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
Based on the character of upward slicing backfilling mining and the condition of Gonggeyingzi coal mine in Inner Mongolia,this paper describes the studies of the strata behavior and the stress distribution in the proc...Based on the character of upward slicing backfilling mining and the condition of Gonggeyingzi coal mine in Inner Mongolia,this paper describes the studies of the strata behavior and the stress distribution in the process of backfilling mining in extra-thick coal seams.This was achieved by setting up and analyzing the elastic foundation beam model using the ABAQUS software.The results show that:(1) With the gradual mining of different slices,the roof appears to bend continuously but does not break.The vertical stress in the roof decreases and the decreasing amplitude reduces,while the tensile stress in the roof grows with the mining slices and the maximum tensile stress will not exceed the allowable tensile stress.(2) The front vertical stress at the working face exceeds the rear vertical stress and both show a trend of decrease with decreasing amplitude of decrease.(3) The slices mined early have more influence on the surrounding rock than the later ones.Similarly,the strata behavior experiences the same trend.The field measured data show that the roof does not break during the mining process,which is consistent with the conclusion.展开更多
Researchers usually detect insider threats by analyzing user behavior.The time information of user behavior is an important concern in internal threat detection.Existing works on insider threat detection fail to make ...Researchers usually detect insider threats by analyzing user behavior.The time information of user behavior is an important concern in internal threat detection.Existing works on insider threat detection fail to make full use of the time information,which leads to their poor detection performance.In this paper,we propose a novel behavioral feature extraction scheme:we implicitly encode absolute time information in the behavioral feature sequences and use a feature sequence construction method taking covariance into account to make our scheme adaptive to users.We select Stacked Bidirectional LSTM and Feedforward Neural Network to build a deep learning-based insider threat detection model:Behavior Rhythm Insider Threat Detection(BRITD).BRITD is universally applicable to various insider threat scenarios,and it has good insider threat detection performance:it achieves an AUC of 0.9730 and a precision of 0.8072 with the CMU CERT dataset,which exceeds all baselines.展开更多
The frame rate of conventional vision systems is restricted to the video signal formats （e.g., NTSC 30 fps and PAL 25 fps） that are designed on the basis of the characteristics of the human eye, which implies that t...The frame rate of conventional vision systems is restricted to the video signal formats （e.g., NTSC 30 fps and PAL 25 fps） that are designed on the basis of the characteristics of the human eye, which implies that the processing speed of these systems is limited to the recognition speed of the human eye. However, there is a strong demand for real-time high-speed vision sensors in many application fields, such as factory automation, biomedicine, and robotics, where high-speed operations are carried out. These high-speed operations can be tracked and inspected by using high-speed vision systems with intelligent sensors that work at hundreds of Hertz or more, especially when the operation is difficult to observe with the human eye. This paper reviews advances in developing real-time high Speed vision systems and their applications in various fields, such as intelligent logging systems, vibration dynamics sensing, vision-based mechanical control, three-dimensional measurement/automated visual inspection, vision-based human interface, and biomedical applications.展开更多
E-learning approaches are one of the most important learning platforms for the learner through electronic equipment.Such study techniques are useful for other groups of learners such as the crowd,pedestrian,sports,tra...E-learning approaches are one of the most important learning platforms for the learner through electronic equipment.Such study techniques are useful for other groups of learners such as the crowd,pedestrian,sports,transports,communication,emergency services,management systems and education sectors.E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and methods.Many of them are currently working on this domain to fulfill the requirements of industry and the environment.In this paper,we proposed a method for pedestrian behavior mining of aerial data,using deep flow feature,graph mining technique,and convocational neural network.For input data,the state-of-the-art crowd activity University of Minnesota(UMN)dataset is adopted,which contains the aerial indoor and outdoor view of the pedestrian,for simplification of extra information and computational cost reduction the pre-processing is applied.Deep flow features are extracted to find more accurate information.Furthermore,to deal with repetition in features data and features mining the graph mining algorithm is applied,while Convolution Neural Network(CNN)is applied for pedestrian behavior mining.The proposed method shows 84.50%of mean accuracy and a 15.50%of error rate.Therefore,the achieved results show more accuracy as compared to state-ofthe-art classification algorithms such as decision tree,artificial neural network(ANN).展开更多
Objective Present a new features selection algorithm. Methods based on rule induction and field knowledge. Results This algorithm can be applied in catching dataflow when detecting network intrusions, only the sub ...Objective Present a new features selection algorithm. Methods based on rule induction and field knowledge. Results This algorithm can be applied in catching dataflow when detecting network intrusions, only the sub dataset including discriminating features is catched. Then the time spend in following behavior patterns mining is reduced and the patterns mined are more precise. Conclusion The experiment results show that the feature subset catched by this algorithm is more informative and the dataset’s quantity is reduced significantly.展开更多
Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy.It necessitates an efficient detection mechanism to identify the vehicles with ...Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy.It necessitates an efficient detection mechanism to identify the vehicles with fake license plates accurately,and further explore the motives through discerning the behaviors of cloned vehicles.The ubiquitous inspection spots that deployed in the city have been collecting moving information of passing vehicles,which opens up a new opportunity for cloned vehicle detection.Existing detection methods cannot detect the cloned vehicle effectively due to that they use the fixed speed threshold.In this paper,we propose a two-phase framework,called CVDF,to detect cloned vehicles and discriminate behavior patterns of vehicles that use the same plate number.In the detection phase,cloned vehicles are identified based on speed thresholds extracted from historical trajectory and behavior abnormality analysis within the local neighborhood.In the behavior analysis phase,consider the traces of vehicles that uses the same license plate will be mixed together,we aim to differentiate the trajectories through matching degree-based clustering and then extract frequent temporal behavior patterns.The experimental results on the real-world data show that CVDF framework has high detection precision and could reveal cloned vehicles’behavior effectively.Our proposal provides a scientific basis for traffic management authority to solve the crime of cloned vehicle.展开更多
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sponsored by the National Key Basic Research Program of China (No.2013CB227905)Qinglan Projects of Jiangsu Province
文摘Based on the character of upward slicing backfilling mining and the condition of Gonggeyingzi coal mine in Inner Mongolia,this paper describes the studies of the strata behavior and the stress distribution in the process of backfilling mining in extra-thick coal seams.This was achieved by setting up and analyzing the elastic foundation beam model using the ABAQUS software.The results show that:(1) With the gradual mining of different slices,the roof appears to bend continuously but does not break.The vertical stress in the roof decreases and the decreasing amplitude reduces,while the tensile stress in the roof grows with the mining slices and the maximum tensile stress will not exceed the allowable tensile stress.(2) The front vertical stress at the working face exceeds the rear vertical stress and both show a trend of decrease with decreasing amplitude of decrease.(3) The slices mined early have more influence on the surrounding rock than the later ones.Similarly,the strata behavior experiences the same trend.The field measured data show that the roof does not break during the mining process,which is consistent with the conclusion.
基金supported by the National Key Research and Development Program of China.
文摘Researchers usually detect insider threats by analyzing user behavior.The time information of user behavior is an important concern in internal threat detection.Existing works on insider threat detection fail to make full use of the time information,which leads to their poor detection performance.In this paper,we propose a novel behavioral feature extraction scheme:we implicitly encode absolute time information in the behavioral feature sequences and use a feature sequence construction method taking covariance into account to make our scheme adaptive to users.We select Stacked Bidirectional LSTM and Feedforward Neural Network to build a deep learning-based insider threat detection model:Behavior Rhythm Insider Threat Detection(BRITD).BRITD is universally applicable to various insider threat scenarios,and it has good insider threat detection performance:it achieves an AUC of 0.9730 and a precision of 0.8072 with the CMU CERT dataset,which exceeds all baselines.
文摘The frame rate of conventional vision systems is restricted to the video signal formats （e.g., NTSC 30 fps and PAL 25 fps） that are designed on the basis of the characteristics of the human eye, which implies that the processing speed of these systems is limited to the recognition speed of the human eye. However, there is a strong demand for real-time high-speed vision sensors in many application fields, such as factory automation, biomedicine, and robotics, where high-speed operations are carried out. These high-speed operations can be tracked and inspected by using high-speed vision systems with intelligent sensors that work at hundreds of Hertz or more, especially when the operation is difficult to observe with the human eye. This paper reviews advances in developing real-time high Speed vision systems and their applications in various fields, such as intelligent logging systems, vibration dynamics sensing, vision-based mechanical control, three-dimensional measurement/automated visual inspection, vision-based human interface, and biomedical applications.
基金This research was supported by a grant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education,Republic of Korea.
文摘E-learning approaches are one of the most important learning platforms for the learner through electronic equipment.Such study techniques are useful for other groups of learners such as the crowd,pedestrian,sports,transports,communication,emergency services,management systems and education sectors.E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and methods.Many of them are currently working on this domain to fulfill the requirements of industry and the environment.In this paper,we proposed a method for pedestrian behavior mining of aerial data,using deep flow feature,graph mining technique,and convocational neural network.For input data,the state-of-the-art crowd activity University of Minnesota(UMN)dataset is adopted,which contains the aerial indoor and outdoor view of the pedestrian,for simplification of extra information and computational cost reduction the pre-processing is applied.Deep flow features are extracted to find more accurate information.Furthermore,to deal with repetition in features data and features mining the graph mining algorithm is applied,while Convolution Neural Network(CNN)is applied for pedestrian behavior mining.The proposed method shows 84.50%of mean accuracy and a 15.50%of error rate.Therefore,the achieved results show more accuracy as compared to state-ofthe-art classification algorithms such as decision tree,artificial neural network(ANN).
文摘Objective Present a new features selection algorithm. Methods based on rule induction and field knowledge. Results This algorithm can be applied in catching dataflow when detecting network intrusions, only the sub dataset including discriminating features is catched. Then the time spend in following behavior patterns mining is reduced and the patterns mined are more precise. Conclusion The experiment results show that the feature subset catched by this algorithm is more informative and the dataset’s quantity is reduced significantly.
基金Our research was supported by NSFC(Grant Nos.U1501252,U1711262,61702423 and U1811264).
文摘Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy.It necessitates an efficient detection mechanism to identify the vehicles with fake license plates accurately,and further explore the motives through discerning the behaviors of cloned vehicles.The ubiquitous inspection spots that deployed in the city have been collecting moving information of passing vehicles,which opens up a new opportunity for cloned vehicle detection.Existing detection methods cannot detect the cloned vehicle effectively due to that they use the fixed speed threshold.In this paper,we propose a two-phase framework,called CVDF,to detect cloned vehicles and discriminate behavior patterns of vehicles that use the same plate number.In the detection phase,cloned vehicles are identified based on speed thresholds extracted from historical trajectory and behavior abnormality analysis within the local neighborhood.In the behavior analysis phase,consider the traces of vehicles that uses the same license plate will be mixed together,we aim to differentiate the trajectories through matching degree-based clustering and then extract frequent temporal behavior patterns.The experimental results on the real-world data show that CVDF framework has high detection precision and could reveal cloned vehicles’behavior effectively.Our proposal provides a scientific basis for traffic management authority to solve the crime of cloned vehicle.
