In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on ...In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.展开更多
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
This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends t...This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].展开更多
Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the eff...Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.展开更多
The quality control system for meteorological real-time data from automatic weather stations in Shandong realized integration of communi- cation system and provincial quality control system, and an interaction platfor...The quality control system for meteorological real-time data from automatic weather stations in Shandong realized integration of communi- cation system and provincial quality control system, and an interaction platform which was mainly created by Web was set up. The system not only was fully guaranteed for the funning of basic business, also improved the reliability of the data.展开更多
[Objective] The aim was to study the rear-end real-time data quality control method of regional automatic weather station. [Method] The basic content and steps of rear-end real-time data quality control of regional au...[Objective] The aim was to study the rear-end real-time data quality control method of regional automatic weather station. [Method] The basic content and steps of rear-end real-time data quality control of regional automatic weather station were introduced. Each element was treated with systematic quality control procedure. The existence of rear-end real time data of regional meteorological station in Guangxi was expounded. Combining with relevant elements and linear changes, improvement based on traditional quality control method was made. By dint of evaluation and relevant check of element, the quality of temperature and pressure was controlled. [Result] The method was optimized based on traditional quality control method, and it narrowed the effectiveness of real-time data quality control. The quality check of hourly precipitation applied relevant check of hourly minimum temperature, vertical consistency check of radar data, which can effectively improve the accuracy and credibility of hourly precipitation quality control. [Conclusion] The method was on trial for one year in the quality control of real-time data in the regional automatic meteorological station in Guangxi and had gained good outcome.展开更多
Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great s...Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great significance for exploiting and protecting the ocean.We used hourly mean wave height,temperature,and pressure real-time observation data taken in the Xiaomaidao station(in Qingdao,China)from June 1,2017,to May 31,2018,to explore the data quality using eight quality control methods,and to discriminate the most effective method for Xiaomaidao station.After using the eight quality control methods,the percentages of the mean wave height,temperature,and pressure data that passed the tests were 89.6%,88.3%,and 98.6%,respectively.With the marine disaster(wave alarm report)data,the values failed in the test mainly due to the influence of aging observation equipment and missing data transmissions.The mean wave height is often affected by dynamic marine disasters,so the continuity test method is not effective.The correlation test with other related parameters would be more useful for the mean wave height.展开更多
Glacier disasters occur frequently in alpine regions around the world,but the current conventional geological disaster measurement technology cannot be directly used for glacier disaster measurement.Hence,in this stud...Glacier disasters occur frequently in alpine regions around the world,but the current conventional geological disaster measurement technology cannot be directly used for glacier disaster measurement.Hence,in this study,a distributed multi-sensor measurement system for glacier deformation was established by integrating piezoelectric sensing,coded sensing,attitude sensing technology and wireless communication technology.The traditional Modbus protocol was optimized to solve the problem of data identification confusion of different acquisition nodes.Through indoor wireless transmission,adaptive performance analysis,error measurement experiment and landslide simulation experiment,the performance of the measurement system was analyzed and evaluated.Using unmanned aerial vehicle technology,the reliability and effectiveness of the measurement system were verified on the site of Galongla glacier in southeastern Tibet,China.The results show that the mean absolute percentage errors were only 1.13%and 2.09%for the displacement and temperature,respectively.The distributed glacier deformation real-time measurement system provides a new means for the assessment of the development process of glacier disasters and disaster prevention and mitigation.展开更多
Solar stills are considered an effective method to solve the scarcity of drinkable water.However,it is still missing a way to forecast its production.Herein,it is proposed that a convenient forecasting model which jus...Solar stills are considered an effective method to solve the scarcity of drinkable water.However,it is still missing a way to forecast its production.Herein,it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data.The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm.The required data to train the model are obtained from daily measurements lasting9 months.To validate the accuracy model,the determination coefficients of two types of solar stills are calculated as 0.935and 0.929,respectively,which are much higher than the value of both multiple linear regression(0.767)and the traditional models(0.829 and 0.847).Moreover,by applying the model,we predicted the freshwater production of four cities in China.The predicted production is approved to be reliable by a high value of correlation(0.868)between the predicted production and the solar insolation.With the help of the forecasting model,it would greatly promote the global application of solar stills.展开更多
The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information ...The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.展开更多
Recently, use of mobile communicational devices in field data collection is increasing such as smart phones and cellular phones due to emergence of embedded Global Position System GPS and Wi-Fi Internet access. Accura...Recently, use of mobile communicational devices in field data collection is increasing such as smart phones and cellular phones due to emergence of embedded Global Position System GPS and Wi-Fi Internet access. Accurate timely and handy field data collection is required for disaster management and emergency quick responses. In this article, we introduce web-based GIS system to collect the field data by personal mobile phone through Post Office Protocol POP3 mail server. The main objective of this work is to demonstrate real-time field data collection method to the students using their mobile phone to collect field data by timely and handy manners, either individual or group survey in local or global scale research.展开更多
Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with o...Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.展开更多
To evaluate and improve the real-time performance of Ethernet for plant automation(EPA) industrial Ethernet,the real-time performance of EPA periodic data transmission was theoretically and experimentally studied.By...To evaluate and improve the real-time performance of Ethernet for plant automation(EPA) industrial Ethernet,the real-time performance of EPA periodic data transmission was theoretically and experimentally studied.By analyzing information transmission regularity and EPA deterministic scheduling mechanism,periodic messages were categorized as different modes according to their entering-queue time.The scheduling characteristics and delivery time of each mode and their interacting relations were studied,during which the models of real-time performance of periodic information transmission in EPA system were established.On this basis,an experimental platform is developed to test the delivery time of periodic messages transmission in EPA system.According to the analysis and the experiment,the main factors that limit the real-time performance of EPA periodic data transmission and the improvement methods were proposed.展开更多
A new system called alternate data acquisition and real-time monitoring system has been developed for long-time discharge in tokamak operation. It can support continuous on-line data acquisition at a high sampling rat...A new system called alternate data acquisition and real-time monitoring system has been developed for long-time discharge in tokamak operation. It can support continuous on-line data acquisition at a high sampling rate and a graphic display of the plasma parameters during the discharge. Thus operators can monitor and control the plasma state in real time. An application of this system has been demonstrated on the HT-7 tokamak.展开更多
GoTaTM from ZTE is the world’s first CDMA-based system. Now, ZTE proudly introduces its third-generation digital trunking system featuring a centralized dispatch,
Go Tafrom ZTE is the world’s first CDMA-based system. Now, ZTE proudly introduces its third-generation digital trunking system featuring a centralized dispatch,
This paper explores the opportunities and challenges of college mental health education from the perspective of big data.Firstly,through literature review,the importance of mental health education and the current issu...This paper explores the opportunities and challenges of college mental health education from the perspective of big data.Firstly,through literature review,the importance of mental health education and the current issues are elucidated.Then,from the perspective of big data,the potential opportunities of big data in college mental health education are analyzed,including data-driven personalized education,real-time monitoring and warning systems,and interdisciplinary research and collaboration.At the same time,the challenges faced by college mental health education under the perspective of big data are also pointed out,such as data privacy and security issues,insufficient data analysis and interpretation capabilities,and inadequate technical facilities and talent support.Lastly,the research content of this paper is summarized,and directions and suggestions for future research are proposed.展开更多
Real-time monitoring and forecast of large scale active population density is of great significance as it can warn and prevent possible public safety accident caused by abnormal population aggregation.Active populatio...Real-time monitoring and forecast of large scale active population density is of great significance as it can warn and prevent possible public safety accident caused by abnormal population aggregation.Active population is defined as the number of people with their mobile phone powered on.Recently,an unfortunate deadly stampede occurred in Shanghai on December 31th 2014 causing the death of 39 people.We hope that our research can help avoid similar unfortunate accident from happening.In this paper we propose a method for active population density real-time monitoring and forecasting based on data from mobile network operators.Our method is based solely on mobile network operators existing infrastructure and barely requires extra investment,and mobile devices play a very limited role in the process of population locating.Four series forecasting methods,namely Simple Exponential Smoothing(SES),Double exponential smoothing(DES),Triple exponential smoothing(TES)and Autoregressive integrated moving average(ARIMA)are used in our experiments.Our experimental results suggest that we can achieve good forecast result for 135 min in future.展开更多
The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction (NWP). By using past multi-time observations and model output, this study proposes a ...The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction (NWP). By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polyno- mial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria: (1) the length of past data sufficient for estimation of the model errors, (2) a proper method of estimating the term "model integration with the exact solution" when solving the inverse problem, and (3) the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that (1) a few data limited by the order of the corrector can be used, (2) trapezoidal approximation can be employed to estimate the "term" in this study; however, a more accurate method should be explored for an operational NWP model, and (3) the correction is sensitive to observational error.展开更多
基金supported by CNPC-CZU Innovation Alliancesupported by the Program of Polar Drilling Environmental Protection and Waste Treatment Technology (2022YFC2806403)。
文摘In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.
基金This work is supported by the National Natural Science Foundation of China(Grant No.51991392)Key Deployment Projects of Chinese Academy of Sciences(Grant No.ZDRW-ZS-2021-3-3)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0904).
文摘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.
文摘This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].
基金supported by National Natural Science Foundation of China(NSFC)under Grant Number T2350710232.
文摘Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.
文摘The quality control system for meteorological real-time data from automatic weather stations in Shandong realized integration of communi- cation system and provincial quality control system, and an interaction platform which was mainly created by Web was set up. The system not only was fully guaranteed for the funning of basic business, also improved the reliability of the data.
文摘[Objective] The aim was to study the rear-end real-time data quality control method of regional automatic weather station. [Method] The basic content and steps of rear-end real-time data quality control of regional automatic weather station were introduced. Each element was treated with systematic quality control procedure. The existence of rear-end real time data of regional meteorological station in Guangxi was expounded. Combining with relevant elements and linear changes, improvement based on traditional quality control method was made. By dint of evaluation and relevant check of element, the quality of temperature and pressure was controlled. [Result] The method was optimized based on traditional quality control method, and it narrowed the effectiveness of real-time data quality control. The quality check of hourly precipitation applied relevant check of hourly minimum temperature, vertical consistency check of radar data, which can effectively improve the accuracy and credibility of hourly precipitation quality control. [Conclusion] The method was on trial for one year in the quality control of real-time data in the regional automatic meteorological station in Guangxi and had gained good outcome.
基金Supported by the National Key Research and Development Program of China(Nos.2016YFC1402000,2018YFC1407003,2017YFC1405300)
文摘Offshore waters provide resources for human beings,while on the other hand,threaten them because of marine disasters.Ocean stations are part of offshore observation networks,and the quality of their data is of great significance for exploiting and protecting the ocean.We used hourly mean wave height,temperature,and pressure real-time observation data taken in the Xiaomaidao station(in Qingdao,China)from June 1,2017,to May 31,2018,to explore the data quality using eight quality control methods,and to discriminate the most effective method for Xiaomaidao station.After using the eight quality control methods,the percentages of the mean wave height,temperature,and pressure data that passed the tests were 89.6%,88.3%,and 98.6%,respectively.With the marine disaster(wave alarm report)data,the values failed in the test mainly due to the influence of aging observation equipment and missing data transmissions.The mean wave height is often affected by dynamic marine disasters,so the continuity test method is not effective.The correlation test with other related parameters would be more useful for the mean wave height.
基金funded by National Key R&D Program of China((Nos.2022YFC3003403 and 2018YFC1505203)Key Research and Development Program of Tibet Autonomous Region(XZ202301ZY0039G)+1 种基金Natural Science Foundation of Hebei Province(No.F2021201031)Geological Survey Project of China Geological Survey(No.DD20221747)。
文摘Glacier disasters occur frequently in alpine regions around the world,but the current conventional geological disaster measurement technology cannot be directly used for glacier disaster measurement.Hence,in this study,a distributed multi-sensor measurement system for glacier deformation was established by integrating piezoelectric sensing,coded sensing,attitude sensing technology and wireless communication technology.The traditional Modbus protocol was optimized to solve the problem of data identification confusion of different acquisition nodes.Through indoor wireless transmission,adaptive performance analysis,error measurement experiment and landslide simulation experiment,the performance of the measurement system was analyzed and evaluated.Using unmanned aerial vehicle technology,the reliability and effectiveness of the measurement system were verified on the site of Galongla glacier in southeastern Tibet,China.The results show that the mean absolute percentage errors were only 1.13%and 2.09%for the displacement and temperature,respectively.The distributed glacier deformation real-time measurement system provides a new means for the assessment of the development process of glacier disasters and disaster prevention and mitigation.
基金Project supported by the National Key Research and Development Program of China(Grant No.2018YFE0127800)the Science,Technology&Innovation Funding Authority(STIFA),Egypt grant(Grant No.40517)+1 种基金China Postdoctoral Science Foundation(Grant No.2020M682411)the Fundamental Research Funds for the Central Universities(Grant No.2019kfy RCPY045)。
文摘Solar stills are considered an effective method to solve the scarcity of drinkable water.However,it is still missing a way to forecast its production.Herein,it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data.The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm.The required data to train the model are obtained from daily measurements lasting9 months.To validate the accuracy model,the determination coefficients of two types of solar stills are calculated as 0.935and 0.929,respectively,which are much higher than the value of both multiple linear regression(0.767)and the traditional models(0.829 and 0.847).Moreover,by applying the model,we predicted the freshwater production of four cities in China.The predicted production is approved to be reliable by a high value of correlation(0.868)between the predicted production and the solar insolation.With the help of the forecasting model,it would greatly promote the global application of solar stills.
文摘The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.
文摘Recently, use of mobile communicational devices in field data collection is increasing such as smart phones and cellular phones due to emergence of embedded Global Position System GPS and Wi-Fi Internet access. Accurate timely and handy field data collection is required for disaster management and emergency quick responses. In this article, we introduce web-based GIS system to collect the field data by personal mobile phone through Post Office Protocol POP3 mail server. The main objective of this work is to demonstrate real-time field data collection method to the students using their mobile phone to collect field data by timely and handy manners, either individual or group survey in local or global scale research.
文摘Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.
基金Supported by the National High Technology Research and Development Program of China (2006AA040301-4,2007AA041301-6)
文摘To evaluate and improve the real-time performance of Ethernet for plant automation(EPA) industrial Ethernet,the real-time performance of EPA periodic data transmission was theoretically and experimentally studied.By analyzing information transmission regularity and EPA deterministic scheduling mechanism,periodic messages were categorized as different modes according to their entering-queue time.The scheduling characteristics and delivery time of each mode and their interacting relations were studied,during which the models of real-time performance of periodic information transmission in EPA system were established.On this basis,an experimental platform is developed to test the delivery time of periodic messages transmission in EPA system.According to the analysis and the experiment,the main factors that limit the real-time performance of EPA periodic data transmission and the improvement methods were proposed.
基金Meg-science Program of the Chinese Academy of Sciences (No. 19981303)
文摘A new system called alternate data acquisition and real-time monitoring system has been developed for long-time discharge in tokamak operation. It can support continuous on-line data acquisition at a high sampling rate and a graphic display of the plasma parameters during the discharge. Thus operators can monitor and control the plasma state in real time. An application of this system has been demonstrated on the HT-7 tokamak.
文摘GoTaTM from ZTE is the world’s first CDMA-based system. Now, ZTE proudly introduces its third-generation digital trunking system featuring a centralized dispatch,
文摘Go Tafrom ZTE is the world’s first CDMA-based system. Now, ZTE proudly introduces its third-generation digital trunking system featuring a centralized dispatch,
文摘This paper explores the opportunities and challenges of college mental health education from the perspective of big data.Firstly,through literature review,the importance of mental health education and the current issues are elucidated.Then,from the perspective of big data,the potential opportunities of big data in college mental health education are analyzed,including data-driven personalized education,real-time monitoring and warning systems,and interdisciplinary research and collaboration.At the same time,the challenges faced by college mental health education under the perspective of big data are also pointed out,such as data privacy and security issues,insufficient data analysis and interpretation capabilities,and inadequate technical facilities and talent support.Lastly,the research content of this paper is summarized,and directions and suggestions for future research are proposed.
文摘Real-time monitoring and forecast of large scale active population density is of great significance as it can warn and prevent possible public safety accident caused by abnormal population aggregation.Active population is defined as the number of people with their mobile phone powered on.Recently,an unfortunate deadly stampede occurred in Shanghai on December 31th 2014 causing the death of 39 people.We hope that our research can help avoid similar unfortunate accident from happening.In this paper we propose a method for active population density real-time monitoring and forecasting based on data from mobile network operators.Our method is based solely on mobile network operators existing infrastructure and barely requires extra investment,and mobile devices play a very limited role in the process of population locating.Four series forecasting methods,namely Simple Exponential Smoothing(SES),Double exponential smoothing(DES),Triple exponential smoothing(TES)and Autoregressive integrated moving average(ARIMA)are used in our experiments.Our experimental results suggest that we can achieve good forecast result for 135 min in future.
基金funded by the Special Scientific Research Project for Public Interest (GYHY201206009)the National Key Technologies Research and Development Program (Grant No. 2012BAC22B02)+2 种基金the National Natural Science Foundation Science Fund for Creative Research Groups (Grant No.41221064)the Special Scientific Research Project for Public Interest (Grant No. GYHY201006013)the National Natural Science Foundation of China (Grant No. 41105070 )
文摘The initial value error and the imperfect numerical model are usually considered as error sources of numerical weather prediction (NWP). By using past multi-time observations and model output, this study proposes a method to estimate imperfect numerical model error. This method can be inversely estimated through expressing the model error as a Lagrange interpolation polynomial, while the coefficients of polyno- mial are determined by past model performance. However, for practical application in the full NWP model, it is necessary to determine the following criteria: (1) the length of past data sufficient for estimation of the model errors, (2) a proper method of estimating the term "model integration with the exact solution" when solving the inverse problem, and (3) the extent to which this scheme is sensitive to the observational errors. In this study, such issues are resolved using a simple linear model, and an advection diffusion model is applied to discuss the sensitivity of the method to an artificial error source. The results indicate that the forecast errors can be largely reduced using the proposed method if the proper length of past data is chosen. To address the three problems, it is determined that (1) a few data limited by the order of the corrector can be used, (2) trapezoidal approximation can be employed to estimate the "term" in this study; however, a more accurate method should be explored for an operational NWP model, and (3) the correction is sensitive to observational error.