Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of indivi...Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of individual prediction methods.This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model(PSO-PIP),which incorporates a particle swarm optimization algorithm enhanced with dy-namic clustering and adaptive parameter tuning(KGPSO).The model integrates multi-source data from the Lattice Boltzmann Method(LBM),Pore Network Modeling(PNM),and Finite Difference Method(FDM).By assigning optimal weight coefficients to the outputs of these methods,the model minimizes deviations from actual values and enhances permeability prediction performance.Initially,the computational performances of the LBM,PNM,and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples.It is observed that these methods exhibit computational biases in certain permeability ranges.The PSOPIP model is proposed to combine the strengths of each computational approach and mitigate their limitations.The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals,significantly enhancing prediction accuracy.The outcomes of this study provide a new tool and perspective for the comprehensive,rapid,and accurate prediction of permeability in porous media.展开更多
Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is p...Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy.Second,the function of Fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of Fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data.展开更多
With the advent of the era of big data,traditional financial management has been unable to meet the needs of modern enterprise business.Enterprises hope that financial management has the function of improving the accu...With the advent of the era of big data,traditional financial management has been unable to meet the needs of modern enterprise business.Enterprises hope that financial management has the function of improving the accuracy of corporate financial data,assisting corporate management to make decisions that are more in line with the actual development of the company,and optimizing corporate management systems,thereby comprehensively improving the overall level of the company and ensuring that the company can be in business with the assistance of financial integration,can better improve and develop themselves.Based on the investigation of enterprises and universities,this article analyzes the problem of accounting talent training from both the demand and supply ends,and puts forward some suggestions for the teaching reform of accounting integration with big data in financial colleges and universities,and strives to promote the integration of business and finance.The optimal allocation of enterprise resources will gradually enhance the market competitiveness of enterprises,and explore the application strategies of big data technology in the integration of enterprise business and finance.展开更多
In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to q...In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.展开更多
Based on the background of ＂big-data-blowout＂, this thesis is about research on college English network teaching resources integration and utilization confronted with the chance of ＂data value, the data assets and ...Based on the background of ＂big-data-blowout＂, this thesis is about research on college English network teaching resources integration and utilization confronted with the chance of ＂data value, the data assets and data say＂, analyzing the inevitable challenge of ＂competition, digital divide and data privacy＂, discussing that the researchers should hold big data thinking of ＂peaks of road ＆ dances with wolves ＆ be interdependent＂, emphasizing the improvement from college English teacher＇s modem education technical information literacy[1] and putting forward to resource integration and utilization strategy in the era of big data.展开更多
With the rapid development of computer technology, the current society has also entered the era of big data. Under this development background, the business activities of enterprises have also undergone profound chang...With the rapid development of computer technology, the current society has also entered the era of big data. Under this development background, the business activities of enterprises have also undergone profound changes under the influence of big data technology. At the same time, in the process of enterprise operation and development, financial and business integration, which integrates business work with financial management, has also become the main development trend and is highly praised by most enterprises. However, judging from the actual situation of social enterprises in our country, many large-scale financial integration activities cannot be carried out smoothly and can only be carried out reluctantly. The process is also facing many difficulties. In view of this, this paper analyzes the significance and difficulties of financial and business integration in the era of big data, and puts forward scientific and effective development strategies.展开更多
Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of ...Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.展开更多
Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-s...Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.展开更多
This paper analyzes the status of existing resources through extensive research and international cooperation on the basis of four typical global monthly surface temperature datasets including the climate research dat...This paper analyzes the status of existing resources through extensive research and international cooperation on the basis of four typical global monthly surface temperature datasets including the climate research dataset of the University of East Anglia(CRUTEM3), the dataset of the U.S. National Climatic Data Center(GHCN-V3), the dataset of the U.S. National Aeronautics and Space Administration(GISSTMP), and the Berkeley Earth surface temperature dataset(Berkeley). China's first global monthly temperature dataset over land was developed by integrating the four aforementioned global temperature datasets and several regional datasets from major countries or regions. This dataset contains information from 9,519 stations worldwide of at least 20 years for monthly mean temperature, 7,073 for maximum temperature, and 6,587 for minimum temperature. Compared with CRUTEM3 and GHCN-V3, the station density is much higher particularly for South America, Africa,and Asia. Moreover, data from significantly more stations were available after the year 1990 which dramatically reduced the uncertainty of the estimated global temperature trend during 1990e2011. The integrated dataset can serve as a reliable data source for global climate change research.展开更多
Energy Internet is deeply integrated by Internet concept, information technology and energy industry, and Energy Internet Big Data are one of core technologies that achieve energy-information-economic interconnection ...Energy Internet is deeply integrated by Internet concept, information technology and energy industry, and Energy Internet Big Data are one of core technologies that achieve energy-information-economic interconnection and improve the development and evolution of Energy Internet. This paper describes the concept and characteristics of Energy Internet Big Data, and feasibility of applying Energy Internet Big Data to integrated energy market. On this basis, as for integrated energy market and multi-subjects of Energy Internet, typical application and technical system based on Energy Internet Big Data in integrated energy market is put forward, which provides a reference for the analysis and decision of integrated energy market in Energy Internet.展开更多
Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF iron...Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.展开更多
There are two key issues in distributed intrusion detection system,that is,maintaining load balance of system and protecting data integrity.To address these issues,this paper proposes a new distributed intrusion detec...There are two key issues in distributed intrusion detection system,that is,maintaining load balance of system and protecting data integrity.To address these issues,this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation.A data allocation strategy based on capacity and workload is introduced to achieve local load balance,and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster.Moreover,data integrity is protected by using session reassemble and session partitioning.The simulation results show that the new model enjoys favorable advantages such as good load balance,higher detection rate and detection efficiency.展开更多
In order to fulfill the requirements of mobile computing and big data environments, we design a mobile campus portal (MCP) providing mobile information services for users in both browser/ server and client/server mode...In order to fulfill the requirements of mobile computing and big data environments, we design a mobile campus portal (MCP) providing mobile information services for users in both browser/ server and client/server modes. We present the topological structure of MCP, system infra- structure, application architecture and the data management (such as data integration, data format translation, data security control and so on) of our mobile campus portal in this paper. After online running in the past two years, our MCP has received good feedbacks from mobile users. In the future, we will focus on the research of data-mining technologies in our big data environment.展开更多
With the strategy of media integration,transformation and upgrading of media has become an important issue.In the era of big data,due to the dual impact of data and technology,the media brings both challenges and oppo...With the strategy of media integration,transformation and upgrading of media has become an important issue.In the era of big data,due to the dual impact of data and technology,the media brings both challenges and opportunities.The paper traces the characteristics of the era of big data,focuses on analyzing the challenges and opportunities in the media industry,and analyzes the transformation and upgrading of the media from the dimensions of news production and distribution to better realize the social functions of media in the era of big data.Some strategic suggestions are put forward to improve the propagation effect.展开更多
CTMRC - Curriculum and Teaching Materials Reform Commission; CTMRI - Curriculum and Teaching Materials Research Institute; ELT - English Language Teaching; MOE - Ministry of Education;
In the era of network information development, the big data technology is constantly developing which is very important to the development of our society. With the development of technology, the demand for computer te...In the era of network information development, the big data technology is constantly developing which is very important to the development of our society. With the development of technology, the demand for computer technology talents in China is increasing. Therefore, in the era of big data, it is the key to improve the teaching level of computer major in colleges and universities and provide more professional and technical talents for social development. In the traditional teaching of computer major, it is influenced by many factors, such as teachers outdated teaching ideas, imperfect computer teaching infrastructure, etc., which leads to the improvement of the teaching level of computer major in colleges and universities in China. Based on the era of big data technology development, it is the key to improve the teaching quality and efficiency by integrating computer major teaching with big data. Based on this, this paper takes college computer major teaching as the research object, relies on big data technology, analyzes the value of big data technology applied to college computer major teaching, and analyzes the practical application of big data technology in college computer major teaching based on big data integration cloud platform teaching mode.展开更多
We propose a novel filter for sparse big data,called an integrated autoencoder(IAE),which utilises auxiliary information to mitigate data sparsity.The proposed model achieves an appropriate balance between prediction ...We propose a novel filter for sparse big data,called an integrated autoencoder(IAE),which utilises auxiliary information to mitigate data sparsity.The proposed model achieves an appropriate balance between prediction accuracy,convergence speed,and complexity.We implement experiments on a GPS trajectory dataset,and the results demonstrate that the IAE is more accurate and robust than some state-of-the-art methods.展开更多
基金supported by the National Key Research and Devel-opment Program of China (Grant No.2022YFC3005503)the National Natural Science Foundation of China (Grant Nos.52322907,52179141,U23B20149,U2340232)+1 种基金the Fundamental Research Funds for the Central Universities (Grant Nos.2042024kf1031,2042024kf0031)the Key Program of Science and Technology of Yunnan Province (Grant Nos.202202AF080004,202203AA080009).
文摘Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of individual prediction methods.This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model(PSO-PIP),which incorporates a particle swarm optimization algorithm enhanced with dy-namic clustering and adaptive parameter tuning(KGPSO).The model integrates multi-source data from the Lattice Boltzmann Method(LBM),Pore Network Modeling(PNM),and Finite Difference Method(FDM).By assigning optimal weight coefficients to the outputs of these methods,the model minimizes deviations from actual values and enhances permeability prediction performance.Initially,the computational performances of the LBM,PNM,and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples.It is observed that these methods exhibit computational biases in certain permeability ranges.The PSOPIP model is proposed to combine the strengths of each computational approach and mitigate their limitations.The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals,significantly enhancing prediction accuracy.The outcomes of this study provide a new tool and perspective for the comprehensive,rapid,and accurate prediction of permeability in porous media.
基金This work was supported in part by the National Natural Science Foundation of China(51435009)Shanghai Sailing Program(19YF1401500)the Fundamental Research Funds for the Central Universities(2232019D3-34).
文摘Industrial big data integration and sharing(IBDIS)is of great significance in managing and providing data for big data analysis in manufacturing systems.A novel fog-computing-based IBDIS approach called Fog-IBDIS is proposed in order to integrate and share industrial big data with high raw data security and low network traffic loads by moving the integration task from the cloud to the edge of networks.First,a task flow graph(TFG)is designed to model the data analysis process.The TFG is composed of several tasks,which are executed by the data owners through the Fog-IBDIS platform in order to protect raw data privacy.Second,the function of Fog-IBDIS to enable data integration and sharing is presented in five modules:TFG management,compilation and running control,the data integration model,the basic algorithm library,and the management component.Finally,a case study is presented to illustrate the implementation of Fog-IBDIS,which ensures raw data security by deploying the analysis tasks executed by the data generators,and eases the network traffic load by greatly reducing the volume of transmitted data.
基金The research was co-completed by School of Journalism and Communication of Hunan Normal University and Financial Big-Data Research Institute of Hunan University of Finance and Economics.This research was funded by the National Natural Science Foundation of China(No.72073041)Open Foundation for the University Innovation Platform in Hunan Province(No.18K103)+2 种基金2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open Project(Nos.20181901CRP03,20181901CRP04,20181901CRP05)2020 Hunan Provincial Higher Education Teaching Reform Research Project(Nos.HNJG-2020-1130,HNJG-2020-1124)2020 General Project of Hunan Social Science Fund(No.20B16).
文摘With the advent of the era of big data,traditional financial management has been unable to meet the needs of modern enterprise business.Enterprises hope that financial management has the function of improving the accuracy of corporate financial data,assisting corporate management to make decisions that are more in line with the actual development of the company,and optimizing corporate management systems,thereby comprehensively improving the overall level of the company and ensuring that the company can be in business with the assistance of financial integration,can better improve and develop themselves.Based on the investigation of enterprises and universities,this article analyzes the problem of accounting talent training from both the demand and supply ends,and puts forward some suggestions for the teaching reform of accounting integration with big data in financial colleges and universities,and strives to promote the integration of business and finance.The optimal allocation of enterprise resources will gradually enhance the market competitiveness of enterprises,and explore the application strategies of big data technology in the integration of enterprise business and finance.
基金Beijing Municipal Social Science Foundation(22GLC062)Research on service function renewal of Beijing subway station living circle driven by multiple big data.Beijing Municipal Education Commission Social Science Project(KM202010009002)Young YuYou Talents Training Plan of North China University of Technology.
文摘In the first-tier cities,subway has become an important carrier and life focus of people’s daily travel activities.By studying the distribution of POIs of public service facilities around Metro Line 10,using GIS to quantitatively analyze the surrounding formats of subway stations,discussing the functional attributes of subway stations,and discussing the distribution of urban functions from a new perspective,this paper provided guidance and advice for the construction of service facilities.
文摘Based on the background of ＂big-data-blowout＂, this thesis is about research on college English network teaching resources integration and utilization confronted with the chance of ＂data value, the data assets and data say＂, analyzing the inevitable challenge of ＂competition, digital divide and data privacy＂, discussing that the researchers should hold big data thinking of ＂peaks of road ＆ dances with wolves ＆ be interdependent＂, emphasizing the improvement from college English teacher＇s modem education technical information literacy[1] and putting forward to resource integration and utilization strategy in the era of big data.
文摘With the rapid development of computer technology, the current society has also entered the era of big data. Under this development background, the business activities of enterprises have also undergone profound changes under the influence of big data technology. At the same time, in the process of enterprise operation and development, financial and business integration, which integrates business work with financial management, has also become the main development trend and is highly praised by most enterprises. However, judging from the actual situation of social enterprises in our country, many large-scale financial integration activities cannot be carried out smoothly and can only be carried out reluctantly. The process is also facing many difficulties. In view of this, this paper analyzes the significance and difficulties of financial and business integration in the era of big data, and puts forward scientific and effective development strategies.
基金Under the auspices of Natural Science Foundation of China(No.41971166)。
文摘Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.
基金funding within the Wheat BigData Project(German Federal Ministry of Food and Agriculture,FKZ2818408B18)。
文摘Genome-wide association mapping studies(GWAS)based on Big Data are a potential approach to improve marker-assisted selection in plant breeding.The number of available phenotypic and genomic data sets in which medium-sized populations of several hundred individuals have been studied is rapidly increasing.Combining these data and using them in GWAS could increase both the power of QTL discovery and the accuracy of estimation of underlying genetic effects,but is hindered by data heterogeneity and lack of interoperability.In this study,we used genomic and phenotypic data sets,focusing on Central European winter wheat populations evaluated for heading date.We explored strategies for integrating these data and subsequently the resulting potential for GWAS.Establishing interoperability between data sets was greatly aided by some overlapping genotypes and a linear relationship between the different phenotyping protocols,resulting in high quality integrated phenotypic data.In this context,genomic prediction proved to be a suitable tool to study relevance of interactions between genotypes and experimental series,which was low in our case.Contrary to expectations,fewer associations between markers and traits were found in the larger combined data than in the individual experimental series.However,the predictive power based on the marker-trait associations of the integrated data set was higher across data sets.Therefore,the results show that the integration of medium-sized to Big Data is an approach to increase the power to detect QTL in GWAS.The results encourage further efforts to standardize and share data in the plant breeding community.
基金supported by the China Meteorological Administration Special Public Welfare Research Fund (GYHY201206012, GYHY201406016)the Climate Change Foundation of the China Meteorological Administration (CCSF201338)
文摘This paper analyzes the status of existing resources through extensive research and international cooperation on the basis of four typical global monthly surface temperature datasets including the climate research dataset of the University of East Anglia(CRUTEM3), the dataset of the U.S. National Climatic Data Center(GHCN-V3), the dataset of the U.S. National Aeronautics and Space Administration(GISSTMP), and the Berkeley Earth surface temperature dataset(Berkeley). China's first global monthly temperature dataset over land was developed by integrating the four aforementioned global temperature datasets and several regional datasets from major countries or regions. This dataset contains information from 9,519 stations worldwide of at least 20 years for monthly mean temperature, 7,073 for maximum temperature, and 6,587 for minimum temperature. Compared with CRUTEM3 and GHCN-V3, the station density is much higher particularly for South America, Africa,and Asia. Moreover, data from significantly more stations were available after the year 1990 which dramatically reduced the uncertainty of the estimated global temperature trend during 1990e2011. The integrated dataset can serve as a reliable data source for global climate change research.
文摘Energy Internet is deeply integrated by Internet concept, information technology and energy industry, and Energy Internet Big Data are one of core technologies that achieve energy-information-economic interconnection and improve the development and evolution of Energy Internet. This paper describes the concept and characteristics of Energy Internet Big Data, and feasibility of applying Energy Internet Big Data to integrated energy market. On this basis, as for integrated energy market and multi-subjects of Energy Internet, typical application and technical system based on Energy Internet Big Data in integrated energy market is put forward, which provides a reference for the analysis and decision of integrated energy market in Energy Internet.
基金financially supported by the General Program of the National Natural Science Foundation of China(No.52274326)the Fundamental Research Funds for the Central Universities (Nos.2125018 and 2225008)China Baowu Low Carbon Metallurgy Innovation Foundation(BWLCF202109)。
文摘Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.
文摘There are two key issues in distributed intrusion detection system,that is,maintaining load balance of system and protecting data integrity.To address these issues,this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation.A data allocation strategy based on capacity and workload is introduced to achieve local load balance,and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster.Moreover,data integrity is protected by using session reassemble and session partitioning.The simulation results show that the new model enjoys favorable advantages such as good load balance,higher detection rate and detection efficiency.
文摘In order to fulfill the requirements of mobile computing and big data environments, we design a mobile campus portal (MCP) providing mobile information services for users in both browser/ server and client/server modes. We present the topological structure of MCP, system infra- structure, application architecture and the data management (such as data integration, data format translation, data security control and so on) of our mobile campus portal in this paper. After online running in the past two years, our MCP has received good feedbacks from mobile users. In the future, we will focus on the research of data-mining technologies in our big data environment.
文摘With the strategy of media integration,transformation and upgrading of media has become an important issue.In the era of big data,due to the dual impact of data and technology,the media brings both challenges and opportunities.The paper traces the characteristics of the era of big data,focuses on analyzing the challenges and opportunities in the media industry,and analyzes the transformation and upgrading of the media from the dimensions of news production and distribution to better realize the social functions of media in the era of big data.Some strategic suggestions are put forward to improve the propagation effect.
文摘CTMRC - Curriculum and Teaching Materials Reform Commission; CTMRI - Curriculum and Teaching Materials Research Institute; ELT - English Language Teaching; MOE - Ministry of Education;
文摘In the era of network information development, the big data technology is constantly developing which is very important to the development of our society. With the development of technology, the demand for computer technology talents in China is increasing. Therefore, in the era of big data, it is the key to improve the teaching level of computer major in colleges and universities and provide more professional and technical talents for social development. In the traditional teaching of computer major, it is influenced by many factors, such as teachers outdated teaching ideas, imperfect computer teaching infrastructure, etc., which leads to the improvement of the teaching level of computer major in colleges and universities in China. Based on the era of big data technology development, it is the key to improve the teaching quality and efficiency by integrating computer major teaching with big data. Based on this, this paper takes college computer major teaching as the research object, relies on big data technology, analyzes the value of big data technology applied to college computer major teaching, and analyzes the practical application of big data technology in college computer major teaching based on big data integration cloud platform teaching mode.
基金supported by the National Social Science Foundation of China[No.16FJY008]the National Planning Office of Philosophy and Social Science[No.11801060]the Natural Science Foundation of Shandong Province[No.ZR2016FM26].
文摘We propose a novel filter for sparse big data,called an integrated autoencoder(IAE),which utilises auxiliary information to mitigate data sparsity.The proposed model achieves an appropriate balance between prediction accuracy,convergence speed,and complexity.We implement experiments on a GPS trajectory dataset,and the results demonstrate that the IAE is more accurate and robust than some state-of-the-art methods.
