To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments,which results from anomaly detection mechanisms in location-based service(LBS)applications,thi...To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments,which results from anomaly detection mechanisms in location-based service(LBS)applications,this paper proposes a novel virtual probe deployment method based on user behavioral feature analysis.The core idea is to circumvent LBS anomaly detection by mimicking real-user behavior patterns.First,we design an automated data extraction algorithm that recognizes graphical user interface(GUI)elements to collect spatio-temporal behavior data.Then,by analyzing the automatically collected user data,we identify normal users’spatio-temporal patterns and extract their features such as high-activity time windows and spatial clustering characteristics.Subsequently,an antidetection scheduling strategy is developed,integrating spatial clustering optimization,load-balanced allocation,and time window control to generate probe scheduling schemes.Additionally,a self-correction mechanism based on an exponential backoff strategy is implemented to rectify anomalous behaviors andmaintain system stability.Experiments in real-world environments demonstrate that the proposed method significantly outperforms baseline methods in terms of both probe ban rate and task completion rate,while maintaining high time efficiency.This study provides a more reliable and clandestine solution for geosocial data collection and lays the foundation for building more robust virtual probe systems.展开更多
To improve the effectiveness of dam safety monitoring database systems,the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mod...To improve the effectiveness of dam safety monitoring database systems,the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mode.The optimal data model was confirmed by identifying data objects,defining relations and reviewing entities.The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely.On this basis,a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established,for which factual tables and dimensional tables have been designed.Finally,based on service design and user interface design,the dam safety monitoring system has been developed with Delphi as the development tool.This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design.It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.展开更多
The question of how to choose a copula model that best fits a given dataset is a predominant limitation of the copula approach, and the present study aims to investigate the techniques of goodness-of-fit tests for mul...The question of how to choose a copula model that best fits a given dataset is a predominant limitation of the copula approach, and the present study aims to investigate the techniques of goodness-of-fit tests for multi-dimensional copulas. A goodness-of-fit test based on Rosenblatt＇s transformation was mathematically expanded from two dimensions to three dimensions and procedures of a bootstrap version of the test were provided. Through stochastic copula simulation, an empirical application of historical drought data at the Lintong Gauge Station shows that the goodness-of-fit tests perform well, revealing that both trivariate Gaussian and Student t copulas are acceptable for modeling the dependence structures of the observed drought duration, severity, and peak. The goodness-of-fit tests for multi-dimensional copulas can provide further support and help a lot in the potential applications of a wider range of copulas to describe the associations of correlated hydrological variables. However, for the application of copulas with the number of dimensions larger than three, more complicated computational efforts as well as exploration and parameterization of corresponding copulas are required.展开更多
With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis o...With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.展开更多
To reduce the risk of traffic accidents significantly caused by the speeding behavior of electric bicycles,this study focuses on the Beijing Yizhuang Economic and Tech-nological Development Zone.This work relies on hi...To reduce the risk of traffic accidents significantly caused by the speeding behavior of electric bicycles,this study focuses on the Beijing Yizhuang Economic and Tech-nological Development Zone.This work relies on high-precision shared electric bicycle Global Positioning System trajectory data,integrating a spatiotemporal analysis model and geographic information system(GIS)technol-ogy to explore the spatial and temporal variability law and formation mechanism of speeding behavior.Through data preprocessing,speeding events are identified,and weekday features are extracted.Four periods are identified:morning peak,midday minipeak,evening peak,and nighttime flat peak.Using the GIS platform,global spatial autocorrela-tion and local clustering analysis are conducted to identify the spatial clustering characteristics of speeding behaviors and hotspot areas.The coldspot and hotspot patterns of speeding events and the dynamic trajectories of their evolu-tion are analyzed using spatiotemporal cube technology.The results show that speeding behaviors are strongly corre-lated with the commuting peak in time and spatially concen-trated in the intersections of urban main roads,the periphery of commercial complexes,and industrial parks,with a dif-fusion tendency.The results of this study provide novel in-sights into the research and analysis of the spatial and tempo-ral characteristics of speeding risk behaviors of electric bi-cycles and effective technical support for nonmotorized traf-fic safety management.展开更多
Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension all...Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.展开更多
This study utilizes wet/dry cyclic corrosion testing combined with corrosion big data technology to investigate the mechanism by which chloride ions(Cl^(-))influence the corrosion behavior of 650 MPa high-strength low...This study utilizes wet/dry cyclic corrosion testing combined with corrosion big data technology to investigate the mechanism by which chloride ions(Cl^(-))influence the corrosion behavior of 650 MPa high-strength low-alloy(HSLA)steel in industrially polluted environments.The corrosion process of 650 MPa HSLA steel occurred in two distinct stages:an initial corrosion stage and a stable corrosion stage.During the initial phase,the weight loss rate increased rapidly owing to the instability of the rust layer.Notably,this study demonstrated that 650 MPa HSLA steel exhibited superior corrosion resistance in Cl-containing environments.The formation of a corrosion-product film eventually reduced the weight-loss rate.However,the intrusion of Cl^(-)at increasing concentrations gradually destabilized theα/γ^(*)phases of the rust layer,leading to a looser structure and lower polarization resistance(R_(p)).The application of corrosion big data technology in this study facilitated the validation and analysis of the experimental results,offering new insights into the corrosion mechanisms of HSLA steel in chloride-rich environments.展开更多
The advent of the digital era has provided unprecedented opportunities for businesses to collect and analyze customer behavior data. Precision marketing, as a key means to improve marketing efficiency, highly depends ...The advent of the digital era has provided unprecedented opportunities for businesses to collect and analyze customer behavior data. Precision marketing, as a key means to improve marketing efficiency, highly depends on a deep understanding of customer behavior. This study proposes a theoretical framework for multi-dimensional customer behavior analysis, aiming to comprehensively capture customer behavioral characteristics in the digital environment. This framework integrates concepts of multi-source data including transaction history, browsing trajectories, social media interactions, and location information, constructing a theoretically more comprehensive customer profile. The research discusses the potential applications of this theoretical framework in precision marketing scenarios such as personalized recommendations, cross-selling, and customer churn prevention. Through analysis, the study points out that multi-dimensional analysis may significantly improve the targeting and theoretical conversion rates of marketing activities. However, the research also explores theoretical challenges that may be faced in the application process, such as data privacy and information overload, and proposes corresponding conceptual coping strategies. This study provides a new theoretical perspective on how businesses can optimize marketing decisions using big data thinking while respecting customer privacy, laying a foundation for future empirical research.展开更多
Background:The phenomenon of multimorbidity in chronic diseases among the elderly is prevalent,and its significant association with depression poses a serious threat to the physical and mental health of older adults.C...Background:The phenomenon of multimorbidity in chronic diseases among the elderly is prevalent,and its significant association with depression poses a serious threat to the physical and mental health of older adults.Current research on the associations between 24-hour movement behaviors(including physical activity,sedentary behavior,and sleep)and depression has largely been confined to examining the effects of single behaviors,overlooking the intrinsic compositional nature and interrelationships among these behaviors.Therefore,it is necessary to investigate the integrated effects of 24-hour movement behaviors on depression in older adults with multimorbidity from a holistic,compositional perspective.Methods:From November 2024 to April 2025,a total of 226 older adult patients with multimorbidity were recruited from a tertiary hospital in Changzhou City.Data were collected using a general information questionnaire,the International Physical Activity Questionnaire–Short Form(IPAQ-SF),the Pittsburgh Sleep Quality Index(PSQI),and the Patient Health Questionnaire-9(PHQ-9).Compositional data analysis and isotemporal substitution models were employed for statistical analysis.Results:The mean daily durations of Light-Intensity Physical Activity(LPA),Moderate-to-Vigorous Physical Activity(MVPA),Sedentary Behavior(SB),and Sleep(SLP)in older adults with multimorbidity were 402.48 min,12.04 min,511.52 min,and 458.68 min,respectively.The prevalence of depressive symptoms was 37.6%.Compositional data analysis revealed that SB was positively associated with depression(βSB=1.005,P=0.006),while SLP was negatively associated with depression(βSLP=−1.736,P<0.001).No statistically significant associations were found between MVPA or LPA and depression(P>0.05).In the 10-minute isotemporal substitution model,replacing SB with any other behavioral component was associated with a decrease in depression scores.Conversely,substituting SLP with either LPA or SB resulted in an increase in depression scores,while substituting SLP with MVPA led to a decrease in depression scores.The dose-response analysis revealed that,among the isotemporal substitution effects,replacing SB with SLP and replacing SLP with MVPA were the substitution pathways associated with the most rapid decline in depression scores,representing the greatest beneficial health effects.Conclusion:The prevalence of depression is notably high among older adults with multimorbidity.Reducing daily sedentary behavior(SB),maintaining adequate sleep(SLP),and increasing moderate-to-vigorous physical activity(MVPA)can improve depressive symptoms and enhance overall health in this population.展开更多
The adoption and usage of electric vehicles(EVs)have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution.As a part of the smart grid,EVs can provide valuable ancillary se...The adoption and usage of electric vehicles(EVs)have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution.As a part of the smart grid,EVs can provide valuable ancillary services beyond consumers of electricity.However,EVs are gradually considered as nonnegligible loads due to their increasing penetration,which may result in negative effects such as voltage deviations,lines saturation,and power losses.Relationship and interaction among EVs,charging stations,and micro grid have to be considered in the next generation of smart grid.Therefore,the topic of smart charging has been the focus of many works where a wide range of control methods have been developed.As one of the bases of simulation,the EV charging behavior and characteristics have also become the focus of many studies.In this work,we review the charging behavior of EVs from the aspects of data,model,and control.We provide the links for most of the data sets reviewed in this work,based on which interested researchers can easily access these data for further investigation.展开更多
Communication behavior recognition is an issue with increasingly importance in the antiterrorism and national defense area.However,the sensing data obtained in actual environment is often not sufficient to accurately ...Communication behavior recognition is an issue with increasingly importance in the antiterrorism and national defense area.However,the sensing data obtained in actual environment is often not sufficient to accurately analyze the communication behavior.Traditional means can hardly utilize the scarce and crude spectrum sensing data captured in a real scene.Thus,communication behavior recognition using raw sensing data under smallsample condition has become a new challenge.In this paper,a data enhanced communication behavior recognition(DECBR)scheme is proposed to meet this challenge.Firstly,a preprocessing method is designed to make the raw spectrum data suitable for the proposed scheme.Then,an adaptive convolutional neural network structure is exploited to carry out communication behavior recognition.Moreover,DCGAN is applied to support data enhancement,which realize communication behavior recognition under small-sample condition.Finally,the scheme is verified by experiments under different data size.The results show that the DECBR scheme can greatly improve the accuracy and efficiency of behavior recognition under smallsample condition.展开更多
Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering p...Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers’driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1%drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency.展开更多
In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and impl...In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.展开更多
The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data s...The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.展开更多
This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-...This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-combat behavior data.The technique of air-combat behavior data mining based on the truncation method is proposed to discover the air-combat rules or patterns.The simulation platform of the air-combat behavior data mining that supports two fighters is implemented.The simulation experimental results show that the proposed air-combat behavior data mining technique based on the truncation method is feasible whether in efficiency or in effectiveness.展开更多
Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and d...Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.展开更多
This paper presents a driver behavior analysis using microscopic video data measures including vehicle speed, lane-changing ratio, and time to collision. An analytical framework was developed to evaluate the effect of...This paper presents a driver behavior analysis using microscopic video data measures including vehicle speed, lane-changing ratio, and time to collision. An analytical framework was developed to evaluate the effect of adverse winter weather conditions on highway driving behavior based on automated （computer） and manual methods. The research was conducted through two case studies. The first case study was conducted to evaluate the feasibility of applying an au- tomated approach to extracting driver behavior data based on 15 video recordings obtained in the winter 2013 at three dif- ferent locations on the Don Valley Parkway in Toronto, Canada. A comparison was made between the automated approach and manual approach, and issues in collecting data using the automated approach under winter conditions were identified. The second case study was based on high quality data collected in the winter 2014, at a location on Highway 25 in Montreal, Canada. The results demonstrate the effectiveness of the automated analytical framework in analyzing driver behavior, as well as evaluating the impact of adverse winter weather conditions on driver behavior. This approach could be applied to evaluate winter maintenance strategies and crash risk on highways during adverse winter weather conditions.展开更多
In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/ C clothing enterprise in China was chosen. The target experimental database containing 2000 ...In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/ C clothing enterprise in China was chosen. The target experimental database containing 2000 data records was obtained based on web service logs of sample enterprise. By means of clustering algorithm of Clementine Data Mining Software, K-means model was set up and 8 clusters of consumer were concluded. Meanwhile, the implicit information existed in consumer＇s characters and preferences for clothing was found. At last, 31 valuable association rules among casual wear, formal wear, and tie-in products were explored by using web analysis and Aprior algorithm. This finding will help to better understand the nature of online apparel consumption behavior and make a good progress in personalization and intelligent recommendation strategies.展开更多
Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a...Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a Web forensic framework proposed in the literature and defined the metrics of footprint,track and movement.Data were obtained from user clickstreams provided by a real estate site’s administrators.There were two phases of data analysis with the first phase on navigation behavior based on user footprints and tracks,and the second phase on navigational transition patterns based on user movements.Findings:Preliminary results suggest that the apartment pages were heavily-trafficked while the agent pages and related information pages were underused to a great extent.Navigation within the same category of pages was prevalent,especially when users navigated among the regional apartment listings.However,navigation of these pages was found to be inefficient.Research limitations:The suggestions for navigation design optimization provided in the paper are specific to this website,and their applicability to other online environments needs to be verified.Preference predications or personal recommendations are not made during the current stage of research.Practical implications:Our clickstream data analysis results offer a base for future research.Meanwhile,website administrators and managers can make better use of the readily available clickstream data to evaluate the effectiveness and efficiency of their site navigation design.Originality/value:Our empirical study is valuable to those seeking analysis metrics for evaluating and improving user navigation experience on informational websites based on clickstream data.Our attempts to analyze the log file in terms of footprint,track and movement will enrich the utilization of such trace data to engender a deeper understanding of users’within-site navigation behavior.展开更多
Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outl...Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.展开更多
基金supported by theNationalNatural Science Foundation of China(No.U23A20305)National Key Research and Development Program of China(No.2022YFB3102900)+1 种基金Innovation Scientists and Technicians Troop Construction Projects of Henan Province,China(No.254000510007)Key Research and Development Project of Henan Province(No.221111321200).
文摘To address the challenge of low survival rates and limited data collection efficiency in current virtual probe deployments,which results from anomaly detection mechanisms in location-based service(LBS)applications,this paper proposes a novel virtual probe deployment method based on user behavioral feature analysis.The core idea is to circumvent LBS anomaly detection by mimicking real-user behavior patterns.First,we design an automated data extraction algorithm that recognizes graphical user interface(GUI)elements to collect spatio-temporal behavior data.Then,by analyzing the automatically collected user data,we identify normal users’spatio-temporal patterns and extract their features such as high-activity time windows and spatial clustering characteristics.Subsequently,an antidetection scheduling strategy is developed,integrating spatial clustering optimization,load-balanced allocation,and time window control to generate probe scheduling schemes.Additionally,a self-correction mechanism based on an exponential backoff strategy is implemented to rectify anomalous behaviors andmaintain system stability.Experiments in real-world environments demonstrate that the proposed method significantly outperforms baseline methods in terms of both probe ban rate and task completion rate,while maintaining high time efficiency.This study provides a more reliable and clandestine solution for geosocial data collection and lays the foundation for building more robust virtual probe systems.
基金supported by the National Natural Science Foundation of China(Grant No.50539010,50539110,50579010,50539030 and 50809025)
文摘To improve the effectiveness of dam safety monitoring database systems,the development process of a multi-dimensional conceptual data model was analyzed and a logic design wasachieved in multi-dimensional database mode.The optimal data model was confirmed by identifying data objects,defining relations and reviewing entities.The conversion of relations among entities to external keys and entities and physical attributes to tables and fields was interpreted completely.On this basis,a multi-dimensional database that reflects the management and analysis of a dam safety monitoring system on monitoring data information has been established,for which factual tables and dimensional tables have been designed.Finally,based on service design and user interface design,the dam safety monitoring system has been developed with Delphi as the development tool.This development project shows that the multi-dimensional database can simplify the development process and minimize hidden dangers in the database structure design.It is superior to other dam safety monitoring system development models and can provide a new research direction for system developers.
基金supported by the Program of Introducing Talents of Disciplines to Universities of the Ministry of Education and State Administration of the Foreign Experts Affairs of China (the 111 Project, Grant No.B08048)the Special Basic Research Fund for Methodology in Hydrology of the Ministry of Sciences and Technology of China (Grant No. 2011IM011000)
文摘The question of how to choose a copula model that best fits a given dataset is a predominant limitation of the copula approach, and the present study aims to investigate the techniques of goodness-of-fit tests for multi-dimensional copulas. A goodness-of-fit test based on Rosenblatt＇s transformation was mathematically expanded from two dimensions to three dimensions and procedures of a bootstrap version of the test were provided. Through stochastic copula simulation, an empirical application of historical drought data at the Lintong Gauge Station shows that the goodness-of-fit tests perform well, revealing that both trivariate Gaussian and Student t copulas are acceptable for modeling the dependence structures of the observed drought duration, severity, and peak. The goodness-of-fit tests for multi-dimensional copulas can provide further support and help a lot in the potential applications of a wider range of copulas to describe the associations of correlated hydrological variables. However, for the application of copulas with the number of dimensions larger than three, more complicated computational efforts as well as exploration and parameterization of corresponding copulas are required.
文摘With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.
基金The National Natural Science Foundation of China(No. 52072012)。
文摘To reduce the risk of traffic accidents significantly caused by the speeding behavior of electric bicycles,this study focuses on the Beijing Yizhuang Economic and Tech-nological Development Zone.This work relies on high-precision shared electric bicycle Global Positioning System trajectory data,integrating a spatiotemporal analysis model and geographic information system(GIS)technol-ogy to explore the spatial and temporal variability law and formation mechanism of speeding behavior.Through data preprocessing,speeding events are identified,and weekday features are extracted.Four periods are identified:morning peak,midday minipeak,evening peak,and nighttime flat peak.Using the GIS platform,global spatial autocorrela-tion and local clustering analysis are conducted to identify the spatial clustering characteristics of speeding behaviors and hotspot areas.The coldspot and hotspot patterns of speeding events and the dynamic trajectories of their evolu-tion are analyzed using spatiotemporal cube technology.The results show that speeding behaviors are strongly corre-lated with the commuting peak in time and spatially concen-trated in the intersections of urban main roads,the periphery of commercial complexes,and industrial parks,with a dif-fusion tendency.The results of this study provide novel in-sights into the research and analysis of the spatial and tempo-ral characteristics of speeding risk behaviors of electric bi-cycles and effective technical support for nonmotorized traf-fic safety management.
基金supported by the National Natural Science Foundation of China(No.61901465,82222032,82172050).
文摘Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.
基金financially supported by the National Natural Science Foundation of China(Nos.52104319 and 52374323)。
文摘This study utilizes wet/dry cyclic corrosion testing combined with corrosion big data technology to investigate the mechanism by which chloride ions(Cl^(-))influence the corrosion behavior of 650 MPa high-strength low-alloy(HSLA)steel in industrially polluted environments.The corrosion process of 650 MPa HSLA steel occurred in two distinct stages:an initial corrosion stage and a stable corrosion stage.During the initial phase,the weight loss rate increased rapidly owing to the instability of the rust layer.Notably,this study demonstrated that 650 MPa HSLA steel exhibited superior corrosion resistance in Cl-containing environments.The formation of a corrosion-product film eventually reduced the weight-loss rate.However,the intrusion of Cl^(-)at increasing concentrations gradually destabilized theα/γ^(*)phases of the rust layer,leading to a looser structure and lower polarization resistance(R_(p)).The application of corrosion big data technology in this study facilitated the validation and analysis of the experimental results,offering new insights into the corrosion mechanisms of HSLA steel in chloride-rich environments.
文摘The advent of the digital era has provided unprecedented opportunities for businesses to collect and analyze customer behavior data. Precision marketing, as a key means to improve marketing efficiency, highly depends on a deep understanding of customer behavior. This study proposes a theoretical framework for multi-dimensional customer behavior analysis, aiming to comprehensively capture customer behavioral characteristics in the digital environment. This framework integrates concepts of multi-source data including transaction history, browsing trajectories, social media interactions, and location information, constructing a theoretically more comprehensive customer profile. The research discusses the potential applications of this theoretical framework in precision marketing scenarios such as personalized recommendations, cross-selling, and customer churn prevention. Through analysis, the study points out that multi-dimensional analysis may significantly improve the targeting and theoretical conversion rates of marketing activities. However, the research also explores theoretical challenges that may be faced in the application process, such as data privacy and information overload, and proposes corresponding conceptual coping strategies. This study provides a new theoretical perspective on how businesses can optimize marketing decisions using big data thinking while respecting customer privacy, laying a foundation for future empirical research.
基金supported by the 2025 Jiangsu Province Graduate Student Practice Innovation Program(No.SJCX25_1696)the 2024 Changzhou University Educational Research Project(No.GJY2024009).
文摘Background:The phenomenon of multimorbidity in chronic diseases among the elderly is prevalent,and its significant association with depression poses a serious threat to the physical and mental health of older adults.Current research on the associations between 24-hour movement behaviors(including physical activity,sedentary behavior,and sleep)and depression has largely been confined to examining the effects of single behaviors,overlooking the intrinsic compositional nature and interrelationships among these behaviors.Therefore,it is necessary to investigate the integrated effects of 24-hour movement behaviors on depression in older adults with multimorbidity from a holistic,compositional perspective.Methods:From November 2024 to April 2025,a total of 226 older adult patients with multimorbidity were recruited from a tertiary hospital in Changzhou City.Data were collected using a general information questionnaire,the International Physical Activity Questionnaire–Short Form(IPAQ-SF),the Pittsburgh Sleep Quality Index(PSQI),and the Patient Health Questionnaire-9(PHQ-9).Compositional data analysis and isotemporal substitution models were employed for statistical analysis.Results:The mean daily durations of Light-Intensity Physical Activity(LPA),Moderate-to-Vigorous Physical Activity(MVPA),Sedentary Behavior(SB),and Sleep(SLP)in older adults with multimorbidity were 402.48 min,12.04 min,511.52 min,and 458.68 min,respectively.The prevalence of depressive symptoms was 37.6%.Compositional data analysis revealed that SB was positively associated with depression(βSB=1.005,P=0.006),while SLP was negatively associated with depression(βSLP=−1.736,P<0.001).No statistically significant associations were found between MVPA or LPA and depression(P>0.05).In the 10-minute isotemporal substitution model,replacing SB with any other behavioral component was associated with a decrease in depression scores.Conversely,substituting SLP with either LPA or SB resulted in an increase in depression scores,while substituting SLP with MVPA led to a decrease in depression scores.The dose-response analysis revealed that,among the isotemporal substitution effects,replacing SB with SLP and replacing SLP with MVPA were the substitution pathways associated with the most rapid decline in depression scores,representing the greatest beneficial health effects.Conclusion:The prevalence of depression is notably high among older adults with multimorbidity.Reducing daily sedentary behavior(SB),maintaining adequate sleep(SLP),and increasing moderate-to-vigorous physical activity(MVPA)can improve depressive symptoms and enhance overall health in this population.
基金This work was supported in part by the National Key Research and Development Program of China(No.2016YFB0901900)the National Natural Science Foundation of China under grants(No.61673229)the 111 International Collaboration Project of China(No.BP2018006).
文摘The adoption and usage of electric vehicles(EVs)have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution.As a part of the smart grid,EVs can provide valuable ancillary services beyond consumers of electricity.However,EVs are gradually considered as nonnegligible loads due to their increasing penetration,which may result in negative effects such as voltage deviations,lines saturation,and power losses.Relationship and interaction among EVs,charging stations,and micro grid have to be considered in the next generation of smart grid.Therefore,the topic of smart charging has been the focus of many works where a wide range of control methods have been developed.As one of the bases of simulation,the EV charging behavior and characteristics have also become the focus of many studies.In this work,we review the charging behavior of EVs from the aspects of data,model,and control.We provide the links for most of the data sets reviewed in this work,based on which interested researchers can easily access these data for further investigation.
基金supported by the National Natural Science Foundation of China(No.61971439 and No.61702543)the Natural Science Foundation of the Jiangsu Province of China(No.BK20191329)+1 种基金the China Postdoctoral Science Foundation Project(No.2019T120987)the Startup Foundation for Introducing Talent of NUIST(No.2020r100).
文摘Communication behavior recognition is an issue with increasingly importance in the antiterrorism and national defense area.However,the sensing data obtained in actual environment is often not sufficient to accurately analyze the communication behavior.Traditional means can hardly utilize the scarce and crude spectrum sensing data captured in a real scene.Thus,communication behavior recognition using raw sensing data under smallsample condition has become a new challenge.In this paper,a data enhanced communication behavior recognition(DECBR)scheme is proposed to meet this challenge.Firstly,a preprocessing method is designed to make the raw spectrum data suitable for the proposed scheme.Then,an adaptive convolutional neural network structure is exploited to carry out communication behavior recognition.Moreover,DCGAN is applied to support data enhancement,which realize communication behavior recognition under small-sample condition.Finally,the scheme is verified by experiments under different data size.The results show that the DECBR scheme can greatly improve the accuracy and efficiency of behavior recognition under smallsample condition.
基金The National Natural Science Foundation of China(No.71641005)the National Key Research and Development Program of China(No.2018YFB1601105)
文摘Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers’driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1%drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency.
文摘In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.
基金Supported by the National 973 Program of China(No.2006CB701305,No.2007CB310804)the National Natural Science Fundation of China(No.60743001)+1 种基金the Best National Thesis Fundation (No.2005047)the National New Century Excellent Talent Fundation (No.NCET-06-0618)
文摘The advanced data mining technologies and the large quantities of remotely sensed Imagery provide a data mining opportunity with high potential for useful results. Extracting interesting patterns and rules from data sets composed of images and associated ground data can be of importance in object identification, community planning, resource discovery and other areas. In this paper, a data field is presented to express the observed spatial objects and conduct behavior mining on them. First, most of the important aspects are discussed on behavior mining and its implications for the future of data mining. Furthermore, an ideal framework of the behavior mining system is proposed in the network environment. Second, the model of behavior mining is given on the observed spatial objects, including the objects described by the first feature data field and the main feature data field by means of the potential function. Finally, a case study about object identification in public is given and analyzed. The experimental results show that the new model is feasible in behavior mining.
文摘This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-combat behavior data.The technique of air-combat behavior data mining based on the truncation method is proposed to discover the air-combat rules or patterns.The simulation platform of the air-combat behavior data mining that supports two fighters is implemented.The simulation experimental results show that the proposed air-combat behavior data mining technique based on the truncation method is feasible whether in efficiency or in effectiveness.
基金Project(2010-0020163) supported by Key Research Institute Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology, Korea
文摘Similarity measure design for discrete data group was proposed. Similarity measure design for continuous membership function was also carried out. Proposed similarity measures were designed based on fuzzy number and distance measure, and were proved. To calculate the degree of similarity of discrete data, relative degree between data and total distribution was obtained. Discrete data similarity measure was completed with combination of mentioned relative degrees. Power interconnected system with multi characteristics was considered to apply discrete similarity measure. Naturally, similarity measure was extended to multi-dimensional similarity measure case, and applied to bus clustering problem.
文摘This paper presents a driver behavior analysis using microscopic video data measures including vehicle speed, lane-changing ratio, and time to collision. An analytical framework was developed to evaluate the effect of adverse winter weather conditions on highway driving behavior based on automated （computer） and manual methods. The research was conducted through two case studies. The first case study was conducted to evaluate the feasibility of applying an au- tomated approach to extracting driver behavior data based on 15 video recordings obtained in the winter 2013 at three dif- ferent locations on the Don Valley Parkway in Toronto, Canada. A comparison was made between the automated approach and manual approach, and issues in collecting data using the automated approach under winter conditions were identified. The second case study was based on high quality data collected in the winter 2014, at a location on Highway 25 in Montreal, Canada. The results demonstrate the effectiveness of the automated analytical framework in analyzing driver behavior, as well as evaluating the impact of adverse winter weather conditions on driver behavior. This approach could be applied to evaluate winter maintenance strategies and crash risk on highways during adverse winter weather conditions.
基金Scientific Research Program Funded by Shaanxi Provincial Education Department,China(No.2013JK0749)
文摘In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/ C clothing enterprise in China was chosen. The target experimental database containing 2000 data records was obtained based on web service logs of sample enterprise. By means of clustering algorithm of Clementine Data Mining Software, K-means model was set up and 8 clusters of consumer were concluded. Meanwhile, the implicit information existed in consumer＇s characters and preferences for clothing was found. At last, 31 valuable association rules among casual wear, formal wear, and tie-in products were explored by using web analysis and Aprior algorithm. This finding will help to better understand the nature of online apparel consumption behavior and make a good progress in personalization and intelligent recommendation strategies.
基金supported by the National Natural Science Foundation of China(Grant No.:71203163)the Foundation for Humanities and Social Sciences of the Chinese Ministry of Education(Grant No.:12YJC870011)
文摘Purpose:The goal of our research is to suggest specific Web metrics that are useful for evaluating and improving user navigation experience on informational websites.Design/methodology/approach:We revised metrics in a Web forensic framework proposed in the literature and defined the metrics of footprint,track and movement.Data were obtained from user clickstreams provided by a real estate site’s administrators.There were two phases of data analysis with the first phase on navigation behavior based on user footprints and tracks,and the second phase on navigational transition patterns based on user movements.Findings:Preliminary results suggest that the apartment pages were heavily-trafficked while the agent pages and related information pages were underused to a great extent.Navigation within the same category of pages was prevalent,especially when users navigated among the regional apartment listings.However,navigation of these pages was found to be inefficient.Research limitations:The suggestions for navigation design optimization provided in the paper are specific to this website,and their applicability to other online environments needs to be verified.Preference predications or personal recommendations are not made during the current stage of research.Practical implications:Our clickstream data analysis results offer a base for future research.Meanwhile,website administrators and managers can make better use of the readily available clickstream data to evaluate the effectiveness and efficiency of their site navigation design.Originality/value:Our empirical study is valuable to those seeking analysis metrics for evaluating and improving user navigation experience on informational websites based on clickstream data.Our attempts to analyze the log file in terms of footprint,track and movement will enrich the utilization of such trace data to engender a deeper understanding of users’within-site navigation behavior.
基金Project(61362021)supported by the National Natural Science Foundation of ChinaProject(2016GXNSFAA380149)supported by Natural Science Foundation of Guangxi Province,China+1 种基金Projects(2016YJCXB02,2017YJCX34)supported by Innovation Project of GUET Graduate Education,ChinaProject(2011KF11)supported by the Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education,China
文摘Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.
