An On-Site Elastic Autonomous Service Network with Efficient Task Assignment

Author(s):  
Jiali You ◽  
Nannan Qiao ◽  
Jinlin Wang ◽  
Guoqiang Zhang ◽  
Yiqiang Sheng ◽  
...  
2020 ◽  
pp. 1-12
Author(s):  
Changxin Sun ◽  
Di Ma

In the research of intelligent sports vision systems, the stability and accuracy of vision system target recognition, the reasonable effectiveness of task assignment, and the advantages and disadvantages of path planning are the key factors for the vision system to successfully perform tasks. Aiming at the problem of target recognition errors caused by uneven brightness and mutations in sports competition, a dynamic template mechanism is proposed. In the target recognition algorithm, the correlation degree of data feature changes is fully considered, and the time control factor is introduced when using SVM for classification,At the same time, this study uses an unsupervised clustering method to design a classification strategy to achieve rapid target discrimination when the environmental brightness changes, which improves the accuracy of recognition. In addition, the Adaboost algorithm is selected as the machine learning method, and the algorithm is optimized from the aspects of fast feature selection and double threshold decision, which effectively improves the training time of the classifier. Finally, for complex human poses and partially occluded human targets, this paper proposes to express the entire human body through multiple parts. The experimental results show that this method can be used to detect sports players with multiple poses and partial occlusions in complex backgrounds and provides an effective technical means for detecting sports competition action characteristics in complex backgrounds.


2004 ◽  
Vol 36 (10) ◽  
pp. 51-55 ◽  
Author(s):  
Rasim Magamed ogly Alguliev ◽  
Ramiz Magamed ogly Aliguliev ◽  
Rashid Kurbanali ogly Alekperov

2010 ◽  
Vol 30 (8) ◽  
pp. 2170-2172
Author(s):  
Hui WANG ◽  
Zhi-yong FENG ◽  
Ju CHEN ◽  
Shi-zhan CHEN

Author(s):  
M.A. Piskunov ◽  

Russian forest sector forms an attractive market for harvesting and logging equipment, however the position of Russian manufacturers is extremely weak. A brief overview of the current state of the market is presented with reference to the open sources. Its features are mentioned as compared to the road construction and agricultural machinery sectors. Three transnational companies dominate the Russian market of harvesting and logging equipment: John Deere, Ponsse and Komatsu. Most of the purchased equipment falls on machines for cut-tolength technology, such as harvester and forwarder. The market volume of new machines is estimated at 330–420 forwarders, 165–300 harvesters, about 30–40 feller bunchers and the same number of skidders. There were two waves in the consolidation of the position of foreign companies in Russia. The first was connected with the delivery of equipment and the development of foreign brands in Russia against the background of still high-profile positions of Russian manufacturers in the market. The second is the takeover of enterprises having a service network and reputation by diversified transnational corporations. The main strategies of the leading companies in the current situation are the export of equipment to Russia and the development of a service network. Companies do not turn to another level associated with the opening of production sites or joint ventures for the production of harvesting and logging machines. The Russian market is characterized by the absence of a strong Russian manufacturer of harvesting and logging machines, which is ready to significantly influence or actively participate in the processes of import substitution. The position of such a manufacturer is gradually occupied by the Belarusian Amkodor Holding. The purchase of new harvesting and logging machines can afford major timber companies. The main production sites of harvesting and logging machines are located in Finland, Sweden, USA, and Canada. In order to support forestry machine engineering, in addition to economic measures of stimulation approved in other sectors, it is proposed: to organize the work of scientific forest engineering centers on the base of public-private partnership with the financial support from the major vertically-integrated timber corporate groups; to stimulate the development of Russian sector-specific information technologies for harvesting and logging; to initiate the partnership with companies from the People’s Republic of China to launch the design and production of new-generation harvesting and logging machines.


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