An Analysis Model of Sports Human Body Based on Computer Vision Tracking Technology

This paper proposes the analysis model of sports human body based on computer vision tracking technology. Visual target tracking is an important research field of the computer vision, motion trajectory and it can provide not only the goal, to provide the initial data movement analysis, scene understanding, behavior or the event detection in intelligent surveillance, human-computer interaction, robot visual navigation and motion recognition based on field has a broad application prospect. For this reason, it is possible to consider the use of a large number of unlabeled samples to assist the training classifier to improve its performance. This type of machine learning method using both labeled and that unlabeled samples is called the semi-supervised learning. This paper proposes the novel idea of the related research topics to propose the new perspective of the model that will be later give us the novel idea of making it efficient for further development of sport science.

Development Track of Renewable Power Technology in China

Introduction Within the context of deeper global energy shortage and increasingly more polluted environment,countries throughout the world have been actively developing renewable energy sources to replace the fossil fuel,

Advances in Terrestrial Mammal Movement Ecology:An Overview

As a research field which is blooming quickly in recent years,movement ecology has been a worldwide concern and interest.However,movement ecology is so comprehensive and complicated that many articles only focus on few aspects or species.As tracking technologies and methods of movement data analysis develop,the abundance of movement data becomes available for demonstrating more scientific facts about animal movement.This article is aimed to summarize the advances of terrestrial mammal movement ecology in the past years to show its critical and potential research fields,as well as trying to ascertain direction of these advances.

Ion track-based nanowire arrays with gradient and programmable diameters towards rational light management

Integrating nanowires with nonuniform diameter and random spatial distribution into an array can afford unconventional and additional means for modulating optical response.However,experimental realization of such a nanowire array is quite challenging.In this work,we propose a new fabrication strategy which takes advantage of ion track technology,via sequential swift heavy ion irradiation and ion track etching.Based on this strategy,we unprecedentedly realize nanowire arrays,using gold as an example,with gradient and programmable diameters in a controlled manner.We further demonstrate that such nanowire arrays can support broadband,tunable,and enhanced plasmonic responses.We believe that our new type of nanowire arrays will find great potential in applications such as light management and optoelectronic devices.

Automatic tracking of multi-fruit targets with oscillation under natural growth conditions

It is important for intelligent orchards to be able to achieve automatic monitoring of fruit growth information within a natural growing environment.The issue of how to track green and oscillating fruits under the influence of wind and farming operations is a key aspect of monitoring of the growth state of the fruit.In order to realize the accurate tracking of green fruit targets,a new method based on target tracking is proposed.First,an optical flow method is applied to realize the automatic detection of green fruit targets,and this lays the foundation for the accurate and automatic tracking of these targets.Then,Kalman and kernelized correlation filter(KCF)algorithms are applied to achieve multi-target tracking and prediction.In order to verify the performance of these different algorithms on various types of green fruit targets,experiments were carried out based on nine video sequences.The experimental results for the tracking of single,double and triple green fruit targets show that the average tracking success rates of the Kalman algorithm are 88.15%,82.30%and 53.10%,respectively,and those of the KCF algorithm are 94.07%,87.35%and 61.46%,respectively,meaning that the average tracking results from KCF are 5.92%,5.05%and 8.36%higher than those from the Kalman algorithm.The time consumed is also reduced by 35.40%,36.27%and 40.86%,respectively.The results show that it is feasible to apply the KCF algorithm to the tracking of green fruit targets.

Coordination of PSS and FACTS Damping Controllers to Improve Small Signal Stability of Large-scale Power Systems

This paper presents an approach for designing parameters of power system stabilizer(PSS)and FACTS damping controllers in a large scale practical power system.The objective is maximizing damping ratio of the target mode,and tracking technology(MTT)is used to avoid frequent alternations of target mode in optimization procedures.An improved planted growth simulation algorithm(IPGSA),which has high search efficiency and quick convergence speed,is proposed to optimize controller parameters coordinately.Based on case study of a large-scale power grid,and by using local and interregional low-frequency oscillation modes as target modes,simulation results verify proposed method in this paper.Furthermore,coordination optimization strategy adapted to multi-operating conditions demonstrates that the proposed approach is robust.