Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are g...Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.展开更多
An ocean state monitor and analysis radar（OSMAR）, developed by Wuhan University in China, have been mounted at six stations along the coasts of East China Sea（ECS） to measure velocities（currents, waves and winds...An ocean state monitor and analysis radar（OSMAR）, developed by Wuhan University in China, have been mounted at six stations along the coasts of East China Sea（ECS） to measure velocities（currents, waves and winds） at the sea surface. Radar-observed surface current is taken as an example to illustrate the operational high-frequency（HF） radar observing and data service platform（OP）, presenting an operational flow from data observing, transmitting, processing, visualizing, to end-user service. Three layers（systems）： radar observing system（ROS）, data service system（DSS） and visualization service system（VSS）, as well as the data flow within the platform are introduced. Surface velocities observed at stations are synthesized at the radar data receiving and preprocessing center of the ROS, and transmitted to the DSS, in which the data processing and quality control（QC） are conducted. Users are allowed to browse the processed data on the portal of the DSS, and access to those data files. The VSS aims to better show the data products by displaying the information on a visual globe. By utilizing the OP, the surface currents in East China Sea are monitored, and hourly and seasonal variabilities of them are investigated.展开更多
Operating System(OS)is a critical piece of software that manages a computer’s hardware and resources,acting as the intermediary between the computer and the user.The existing OS is not designed for Big Data and Cloud...Operating System(OS)is a critical piece of software that manages a computer’s hardware and resources,acting as the intermediary between the computer and the user.The existing OS is not designed for Big Data and Cloud Computing,resulting in data processing and management inefficiency.This paper proposes a simplified and improved kernel on an x86 system designed for Big Data and Cloud Computing purposes.The proposed algorithm utilizes the performance benefits from the improved Input/Output(I/O)performance.The performance engineering runs the data-oriented design on traditional data management to improve data processing speed by reducing memory access overheads in conventional data management.The OS incorporates a data-oriented design to“modernize”various Data Science and management aspects.The resulting OS contains a basic input/output system(BIOS)bootloader that boots into Intel 32-bit protected mode,a text display terminal,4 GB paging memory,4096 heap block size,a Hard Disk Drive(HDD)I/O Advanced Technology Attachment(ATA)driver and more.There are also I/O scheduling algorithm prototypes that demonstrate how a simple Sweeping algorithm is superior to more conventionally known I/O scheduling algorithms.A MapReduce prototype is implemented using Message Passing Interface(MPI)for big data purposes.An attempt was made to optimize binary search using modern performance engineering and data-oriented design.展开更多
Objective: To measure the hospital operation efficiency, study the correlation between average length of stay and hospital operation efficiency, analyze the importance of shortening average length of stay to the impro...Objective: To measure the hospital operation efficiency, study the correlation between average length of stay and hospital operation efficiency, analyze the importance of shortening average length of stay to the improvement of the hospital operation efficiency and put forward relevant policy suggestion. Methods: Based on China provincial panel data from 2003 to 2012, the hospital operation efficiencies are calculated using Super Efficiency Data Envelopment Analysis model, and the correlation between average length of stay and hospital operation efficiency is tested using Spearman rank correlation coefficient test. Results: From 2003 to 2012, the average of national hospital operation efficiency was increasing slowly and the hospital operations were inefficient in most of the areas. The national hospital operation efficiency is negatively correlated to the average length of stay. Conclusion: Measures should be taken to set average length of stay in a scientific and reasonable way, improve social and economic benefits based on the improvement of efficiency.展开更多
Objective:To discuss the efficacy of Bispectral index (BIS)-monitored closed-loop targeted-controlled infusion of propofol for laparoscopic radical operation for gastric cancer.Methods:A total of 106 patients with pri...Objective:To discuss the efficacy of Bispectral index (BIS)-monitored closed-loop targeted-controlled infusion of propofol for laparoscopic radical operation for gastric cancer.Methods:A total of 106 patients with primary gastric cancer who underwent laparoscopic radical operation for gastric cancer in our hospital between August 2015 and February 2018 were chosen as the research subjects and divided into the control group (n=53) and the observation group (n=53) according to the different anesthesia methods. Control group of patients received BIS-monitored manually adjusted targeted-controlled infusion concentration of propofol, and observation group of patients received BIS-monitored closed-loop targeted-controlled infusion of propofol. The differences in hemodynamic index levels as well as serum contents of inflammatory factors and stress hormones were compared between the two groups of patients before anesthesia (T0), 30 min after surgery started (T1) and 30 min before surgery ended (T2).Results:At T0, the differences in hemodynamic index levels as well as serum contents of inflammatory factors and stress hormones were not statistically significant between the two groups. At T1 and T2, hemodynamic indexes MAP and HR levels of observation group were lower than those of control group at the corresponding time points;serum inflammatory factors sICAM-1, IL-1β, IL-8 and TNF-α contents were lower than those of control group at the corresponding time points;serum stress hormones Cor, T4 and glucagon contents were lower than those of control group at the corresponding time points.Conclusion: BIS-monitored closed-loop targeted-controlled infusion of propofol can effectively stabilize the intraoperative hemodynamics and inhibit the systemic inflammatory stress response in patients with laparoscopic radical operation for gastric cancer.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud...With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.展开更多
Traditional grid computing focuses on the movement of data to compute resources and the management of large scale simulations. Data grid computing focuses on moving the operations to the storage location and on operat...Traditional grid computing focuses on the movement of data to compute resources and the management of large scale simulations. Data grid computing focuses on moving the operations to the storage location and on operations on data collections. We present three types of data grid operations that facilitate data driven research: the manipulation of time series data, the reproducible execution of workflows, and the mapping of data access to software-defined networks. These data grid operations have been implemented as operations on collections within the NSF DataNet Federation Consortium project. The operations can be applied at the remote resource where data are stored, improving the ability of researchers to interact with large collections.展开更多
A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns r...A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns revealed from their mobile phones,researchers and practitioners are now equipped to derive the largest trip samples for a region.Because of its ubiquitous use,extensive coverage of telecommunication services and high penetration rates,travel demand can be studied continuously in fine spatial and temporal resolutions.The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origindestination(OD)matrices.The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling,integrated corridor management and online traffic simulations.展开更多
As a global financial center, the transportation system in New York City (NYC) has always been studied from various aspects. Since 2009, NYC Taxi and Limousine Commission have made public the information on NYC taxi o...As a global financial center, the transportation system in New York City (NYC) has always been studied from various aspects. Since 2009, NYC Taxi and Limousine Commission have made public the information on NYC taxi operations, offering an opportunity for detailed analysis. Thus, this research project investigates taxi operations in New York City based on big data analysis. The correlation between taxi operations and different types of weather, including precipitation, snow depth, and snowfall is discussed in this paper. The research also evaluates taxi trip distribution in each NTA area using Geopandas, and presents its density on an NYC map.展开更多
In this paper, we conduct research on the database technology under the basic operation of the IOT huge amounts of data. Spatial data service is the process of basic customer application system service request, direct...In this paper, we conduct research on the database technology under the basic operation of the IOT huge amounts of data. Spatial data service is the process of basic customer application system service request, directory service system according to the request is mapped into physical addresses, according to the data block physical disk server address block of data read from the disk in parallel and sent to the client the process of application system. In addition, spatial data distribution system should possess unloading, transfer, replication, and optimization of spatial data, and other functions, to achieve comprehensive management and maintenance of spatial data. Under this basis, we propose the enhanced database technology that optimizes the operation with better performance.展开更多
With the rapid development of China＇ s economy and the accelerating pace of economic globalization, the rapid expansion of trade appears in goods, materials; space to move also will expand in breadth and depth, and t...With the rapid development of China＇ s economy and the accelerating pace of economic globalization, the rapid expansion of trade appears in goods, materials; space to move also will expand in breadth and depth, and thus the efficiency of the logistics activities, rapid response capabilities and the level of information logistics put forward higher requirements. Meanwhile, the logistics needs of personalization, diversification and sophistication, require that logistics service companies must constantly improve and optimize enterprise business model, and target to develop new logistics services to adapt to changes in the logistics market, and improve the competitiveness of enterprises.Modem logistics enterprises refer to the concept of modem logistics as a guide, and the use of modem logistics and organization of modem logistics technology, it is to provide customers help and reduce logistics costs and improve the level of integrated logistics services to rationalize logistics enterprises. Modem logistics enterprises are in terms of philosophy, mode of operation, services, information technology degree, logistics technology, enterprise systems and others have higher requirements to react quickly, service serialization, standardization of operations, the target of systematic, modem means are the features of the organization which are different from the traditional network of logistics enterprises.展开更多
Wind power generation is a mature kind of technology, economic, environmental protection of new energy, it has a broad space for development. Wind turbine is a complex electromechanical system with complex structure a...Wind power generation is a mature kind of technology, economic, environmental protection of new energy, it has a broad space for development. Wind turbine is a complex electromechanical system with complex structure and many components. It is often located in remote mountainous areas, often encountering storms, lightning, ice, salt fog and other extreme weather. Due to its harsh working environment, it has appeared a lot of faults, and its low reliability and high maintenance cost, which brings great challenges to the safe operation of wind power generation equipment. Based on this, this paper is based on the actual failure data of domestic wind turbine, uses the Pareto curve and other analysis methods of the main components of domestic wind turbine, and accordingly formulated the corresponding maintenance countermeasures.展开更多
In the process of the development of electric power communication system, the data also played a role in intelligence, the data intelligent maintenance technology used in the electric power communication system, promp...In the process of the development of electric power communication system, the data also played a role in intelligence, the data intelligent maintenance technology used in the electric power communication system, prompted the electric power communication system become more perfect, it will give people more convenient, but also promoted the development of electric power and improvement, in order to ensure the quality of electric power communication operation maintenance schedule and, In view of the current problems in the operation and maintenance of electric power communication, it has become the focus of promoting the development of electric power communication data to adopt reasonable processing strategies. This paper studies and analyzes the practice of operation and maintenance big data and intelligent operation and maintenance in the electric power industry for reference.展开更多
Based on the target analysis of the operation optimization for power plants, a novel system scheme called operation optimization decision support system (OODSS) is brought forward. According to the structure and desig...Based on the target analysis of the operation optimization for power plants, a novel system scheme called operation optimization decision support system (OODSS) is brought forward. According to the structure and design thinking of decision support system (DSS), the overall structure of the OODSS is studied, and the scheme of the sub systems in the OODSS such as the user interface system, the problem processing system, the database system, the model base system, the expert system (ES) and the data mining sy...展开更多
An observation operator is a bridge linking the system state vector and observations in a data assimilation system. Despite its importance, the degree to which an observation operator influences the performance of dat...An observation operator is a bridge linking the system state vector and observations in a data assimilation system. Despite its importance, the degree to which an observation operator influences the performance of data assimilation methods is still poorly understood. This study aimed to analyze the influences of linear and nonlinear observation operators on the sequential data assimilation through soil temperature simulation using the unscented particle filter(UPF) and the common land model. The linear observation operator between unprocessed simulations and observations was first established. To improve the correlation between simulations and observations, both were processed based on a series of equations. This processing essentially resulted in a nonlinear observation operator. The linear and nonlinear observation operators were then used along with the UPF in three assimilation experiments: an hourly in situ soil surface temperature assimilation, a daily in situ soil surface temperature assimilation, and a moderate resolution imaging spectroradiometer(MODIS) land surface temperature(LST) assimilation. The results show that the filter improved the soil temperature simulation significantly with the linear and nonlinear observation operators. The nonlinear observation operator improved the UPF's performance more significantly for the hourly and daily in situ observation assimilations than the linear observation operator did, while the situation was opposite for the MODIS LST assimilation. Because of the high assimilation frequency and data quality, the simulation accuracy was significantly improved in all soil layers for hourly in situ soil surface temperature assimilation, while the significant improvements of the simulation accuracy were limited to the lower soil layers for the assimilation experiments with low assimilation frequency or low data quality.展开更多
Operational disposition of electronic countermeasures(ECM)is a hot topic in modern warfare research.Through fully analyzing the characteristics and shortcomings of the traditional operational disposition scheme,a supe...Operational disposition of electronic countermeasures(ECM)is a hot topic in modern warfare research.Through fully analyzing the characteristics and shortcomings of the traditional operational disposition scheme,a super-efficient data envelopment analysis support vector machine(SE-DEA-SVM)method for evaluating the operational configuration scheme of ECM is proposed.Firstly,considering the subjective and objective factors affecting the operational disposition of ECM,the index system of operational disposition scheme is established,and we explain the solution method of terminal indexs.Secondly,the evaluation and algorithm process of SE-DEA-SVM evaluation method are introduced.In this method,the super-efficient data envelopment analysis(SE-DEA)model is used to calculate the weight of index system,and the support vector machine(SVM)method combined with the training samples of evaluation index is used to obtain the input-output model of evaluation value of combat configuration.Finally,by an example(obtaining five schemes),we verify the SE-DEA-SVM evaluation method and analyze the results.The efficiency analysis,comparison analysis,and error analysis of this method are carried out.The results show that this method is more suitable for military evaluation with small samples,and it has high efficiency,applicability,and popularization value.展开更多
Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP)...Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP).To realize this potential,an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables.For this purpose,a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain,snow,hail,and graupel.The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution.The calculated polarimetric variables are then fitted to simple functions of water content and volumeweighted mean diameter of the hydrometeor particle size distribution.The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting(WRF)model to have simulated PRD,which are compared with existing operators and real observations to show their validity and applicability.The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations,making it efficient in PRD simulation and assimilation usage.展开更多
The Indiana Department of Transportation (INDOT) maintains 29,000 lane miles of roadway and operates a fleet of nearly 1100 snowplows and spends upwards of $60 million annually on winter maintenance operations. Since ...The Indiana Department of Transportation (INDOT) maintains 29,000 lane miles of roadway and operates a fleet of nearly 1100 snowplows and spends upwards of $60 million annually on winter maintenance operations. Since winter weather varies considerably, allocation of snow removal and deicing resources are highly decentralized to facilitate agile response. Historically, real-time two-way radio communication with drivers has been the primary monitoring system, but with 6 districts, 29 subdistricts, and over one hundred units it does not scale well for systematic data collection. Emerging technology such as real-time truck telematics, hi-resolution NOAA data, dash camera imagery, and crowdsourced traffic speeds can now be fused into dashboards. These real-time dashboards can be used for systematic monitoring and allocation of resources during critical weather events. This paper reports on dashboards used during the 2020-2021 winter season derived from that data. Nearly 13 million location records and 11 million dash camera images were collected from telematics onboard 1105 trucks. Peak impact of nearly 1570 congested miles and 610 trucks deployed was observed for a winter storm on February 15<sup><span style="font-family:Verdana;">th</span></sup><span style="font-family:Verdana;">, 2021 chosen for further analysis. In addition to tactical adjustments of resources during storms, this system-wide collection of resources allows agencies to monitor multiple seasons and make long</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">term strategic asset allocation decisions. Also, from a public information perspective, these resources were found to be very useful to agencies that interface with the media (and social media) during large storms to provide real-time visual updates on conditions throughout the state from pre-treatment, through cleanup.</span>展开更多
A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates f...A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.展开更多
基金funded by the National Natural Science Foundation of China(Grant No.41941019)the State Key Laboratory of Hydroscience and Engineering(Grant No.2019-KY-03)。
文摘Real-time prediction of the rock mass class in front of the tunnel face is essential for the adaptive adjustment of tunnel boring machines(TBMs).During the TBM tunnelling process,a large number of operation data are generated,reflecting the interaction between the TBM system and surrounding rock,and these data can be used to evaluate the rock mass quality.This study proposed a stacking ensemble classifier for the real-time prediction of the rock mass classification using TBM operation data.Based on the Songhua River water conveyance project,a total of 7538 TBM tunnelling cycles and the corresponding rock mass classes are obtained after data preprocessing.Then,through the tree-based feature selection method,10 key TBM operation parameters are selected,and the mean values of the 10 selected features in the stable phase after removing outliers are calculated as the inputs of classifiers.The preprocessed data are randomly divided into the training set(90%)and test set(10%)using simple random sampling.Besides stacking ensemble classifier,seven individual classifiers are established as the comparison.These classifiers include support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),gradient boosting decision tree(GBDT),decision tree(DT),logistic regression(LR)and multilayer perceptron(MLP),where the hyper-parameters of each classifier are optimised using the grid search method.The prediction results show that the stacking ensemble classifier has a better performance than individual classifiers,and it shows a more powerful learning and generalisation ability for small and imbalanced samples.Additionally,a relative balance training set is obtained by the synthetic minority oversampling technique(SMOTE),and the influence of sample imbalance on the prediction performance is discussed.
基金The National Natural Science Foundation of China under contract No.41206012
文摘An ocean state monitor and analysis radar（OSMAR）, developed by Wuhan University in China, have been mounted at six stations along the coasts of East China Sea（ECS） to measure velocities（currents, waves and winds） at the sea surface. Radar-observed surface current is taken as an example to illustrate the operational high-frequency（HF） radar observing and data service platform（OP）, presenting an operational flow from data observing, transmitting, processing, visualizing, to end-user service. Three layers（systems）： radar observing system（ROS）, data service system（DSS） and visualization service system（VSS）, as well as the data flow within the platform are introduced. Surface velocities observed at stations are synthesized at the radar data receiving and preprocessing center of the ROS, and transmitted to the DSS, in which the data processing and quality control（QC） are conducted. Users are allowed to browse the processed data on the portal of the DSS, and access to those data files. The VSS aims to better show the data products by displaying the information on a visual globe. By utilizing the OP, the surface currents in East China Sea are monitored, and hourly and seasonal variabilities of them are investigated.
文摘Operating System(OS)is a critical piece of software that manages a computer’s hardware and resources,acting as the intermediary between the computer and the user.The existing OS is not designed for Big Data and Cloud Computing,resulting in data processing and management inefficiency.This paper proposes a simplified and improved kernel on an x86 system designed for Big Data and Cloud Computing purposes.The proposed algorithm utilizes the performance benefits from the improved Input/Output(I/O)performance.The performance engineering runs the data-oriented design on traditional data management to improve data processing speed by reducing memory access overheads in conventional data management.The OS incorporates a data-oriented design to“modernize”various Data Science and management aspects.The resulting OS contains a basic input/output system(BIOS)bootloader that boots into Intel 32-bit protected mode,a text display terminal,4 GB paging memory,4096 heap block size,a Hard Disk Drive(HDD)I/O Advanced Technology Attachment(ATA)driver and more.There are also I/O scheduling algorithm prototypes that demonstrate how a simple Sweeping algorithm is superior to more conventionally known I/O scheduling algorithms.A MapReduce prototype is implemented using Message Passing Interface(MPI)for big data purposes.An attempt was made to optimize binary search using modern performance engineering and data-oriented design.
文摘Objective: To measure the hospital operation efficiency, study the correlation between average length of stay and hospital operation efficiency, analyze the importance of shortening average length of stay to the improvement of the hospital operation efficiency and put forward relevant policy suggestion. Methods: Based on China provincial panel data from 2003 to 2012, the hospital operation efficiencies are calculated using Super Efficiency Data Envelopment Analysis model, and the correlation between average length of stay and hospital operation efficiency is tested using Spearman rank correlation coefficient test. Results: From 2003 to 2012, the average of national hospital operation efficiency was increasing slowly and the hospital operations were inefficient in most of the areas. The national hospital operation efficiency is negatively correlated to the average length of stay. Conclusion: Measures should be taken to set average length of stay in a scientific and reasonable way, improve social and economic benefits based on the improvement of efficiency.
文摘Objective:To discuss the efficacy of Bispectral index (BIS)-monitored closed-loop targeted-controlled infusion of propofol for laparoscopic radical operation for gastric cancer.Methods:A total of 106 patients with primary gastric cancer who underwent laparoscopic radical operation for gastric cancer in our hospital between August 2015 and February 2018 were chosen as the research subjects and divided into the control group (n=53) and the observation group (n=53) according to the different anesthesia methods. Control group of patients received BIS-monitored manually adjusted targeted-controlled infusion concentration of propofol, and observation group of patients received BIS-monitored closed-loop targeted-controlled infusion of propofol. The differences in hemodynamic index levels as well as serum contents of inflammatory factors and stress hormones were compared between the two groups of patients before anesthesia (T0), 30 min after surgery started (T1) and 30 min before surgery ended (T2).Results:At T0, the differences in hemodynamic index levels as well as serum contents of inflammatory factors and stress hormones were not statistically significant between the two groups. At T1 and T2, hemodynamic indexes MAP and HR levels of observation group were lower than those of control group at the corresponding time points;serum inflammatory factors sICAM-1, IL-1β, IL-8 and TNF-α contents were lower than those of control group at the corresponding time points;serum stress hormones Cor, T4 and glucagon contents were lower than those of control group at the corresponding time points.Conclusion: BIS-monitored closed-loop targeted-controlled infusion of propofol can effectively stabilize the intraoperative hemodynamics and inhibit the systemic inflammatory stress response in patients with laparoscopic radical operation for gastric cancer.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00399401,Development of Quantum-Safe Infrastructure Migration and Quantum Security Verification Technologies).
文摘With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.
文摘Traditional grid computing focuses on the movement of data to compute resources and the management of large scale simulations. Data grid computing focuses on moving the operations to the storage location and on operations on data collections. We present three types of data grid operations that facilitate data driven research: the manipulation of time series data, the reproducible execution of workflows, and the mapping of data access to software-defined networks. These data grid operations have been implemented as operations on collections within the NSF DataNet Federation Consortium project. The operations can be applied at the remote resource where data are stored, improving the ability of researchers to interact with large collections.
文摘A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions.By analyzing individuals’mobility patterns revealed from their mobile phones,researchers and practitioners are now equipped to derive the largest trip samples for a region.Because of its ubiquitous use,extensive coverage of telecommunication services and high penetration rates,travel demand can be studied continuously in fine spatial and temporal resolutions.The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origindestination(OD)matrices.The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling,integrated corridor management and online traffic simulations.
文摘As a global financial center, the transportation system in New York City (NYC) has always been studied from various aspects. Since 2009, NYC Taxi and Limousine Commission have made public the information on NYC taxi operations, offering an opportunity for detailed analysis. Thus, this research project investigates taxi operations in New York City based on big data analysis. The correlation between taxi operations and different types of weather, including precipitation, snow depth, and snowfall is discussed in this paper. The research also evaluates taxi trip distribution in each NTA area using Geopandas, and presents its density on an NYC map.
文摘In this paper, we conduct research on the database technology under the basic operation of the IOT huge amounts of data. Spatial data service is the process of basic customer application system service request, directory service system according to the request is mapped into physical addresses, according to the data block physical disk server address block of data read from the disk in parallel and sent to the client the process of application system. In addition, spatial data distribution system should possess unloading, transfer, replication, and optimization of spatial data, and other functions, to achieve comprehensive management and maintenance of spatial data. Under this basis, we propose the enhanced database technology that optimizes the operation with better performance.
文摘With the rapid development of China＇ s economy and the accelerating pace of economic globalization, the rapid expansion of trade appears in goods, materials; space to move also will expand in breadth and depth, and thus the efficiency of the logistics activities, rapid response capabilities and the level of information logistics put forward higher requirements. Meanwhile, the logistics needs of personalization, diversification and sophistication, require that logistics service companies must constantly improve and optimize enterprise business model, and target to develop new logistics services to adapt to changes in the logistics market, and improve the competitiveness of enterprises.Modem logistics enterprises refer to the concept of modem logistics as a guide, and the use of modem logistics and organization of modem logistics technology, it is to provide customers help and reduce logistics costs and improve the level of integrated logistics services to rationalize logistics enterprises. Modem logistics enterprises are in terms of philosophy, mode of operation, services, information technology degree, logistics technology, enterprise systems and others have higher requirements to react quickly, service serialization, standardization of operations, the target of systematic, modem means are the features of the organization which are different from the traditional network of logistics enterprises.
文摘Wind power generation is a mature kind of technology, economic, environmental protection of new energy, it has a broad space for development. Wind turbine is a complex electromechanical system with complex structure and many components. It is often located in remote mountainous areas, often encountering storms, lightning, ice, salt fog and other extreme weather. Due to its harsh working environment, it has appeared a lot of faults, and its low reliability and high maintenance cost, which brings great challenges to the safe operation of wind power generation equipment. Based on this, this paper is based on the actual failure data of domestic wind turbine, uses the Pareto curve and other analysis methods of the main components of domestic wind turbine, and accordingly formulated the corresponding maintenance countermeasures.
文摘In the process of the development of electric power communication system, the data also played a role in intelligence, the data intelligent maintenance technology used in the electric power communication system, prompted the electric power communication system become more perfect, it will give people more convenient, but also promoted the development of electric power and improvement, in order to ensure the quality of electric power communication operation maintenance schedule and, In view of the current problems in the operation and maintenance of electric power communication, it has become the focus of promoting the development of electric power communication data to adopt reasonable processing strategies. This paper studies and analyzes the practice of operation and maintenance big data and intelligent operation and maintenance in the electric power industry for reference.
文摘Based on the target analysis of the operation optimization for power plants, a novel system scheme called operation optimization decision support system (OODSS) is brought forward. According to the structure and design thinking of decision support system (DSS), the overall structure of the OODSS is studied, and the scheme of the sub systems in the OODSS such as the user interface system, the problem processing system, the database system, the model base system, the expert system (ES) and the data mining sy...
基金supported by the National Key Research and Development Program of China(Grants No.2016YFC0402706 and 2016YFC0402710)the National Natural Science Foundation of China(Grants No.51709046 and41323001)the Open Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University(Grant No.2015490311)
文摘An observation operator is a bridge linking the system state vector and observations in a data assimilation system. Despite its importance, the degree to which an observation operator influences the performance of data assimilation methods is still poorly understood. This study aimed to analyze the influences of linear and nonlinear observation operators on the sequential data assimilation through soil temperature simulation using the unscented particle filter(UPF) and the common land model. The linear observation operator between unprocessed simulations and observations was first established. To improve the correlation between simulations and observations, both were processed based on a series of equations. This processing essentially resulted in a nonlinear observation operator. The linear and nonlinear observation operators were then used along with the UPF in three assimilation experiments: an hourly in situ soil surface temperature assimilation, a daily in situ soil surface temperature assimilation, and a moderate resolution imaging spectroradiometer(MODIS) land surface temperature(LST) assimilation. The results show that the filter improved the soil temperature simulation significantly with the linear and nonlinear observation operators. The nonlinear observation operator improved the UPF's performance more significantly for the hourly and daily in situ observation assimilations than the linear observation operator did, while the situation was opposite for the MODIS LST assimilation. Because of the high assimilation frequency and data quality, the simulation accuracy was significantly improved in all soil layers for hourly in situ soil surface temperature assimilation, while the significant improvements of the simulation accuracy were limited to the lower soil layers for the assimilation experiments with low assimilation frequency or low data quality.
基金This work was supported by the Military Postgraduate Funding Project(JY2019C055)Hunan Province Postgraduate Scientific Research Innovation Project(CX20200029).
文摘Operational disposition of electronic countermeasures(ECM)is a hot topic in modern warfare research.Through fully analyzing the characteristics and shortcomings of the traditional operational disposition scheme,a super-efficient data envelopment analysis support vector machine(SE-DEA-SVM)method for evaluating the operational configuration scheme of ECM is proposed.Firstly,considering the subjective and objective factors affecting the operational disposition of ECM,the index system of operational disposition scheme is established,and we explain the solution method of terminal indexs.Secondly,the evaluation and algorithm process of SE-DEA-SVM evaluation method are introduced.In this method,the super-efficient data envelopment analysis(SE-DEA)model is used to calculate the weight of index system,and the support vector machine(SVM)method combined with the training samples of evaluation index is used to obtain the input-output model of evaluation value of combat configuration.Finally,by an example(obtaining five schemes),we verify the SE-DEA-SVM evaluation method and analyze the results.The efficiency analysis,comparison analysis,and error analysis of this method are carried out.The results show that this method is more suitable for military evaluation with small samples,and it has high efficiency,applicability,and popularization value.
基金the University of Oklahoma(OU)Supercomputing Center for Education&Research(OSCER).
文摘Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP).To realize this potential,an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables.For this purpose,a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain,snow,hail,and graupel.The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution.The calculated polarimetric variables are then fitted to simple functions of water content and volumeweighted mean diameter of the hydrometeor particle size distribution.The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting(WRF)model to have simulated PRD,which are compared with existing operators and real observations to show their validity and applicability.The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations,making it efficient in PRD simulation and assimilation usage.
文摘The Indiana Department of Transportation (INDOT) maintains 29,000 lane miles of roadway and operates a fleet of nearly 1100 snowplows and spends upwards of $60 million annually on winter maintenance operations. Since winter weather varies considerably, allocation of snow removal and deicing resources are highly decentralized to facilitate agile response. Historically, real-time two-way radio communication with drivers has been the primary monitoring system, but with 6 districts, 29 subdistricts, and over one hundred units it does not scale well for systematic data collection. Emerging technology such as real-time truck telematics, hi-resolution NOAA data, dash camera imagery, and crowdsourced traffic speeds can now be fused into dashboards. These real-time dashboards can be used for systematic monitoring and allocation of resources during critical weather events. This paper reports on dashboards used during the 2020-2021 winter season derived from that data. Nearly 13 million location records and 11 million dash camera images were collected from telematics onboard 1105 trucks. Peak impact of nearly 1570 congested miles and 610 trucks deployed was observed for a winter storm on February 15<sup><span style="font-family:Verdana;">th</span></sup><span style="font-family:Verdana;">, 2021 chosen for further analysis. In addition to tactical adjustments of resources during storms, this system-wide collection of resources allows agencies to monitor multiple seasons and make long</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">term strategic asset allocation decisions. Also, from a public information perspective, these resources were found to be very useful to agencies that interface with the media (and social media) during large storms to provide real-time visual updates on conditions throughout the state from pre-treatment, through cleanup.</span>
文摘A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.
