In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets w...In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets with non-zero flow payload sizes are selected and their payload sizes are used as the early-stage flow features. Such features can be easily and rapidly extracted at the early flow stage, which makes them outstanding. The behavior patterns of different Intemet applications are analyzed by visualizing the early-stage packet size values. Analysis results show that most Internet applications can reflect their own early packet size behavior patterns. Early packet sizes are assumed to carry enough information for effective traffic identification. Three classical machine learning classifiers, classifier, naive Bayesian trees, i. e., the naive Bayesian and the radial basis function neural networks, are used to validate the effectiveness of the proposed assumption. The experimental results show that the early stage packet sizes can be used as features for traffic identification.展开更多
This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first ...This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the repre...展开更多
A framework is proposed to characterize and forecast the displacement trends of slow-moving landslides, defined as the reactivation stage of phenomena in rocks or fine-grained soils, with movements localized along one...A framework is proposed to characterize and forecast the displacement trends of slow-moving landslides, defined as the reactivation stage of phenomena in rocks or fine-grained soils, with movements localized along one or several existing shear surfaces. The framework is developed based on a thorough analysis of the scientific literature and with reference to significant reported case studies for which a consistent dataset of continuous displacement measurements is available. Three distinct trends of movement are defined to characterize the kinematic behavior of the active stages of slow-moving landslides in a velocity-time plot: a linear trend-type I, which is appropriate for stationary phenomena; a convex shaped trend-type II, which is associated with rapid increases in pore water pressure due to rainfall, followed by a slow decrease in the groundwater level with time; and a concave shaped trend-type III, which denotes a non-stationary process related to the presence of new boundary conditions such as those associated with the development of a newly formed local slip surface that connects with the main existing slip surface. Within the proposed framework, a model is developed to forecast future displacements for active stages of trend-type II based on displacement measurements at the beginning of the stage. The proposed model is validated by application to two case studies.展开更多
To provide system designer a valid measure to evaluate the structure complexityof class diagrams objectively, this letter first proposes a method to transform a class diagramsinto a weighted class dependence graph, th...To provide system designer a valid measure to evaluate the structure complexityof class diagrams objectively, this letter first proposes a method to transform a class diagramsinto a weighted class dependence graph, then presents a structure complexity measure for classdiagrams based on entropy distance.展开更多
基金The Program for New Century Excellent Talents in University(No.NCET-11-0565)the Fundamental Research Funds for the Central Universities(No.K13JB00160,2012JBZ010,2011JBM217)+2 种基金the Ph.D.Programs Foundation of Ministry of Education of China(No.20120009120010)the Program for Innovative Research Team in University of Ministry of Education of China(No.IRT201206)the Natural Science Foundation of Shandong Province(No.ZR2012FM010,ZR2011FZ001)
文摘In order to classify the Intemet traffic of different Internet applications more quickly, two open Internet traffic traces, Auckland I1 and UNIBS traffic traces, are employed as study objects. Eight earliest packets with non-zero flow payload sizes are selected and their payload sizes are used as the early-stage flow features. Such features can be easily and rapidly extracted at the early flow stage, which makes them outstanding. The behavior patterns of different Intemet applications are analyzed by visualizing the early-stage packet size values. Analysis results show that most Internet applications can reflect their own early packet size behavior patterns. Early packet sizes are assumed to carry enough information for effective traffic identification. Three classical machine learning classifiers, classifier, naive Bayesian trees, i. e., the naive Bayesian and the radial basis function neural networks, are used to validate the effectiveness of the proposed assumption. The experimental results show that the early stage packet sizes can be used as features for traffic identification.
文摘This paper presents a fuzzy logic approach to efficiently perform unsupervised character classification for improvement in robustness, correctness and speed of a character recognition system. The characters are first split into eight typographical categories. The classification scheme uses pattern matching to classify the characters in each category into a set of fuzzy prototypes based on a nonlinear weighted similarity function. The fuzzy unsupervised character classification, which is natural in the repre...
基金partially supported by the University of Salerno (Italy) through the Civil and Environmental Engineering Ph.D. programme and FARB research funding
文摘A framework is proposed to characterize and forecast the displacement trends of slow-moving landslides, defined as the reactivation stage of phenomena in rocks or fine-grained soils, with movements localized along one or several existing shear surfaces. The framework is developed based on a thorough analysis of the scientific literature and with reference to significant reported case studies for which a consistent dataset of continuous displacement measurements is available. Three distinct trends of movement are defined to characterize the kinematic behavior of the active stages of slow-moving landslides in a velocity-time plot: a linear trend-type I, which is appropriate for stationary phenomena; a convex shaped trend-type II, which is associated with rapid increases in pore water pressure due to rainfall, followed by a slow decrease in the groundwater level with time; and a concave shaped trend-type III, which denotes a non-stationary process related to the presence of new boundary conditions such as those associated with the development of a newly formed local slip surface that connects with the main existing slip surface. Within the proposed framework, a model is developed to forecast future displacements for active stages of trend-type II based on displacement measurements at the beginning of the stage. The proposed model is validated by application to two case studies.
基金Supported in part by the National Natural Science Foundation of China(60073012),National Grand Fundamental Research 973 Program of China(G1999032701),Natural Research Foundation for the Doctoral Program of Higher Education of China,Natural Science Founda
文摘To provide system designer a valid measure to evaluate the structure complexityof class diagrams objectively, this letter first proposes a method to transform a class diagramsinto a weighted class dependence graph, then presents a structure complexity measure for classdiagrams based on entropy distance.
