Fibre channel （FC） is the main candidate architecture for ＂unified network＂. Flow control deals with the problem in which a device receives frames faster than it can process them. Credit is an important service pa...Fibre channel （FC） is the main candidate architecture for ＂unified network＂. Flow control deals with the problem in which a device receives frames faster than it can process them. Credit is an important service parameter for fibre channel flow control. Configuring the credit reasonably can avoid buffer overflow in nodes. This paper derives the mathematic relationships among credit, bandwidth and message sets under real-time condition according as three main topologies of fibre channel, and proposes the credit determination and the optimal credit for typical message sets. This study is based on the features of hard real-time communications in avionics environment.展开更多
Recently,switched Ethernet has become an active area of research because of its wide uses in industry.However,its uses have various real-time constraints on data communications.This paper analyzes the performance of t...Recently,switched Ethernet has become an active area of research because of its wide uses in industry.However,its uses have various real-time constraints on data communications.This paper analyzes the performance of the line topology switched Ethernet as a data acquisition network.Network calculus theory,which has been successfully applied to assess the real-time performance of packet-switched networks,is used to analyze the networks.To properly describe the activity of switches,a novel approach of modeling data flows into or out of switches is addressed.Based on our model,a concisely analytical expression of the maximal end-to-end delay in line topology switched Ethernet is derived.Finally,the relative simulation results are demonstrated.These results agree well with the analytical results,and thus they validate the data flow modeling techniques.展开更多
Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challe...Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples.展开更多
基金National Natural Science Foundation of China (10477005)
文摘Fibre channel （FC） is the main candidate architecture for ＂unified network＂. Flow control deals with the problem in which a device receives frames faster than it can process them. Credit is an important service parameter for fibre channel flow control. Configuring the credit reasonably can avoid buffer overflow in nodes. This paper derives the mathematic relationships among credit, bandwidth and message sets under real-time condition according as three main topologies of fibre channel, and proposes the credit determination and the optimal credit for typical message sets. This study is based on the features of hard real-time communications in avionics environment.
文摘Recently,switched Ethernet has become an active area of research because of its wide uses in industry.However,its uses have various real-time constraints on data communications.This paper analyzes the performance of the line topology switched Ethernet as a data acquisition network.Network calculus theory,which has been successfully applied to assess the real-time performance of packet-switched networks,is used to analyze the networks.To properly describe the activity of switches,a novel approach of modeling data flows into or out of switches is addressed.Based on our model,a concisely analytical expression of the maximal end-to-end delay in line topology switched Ethernet is derived.Finally,the relative simulation results are demonstrated.These results agree well with the analytical results,and thus they validate the data flow modeling techniques.
基金supported by the National Key Research and Development Projects (Grant Nos.2021YFB3300601,2021YFB3300603,2021YFB3300604)Fundamental Research Funds for the Central Universities (No.DUT22QN241).
文摘Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples.
