In high-level synthesis of VLSI circuits, good lower bound prediction canefficiently narrow down the large space of possible designs. Previous approaches predict the lowerbound by relaxing or even ignoring the precede...In high-level synthesis of VLSI circuits, good lower bound prediction canefficiently narrow down the large space of possible designs. Previous approaches predict the lowerbound by relaxing or even ignoring the precedence constraints of the data flow graph (DFG), andresult in inaccuracy of the lower bound. The loop folding and conditional branch were also notconsidered. In this paper, a new stepwise refinement algorithm is proposed, which takesconsideration of precedence constraints of the DFG to estimate the lower bound of hardware resourcesunder time constraints. Processing techniques to handle multi-cycle, chaining, pipelining, as wellas loop folding and mutual exclusion among conditional branches are also incorporated in thealgorithm. Experimental results show that the algorithm can produce a very tight and close tooptimal lower bound in reasonable computation time.展开更多
A finite element asymptotic analysis for determining the lower bound dynamic buckling estimates of imperfection-sensitive structures under step load of infinite duration is presented. The lower bound dynamic buckling ...A finite element asymptotic analysis for determining the lower bound dynamic buckling estimates of imperfection-sensitive structures under step load of infinite duration is presented. The lower bound dynamic buckling loads and the corresponding displacements are sought in the form of asymptotic expansions based on the static stability criterion and they can be determined by solving numerically (FEM) several linear problems with a single nonsingular sub-stiffness matrix.展开更多
This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sam...This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.展开更多
By analyzing the bus operation environment and accounting for prediction uncertainties,a bus arrival interval prediction model was developed utilizing a gated recur-rent unit(GRU)neural network.To reduce the impact of...By analyzing the bus operation environment and accounting for prediction uncertainties,a bus arrival interval prediction model was developed utilizing a gated recur-rent unit(GRU)neural network.To reduce the impact of irrelevant data and boost prediction accuracy,an attention mechanism was integrated into the point model to concen-trate on important input sequence information.Based on the point predictions,the lower upper bound estimation(LUBE)method was used,providing a range for the bus interval times predicted by the model.The model was vali-dated using data from 169 bus routes in Nanchang,Jiangxi Province.The results indicated that the attention-GRU model outperformed neural network,long short-term memory and GRU models.Compared with the Bootstrap method,the LUBE method has a narrower average interval width.The coverage width-based criterion(CWC)was reduced by 8.1%,2.2%,and 5.7%at confidence levels of 85%,90%,and 95%,respectively,during the off-peak period,and by 23.2%,26.9%,and 27.3%at confidence levels of 85%,90%,and 95%,respectively,during the peak period.Therefore,it can accurately describe the fluctuation range in bus arrival times with higher accuracy and stability.展开更多
We present the convergence analysis of the rectangular Morley element scheme utilised on the second order problem in arbitrary dimensions. Specifically, we prove that the convergence of the scheme is of （D（h） order...We present the convergence analysis of the rectangular Morley element scheme utilised on the second order problem in arbitrary dimensions. Specifically, we prove that the convergence of the scheme is of （D（h） order in energy norm and of O（h2） order in L2 norm on general d-rectangular triangulations. Moreover, when the triangulation is uniform, the convergence rate can be of O（h2） order in energy norm, and the convergence rate in L2 norm is still of O（h2） order, which cannot be improved. Numerical examples are presented to demonstrate our theoretical results.展开更多
文摘In high-level synthesis of VLSI circuits, good lower bound prediction canefficiently narrow down the large space of possible designs. Previous approaches predict the lowerbound by relaxing or even ignoring the precedence constraints of the data flow graph (DFG), andresult in inaccuracy of the lower bound. The loop folding and conditional branch were also notconsidered. In this paper, a new stepwise refinement algorithm is proposed, which takesconsideration of precedence constraints of the DFG to estimate the lower bound of hardware resourcesunder time constraints. Processing techniques to handle multi-cycle, chaining, pipelining, as wellas loop folding and mutual exclusion among conditional branches are also incorporated in thealgorithm. Experimental results show that the algorithm can produce a very tight and close tooptimal lower bound in reasonable computation time.
基金The project supported by the State Education Commission of China
文摘A finite element asymptotic analysis for determining the lower bound dynamic buckling estimates of imperfection-sensitive structures under step load of infinite duration is presented. The lower bound dynamic buckling loads and the corresponding displacements are sought in the form of asymptotic expansions based on the static stability criterion and they can be determined by solving numerically (FEM) several linear problems with a single nonsingular sub-stiffness matrix.
基金supported in part by National Natural Science Foundation of China(Nos.61563009 and 61065010)Doctoral Fund of Ministry of Education of China(No.20125201110003)
文摘This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.
基金The National Natural Science Foundation of China(No.52162042)General Science and Technology Project of Jiangxi Provincial Department of Transportation(No.2024YB039).
文摘By analyzing the bus operation environment and accounting for prediction uncertainties,a bus arrival interval prediction model was developed utilizing a gated recur-rent unit(GRU)neural network.To reduce the impact of irrelevant data and boost prediction accuracy,an attention mechanism was integrated into the point model to concen-trate on important input sequence information.Based on the point predictions,the lower upper bound estimation(LUBE)method was used,providing a range for the bus interval times predicted by the model.The model was vali-dated using data from 169 bus routes in Nanchang,Jiangxi Province.The results indicated that the attention-GRU model outperformed neural network,long short-term memory and GRU models.Compared with the Bootstrap method,the LUBE method has a narrower average interval width.The coverage width-based criterion(CWC)was reduced by 8.1%,2.2%,and 5.7%at confidence levels of 85%,90%,and 95%,respectively,during the off-peak period,and by 23.2%,26.9%,and 27.3%at confidence levels of 85%,90%,and 95%,respectively,during the peak period.Therefore,it can accurately describe the fluctuation range in bus arrival times with higher accuracy and stability.
基金supported by National Natural Science Foundation of China (Grant Nos. 11471026, 11271035, 91430213, 11421101 and 11101415)
文摘We present the convergence analysis of the rectangular Morley element scheme utilised on the second order problem in arbitrary dimensions. Specifically, we prove that the convergence of the scheme is of （D（h） order in energy norm and of O（h2） order in L2 norm on general d-rectangular triangulations. Moreover, when the triangulation is uniform, the convergence rate can be of O（h2） order in energy norm, and the convergence rate in L2 norm is still of O（h2） order, which cannot be improved. Numerical examples are presented to demonstrate our theoretical results.
