In order to analyze the heterogeneity in vehicular traffic speed, a new method that integrates cluster analysis and probability distribution function fitting is presented. First, for identifying the optimal number of ...In order to analyze the heterogeneity in vehicular traffic speed, a new method that integrates cluster analysis and probability distribution function fitting is presented. First, for identifying the optimal number of clusters, the two-step cluster method is applied to analyze actual speed data, which suggests that dividing speed data into two clusters can best reflect the intrinsic patterns of traffic flows. Such information is then taken as guidance in probability distribution function fitting. The normal, skew-normal and skew-t distribution functions are used to fit the probability distribution of each cluster respectively, which suggests that the skew-t distribution has the highest fitting accuracy; the second is skew-normal distribution; the worst is normal distribution. Model analysis results demonstrate that the proposed mixture model has a better fitting and generalization capability than the conventional single model. In addition, the new method is more flexible in terms of data fitting and can provide a more accurate model of speed distribution.展开更多
Most existing applications of centroidal Voronoi tessellations(CVTs) lack consideration of the length of the cluster boundaries.In this paper we propose a new model and algorithms to produce segmentations which would ...Most existing applications of centroidal Voronoi tessellations(CVTs) lack consideration of the length of the cluster boundaries.In this paper we propose a new model and algorithms to produce segmentations which would minimize the total energy—a sum of the classic CVT energy and the weighted length of cluster boundaries.To distinguish it with the classic CVTs,we call it an Edge-Weighted CVT(EWCVT).The concept of EWCVT is expected to build a mathematical base for all CVT related data classifications with requirement of smoothness of the cluster boundaries.The EWCVT method is easy in implementation,fast in computation,and natural for any number of clusters.展开更多
Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many s...Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.展开更多
In order to solve the difficulty of detailed recognition of subdivisions of structural coal types,a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed.Structural coa...In order to solve the difficulty of detailed recognition of subdivisions of structural coal types,a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed.Structural coal types are recognized based on a suitable consideration of ultrasonic speed,an ultrasonic attenuation coefficient,characteristics of ultrasonic transmission and other parameters relating to structural coal types.We have focused on a computational model of ultrasonic speed,attenuation coefficient in coal and differentiation algorithm of structural coal types based on a BP neural network.Experiments demonstrate that the model can distinguish structural coal types effectively.It is important for the improved ultrasonic differentiation model to predict coal and gas outbursts.展开更多
In the past two decades,many statistical depth functions seemed as powerful exploratoryand inferential tools for multivariate data analysis have been presented.In this paper,a new depthfunction family that meets four ...In the past two decades,many statistical depth functions seemed as powerful exploratoryand inferential tools for multivariate data analysis have been presented.In this paper,a new depthfunction family that meets four properties mentioned in Zuo and Serfling(2000)is proposed.Then aclassification rule based on the depth function family is proposed.The classification parameter b couldbe modified according to the type-Ⅰ error α,and the estimator of b has the consistency and achievesthe convergence rate n^(-1/2).With the help of the proper selection for depth family parameter c,theapproach for discriminant analysis could minimize the type-Ⅱ error β.A simulation study and a realdata example compare the performance of the different discriminant methods.展开更多
Nanopores have been studied as a unique DNA sequencing technology that can quickly read long stretched DNA sequences. A DNA molecule could pass through a nanopore in a speed of microsecond per base and even faster. Wi...Nanopores have been studied as a unique DNA sequencing technology that can quickly read long stretched DNA sequences. A DNA molecule could pass through a nanopore in a speed of microsecond per base and even faster. With this speed, a human genome can potentially be sequenced by one nanopore in 〈1 h. In contrast to next- generation DNA sequencing （NGS）, the nanopore sequencing is enzyme free without need of sample amplification due to its single-molecule nature. The nanopore sequencing has been envisioned as a new generation of DNA sequencing technology in the post-NGS era. This progress focuses on status quo of the nanopore DNA sequencing and discusses the opportunities and challenges in this rapidly growing field.展开更多
基金The National Science Foundation by Changjiang Scholarship of Ministry of Education of China(No.BCS-0527508)the Joint Research Fund for Overseas Natural Science of China(No.51250110075)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK200910046)the Postdoctoral Science Foundation of Jiangsu Province(No.0901005C)
文摘In order to analyze the heterogeneity in vehicular traffic speed, a new method that integrates cluster analysis and probability distribution function fitting is presented. First, for identifying the optimal number of clusters, the two-step cluster method is applied to analyze actual speed data, which suggests that dividing speed data into two clusters can best reflect the intrinsic patterns of traffic flows. Such information is then taken as guidance in probability distribution function fitting. The normal, skew-normal and skew-t distribution functions are used to fit the probability distribution of each cluster respectively, which suggests that the skew-t distribution has the highest fitting accuracy; the second is skew-normal distribution; the worst is normal distribution. Model analysis results demonstrate that the proposed mixture model has a better fitting and generalization capability than the conventional single model. In addition, the new method is more flexible in terms of data fitting and can provide a more accurate model of speed distribution.
基金supported in part by the U.S.National Science Foundation under grant number DMS-0913491.
文摘Most existing applications of centroidal Voronoi tessellations(CVTs) lack consideration of the length of the cluster boundaries.In this paper we propose a new model and algorithms to produce segmentations which would minimize the total energy—a sum of the classic CVT energy and the weighted length of cluster boundaries.To distinguish it with the classic CVTs,we call it an Edge-Weighted CVT(EWCVT).The concept of EWCVT is expected to build a mathematical base for all CVT related data classifications with requirement of smoothness of the cluster boundaries.The EWCVT method is easy in implementation,fast in computation,and natural for any number of clusters.
文摘Vehicular Ad Hoc Network （VANET） has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks （clusters） so that channel interferences and flooding messages can be limited. This research presents a novel Multi-Resolution Relative Speed Detection （MRSD） model to improve the clustering algorithm in VANET without using Global Positioning System （GPS）. MRSD uses the Moving Average Convergence Divergence （MACD）, the Momentum of Received Signal Strength （MRSS）, and Artificial Neural Networks （ANNs） to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.
基金Projects 50674093 supported by the National Natural Science Foundation of China20050290010 by the Doctoral Foundation of the Chinese Education Ministry
文摘In order to solve the difficulty of detailed recognition of subdivisions of structural coal types,a differentiation model that combines BP neural network with an ultrasonic reflection method is proposed.Structural coal types are recognized based on a suitable consideration of ultrasonic speed,an ultrasonic attenuation coefficient,characteristics of ultrasonic transmission and other parameters relating to structural coal types.We have focused on a computational model of ultrasonic speed,attenuation coefficient in coal and differentiation algorithm of structural coal types based on a BP neural network.Experiments demonstrate that the model can distinguish structural coal types effectively.It is important for the improved ultrasonic differentiation model to predict coal and gas outbursts.
基金supported by the Natural Science Foundation of China under Grant Nos.10901020,10726013 and 10771017
文摘In the past two decades,many statistical depth functions seemed as powerful exploratoryand inferential tools for multivariate data analysis have been presented.In this paper,a new depthfunction family that meets four properties mentioned in Zuo and Serfling(2000)is proposed.Then aclassification rule based on the depth function family is proposed.The classification parameter b couldbe modified according to the type-Ⅰ error α,and the estimator of b has the consistency and achievesthe convergence rate n^(-1/2).With the help of the proper selection for depth family parameter c,theapproach for discriminant analysis could minimize the type-Ⅱ error β.A simulation study and a realdata example compare the performance of the different discriminant methods.
基金supported by the National Natural Science Foundation of China (21372183)the Hubei Province Natural Science Foundation (2013CFB328)+1 种基金the Key Laboratory of Analytical Chemistry for Biology and Medicine (Wuhan University), Ministry of Education (ACBM2014001)the Start-Up-Fund grant provided by Wuhan University of Science and Technology
文摘Nanopores have been studied as a unique DNA sequencing technology that can quickly read long stretched DNA sequences. A DNA molecule could pass through a nanopore in a speed of microsecond per base and even faster. With this speed, a human genome can potentially be sequenced by one nanopore in 〈1 h. In contrast to next- generation DNA sequencing （NGS）, the nanopore sequencing is enzyme free without need of sample amplification due to its single-molecule nature. The nanopore sequencing has been envisioned as a new generation of DNA sequencing technology in the post-NGS era. This progress focuses on status quo of the nanopore DNA sequencing and discusses the opportunities and challenges in this rapidly growing field.
