The improvement of question soils with cement shows great technical, economic and environmental advantages. And interest in introducing electrical resistivity measurement to assess the quality of cement treated soils ...The improvement of question soils with cement shows great technical, economic and environmental advantages. And interest in introducing electrical resistivity measurement to assess the quality of cement treated soils has increased markedly recently due to its economical, non-destructive, and relatively non-invasive advantages. This work aims to quantify the effect of cement content （aw）, porosity （nt）, and curing time（T） on the electrical resistivity （p） and unconfined compression strength （UCS） of cement treated soil. A series of electrical resistivity tests and UCS tests of cement treated soil specimen after various curing periods were carried out. A modified Archie empirical law was proposed taking into account the effect of cement content and curing period on the electrical resistivity of cement treated soil. The results show that nt/（aw·T） and nt/（aw·T^1/2） ratio are appropriate parameters to assess electrical resistivity and UCS of cement treated soil, respectively. Finally, the relationship between UCS and electrical resistivity was also established.展开更多
Despite the rapid development of mobile and embedded hardware, directly executing computationexpensive and storage-intensive deep learning algorithms on these devices’ local side remains constrained for sensory data ...Despite the rapid development of mobile and embedded hardware, directly executing computationexpensive and storage-intensive deep learning algorithms on these devices’ local side remains constrained for sensory data analysis. In this paper, we first summarize the layer compression techniques for the state-of-theart deep learning model from three categories: weight factorization and pruning, convolution decomposition, and special layer architecture designing. For each category of layer compression techniques, we quantify their storage and computation tunable by layer compression techniques and discuss their practical challenges and possible improvements. Then, we implement Android projects using TensorFlow Mobile to test these 10 compression methods and compare their practical performances in terms of accuracy, parameter size, intermediate feature size,computation, processing latency, and energy consumption. To further discuss their advantages and bottlenecks,we test their performance over four standard recognition tasks on six resource-constrained Android smartphones.Finally, we survey two types of run-time Neural Network(NN) compression techniques which are orthogonal with the layer compression techniques, run-time resource management and cost optimization with special NN architecture,which are orthogonal with the layer compression techniques.展开更多
Multipass plain strain compression test of 7055 alloy was carried out on Gleeble 1500D thermomechanical simulator to study the effect of interval time on static softening behavior between two passes. Microstructural f...Multipass plain strain compression test of 7055 alloy was carried out on Gleeble 1500D thermomechanical simulator to study the effect of interval time on static softening behavior between two passes. Microstructural features of the alloy deformed with delay times varying from 0 to 180 s after achieving a reduction of ,-~52 % in the 13 stages was investigated through TEM and EBSD observations. The 14th pass of peak stresses after different delay times were gained. The peak stress decreases with the interstage delay time increasing, but the decreasing trend is gradually slower. Static recovery, metadynamic recrystallization, and/or static recrystallization can be found in the alloy during two passes. The recovery and recrystallization degree increases with longer interstage delay time. The static recovery is the main softening mechanism. Subgrain coalescence and subgrain growth together with particle-stimulated nucleation are the main nucleation mechanisms for static recrystallization.展开更多
基金Project(BK2011618) supported by the Natural Science Foundation of Jiangsu Province, ChinaProject(51108288) supported by the National Natural Science Foundation of China
文摘The improvement of question soils with cement shows great technical, economic and environmental advantages. And interest in introducing electrical resistivity measurement to assess the quality of cement treated soils has increased markedly recently due to its economical, non-destructive, and relatively non-invasive advantages. This work aims to quantify the effect of cement content （aw）, porosity （nt）, and curing time（T） on the electrical resistivity （p） and unconfined compression strength （UCS） of cement treated soil. A series of electrical resistivity tests and UCS tests of cement treated soil specimen after various curing periods were carried out. A modified Archie empirical law was proposed taking into account the effect of cement content and curing period on the electrical resistivity of cement treated soil. The results show that nt/（aw·T） and nt/（aw·T^1/2） ratio are appropriate parameters to assess electrical resistivity and UCS of cement treated soil, respectively. Finally, the relationship between UCS and electrical resistivity was also established.
基金supported by the National Key Research and Development Program of China (No. 2018YFB1003605)Foundations of CARCH (No. CARCH201704)+3 种基金the National Natural Science Foundation of China (No. 61472312)Foundations of Shaanxi Province and Xi’an ScienceTechnology Plan (Nos. B018230008 and BD34017020001)the Foundations of Xidian University (No. JBZ171002)
文摘Despite the rapid development of mobile and embedded hardware, directly executing computationexpensive and storage-intensive deep learning algorithms on these devices’ local side remains constrained for sensory data analysis. In this paper, we first summarize the layer compression techniques for the state-of-theart deep learning model from three categories: weight factorization and pruning, convolution decomposition, and special layer architecture designing. For each category of layer compression techniques, we quantify their storage and computation tunable by layer compression techniques and discuss their practical challenges and possible improvements. Then, we implement Android projects using TensorFlow Mobile to test these 10 compression methods and compare their practical performances in terms of accuracy, parameter size, intermediate feature size,computation, processing latency, and energy consumption. To further discuss their advantages and bottlenecks,we test their performance over four standard recognition tasks on six resource-constrained Android smartphones.Finally, we survey two types of run-time Neural Network(NN) compression techniques which are orthogonal with the layer compression techniques, run-time resource management and cost optimization with special NN architecture,which are orthogonal with the layer compression techniques.
基金financially supported by the Natural Science Foundation of Inner Mongolia (No. 2011bs0802)Research Fund for the Higher Education of Inner Mongolia (No. NJZY11075)
文摘Multipass plain strain compression test of 7055 alloy was carried out on Gleeble 1500D thermomechanical simulator to study the effect of interval time on static softening behavior between two passes. Microstructural features of the alloy deformed with delay times varying from 0 to 180 s after achieving a reduction of ,-~52 % in the 13 stages was investigated through TEM and EBSD observations. The 14th pass of peak stresses after different delay times were gained. The peak stress decreases with the interstage delay time increasing, but the decreasing trend is gradually slower. Static recovery, metadynamic recrystallization, and/or static recrystallization can be found in the alloy during two passes. The recovery and recrystallization degree increases with longer interstage delay time. The static recovery is the main softening mechanism. Subgrain coalescence and subgrain growth together with particle-stimulated nucleation are the main nucleation mechanisms for static recrystallization.
