A METHOD OF RAPID CLASSIFICATION OF TROPICAL CYCLONE TRACKS OVER CHINA AND ITS APPLICATION

Using data of tropical cyclones making landfall in China between May and October each year during the1951-2015 period from the Shanghai Typhoon Institute, China Meteorological Administration(CMA-STI) Tropical Cyclone(TC) Best Track Dataset, we developed a method of rapid classification of TC tracks based on their average movement velocities and noted three types of tracks: a westward type, a northwestward type, and a northward type. We compared the climate characteristics of the westward and northward types and discuss their corresponding causes. The results show that the westward and northward types account for more than 80% of all TCs making landfall in China.Their climate characteristics, such as the frequency, landfall intensity, duration over land, velocity over land, movement distance over land, and other changes, show both similarities and differences. Both TC types show significant increases in their over-land durations, indicating that the effects of these landfalling TCs are increasing. However, the causes of these two TC types are similar and different in certain respects. The changes in large-scale steering flows have significantly affected the frequencies and over-land velocities of the landfalling TCs of the westward and northward types. In addition, differences between the changes in formation locations of the westward and northward types may lead to significant difference in their landfall intensities.

Modified joint probabilistic data association with classification-aided for multitarget tracking

Joint probabilistic data association is an effective method for tracking multiple targets in clutter, but only the target kinematic information is used in measure-to-track association. If the kinematic likelihoods are similar for different closely spaced targets, there is ambiguity in using the kinematic information alone; the correct association probability will decrease in conventional joint probabilistic data association algorithm and track coalescence will occur easily. A modified algorithm of joint probabilistic data association with classification-aided is presented, which avoids track coalescence when tracking multiple neighboring targets. Firstly, an identification matrix is defined, which is used to simplify validation matrix to decrease computational complexity. Then, target class information is integrated into the data association process. Performance comparisons with and without the use of class information in JPDA are presented on multiple closely spaced maneuvering targets tracking problem. Simulation results quantify the benefits of classification-aided JPDA for improved multiple targets tracking, especially in the presence of association uncertainty in the kinematic measurement and target maneuvering. Simulation results indicate that the algorithm is valid.

K-MEANS CLUSTERING FOR CLASSIFICATION OF THE NORTHWESTERN PACIFIC TROPICAL CYCLONE TRACKS

Based on the Joint Typhoon Warning Center(JTWC) best-track dataset between 1965 and 2009 and the characteristic parameters including tropical cyclone(TC) position,intensity,path length and direction,a method for objective classification of the Northwestern Pacific tropical cyclone tracks is established by using k-means Clustering.The TC lifespan,energy,active season and landfall probability of seven clusters of tropical cyclone tracks are comparatively analyzed.The characteristics of these parameters are quite different among different tropical cyclone track clusters.From the trend of the past two decades,the frequency of the western recurving cluster(accounting for 21.3% of the total) increased,and the lifespan elongated slightly,which differs from the other clusters.The annual variation of the Power Dissipation Index(PDI) of most clusters mainly depended on the TC intensity and frequency.However,the annual variation of the PDI in the northwestern moving then recurving cluster and the pelagic west-northwest moving cluster mainly depended on the frequency.

Label-free in-vivo classi-cation and tracking of red blood cells and platelets using Dynamic-YOLOv4 network

In-vivo flow cytometry is a noninvasive real-time diagnostic technique that facilitates continuous monitoring of cells without perturbing their natural biological environment,which renders it a valuable tool for both scientific research and clinical applications.However,the conventional approach for improving classification accuracy often involves labeling cells with fluorescence,which can lead to potential phototoxicity.This study proposes a label-free in-vivo flow cytometry technique,called dynamic YOLOv4(D-YOLOv4),which improves classification accuracy by integrating absorption intensity fluctuation modulation(AIFM)into YOLOv4 to demodulate the temporal features of moving red blood cells(RBCs)and platelets.Using zebrafish as an experimental model,the D-YOLOv4 method achieved average precisions(APs)of 0.90 for RBCs and 0.64 for thrombocytes(similar to platelets in mammals),resulting in an overall AP of 0.77.These scores notably surpass those attained by alternative network models,thereby demonstrating that the combination of physical models with neural networks provides an innovative approach toward developing label-free in-vivoflow cytometry,which holds promise for diverse in-vivo cell classification applications.

Joint tracking and classification of extended targets with complex shapes

This paper addresses the problem of joint tracking and classification(JTC) of a single extended target with a complex shape. To describe this complex shape, the spatial extent state is first modeled by star-convex shape via a random hypersurface model(RHM), and then used as feature information for target classification. The target state is modeled by two vectors to alleviate the influence of the high-dimensional state space and the severely nonlinear observation model on target state estimation, while the Euclidean distance metric of the normalized Fourier descriptors is applied to obtain the analytical solution of the updated class probability. Consequently, the resulting method is called the "JTC-RHM method." Besides, the proposed JTC-RHM is integrated into a Bernoulli filter framework to solve the JTC of a single extended target in the presence of detection uncertainty and clutter, resulting in a JTC-RHM-Ber filter. Specifically, the recursive expressions of this filter are derived. Simulations indicate that:(1) the proposed JTC-RHM method can classify the targets with complex shapes and similar sizes more correctly, compared with the JTC method based on the random matrix model,(2) the proposed method performs better in target state estimation than the star-convex RHM based extended target tracking method,(3) the proposed JTC-RHM-Ber filter has a promising performance in state detection and estimation, and can achieve target classification correctly.

Experimental analysis of operational data for roundabouts through advanced image processing

Roundabout is still the focus of several investigations due to the relevant number of variables affecting their operational performances(i.e.,capacity,safety,emissions).To develop reliable models,investigations should be supported by devices and relate d sensors to extract variables of interest(i.e.,flow,speed,gap,lag,follow-up time,vehicle classification and trajectory).Notwithstanding that several sensors and technolo gies are currently used for data collection,most of them present limitations.The paper presents the investigation carried out to survey vehicle movem ents at roundabouts as a comprehensive video image analysis system is able to derive the origin/destination(O/D)matrix,compile a vehicle classification,track individual vehicle trajectories together with corresponding speeds and accelerations along paths.To this end,the authors collected video-sequences that were analysed with a piece of software developed for that task.To minimize the problems due to perspective distortion,environmental effects,and obstructions,a number of camera set-up configurations were adopted with equipment being placed on central or external poles,and on permanent fixtures such as raised working platforms outside the confines of the intersection area.Performance of those installation set-ups with different vehicle tracking strategies has been evaluated.Particularly,speed has been successfully related to trajectory tortuosity,the result of which emphasizes the tremendous potential of image analysis and opens up to further studies on the evaluation of the operational effects of roundabout geometrics.