With the rapid development of sports,the number of sports images has increased dramatically.Intelligent and automatic processing and analysis of moving images are significant,which can not only facilitate users to qui...With the rapid development of sports,the number of sports images has increased dramatically.Intelligent and automatic processing and analysis of moving images are significant,which can not only facilitate users to quickly search and access moving images but also facilitate staff to store and manage moving image data and contribute to the intellectual development of the sports industry.In this paper,a method of table tennis identification and positioning based on a convolutional neural network is proposed,which solves the problem that the identification and positioning method based on color features and contour features is not adaptable in various environments.At the same time,the learning methods and techniques of table tennis detection,positioning,and trajectory prediction are studied.A deep learning framework for recognition learning of rotating flying table tennis is put forward.The mechanism and methods of positioning,trajectory prediction,and intelligent automatic processing of moving images are studied,and the self-built data sets are trained and verified.展开更多
The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things(IoT).In recent years,researchers have ...The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things(IoT).In recent years,researchers have tried to develop hardware-based solutions for the classification of Internet packets.Due to higher throughput and shorter delays,these solutions are considered as a major key to improving the quality of services.Most of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the throughput.The proposed architectures,however,cannot reach a compromise among power consumption,memory usage,and throughput rate.In view of this,the architecture proposed in this paper contains a pipelinebased micro-core that is used in network processors to classify packets.To this end,three architectures have been implemented using the proposed micro-core.The first architecture performs parallel classification based on header fields.The second one classifies packets in a serial manner.The last architecture is the pipeline-based classifier,which can increase performance by nine times.The proposed architectures have been implemented on an FPGA chip.The results are indicative of a reduction in memory usage as well as an increase in speedup and throughput.The architecture has a power consumption of is 1.294w,and its throughput with a frequency of 233 MHz exceeds 147 Gbps.展开更多
September 10 ~12 ,2008,Lyon,France The IIP conference series provides aforumfor engineers andscientistsin academia,university andindustryto present theirlatest researchfindings in any aspects of intelligent informatio...September 10 ~12 ,2008,Lyon,France The IIP conference series provides aforumfor engineers andscientistsin academia,university andindustryto present theirlatest researchfindings in any aspects of intelligent information processing.This ti me , we especially encourage papers on Knowledge Discovery , Knowledge Management ,Intelligent Agents , Machine Learning, Autonomic Reasoning etc.We also welcome papers that highlight successful modern applications of IIP,such as Biomedicine ,Bioinformatics ,e-Services ,e-Learning, Business Intelligence.IIP2008 attempts to meet the needs of a large and diverse community.展开更多
Presently,the service performance of new-generation high-tech equipment is directly affected by the manufacturing quality of complex thin-walled components.A high-efficiency and quality manufacturing of these complex ...Presently,the service performance of new-generation high-tech equipment is directly affected by the manufacturing quality of complex thin-walled components.A high-efficiency and quality manufacturing of these complex thin-walled components creates a bottleneck that needs to be solved urgently in machinery manufacturing.To address this problem,the collaborative manufacturing of structure shape and surface integrity has emerged as a new process that can shorten processing cycles,improve machining qualities,and reduce costs.This paper summarises the research status on the material removal mechanism,precision control of structure shape,machined surface integrity control and intelligent process control technology of complex thin-walled components.Numerous solutions and technical approaches are then put forward to solve the critical problems in the high-performance manufacturing of complex thin-wall components.The development status,challenge and tendency of collaborative manufacturing technologies in the high-efficiency and quality manufacturing of complex thin-wall components is also discussed.展开更多
文摘With the rapid development of sports,the number of sports images has increased dramatically.Intelligent and automatic processing and analysis of moving images are significant,which can not only facilitate users to quickly search and access moving images but also facilitate staff to store and manage moving image data and contribute to the intellectual development of the sports industry.In this paper,a method of table tennis identification and positioning based on a convolutional neural network is proposed,which solves the problem that the identification and positioning method based on color features and contour features is not adaptable in various environments.At the same time,the learning methods and techniques of table tennis detection,positioning,and trajectory prediction are studied.A deep learning framework for recognition learning of rotating flying table tennis is put forward.The mechanism and methods of positioning,trajectory prediction,and intelligent automatic processing of moving images are studied,and the self-built data sets are trained and verified.
文摘The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things(IoT).In recent years,researchers have tried to develop hardware-based solutions for the classification of Internet packets.Due to higher throughput and shorter delays,these solutions are considered as a major key to improving the quality of services.Most of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the throughput.The proposed architectures,however,cannot reach a compromise among power consumption,memory usage,and throughput rate.In view of this,the architecture proposed in this paper contains a pipelinebased micro-core that is used in network processors to classify packets.To this end,three architectures have been implemented using the proposed micro-core.The first architecture performs parallel classification based on header fields.The second one classifies packets in a serial manner.The last architecture is the pipeline-based classifier,which can increase performance by nine times.The proposed architectures have been implemented on an FPGA chip.The results are indicative of a reduction in memory usage as well as an increase in speedup and throughput.The architecture has a power consumption of is 1.294w,and its throughput with a frequency of 233 MHz exceeds 147 Gbps.
文摘September 10 ~12 ,2008,Lyon,France The IIP conference series provides aforumfor engineers andscientistsin academia,university andindustryto present theirlatest researchfindings in any aspects of intelligent information processing.This ti me , we especially encourage papers on Knowledge Discovery , Knowledge Management ,Intelligent Agents , Machine Learning, Autonomic Reasoning etc.We also welcome papers that highlight successful modern applications of IIP,such as Biomedicine ,Bioinformatics ,e-Services ,e-Learning, Business Intelligence.IIP2008 attempts to meet the needs of a large and diverse community.
基金supported by the National Natural Science Foundation of China(Nos.51921003,92160301,52175415 and 52205475)the Science Center for Gas Turbine Project(No.P2022-A-IV-002-001)Natural Science Foundation of Jiangsu Province(No.BK20210295).
文摘Presently,the service performance of new-generation high-tech equipment is directly affected by the manufacturing quality of complex thin-walled components.A high-efficiency and quality manufacturing of these complex thin-walled components creates a bottleneck that needs to be solved urgently in machinery manufacturing.To address this problem,the collaborative manufacturing of structure shape and surface integrity has emerged as a new process that can shorten processing cycles,improve machining qualities,and reduce costs.This paper summarises the research status on the material removal mechanism,precision control of structure shape,machined surface integrity control and intelligent process control technology of complex thin-walled components.Numerous solutions and technical approaches are then put forward to solve the critical problems in the high-performance manufacturing of complex thin-wall components.The development status,challenge and tendency of collaborative manufacturing technologies in the high-efficiency and quality manufacturing of complex thin-wall components is also discussed.
