scholarly journals Intelligent early warning method based on drone inspection

2022 ◽  
Author(s):  
Qijuan Li ◽  
Yue Xu
Keyword(s):  
Real Time ◽  
Information Flow ◽  
Early Warning ◽  
Critical Time ◽  
Early Warning Systems ◽  
Rapid Response ◽  
Measuring System ◽  
Risk Level ◽  
Command Center ◽  
Response Capability

Commonly used UAV emergency inspection methods are executed by the instructions of the ground command center. The response rate depends on the stability of the communication network and the rapid response ability of the commander. The critical time window is fleeting, which is likely to cause unnecessary loss. Crisis rapid response capability has become the key to measuring system capabilities. In order to improve the system’s rapid response capability, a method of deploying decision-making agents on airborne computers and ground early warning systems is proposed. This early warning method uses key technologies such as multi-network integration, situation assessment, neural network architecture, deep learning, reinforcement learning, and intelligent cognitive reasoning to effectively ensure the effectiveness of crisis warning. The early warning method of the early warning system is as follows: the mission computer uniformly collects the flight control status parameters, the load status parameters and the load real-time data form a composite information flow. The task computer adopts the methods of protocol conversion, data classification, and danger recognition to the compound information flow to identify the crisis information and make a preliminary analysis and judgment of the crisis state. If it is determined that it is necessary to track the target in real time, the initial task assignment and parameter adjustment of the load are carried out, and the continuous tracking of the task target is carried out to realize the rapid response to the crisis on the edge side. At the same time, the composite data are downloaded to the command center. The command center performs the secondary crisis analysis and risk level determination and outputs the crisis plan deduced by the agent to realize the strategy assistance. The accuser refers to the plan strategy and issues instructions to the task computer, and the task computer receives it. Instruction and secondary adjustment and optimization of the load parameters. If there is a flight route adjustment instruction, the adjustment route will be sent to the flight controller, which greatly improves the flexibility and efficiency of handling the crisis in the UAV inspection process. By adopting this set of early warning methods, it can provide users with an updated, faster and more efficient way to realize the early warning requirements in drone inspections, which is a new breakthrough in the field of drone command methods.

scholarly journals Near real-time GPS applications for tsunami early warning systems

2010 ◽  
Vol 10 (2) ◽  
pp. 181-189 ◽  
Author(s):  
C. Falck ◽  
M. Ramatschi ◽  
C. Subarya ◽  
M. Bartsch ◽  
A. Merx ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Tide Gauge ◽  
Warning System ◽  
Early Warning Systems ◽  
Time Data ◽  
Essential Information ◽  
Tsunami Early Warning ◽  
Land Mass

Abstract. GPS (Global Positioning System) technology is widely used for positioning applications. Many of them have high requirements with respect to precision, reliability or fast product delivery, but usually not all at the same time as it is the case for early warning applications. The tasks for the GPS-based components within the GITEWS project (German Indonesian Tsunami Early Warning System, Rudloff et al., 2009) are to support the determination of sea levels (measured onshore and offshore) and to detect co-seismic land mass displacements with the lowest possible latency (design goal: first reliable results after 5 min). The completed system was designed to fulfil these tasks in near real-time, rather than for scientific research requirements. The obtained data products (movements of GPS antennas) are supporting the warning process in different ways. The measurements from GPS instruments on buoys allow the earliest possible detection or confirmation of tsunami waves on the ocean. Onshore GPS measurements are made collocated with tide gauges or seismological stations and give information about co-seismic land mass movements as recorded, e.g., during the great Sumatra-Andaman earthquake of 2004 (Subarya et al., 2006). This information is important to separate tsunami-caused sea height movements from apparent sea height changes at tide gauge locations (sensor station movement) and also as additional information about earthquakes' mechanisms, as this is an essential information to predict a tsunami (Sobolev et al., 2007). This article gives an end-to-end overview of the GITEWS GPS-component system, from the GPS sensors (GPS receiver with GPS antenna and auxiliary systems, either onshore or offshore) to the early warning centre displays. We describe how the GPS sensors have been installed, how they are operated and the methods used to collect, transfer and process the GPS data in near real-time. This includes the sensor system design, the communication system layout with real-time data streaming, the data processing strategy and the final products of the GPS-based early warning system components.

Laboratory Experiment Study on Real-Time Monitoring and Safety Early Warning System of Structure Settlement and Deformation

2015 ◽  
Vol 775 ◽  
pp. 264-267
Author(s):  
Xiao Dong Pan ◽  
Lei Zhao
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Transport Layer ◽  
Measuring Instruments ◽  
Test Scheme ◽  
Static Force ◽  
Force Level ◽  
Real Time Monitoring ◽  
Time Deformation

Currently the settlement and deformation of factory building structure is monitored using total stations and other more conventional measuring instruments, it is difficult to reflect the health of the structure timely and accurately. In order to change the situation, we establish a set of system for real-time monitoring of deformation and safety warning. The system is formed of sensing layer, transport layer and application layer. Sensing layer is composed of static force level and biaxial inclinometer. The system can be used in dynamic real-time factory structure safety monitoring, also applied to other similar structural monitoring. This paper will study the system components and principle, early warning systems grading, calculation of real-time deformation of roof frame, laboratory test scheme and verification. Experiments showed that the system is suitable for the actual factory structure monitoring, while the choice of static force level and biaxial inclinometer of precision to meet the requirements.

Real-time risk assessment in seismic early warning and rapid response: a feasibility study in Bishkek (Kyrgyzstan)

2012 ◽  
Vol 17 (2) ◽  
pp. 485-505 ◽  
Author(s):  
M. Picozzi ◽  
D. Bindi ◽  
M. Pittore ◽  
K. Kieling ◽  
S. Parolai
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Feasibility Study ◽  
Early Warning ◽  
Rapid Response ◽  
Seismic Early Warning

scholarly journals An Ambulatory Blood Pressure Monitor Mobile Health System for Early Warning for Stroke Risk: Longitudinal Observational Study (Preprint)

2019 ◽  
Author(s):  
Guangyu Wang ◽  
Silu Zhou ◽  
Shahbaz Rezaei ◽  
Xin Liu ◽  
Anpeng Huang
Keyword(s):  
Blood Pressure ◽  
Observational Study ◽  
Real Time ◽  
Early Warning ◽  
Stroke Risk ◽  
Risk Level ◽  
Blood Pressure Monitor ◽  
Pressure Monitor ◽  
Blood Pressure Data ◽  
Longitudinal Observational Studies

BACKGROUND Stroke, as a leading cause of death around the globe, has become a heavy burden on our society. Studies show that stroke can be predicted and prevented if a person’s blood pressure (BP) status is appropriately monitored via an ambulatory blood pressure monitor (ABPM) system. However, currently there exists no efficient and user-friendly ABPM system to provide early warning for stroke risk in real-time. Moreover, most existing ABPM devices measure BP during the deflation of the cuff, which fails to reflect blood pressure accurately. OBJECTIVE In this study, we sought to develop a new ABPM mobile health (mHealth) system that was capable of monitoring blood pressure during inflation and could detect early stroke-risk signals in real-time. METHODS We designed an ABPM mHealth system that is based on mobile network infrastructure and mobile apps. The proposed system contains two major parts: a new ABPM device in which an inflation-type BP measurement algorithm is embedded, and an abnormal blood pressure data analysis algorithm for stroke-risk prediction services at our health data service center. For evaluation, the ABPM device was first tested using simulated signals and compared with the gold standard of a mercury sphygmomanometer. Then, the performance of our proposed mHealth system was evaluated in an observational study. RESULTS The results are presented in two main parts: the device test and the longitudinal observational studies of the presented system. The average measurement error of the new ABPM device with the inflation-type algorithm was less than 0.55 mmHg compared to a reference device using simulated signals. Moreover, the results of correlation coefficients and agreement analyses show that there is a strong linear correlation between our device and the standard mercury sphygmomanometer. In the case of the system observational study, we collected a data set with 88 features, including real-time data, user information, and user records. Our abnormal blood pressure data analysis algorithm achieved the best performance, with an area under the curve of 0.904 for the low risk level, 0.756 for the caution risk level, and 0.912 for the high-risk level. Our system enables a patient to be aware of their risk in real-time, which improves medication adherence with risk self-management. CONCLUSIONS To our knowledge, this device is the first ABPM device that measures blood pressure during the inflation process and has obtained a government medical license. Device tests and longitudinal observational studies were conducted in Peking University hospitals, and they showed the device’s high accuracy for BP measurements, its efficiency in detecting early signs of stroke, and its efficiency at providing an early warning for stroke risk.

scholarly journals An Ambulatory Blood Pressure Monitor Mobile Health System for Early Warning for Stroke Risk: Longitudinal Observational Study

10.2196/14926 ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. e14926 ◽  
Author(s):  
Guangyu Wang ◽  
Silu Zhou ◽  
Shahbaz Rezaei ◽  
Xin Liu ◽  
Anpeng Huang
Keyword(s):  
Blood Pressure ◽  
Observational Study ◽  
Real Time ◽  
Early Warning ◽  
Stroke Risk ◽  
Risk Level ◽  
Blood Pressure Monitor ◽  
Pressure Monitor ◽  
Blood Pressure Data ◽  
Longitudinal Observational Studies

Background Stroke, as a leading cause of death around the globe, has become a heavy burden on our society. Studies show that stroke can be predicted and prevented if a person’s blood pressure (BP) status is appropriately monitored via an ambulatory blood pressure monitor (ABPM) system. However, currently there exists no efficient and user-friendly ABPM system to provide early warning for stroke risk in real-time. Moreover, most existing ABPM devices measure BP during the deflation of the cuff, which fails to reflect blood pressure accurately. Objective In this study, we sought to develop a new ABPM mobile health (mHealth) system that was capable of monitoring blood pressure during inflation and could detect early stroke-risk signals in real-time. Methods We designed an ABPM mHealth system that is based on mobile network infrastructure and mobile apps. The proposed system contains two major parts: a new ABPM device in which an inflation-type BP measurement algorithm is embedded, and an abnormal blood pressure data analysis algorithm for stroke-risk prediction services at our health data service center. For evaluation, the ABPM device was first tested using simulated signals and compared with the gold standard of a mercury sphygmomanometer. Then, the performance of our proposed mHealth system was evaluated in an observational study. Results The results are presented in two main parts: the device test and the longitudinal observational studies of the presented system. The average measurement error of the new ABPM device with the inflation-type algorithm was less than 0.55 mmHg compared to a reference device using simulated signals. Moreover, the results of correlation coefficients and agreement analyses show that there is a strong linear correlation between our device and the standard mercury sphygmomanometer. In the case of the system observational study, we collected a data set with 88 features, including real-time data, user information, and user records. Our abnormal blood pressure data analysis algorithm achieved the best performance, with an area under the curve of 0.904 for the low risk level, 0.756 for the caution risk level, and 0.912 for the high-risk level. Our system enables a patient to be aware of their risk in real-time, which improves medication adherence with risk self-management. Conclusions To our knowledge, this device is the first ABPM device that measures blood pressure during the inflation process and has obtained a government medical license. Device tests and longitudinal observational studies were conducted in Peking University hospitals, and they showed the device’s high accuracy for BP measurements, its efficiency in detecting early signs of stroke, and its efficiency at providing an early warning for stroke risk.

scholarly journals P-wave picking for earthquake early warning: Refinement of a T pd method

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Jim Mori
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Arrival Time ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Warning Systems ◽  
Wave Detection ◽  
Earthquake Early Warning Systems ◽  
Wave Arrival

Summary Detecting P-wave onsets for on-line processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection method designed for real-time processing of continuous waveforms. We used the new Tpd method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 s to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the Tpd method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The Tpd method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The Tpd method can be computed quickly so it would be suitable for the implementation in earthquake early warning systems.

scholarly journals A Study on the Slope Failure Monitoring of a Model Slope by the Application of a Displacement Sensor

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Sungyong Park ◽  
Hyuntaek Lim ◽  
Bibek Tamang ◽  
Jihuan Jin ◽  
Seungjoo Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Slope Failure ◽  
Early Warning Systems ◽  
Displacement Sensor ◽  
Scale Model ◽  
Economic Losses ◽  
Real Time Monitoring ◽  
Landslide Occurrence ◽  
Model Slope

Many causalities and economic losses are caused by natural disasters, such as landslides and slope failures, every year. This suggests that there is a need for an early warning system to mitigate casualties and economic losses. Most of the studies on early warning systems have been carried out by predicting landslide-prone areas, but studies related to the prediction of landslide occurrence time points by the real-time monitoring of slope displacement are still insufficient. In this study, a displacement sensor and an Internet of Things (IoT) monitoring system were combined together, to monitor slope failure through cutting experiments of a real-scale model slope. Real-time monitoring of the slope movement was performed simultaneously via a low-cost, efficient, and easy-to-use IoT system. Based on the obtained displacement data, an inverse displacement analysis was performed. Finally, a slope instrumentation standard was proposed based on the slope of the inverse displacement for early evacuation before slope failure.

scholarly journals Towards an operational database for real-time environmental monitoring and early warning systems

2017 ◽  
Vol 108 ◽  
pp. 2250-2259 ◽  
Author(s):  
Bartosz Balis ◽  
Marian Bubak ◽  
Daniel Harezlak ◽  
Piotr Nowakowski ◽  
Maciej Pawlik ◽  
...  
Keyword(s):  
Environmental Monitoring ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Monitoring And Early Warning

Disability inclusiveness of early warning system: a study on flood-prone areas of Bangladesh

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Musabber Ali Chisty ◽  
Ashrafuzzaman Nazim ◽  
Md. Mostafizur Rahman ◽  
Syeda Erena Alam Dola ◽  
Nesar Ahmed Khan
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Early Warning Systems ◽  
Depth Information ◽  
Extensive Literature ◽  
Persons With Disabilities ◽  
Content Type ◽  
Flood Prone Areas ◽  
Response Capability

PurposePersons with disabilities face the impacts of disasters differently. Early warning systems can be one of the powerful tools to reduce the vulnerabilities of persons with disabilities and mitigate the impacts of disasters. The main objective of this study was to assess the disability inclusiveness of the current early warning system (EWS) in flood-prone areas of Bangladesh.Design/methodology/approachA qualitative method was focused on getting in-depth information. Persons with disabilities participated in focus group discussions (FGDs) and shared the inclusiveness and gaps of the current EWS. Through extensive literature review, a checklist was developed to conduct the FGDs. QDA Miner 6.0.6 software was used for coding and analyzing the data.FindingsResults indicated that, though persons with disabilities have proper risk knowledge, the current monitoring and warning service, dissemination and communication, and response capability are not fully inclusive. A significant gap in the EWS was found in response capability. Even if somehow persons with disabilities manage to receive a warning about a flood, they lack the capacity to respond to the warning.Research limitations/implicationsThe study proposed that to make an EWS inclusive and effective, the concerned authorities should focus on all four parts of the EWS.Originality/valueStudies related to disability and disaster management are not very common. Conducting a qualitative study provided the persons with disabilities the opportunity to share their perspectives. Future studies can focus on vulnerability and capacity assessment of persons with disabilities to identify areas requiring interventions to enhance resilience.

Integration of operational Multi-Hazard Early Warning Systems and COVID-19 data for the humanitarian community

2021 ◽  
Author(s):  
Chiara Proietti ◽  
Alessandro Annunziato ◽  
Pamela Probst ◽  
Stefano Paris ◽  
Thomas Peter
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Forest Fires ◽  
Large Scale ◽  
Warning System ◽  
Early Warning Systems ◽  
Research Centre ◽  
Time Data ◽  
Joint Research ◽  
Coordination System

<p>To improve preparedness and response in case of large-scale disasters, the international humanitarian community needs to understand the anticipated impact of an event as soon as possible in order to take informed operational decisions. The European Commission’s Joint Research Centre (JRC), DG ECHO, and the United Nations’ OCHA and UNOSAT launched the Global Disaster Alert and Coordination System (www.GDACS.org) in 2002-03 as cooperation platform to provide early disaster warning and coordination services to humanitarian actors. After more than 15 years, GDACS has around 30k registered users among humanitarian organisations at global level.</p><p>At the beginning, one of GDACS’s main tasks was the dissemination of automatic alerts for earthquakes, tsunamis and tropical cyclones; today, the system has been augmented to include also floods, droughts and volcanoes, and it will soon include forest fires.  Alerts are sent to the international humanitarian community to ensure timely warning in severe events that are expected to require international assistance. Alert levels are determined by automated algorithms without, or with very limited, human intervention, using automatic real-time data-feeds from various scientific institutes or the JRC’s own systems.</p><p>From 2020, because of the potential impact of the COVID-19 emergency on international preparedness and response activities, the COVID-19 situation in affected countries is now also monitored by the system, providing real time information updates on the website. This new feature allows to consider in the planning of the emergency response, the severity of the outbreak in the affected countries.</p><p>This contribution presents the challenges and outcomes of combining science-based information from different independent systems into a single Multi-Hazard Early Warning System and introduces new functionalities that were recently developed to address the new challenges related to the COVID-19 emergency.</p>

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