations following seismic events. The aim of this work is to combine these two observations in one real-time method based on the Total Variometric Approach (TVA) to include the GNSS real-time data stream in future warning systems and tsunami genesis estimation observing both, ground motion and TEC. Our TVA couples together the Variometric Approach for Displacement Analysis Stand-alone Engine (VADASE) with the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithms. We apply the TVA to the Mw 8.3 Illapel earthquake, that occurred in Chile on September 16, 2015, and we demonstrate the coherence of the earthquake ground shaking and the TEC perturbation by using the same GNSS data stream in a real-time scenario. Nominally, we also highlight a stronger kinetic energy released in the north of the epicenter and visible in both, the ground motion and the TEC perturbation detect at 30 s and around 9.5 min after the rupture respectively. The high spatial resolution of ionospheric TEC measurement seems to match with the extent of the seismic source. The GNSS data stream by TVA of both the ground and ionospheric measurement opens today new perspectives to real-time warning systems for tsunami genesis estimation.

Real-Time Warning Model of Highway Engineering Construction Safety Based on Internet of Things

Real-time and effective early warning of highway engineering construction sites is the key to ensuring the safety of highway engineering construction. At present, highway engineering construction safety early warning is limited by the experience of relevant personnel at the site and the dynamic changes of the project site environment. Therefore, the creation of a more active, smarter, and more effective real-time early warning model for construction safety is a strong complement to current research and has important theoretical and practical implications. The Internet of Things is the third wave of the information industry after computers, the Internet, and mobile communication networks. It is of great significance to promote the development of science and technology, economic growth, and social progress. Aiming at the shortcomings of the inadequate safety management methods for highway engineering construction in China, the inefficient efficiency of safety production supervision and management, and the emphasis on single and sporty supervision methods, a real-time early warning model for highway engineering construction safety based on the Internet of Things technology was constructed. By quantifying, scoring, and statistics of the safety situation during the construction process, the model achieves the goals of real-time monitoring, early warning, and handling hidden safety hazards. It overcomes problems such as untimely and unscientific safety issues in the past and effectively improves China’s highway engineering construction. The experimental comparison between the real-time early warning model and the traditional early warning model in this paper shows that the accuracy of the early warning model proposed in this paper is improved by nearly 5%, and the false alarm rate is reduced by nearly 4%.

The Efficacy of Operational Bird Strike Prevention

Involving air traffic controllers and pilots into the bird strike prevention process is considered an essential step to increase aviation and avian safety. Prior to implementing operational measures such as real-time warning systems, it is vital to evaluate their feasibility. This paper studies the efficacy of a bird strike advisory system for air traffic control. In addition to the potential safety benefit, the possible impact on airport operations is analyzed. To this end, a previously developed collision avoidance algorithm underlying the system was tested in fast-time Monte Carlo simulations involving various air traffic and bird densities to obtain representative conclusions for different operational conditions. The results demonstrate the strong safety potential of operational bird strike prevention in case of precise bird movement prediction. Unless airports operate close to their capacity limits while bird abundance is high, the induced delays remain tolerable. Prioritization of hazardous strikes involving large individuals as well as flocks of birds are expected to support operational feasibility in all conditions.

A Study on the Early Warning of Athletes’ Safety in Sport Events Based on the “Fusion of Data and Intelligence”

As there are many safety factors involved in sports events, the uncertainty and complexity of event risks have led to the frequent occurrence of sudden death and injury accidents among athletes. Starting from the analysis of the data in the 522 Gansu Baiyin Cross Country Race accident report, this paper discusses the feasibility and effectiveness of applying big data and intelligent techniques to guarantee the safety of the athletes in sport events, by all-round monitoring the relevant elements during the whole event’s process. Real-time warning is accomplished on the basis of intelligent analysis of the monitoring data.

Construction and application of comprehensive early warning system for coal and gas outburst in Xinjing Coal Mine

In order to further strengthen the prevention and control of coal and gas outburst in Xinjing Coal Mine, the online comprehensive analysis and early warning index system and rules of coal and gas outburst suitable for Xinjing Coal Mine were studied. Based on the corresponding early warning computer system and guarantee mechanism, a comprehensive early warning system for coal and gas outbursts in Xinjing Coal Mine was established, realizing real-time intelligent early warning of outburst dangers in working faces. The system realizes the standardization and dynamic management of outburst prevention information at the working face, as well as the real-time dynamic update and sharing of outburst prevention information, which improves the efficiency of mine outburst prevention management and the level of mine safety. The actual application of the system in Xinjing Coal Mine shows that the system can provide an effective reference for the comprehensive early warning of outbursts in other outburst mines of Yangquan Coal Industry Group.

The Application of Visual Sensors in the Artistic Design of College Campus Landscape

The research purpose is to solve the problems of low efficiency, low accuracy, and high cost of traditional environmental landscape mapping and landscape volume measurement methods in the artistic design of college campus landscape and make up the defects that the traditional campus monitoring is vulnerable to adverse weather, which results in low monitoring accuracy. Primarily, a binocular stereo vision measurement based on Scale Invariant Feature Transform (SIFT) matching algorithm is proposed, which can realize accurate collection of environmental spatial information and measurement of landscape volume without contact in the process of campus landscape design. Then, the visual monitoring system of college landscape based on the Internet of Things (IoT) is constructed to realize real‐time monitoring and early warning of human damage to campus landscape. The proposed method is verified by actual measurement of different objects and simulation experiments using simulation software. Ultimately, the application of visual sensors in artistic design of college campus landscape is analysed by literature analysis. The results show that (1) the error of the improved binocular stereo vision measurement designed here is 52.32% and 59.69% lower than that of the traditional measurement method when measuring the same object with different volumes and the volumes of different objects, respectively, which indicates that the measurement accuracy of the new method is higher. (2) The proposed landscape visual monitoring method based on IoT improves the image recognition accuracy by 21% compared with the traditional digital image monitoring method. The average recognition time is shortened by 12 ms, which ensures the accuracy and improves the recognition efficiency. (3) Through the analysis of existing literature, it is found that the binocular stereo vision sensor can be used to monitor the whole process of landscape construction in real time. The sensor can be combined with social networks, mobile terminals, and physiological monitoring equipment to comprehensively analyse and evaluate people’s preference for campus landscape. The proposed method has broad application prospects in campus landscape design, construction, and maintenance. The research purpose is to provide important technical support for the improvement of the overall image of the college campus and even the city for the design of landscape environment and the technical upgrading of maintenance work in the college campus.

Assessment of agricultural drought in Morocco based on a composite of the Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI)

According to IPCC, Morocco is a highly vulnerable country to extreme climate events, especially droughts; this will affect different socioeconomic sectors, mainly the agriculture sector. Droughts are controlled by the variability of precipitation and evapotranspiration but also not neglecting the effect of land surface conditions such as land surface temperature. In this present study, the remote sense observations MODIS Normalized Difference Vegetation Index (NDVI) and CMSAF Land Surface Temperature (LST) were used for calculating the Vegetation Health Index (VHI). The main advantage of remote sensing products is that they are reasonably efficient in terms of temporal and spatial coverage, and they are useful for the monitoring and assessment of drought in the near real-time. Furthermore, ERA5 Reanalysis-based SPEI is calculated. The goal of this study is to assess the spatial and temporal patterns of drought, this study offers the composite of SPEI and VHI drought monitoring obtained by plotting maps and graphs to show the monthly and annual variability of drought for the period 2000–2015 over the whole of Morocco. This monitoring can be used as a near real-time warning system in a changing climate.