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Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department
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作者 Rex Pui Kin Lam Zonglin Dai +6 位作者 Eric Ho Yin Lau Carrie Yuen Ting Ip Ho Ching Chan Lingyun Zhao Tat ChiTsang Matthew Sik Hon Tsui Timothy Hudson Rainer 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2024年第4期273-282,共10页
BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We per... BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong.The primary outcome was sepsis(Sepsis-3 definition)within 48 h of ED presentation.Using c-statistics and the DeLong test,we compared 11 EWSs,including the National Early Warning Score 2(NEWS2),Modified Early Warning Score,and Worthing Physiological Scoring System(WPS),etc.,and three shock indices(the shock index[SI],modified shock index[MSI],and diastolic shock index[DSI]),with Systemic Inflammatory Response Syndrome(SIRS)and quick Sequential Organ Failure Assessment(qSOFA)in predicting the primary outcome,intensive care unit admission,and mortality at different time points.RESULTS:We analyzed 601 patients,of whom 166(27.6%)developed sepsis.NEWS2 had the highest point estimate(area under the receiver operating characteristic curve[AUROC]0.75,95%CI 0.70-0.79)and was significantly better than SIRS,qSOFA,other EWSs and shock indices,except WPS,at predicting the primary outcome.However,the pooled sensitivity and specificity of NEWS2≥5 for the prediction of sepsis were 0.45(95%CI 0.37-0.52)and 0.88(95%CI 0.85-0.91),respectively.The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.CONCLUSION:NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening. 展开更多
关键词 SEPSIS Emergency department Clinical prediction rule Early warning score Shock index
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Real-time 3-D space numerical shake prediction for earthquake early warning 被引量:3
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作者 Tianyun Wang Xing Jin +1 位作者 Yandan Huang Yongxiang Wei 《Earthquake Science》 CSCD 2017年第5期269-281,共13页
In earthquake early warning systems, real-time shake prediction through wave propagation simulation is a promising approach. Compared with traditional methods, it does not suffer from the inaccurate estimation of sour... In earthquake early warning systems, real-time shake prediction through wave propagation simulation is a promising approach. Compared with traditional methods, it does not suffer from the inaccurate estimation of source parameters. For computation efficiency, wave direction is assumed to propagate on the 2-D surface of the earth in these methods. In fact, since the seismic wave propagates in the 3-D sphere of the earth, the 2-D space modeling of wave direction results in inaccurate wave estimation. In this paper, we propose a 3-D space numerical shake pre- diction method, which simulates the wave propagation in 3-D space using radiative transfer theory, and incorporate data assimilation technique to estimate the distribution of wave energy. 2011 Tohoku earthquake is studied as an example to show the validity of the proposed model. 2-D space model and 3-D space model are compared in this article, and the prediction results show that numerical shake prediction based on 3-D space model can estimate the real-time ground motion precisely, and overprediction is alleviated when using 3-D space model. 展开更多
关键词 real-time numerical shake prediction· 3-Dspace model · Radiative transfer theory · Data assimilation
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Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data 被引量:2
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作者 Xuyan Tan Weizhong Chen +2 位作者 Tao Zou Jianping Yang Bowen Du 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2023年第4期886-895,共10页
Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of i... Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run.In addition to the incomplete consideration of influencing factors,the prediction time scale of existing studies is rough.Therefore,this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network(ATENet)based on structural health monitoring(SHM)data.An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions,and the recurrent neural network is applied to understanding the temporal correlation from the time series.Then,the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h.As a case study,the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel.The robustness study is carried out to verify the reliability and the prediction capability of the proposed model.Finally,the ATENet model is compared with some typical models,and the results indicate that it has the best performance.ATENet model is of great value to predict the realtime evolution trend of tunnel structure. 展开更多
关键词 Shied tunnel Machine learning MONITORING real-time prediction Data analysis
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Real-time numerical shake prediction and updating for earthquake early warning
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作者 Tianyun Wang Xing Jin +1 位作者 Yongxiang Wei Yandan Huang 《Earthquake Science》 CSCD 2017年第5期251-267,共17页
Ground motion prediction is important for earthquake early warning systems, because the region's peak ground motion indicates the potential disaster. In order to predict the peak ground motion quickly and pre- cisely... Ground motion prediction is important for earthquake early warning systems, because the region's peak ground motion indicates the potential disaster. In order to predict the peak ground motion quickly and pre- cisely with limited station wave records, we propose a real- time numerical shake prediction and updating method. Our method first predicts the ground motion based on the ground motion prediction equation after P waves detection of several stations, denoted as the initial prediction. In order to correct the prediction error of the initial prediction, an updating scheme based on real-time simulation of wave propagation is designed. Data assimilation technique is incorporated to predict the distribution of seismic wave energy precisely. Radiative transfer theory and Monte Carlo simulation are used for modeling wave propagation in 2-D space, and the peak ground motion is calculated as quickly as possible. Our method has potential to predict shakemap, making the potential disaster be predicted before the real disaster happens. 2008 Ms8.0 Wenchuan earthquake is studied as an example to show the validity of the proposed method. 展开更多
关键词 real-time numerical shake prediction· 2-Dspace model · Radiative transfer theory · Dataassimilation · Shakemap prediction
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Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing
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作者 YUAN Bin ZHAO Mingze +2 位作者 MENG Siwei ZHANG Wei ZHENG He 《Petroleum Exploration and Development》 SCIE 2023年第6期1487-1496,共10页
The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algori... The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algorithm, together with the analysis on data and information of horizontal well fracturing in shale gas reservoirs, this paper presents a method for intelligent identification and real-time warning of diverse complex events in horizontal well fracturing. An identification model for "point" events in fracturing is established based on the Att-BiLSTM neural network, along with the broad learning system (BLS) and the BP neural network, and it realizes the intelligent identification of the start/end of fracturing, formation breakdown, instantaneous shut-in, and other events, with an accuracy of over 97%. An identification model for "phase" events in fracturing is established based on enhanced Unet++ network, and it realizes the intelligent identification of pump ball, pre-acid treatment, temporary plugging fracturing, sand plugging, and other events, with an error of less than 0.002. Moreover, a real-time prediction model for fracturing pressure is built based on the Att-BiLSTM neural network, and it realizes the real-time warning of diverse events in fracturing. The proposed method can provide an intelligent, efficient and accurate identification of events in fracturing to support the decision-making. 展开更多
关键词 horizontal well fracturing fracturing events intelligent identification real-time warning deep learning
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Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling
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作者 Seungwoo Kang Seyha Ros +3 位作者 Inseok Song Prohim Tam Sa Math Seokhoon Kim 《Computers, Materials & Continua》 SCIE EI 2023年第11期1967-1983,共17页
Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requi... Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation. 展开更多
关键词 Edge computing federated logistic regression intelligent healthcare networks prediction modeling privacy-aware and real-time learning
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Real-time prediction of earthquake potential damage:A case study for the January 8,2022 M_(S) 6.9 Menyuan earthquake in Qinghai,China
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作者 Jindong Song Jingbao Zhu +2 位作者 Yongxiang Wei Shuilong Li Shanyou Li 《Earthquake Research Advances》 CSCD 2023年第1期52-60,共9页
It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster reduction.Here,we propose a real-time Earthquake Potential Damage pre... It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster reduction.Here,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of sites.The EPDor is composed of three parts:(1)predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models;(2)predicting the PGVs at distant sites based on the empirical ground motion prediction equation;(3)generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in(1)and(2).We apply the EPDor to the 2022 M_(S) 6.9 Menyuan earthquake in Qinghai Province,China to predict its potential damage.Within the initial few seconds after the first station is triggered,the EPDor can determine directly whether there is potential damage for some sites to a certain degree.Hence,we infer that the EPDor has potential application for future earthquakes.Meanwhile,it also has potential in Chinese earthquake early warning system. 展开更多
关键词 Earthquake early warning Potential damage Machine learning 2022 M_(S)6.9 Menyuan earthquake Magnitude estimation On-site peak ground velocity prediction
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Construction of multi-factor identification model for real-time monitoring and early warning of mine water inrush 被引量:4
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作者 Xin Wang Zhimin Xu +3 位作者 Yajun Sun Jieming Zheng Chenghang Zhang Zhongwen Duan 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2021年第5期853-866,共14页
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
关键词 Mine water inrush Automatic monitoring real-time warning Recognition model
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Research and application of real-time monitoring and early warning thresholds for multi-temporal agricultural products information 被引量:2
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作者 XU Shi-wei WANG Yu +1 位作者 WANG Sheng-wei LI Jian-zheng 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2020年第10期2582-2596,共15页
Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background... Monitoring and early warning is an important means to effectively prevent risks in agricultural production,consumption and price.In particular,with the change of modes of national administration against the background of big data,improving the capacity to monitor agricultural products is of great significance for macroeconomic decision-making.Agricultural product information early warning thresholds are the core of agricultural product monitoring and early warning.How to appropriately determine the early warning thresholds of multi-temporal agricultural product information is a key question to realize real-time and dynamic monitoring and early warning.Based on the theory of abnormal fluctuation of agricultural product information and the research of substantive impact on the society,this paper comprehensively discussed the methods to determine the thresholds of agricultural product information fluctuation in different time dimensions.Based on the data of the National Bureau of Statistics of China(NBSC)and survey data,this paper used a variety of statistical methods to determine the early warning thresholds of the production,consumption and prices of agricultural products.Combined with Delphi expert judgment correction method,it finally determined the early warning thresholds of agricultural product information in multiple time,and carried out early warning analysis on the fluctuation of agricultural product monitoring information in 2018.The results show that:(1)the daily,weekly and monthly monitoring and early warning thresholds of agricultural products play an important early warning role in monitoring abnormal fluctuations with agricultural products;(2)the multitemporal monitoring and early warning thresholds of agricultural product information identified by the research institute can provide effective early warning on current abnormal fluctuation of agricultural product information,provide a benchmarking standard for China's agricultural production,consumption and price monitoring and early warning at the national macro level,and further improve the application of China's agricultural product monitoring and early warning. 展开更多
关键词 agricultural product information monitoring and early warning THRESHOLD MULTI-TEMPORAL real-time dynamics
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A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm 被引量:6
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作者 Xing Huang Quantai Zhang +4 位作者 Quansheng Liu Xuewei Liu Bin Liu Junjie Wang Xin Yin 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2022年第3期798-812,共15页
Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented... Based on data from the Jilin Water Diversion Tunnels from the Songhua River(China),an improved and real-time prediction method optimized by multi-algorithm for tunnel boring machine(TBM)cutter-head torque is presented.Firstly,a function excluding invalid and abnormal data is established to distinguish TBM operating state,and a feature selection method based on the SelectKBest algorithm is proposed.Accordingly,ten features that are most closely related to the cutter-head torque are selected as input variables,which,in descending order of influence,include the sum of motor torque,cutter-head power,sum of motor power,sum of motor current,advance rate,cutter-head pressure,total thrust force,penetration rate,cutter-head rotational velocity,and field penetration index.Secondly,a real-time cutterhead torque prediction model’s structure is developed,based on the bidirectional long short-term memory(BLSTM)network integrating the dropout algorithm to prevent overfitting.Then,an algorithm to optimize hyperparameters of model based on Bayesian and cross-validation is proposed.Early stopping and checkpoint algorithms are integrated to optimize the training process.Finally,a BLSTMbased real-time cutter-head torque prediction model is developed,which fully utilizes the previous time-series tunneling information.The mean absolute percentage error(MAPE)of the model in the verification section is 7.3%,implying that the presented model is suitable for real-time cutter-head torque prediction.Furthermore,an incremental learning method based on the above base model is introduced to improve the adaptability of the model during the TBM tunneling.Comparison of the prediction performance between the base and incremental learning models in the same tunneling section shows that:(1)the MAPE of the predicted results of the BLSTM-based real-time cutter-head torque prediction model remains below 10%,and both the coefficient of determination(R^(2))and correlation coefficient(r)between measured and predicted values exceed 0.95;and(2)the incremental learning method is suitable for realtime cutter-head torque prediction and can effectively improve the prediction accuracy and generalization capacity of the model during the excavation process. 展开更多
关键词 Tunnel boring machine(TBM) real-time cutter-head torque prediction Bidirectional long short-term memory (BLSTM) Bayesian optimization Multi-algorithm fusion optimization Incremental learning
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A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network 被引量:1
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作者 JI Tian Jiao CHENG Qiang +5 位作者 ZHANG Yong ZENG Han Ri WANG Jian Xing YANG Guan Yu XU Wen Bo LIU Hong Tu 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2022年第6期494-503,共10页
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
关键词 HFMD Early warning model STGCN Disease prediction
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Real-time lane departure warning system based on principal component analysis of grayscale distribution and risk evaluation model 被引量:4
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作者 张伟伟 宋晓琳 张桂香 《Journal of Central South University》 SCIE EI CAS 2014年第4期1633-1642,共10页
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
关键词 lane departure warning system lane detection lane tracking principal component analysis risk evaluation model ARM-based real-time system
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Real-Time Warning System of Regional Landslides Supported by WEGISB and Its Application in Typhoon Rananim,Zhejiang Province,China
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作者 Guirong Zhang~1,Kunlong Yin~2,Lixia Chen~2 1.Geotechnical Engineering Institute of Nanjing Hydraulic Research Institute,Nanjing 210036,China. 2.China University of Geosciences(Beijing),Beijing 100083,China 《地学前缘》 EI CAS CSCD 北大核心 2009年第S1期68-69,共2页
As one of the provinces of highest economic growth in coastal China,Zhejiang Province is experiencing serious geological disasters during the past development of economy.The main kinds of geo-hazards include landslide... As one of the provinces of highest economic growth in coastal China,Zhejiang Province is experiencing serious geological disasters during the past development of economy.The main kinds of geo-hazards include landslides,rock falls and debris-flows in Zhejiang Province,which are mainly induced by intensive rainfall during typhoon season or by long-term rainfall from May to June every year.Thus, 展开更多
关键词 LANDSLIDE prediction real-time warning effective RAINFALL RAINFALL intensity WEBGIS TYPHOON
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Efficient Harmonic Analysis Technique for Prediction of IGS Real-Time Satellite Clock Corrections
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作者 Mohamed Elsayed Elsobeiey 《Positioning》 2017年第3期37-45,共9页
Real-time satellite orbit and clock corrections obtained from the broadcast ephemerides can be improved using IGS real-time service (RTS) products. Recent research showed that applying such corrections for broadcast e... Real-time satellite orbit and clock corrections obtained from the broadcast ephemerides can be improved using IGS real-time service (RTS) products. Recent research showed that applying such corrections for broadcast ephemerides can significantly improve the RMS of the estimated coordinates. However, unintentional streaming interruption may happen for many reasons such as software or hardware failure. Streaming interruption, if happened, will cause sudden degradation of the obtained solution if only the broadcast ephemerides are used. A better solution can be obtained in real-time if the predicted part of the ultra-rapid products is used. In this paper, Harmonic analysis technique is used to predict the IGS RTS corrections using historical broadcasted data. It is shown that using the predicted clock corrections improves the RMS of the estimated coordinates by about 72%, 58%, and 72% in latitude, longitude, and height directions, respectively and reduces the 2D and 3D errors by about 80% compared with the predicted part of the IGS ultra-rapid clock corrections. 展开更多
关键词 real-time Service CLOCK prediction PRECISE Point Positioning
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Forage Monitoring and Prediction Model for Early Warning Application over the East of Africa Region
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作者 Jully Ouma Dereje Wakjira +7 位作者 Ahmed Amdihun Eva Nyaga Franklin Opijah John Muthama Viola Otieno Eugene Kayijamahe Solomon Munywa Guleid Artan 《Journal of Atmospheric Science Research》 2022年第4期1-9,共9页
Rangelands dominate arid and semi-arid lands of the Greater Horn of Africa(GHA)region,whereby pastoralism being the primary source of livelihood.The pastoral livelihood is affected by the seasonal variability of pastu... Rangelands dominate arid and semi-arid lands of the Greater Horn of Africa(GHA)region,whereby pastoralism being the primary source of livelihood.The pastoral livelihood is affected by the seasonal variability of pasture and water resources.This research sought to design a grid-based forage monitoring and prediction model for the cross-border areas of the GHA region.A technique known as Geographically Weighted Regression was used in developing the model with monthly rainfall,temperature,soil moisture,and the Normalized Difference Vegetation Index(NDVI).Rainfall and soil moisture had a high correlation with NDVI,and thus formed the model development parameters.The model performed well in predicting the available forage biomass at each grid-cell with March-May and October-December seasons depicting a similar pattern but with a different magnitude in ton/ha.The output is critical for actionable early warning over the GHA region’s rangeland areas.It is expected that this mode can be used operationally for forage monitoring and prediction over the eastern Africa region and further guide the regional,national,sub-national actors and policymakers on issuing advisories before the season. 展开更多
关键词 prediction Forage biomass RANGELANDS Pastoralism Early warning East Africa
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Continuous prediction method of earthquake early warning magnitude for high-speed railway based on support vector machine
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作者 Jindong Song Jingbao Zhu Shanyou Li 《Railway Sciences》 2022年第2期307-323,共17页
Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wa... Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction. 展开更多
关键词 High-speed railway Earthquake early warning Magnitude prediction Support vector machine Characteristic parameters
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Yellow River Valley flood and drought disaster:spatial-temporal distribution prediction and early-warning
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作者 Gao Lin, Sha Wanying, Liu Huaiquan, Yang Xinhai(Research Center for Eco-Environmental Sciences . ChineseAcademy of Sciences, Beijing 100085, China) 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 1994年第4期422-431,共10页
By means of analysing the historical data of flood-drought grade series in the past 2000 years(A.D.0-1900),especially in the last 5000 years (1470-1900) , this paper revealed the spatial-temporaldistribution features ... By means of analysing the historical data of flood-drought grade series in the past 2000 years(A.D.0-1900),especially in the last 5000 years (1470-1900) , this paper revealed the spatial-temporaldistribution features of severe flood and drought in Yellow River Valley. Statistical methods of varianceanalysis, probability transition and the principles of scale correspondence were employed tocomprehensively predicate 90's tendency of severe flood and drought in the Yellow River Valley. In addi-tion, this paper pointed out the possible breaching dikes, sectors and the flooding ranges by future's se-vere flood, meanwhile estimating the associated economic losses and impact to environment. 展开更多
关键词 Yellow River Valley i flood and drought disaster i spatial-temporal distribution prediction andearly-warning.
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Application of predictive control scheduling method to real-time periodic control tasks overrun 被引量:1
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作者 沈青 桂卫华 +1 位作者 阳春华 杨铁军 《Journal of Central South University of Technology》 EI 2007年第2期266-270,共5页
Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to t... Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to the control objective, the new strategy was expected to optimize the control system for better performance and yet guarantee the schedulability of all tasks under overrun. The schedulability of the real-time systems with p-period overruns was analyzed, and the corresponding stability criteria was given as well. The simulation results show that the new approach can improve the performance of control system compared to that of conventional abort strategy, it can reduce the overshoot and adjust time as well as ensure the schedulability and stability. 展开更多
关键词 real-time system OVERRUN predictive control scheduling STABILITY
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A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation
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作者 杨国军 李秀喜 钱宇 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2014年第3期318-329,共12页
Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computati... Nonlinear model predictive control(NMPC) is an appealing control technique for improving the performance of batch processes, but its implementation in industry is not always possible due to its heavy on-line computation. To facilitate the implementation of NMPC in batch processes, we propose a real-time updated model predictive control method based on state estimation. The method includes two strategies: a multiple model building strategy and a real-time model updated strategy. The multiple model building strategy is to produce a series of sim-plified models to reduce the on-line computational complexity of NMPC. The real-time model updated strategy is to update the simplified models to keep the accuracy of the models describing dynamic process behavior. The me-thod is validated with a typical batch reactor. Simulation studies show that the new method is efficient and robust with respect to model mismatch and changes in process parameters. 展开更多
关键词 batch process exothermic batch reactor nonlinear model predictive control state estimation real-time model update
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Imprecise Computation Based Real-time Fault Tolerant Implementation for Model Predictive Control
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作者 周平方 谢剑英 《Journal of Donghua University(English Edition)》 EI CAS 2006年第1期148-150,共3页
Model predictive control (MPC) could not be deployed in real-time control systems for its computation time is not well defined. A real-time fault tolerant implementation algorithm based on imprecise computation is pro... Model predictive control (MPC) could not be deployed in real-time control systems for its computation time is not well defined. A real-time fault tolerant implementation algorithm based on imprecise computation is proposed for MPC, according to the solving process of quadratic programming (QP) problem. In this algorithm, system stability is guaranteed even when computation resource is not enough to finish optimization completely. By this kind of graceful degradation, the behavior of real-time control systems is still predictable and determinate. The algorithm is demonstrated by experiments on servomotor, and the simulation results show its effectiveness. 展开更多
关键词 model predictive control fault tolerance imprecise computation real-time control
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