The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warni...The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.展开更多
To address the issues of single warning indicators,fixed thresholds,and insufficient adaptability in coal and gas outburst early warning models,this study proposes a dynamic early warning model for gas outbursts based...To address the issues of single warning indicators,fixed thresholds,and insufficient adaptability in coal and gas outburst early warning models,this study proposes a dynamic early warning model for gas outbursts based on adaptive fractal dimension characterization.By analyzing the nonlinear characteristics of gas concentration data,an adaptive window fractal analysis method is introduced.Combined with boxcounting dimension and variation of box dimension metrics,a cross-scale dynamic warning model for disaster prevention is established.The implementation involves three key phases:First,wavelet denoising and interpolation methods are employed for raw data preprocessing,followed by validation of fractal characteristics.Second,an adaptive window cross-scale fractal dimension method is proposed to calculate the box-counting dimension of gas concentration,enabling effective capture of multi-scale complex features.Finally,dynamic threshold partitioning is achieved through membership functions and the 3σprinciple,establishing a graded classification standard for the mine gas disaster(MGD)index.Validated through engineering applications at Shoushan#1 Coal Mine in Henan Province,the results demonstrate that the adaptive window fractal dimension curve exhibits significantly enhanced fluctuation characteristics compared to fixed window methods,with local feature detection capability improved and warning accuracy reaching 86.9%.The research reveals that this model effectively resolves the limitations of traditional methods in capturing local features and dependency on subjective thresholds through multiindicator fusion and threshold optimization,providing both theoretical foundation and practical tool for coal mine gas outburst early warning.展开更多
Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall ...Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.展开更多
The study established daily comprehensive precipitation equations and calculated respective critical daily comprehensive precipitation value of loess-collapse disasters and landslide disasters by dint of the geologica...The study established daily comprehensive precipitation equations and calculated respective critical daily comprehensive precipitation value of loess-collapse disasters and landslide disasters by dint of the geological disasters and corresponding precipitation data in 47 years.Considering geological disaster risk divisions,precipitation influence coefficient and daily comprehensive precipitation,hourly rolling daily-forecasting and hourly warning fine and no-gap models on the base of high temporal and spatial resolution rainfall data of automatic meteorological station were developed.Through the verifying of combination of dynamical forecasting model and warning model,the results showed that it can improve efficiency of forecast and have good response at the same time.展开更多
Human beings have experienced a serious public health event as the new pneumonia(COVID-19), caused by the severe acute respiratory syndrome coronavirus has killed more than 3000 people in China, most of them elderly o...Human beings have experienced a serious public health event as the new pneumonia(COVID-19), caused by the severe acute respiratory syndrome coronavirus has killed more than 3000 people in China, most of them elderly or people with underlying chronic diseases or immunosuppressed states. Rapid assessment and early warning are essential for outbreak analysis in response to serious public health events. This paper reviews the current model analysis methods and conclusions from both micro and macro perspectives. The establishment of a comprehensive assessment model, and the use of model analysis prediction, is very efficient for the early warning of infectious diseases. This would significantly improve global surveillance capacity, particularly in developing regions, and improve basic training in infectious diseases and molecular epidemiology.展开更多
Early warning model of debris flow is important for providing local residents with reliable and accurate warning information to escape from debris flow hazards. This research studied the debris flow initiation in the ...Early warning model of debris flow is important for providing local residents with reliable and accurate warning information to escape from debris flow hazards. This research studied the debris flow initiation in the Yindongzi gully in Dujiangyan City, Sichuan province, China with scaled-down model experiments. We set rainfall intensity and slope angle as dominating parameters and carried out 20 scaled-down model tests under artificial rainfall conditions. The experiments set four slope angles(32°, 34°, 37°, 42°) and five rainfall intensities(60 mm/h, 90 mm/h, 120 mm/h, 150 mm/h, and 180 mm/h) treatments. The characteristic variables in the experiments, such as, rainfall duration, pore water pressure, moisture content, surface inclination, and volume were monitored. The experimental results revealed the failure mode of loose slope material and the process of slope debris flow initiation, as well as the relationship between the surface deformation and the physical parameters of experimental model. A traditional rainfall intensity-duration early warning model(I-D model) was firstly established by using a mathematical regression analysis, and it was then improved into ISD model and ISM model(Here, I is rainfall Intensity, S is Slope angle, D is rainfall Duration, and M is Moisture content). The warning model can provide reliable early warning of slope debris flow initiation.展开更多
As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.D...As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.展开更多
The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines.However,three technical difficulties need to be solved:(1)low modeling precision caused by indiv...The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines.However,three technical difficulties need to be solved:(1)low modeling precision caused by individual differences between gas turbines,(2)poor solution efficiency due to excessive iterations,and(3)the false alarm and missing alarm brought by the traditional fixed threshold method.This paper proposes a digital twin model-based early warning method for gas-path faults that breaks through the above obstacles from three aspects.Firstly,a novel performance modeling strategy is proposed to make the simulation effect close to the actual gas turbine by fusing the mechanism model and measurement data.Secondly,the idea of controlling the relative accuracy of model parameters is developed.The introduction of an error module to the existing model can greatly shorten the modeling cycle.The third solution focuses on the early warning based on the digital twin model,which self-learns the alarm threshold of the warning feature of gas-path parameters using the kernel density estimation.The proposed method is utilized to analyze actual measured data of LM2500+,and the results verify that the new-built digital model has higher accuracy and better efficiency.The comparisons show that the proposed method shows evident superiority in early warning of performance faults for gas turbines over other methods.展开更多
Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel w...Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of HFMD.Methods We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019.The 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction set.Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.Results As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant concern.Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.展开更多
In view of the cumbersome and often untimely process of manual collection and observation of frozen soil data parameters,and the damage caused to dams by frost heaving of frozen soil,a remote monitoring and an early w...In view of the cumbersome and often untimely process of manual collection and observation of frozen soil data parameters,and the damage caused to dams by frost heaving of frozen soil,a remote monitoring and an early warning model for frozen soil in dam areas was presented.The Pt100 temperature sensors and JM seam gauges were used as measurement tools in the system.The sensor layout was designed,based on the actual situation in the monitoring area.A 4G network was used for wireless transmission to monitor frozen soil data in real time.BP neural network was used to predict the parameters of frozen soil.After analysis,four factors including the average temperature of frozen soil,the type of frozen soil,the artificial upper limit of frozen soil and the building construction time were selected to establish an early warning model using fuzzy reasoning.The experimental results showed that the early warning model could reflect the influence on dam buildings of frost heaving and sinking of frozen soil,and provided technical support for predicting the hazard level.展开更多
A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and...A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.展开更多
Safety accidents occure frequently during metro construction, which are mainly caused by human factors, and the incidence of accidents can be increased due to the overlap of human factors and physical factors. The hum...Safety accidents occure frequently during metro construction, which are mainly caused by human factors, and the incidence of accidents can be increased due to the overlap of human factors and physical factors. The human factors are taken as breakthrough to make early warning for the human insecurity factors in the metro construction, which can effectively reduce the occurrence of safety accidents. This paper proposes the principle of total monitoring and early-warning management. The unsafe behaviors in metro construction when approaching the hazardous area and non-standard safety prevention measures are analyzed to design and model the early warning process of unsafe behaviors in metro construction. Finally, the model is analyzed and verified using actual examples.展开更多
In order to improve the accuracy and efficiency of early warning system, the incident chain model and the targeted dissemination technology are proposed in this paper. Firstly, the occurrence probability, affected are...In order to improve the accuracy and efficiency of early warning system, the incident chain model and the targeted dissemination technology are proposed in this paper. Firstly, the occurrence probability, affected area and duration of disaster are predicted with the incident chain model and GIS. According to prediction results, the early warning system can accurately deliver early warning information specifically to the affected areas through targeted dissemination. Moreover, dissemination performance can also be evaluated in real time after early warning information dissemination, so that everyone in the affected area can receive early warning information successfully. The incident chain model and the targeted dissemination technology presented in this study are of great significance for improving the information dissemination ability of early warning system.展开更多
Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning fo...Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.展开更多
The phenomenon of car-following is special in traffic operations. Traditional car-following models can well describe the reactions of the movements between two concessive vehicles in the same lane within a certain dis...The phenomenon of car-following is special in traffic operations. Traditional car-following models can well describe the reactions of the movements between two concessive vehicles in the same lane within a certain distance. With the invention of connected vehicle technologies, more and more advisory messages are in development and applied in our daily lives, some of which are related to the measures and warnings of speed and headway distance between the two concessive vehicles. Such warnings may change the conventional car-following mechanisms. This paper intends to consider the possible impacts of in-vehicle warning messages to improve the traditional car-following models, including the General Motor (GM) Model and the Linear (Helly) Model, by calibrating model parameters using field data from an arterial road in Houston, Texas, U.S.A. The safety messages were provided by a tablet/smartphone application. One exponent was applied to the GM model, while another one applied to the Linear (Helly) model, both were on the stimuli term “difference in velocity between two concessive vehicles”. The calibration and validation were separately conducted for deceleration and acceleration conditions. Results showed that, the parameters of the traditional GM model failed to be properly calibrated with the interference of in-vehicle safety messages, and the parameters calibrated from the traditional Linear (Helly) Model with no in-vehicle messages could not be directly used in the case with such messages. However, both updated models can be well calibrated even if those messages were provided. The entire research process, as well as the calibrated models and parameters could be a reference in the on-going connected vehicle program and micro/macroscopic traffic simulations.展开更多
In this paper,a cybersecurity threat warning model based on ant colony algorithm is designed to strengthen the accuracy of the cybersecurity threat warning model in the warning process and optimize its algorithm struc...In this paper,a cybersecurity threat warning model based on ant colony algorithm is designed to strengthen the accuracy of the cybersecurity threat warning model in the warning process and optimize its algorithm structure.Through the ant colony algorithm structure,the local global optimal solution is obtained;and the cybersecurity threat warning index system is established.Next,the above two steps are integrated to build the cybersecurity threat warning model based on ant colony algorithm,and comparative experiment is also designed.The experimental results show that,compared with the traditional qualitative differential game-based cybersecurity threat warning model,the cybersecurity threat warning model based on ant colony algorithm has a higher correct rate in the warning process,and the algorithm program is simpler with higher use value.展开更多
Objective:To explore the appropriate modeling method of the early warning model of ischemic stroke recurrence in TCM.Methods:This was a prospective,multi-center and registered study conducted in 7 clinical subcenters ...Objective:To explore the appropriate modeling method of the early warning model of ischemic stroke recurrence in TCM.Methods:This was a prospective,multi-center and registered study conducted in 7 clinical subcenters from 8 provinces and 10 cities in China between 3rd November 2016 and 27th April,2019.1,741 patients with first-ever ischemic stroke were recruited.Univariate analysis was carried out using distance correlation coefficient,mutual information entropy,and statistical correlation test.Multivariate analysis adopted multi-factor Cox regression model and combined with expert opinions in the field of stroke to determine modeling variables.The generalized estimating equation of longitudinal data and the Cox proportional hazard regression model of cross-sectional data were used to construct and compare in the early warning model of ischemic stroke recalls.The area under the ROC curve(AUC value)was used to evaluate the early warning capability of the model.Results:The follow-up time was 1-3 years,and the median follow-up time was 1.42 years(95%CI:1.37-1.47).Recurrence events occurred in 175 cases,and the cumulative recurrence rate was 10.05%(95%CI:8.64%-11.47%).The AUC values of the TCM syndrome and TCM constitution model were 0.71809 and 0.72668 based on the generalized estimating equation and the AUC values.Conclusion:The generalized estimating equation may be more suitable for the construction of early warning models of stroke recurrence with TCM characteristics,which provides a certain reference for the evaluation of secondary prevention of ischemic stroke.展开更多
ln order to explore the design and construction of cucumber powdery mildew warning system in solar greenhouse, internet of things technology was used to conduct the real-time dynamic monitoring of the incidence of cuc...ln order to explore the design and construction of cucumber powdery mildew warning system in solar greenhouse, internet of things technology was used to conduct the real-time dynamic monitoring of the incidence of cucumber powdery mildew and cucumber growth environment in solar greenhouse. The growth environ-ment included temperature and humidity of air and soil. Logistic regression model was used to construct cucumber powdery mildew warning model. The results showed that humidity characteristic variable （maximum air humidity） and temperature characteristic variable （maximum air temperature） had significant effects on the inci-dence probability of cucumber powdery mildew in solar greenhouse. And it was fea-sible to construct cucumber powdery mildew warning system in solar greenhouse with internet of things.展开更多
The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation...The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.展开更多
基金support from the Sichuan Science and Technology Program(Grant No.2023NSFSC0812).
文摘The effective early warning of surrounding rock mass deformation is crucial in geotechnical engineering for ensuring the safety and stability of underground constructions.This study introduces a novel risk early warning model based on multi-parameter fuzzy comprehensive evaluation,which quantitatively assesses the risk state of the surrounding rock mass.The microseismic(MS)monitoring system is set up for the underground powerhouse.The spatial and temporal distribution of MS events and the frequency characteristics of MS signals are analyzed during the top arch excavation.The early warning indices for characterizing MS spatial aggregation and frequency-energy dispersion are proposed based on the octree theory to assess the deformation of the surrounding rock mass.The risk warning model for the surrounding rock mass in underground engineering is developed through the integration of the formulated index and the frequency characteristics of MS signals.The results indicate that the multiparameter fuzzy comprehensive assessment model can achieve three-dimensional visualization of risk warnings for the surrounding rock mass.The quantitative results regarding warning time and potential deformation areas are highly consistent with the characteristics of MS precursors.These research results can provide an important reference for early warning of surrounding rock mass risk in similar underground projects.
基金funded by the National Key Research and Development ProgramFund for Young Scientists(No.2021YFC2900400)+5 种基金the National Natural Science Foundation of China(No.52304123)Fundamental Research Funds for the Central Universities(No.2024CDJXY025)Sichuan-Chongqing Science and Technology Innovation Cooperation Program Project(No.CSTB2024TIAD-CYKJCXX0016)Postdoctoral Research Foundation of China(No.2023M730412)Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(No.GZB20230914)Chongqing Outstanding Youth Science Foundation Program(No.CSTB2023NSCQ-JQX0027)。
文摘To address the issues of single warning indicators,fixed thresholds,and insufficient adaptability in coal and gas outburst early warning models,this study proposes a dynamic early warning model for gas outbursts based on adaptive fractal dimension characterization.By analyzing the nonlinear characteristics of gas concentration data,an adaptive window fractal analysis method is introduced.Combined with boxcounting dimension and variation of box dimension metrics,a cross-scale dynamic warning model for disaster prevention is established.The implementation involves three key phases:First,wavelet denoising and interpolation methods are employed for raw data preprocessing,followed by validation of fractal characteristics.Second,an adaptive window cross-scale fractal dimension method is proposed to calculate the box-counting dimension of gas concentration,enabling effective capture of multi-scale complex features.Finally,dynamic threshold partitioning is achieved through membership functions and the 3σprinciple,establishing a graded classification standard for the mine gas disaster(MGD)index.Validated through engineering applications at Shoushan#1 Coal Mine in Henan Province,the results demonstrate that the adaptive window fractal dimension curve exhibits significantly enhanced fluctuation characteristics compared to fixed window methods,with local feature detection capability improved and warning accuracy reaching 86.9%.The research reveals that this model effectively resolves the limitations of traditional methods in capturing local features and dependency on subjective thresholds through multiindicator fusion and threshold optimization,providing both theoretical foundation and practical tool for coal mine gas outburst early warning.
文摘Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.
基金Supported by Important Investigation Program of National Land and Resources Department(Water[2007]017-07)Key Program of Shaanxi Meteorological Bureau(2008Z-2)
文摘The study established daily comprehensive precipitation equations and calculated respective critical daily comprehensive precipitation value of loess-collapse disasters and landslide disasters by dint of the geological disasters and corresponding precipitation data in 47 years.Considering geological disaster risk divisions,precipitation influence coefficient and daily comprehensive precipitation,hourly rolling daily-forecasting and hourly warning fine and no-gap models on the base of high temporal and spatial resolution rainfall data of automatic meteorological station were developed.Through the verifying of combination of dynamical forecasting model and warning model,the results showed that it can improve efficiency of forecast and have good response at the same time.
基金supported by grants from the National Science and Technology Major Project (2018ZX10101004)the Youth Innovation Promotion Association of CAS (2019091)。
文摘Human beings have experienced a serious public health event as the new pneumonia(COVID-19), caused by the severe acute respiratory syndrome coronavirus has killed more than 3000 people in China, most of them elderly or people with underlying chronic diseases or immunosuppressed states. Rapid assessment and early warning are essential for outbreak analysis in response to serious public health events. This paper reviews the current model analysis methods and conclusions from both micro and macro perspectives. The establishment of a comprehensive assessment model, and the use of model analysis prediction, is very efficient for the early warning of infectious diseases. This would significantly improve global surveillance capacity, particularly in developing regions, and improve basic training in infectious diseases and molecular epidemiology.
基金financially supported by the CAS Pioneer Hundred Talents Programpthe Institute of Mountain Hazards and Environment(Grant No.SDS-135-1705)+1 种基金support from the National Natural Science Foundation of China(Grant No.41771021,41471429,and 41790443)the National Key Research and Development Program of China(Grant No.2017YFD0800501)
文摘Early warning model of debris flow is important for providing local residents with reliable and accurate warning information to escape from debris flow hazards. This research studied the debris flow initiation in the Yindongzi gully in Dujiangyan City, Sichuan province, China with scaled-down model experiments. We set rainfall intensity and slope angle as dominating parameters and carried out 20 scaled-down model tests under artificial rainfall conditions. The experiments set four slope angles(32°, 34°, 37°, 42°) and five rainfall intensities(60 mm/h, 90 mm/h, 120 mm/h, 150 mm/h, and 180 mm/h) treatments. The characteristic variables in the experiments, such as, rainfall duration, pore water pressure, moisture content, surface inclination, and volume were monitored. The experimental results revealed the failure mode of loose slope material and the process of slope debris flow initiation, as well as the relationship between the surface deformation and the physical parameters of experimental model. A traditional rainfall intensity-duration early warning model(I-D model) was firstly established by using a mathematical regression analysis, and it was then improved into ISD model and ISM model(Here, I is rainfall Intensity, S is Slope angle, D is rainfall Duration, and M is Moisture content). The warning model can provide reliable early warning of slope debris flow initiation.
基金financially supported by the National Key Research and Development Program of China(No.2019YFC1805400)。
文摘As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.
基金co-supported by the National Postdoctoral Program for Innovative Talent(No.BX20180031)。
文摘The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines.However,three technical difficulties need to be solved:(1)low modeling precision caused by individual differences between gas turbines,(2)poor solution efficiency due to excessive iterations,and(3)the false alarm and missing alarm brought by the traditional fixed threshold method.This paper proposes a digital twin model-based early warning method for gas-path faults that breaks through the above obstacles from three aspects.Firstly,a novel performance modeling strategy is proposed to make the simulation effect close to the actual gas turbine by fusing the mechanism model and measurement data.Secondly,the idea of controlling the relative accuracy of model parameters is developed.The introduction of an error module to the existing model can greatly shorten the modeling cycle.The third solution focuses on the early warning based on the digital twin model,which self-learns the alarm threshold of the warning feature of gas-path parameters using the kernel density estimation.The proposed method is utilized to analyze actual measured data of LM2500+,and the results verify that the new-built digital model has higher accuracy and better efficiency.The comparisons show that the proposed method shows evident superiority in early warning of performance faults for gas turbines over other methods.
基金supported by grants from the Key Technologies Research and Development Program from the Ministry of Science and Technology[grant number:ZDZX-2018ZX102001002-003-003]the Beijing Natural Science Foundation[project number:L192014]
文摘Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of HFMD.Methods We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019.The 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction set.Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.Results As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant concern.Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.
基金Supported by the Application Technology Research and Development Plan Project of Heilongjiang Province(GY2014ZB0011)the 13th Five-year National Key R&D Program(2016YFD0300610)
文摘In view of the cumbersome and often untimely process of manual collection and observation of frozen soil data parameters,and the damage caused to dams by frost heaving of frozen soil,a remote monitoring and an early warning model for frozen soil in dam areas was presented.The Pt100 temperature sensors and JM seam gauges were used as measurement tools in the system.The sensor layout was designed,based on the actual situation in the monitoring area.A 4G network was used for wireless transmission to monitor frozen soil data in real time.BP neural network was used to predict the parameters of frozen soil.After analysis,four factors including the average temperature of frozen soil,the type of frozen soil,the artificial upper limit of frozen soil and the building construction time were selected to establish an early warning model using fuzzy reasoning.The experimental results showed that the early warning model could reflect the influence on dam buildings of frost heaving and sinking of frozen soil,and provided technical support for predicting the hazard level.
基金Project(51175159)supported by the National Natural Science Foundation of ChinaProject(2013WK3024)supported by the Science andTechnology Planning Program of Hunan Province,ChinaProject(CX2013B146)supported by the Hunan Provincial InnovationFoundation for Postgraduate,China
文摘A technology for unintended lane departure warning was proposed. As crucial information, lane boundaries were detected based on principal component analysis of grayscale distribution in search bars of given number and then each search bar was tracked using Kalman filter between frames. The lane detection performance was evaluated and demonstrated in ways of receiver operating characteristic, dice similarity coefficient and real-time performance. For lane departure detection, a lane departure risk evaluation model based on lasting time and frequency was effectively executed on the ARM-based platform. Experimental results indicate that the algorithm generates satisfactory lane detection results under different traffic and lighting conditions, and the proposed warning mechanism sends effective warning signals, avoiding most false warning.
基金Supported by the National Natural Science Foundation of China(71603284)the Humanity and Social Science Research Foundation of Ministry of Education PRC(16YJC630068)the China Postdoctoral Science Foundation(2019T120718,2018M630918).
文摘Safety accidents occure frequently during metro construction, which are mainly caused by human factors, and the incidence of accidents can be increased due to the overlap of human factors and physical factors. The human factors are taken as breakthrough to make early warning for the human insecurity factors in the metro construction, which can effectively reduce the occurrence of safety accidents. This paper proposes the principle of total monitoring and early-warning management. The unsafe behaviors in metro construction when approaching the hazardous area and non-standard safety prevention measures are analyzed to design and model the early warning process of unsafe behaviors in metro construction. Finally, the model is analyzed and verified using actual examples.
文摘In order to improve the accuracy and efficiency of early warning system, the incident chain model and the targeted dissemination technology are proposed in this paper. Firstly, the occurrence probability, affected area and duration of disaster are predicted with the incident chain model and GIS. According to prediction results, the early warning system can accurately deliver early warning information specifically to the affected areas through targeted dissemination. Moreover, dissemination performance can also be evaluated in real time after early warning information dissemination, so that everyone in the affected area can receive early warning information successfully. The incident chain model and the targeted dissemination technology presented in this study are of great significance for improving the information dissemination ability of early warning system.
基金Supported by the National Natural Science Foundation of China(11072035)
文摘Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.
文摘The phenomenon of car-following is special in traffic operations. Traditional car-following models can well describe the reactions of the movements between two concessive vehicles in the same lane within a certain distance. With the invention of connected vehicle technologies, more and more advisory messages are in development and applied in our daily lives, some of which are related to the measures and warnings of speed and headway distance between the two concessive vehicles. Such warnings may change the conventional car-following mechanisms. This paper intends to consider the possible impacts of in-vehicle warning messages to improve the traditional car-following models, including the General Motor (GM) Model and the Linear (Helly) Model, by calibrating model parameters using field data from an arterial road in Houston, Texas, U.S.A. The safety messages were provided by a tablet/smartphone application. One exponent was applied to the GM model, while another one applied to the Linear (Helly) model, both were on the stimuli term “difference in velocity between two concessive vehicles”. The calibration and validation were separately conducted for deceleration and acceleration conditions. Results showed that, the parameters of the traditional GM model failed to be properly calibrated with the interference of in-vehicle safety messages, and the parameters calibrated from the traditional Linear (Helly) Model with no in-vehicle messages could not be directly used in the case with such messages. However, both updated models can be well calibrated even if those messages were provided. The entire research process, as well as the calibrated models and parameters could be a reference in the on-going connected vehicle program and micro/macroscopic traffic simulations.
基金This work was supported by the Natural Science Foundation of Fujian Province,ChinaResearch on Network Risk Assessment Method Based on Dynamic Attack Behavior(Grant No.2019J01889)+1 种基金the Education-Scientific research Project for Middle-Aged and Young of Fujian Province,ChinaResearch on Analysis System of Malicious Code Based on API Relevance(Grant No.JT180626).
文摘In this paper,a cybersecurity threat warning model based on ant colony algorithm is designed to strengthen the accuracy of the cybersecurity threat warning model in the warning process and optimize its algorithm structure.Through the ant colony algorithm structure,the local global optimal solution is obtained;and the cybersecurity threat warning index system is established.Next,the above two steps are integrated to build the cybersecurity threat warning model based on ant colony algorithm,and comparative experiment is also designed.The experimental results show that,compared with the traditional qualitative differential game-based cybersecurity threat warning model,the cybersecurity threat warning model based on ant colony algorithm has a higher correct rate in the warning process,and the algorithm program is simpler with higher use value.
基金National Key R&D Program of the Ministry of Science and TechnologyConstruction of the Technical System for"Treating the Disease"in Traditional Chinese Medicine(No.2018YFC1704705)2015 Special Research Project of the Chinese Medicine Industry of the National Administration of Traditional Chinese Medicine:R&D and Demonstration of Recurrence Risk Assessment System for Ischemic Stroke Disease with Chinese Medicine Characteristic Health Management(No.201507003-8).
文摘Objective:To explore the appropriate modeling method of the early warning model of ischemic stroke recurrence in TCM.Methods:This was a prospective,multi-center and registered study conducted in 7 clinical subcenters from 8 provinces and 10 cities in China between 3rd November 2016 and 27th April,2019.1,741 patients with first-ever ischemic stroke were recruited.Univariate analysis was carried out using distance correlation coefficient,mutual information entropy,and statistical correlation test.Multivariate analysis adopted multi-factor Cox regression model and combined with expert opinions in the field of stroke to determine modeling variables.The generalized estimating equation of longitudinal data and the Cox proportional hazard regression model of cross-sectional data were used to construct and compare in the early warning model of ischemic stroke recalls.The area under the ROC curve(AUC value)was used to evaluate the early warning capability of the model.Results:The follow-up time was 1-3 years,and the median follow-up time was 1.42 years(95%CI:1.37-1.47).Recurrence events occurred in 175 cases,and the cumulative recurrence rate was 10.05%(95%CI:8.64%-11.47%).The AUC values of the TCM syndrome and TCM constitution model were 0.71809 and 0.72668 based on the generalized estimating equation and the AUC values.Conclusion:The generalized estimating equation may be more suitable for the construction of early warning models of stroke recurrence with TCM characteristics,which provides a certain reference for the evaluation of secondary prevention of ischemic stroke.
基金Supported by the Science and Technology Support Program of Tianjin(15ZCZDNC00120)~~
文摘ln order to explore the design and construction of cucumber powdery mildew warning system in solar greenhouse, internet of things technology was used to conduct the real-time dynamic monitoring of the incidence of cucumber powdery mildew and cucumber growth environment in solar greenhouse. The growth environ-ment included temperature and humidity of air and soil. Logistic regression model was used to construct cucumber powdery mildew warning model. The results showed that humidity characteristic variable （maximum air humidity） and temperature characteristic variable （maximum air temperature） had significant effects on the inci-dence probability of cucumber powdery mildew in solar greenhouse. And it was fea-sible to construct cucumber powdery mildew warning system in solar greenhouse with internet of things.
基金supported in part by the Basic Public Welfare Research Program of Zhejiang Province under Grant LGF20G030001.
文摘The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.
