The change rules associated with hot deformation of FGH96 alloy were investigated by isothermal two-pass hot deformation tests in the temperature range 1050–1125°C and at strain rates ranging from 0.001 to 0.1 s...The change rules associated with hot deformation of FGH96 alloy were investigated by isothermal two-pass hot deformation tests in the temperature range 1050–1125°C and at strain rates ranging from 0.001 to 0.1 s^(-1) on a Gleeble 3500 thermo-simulation machine. The results showed that the softening degree of the alloy between passes decreases with increasing temperature and decreasing strain rates. The critical strain of the first-pass is greater than that of the second-pass. The true stress–true strain curves showed that single-peak dynamic recrystallization, multi-peak dynamic recrystallization, and dynamic response occur when the strain rate is 0.1, 0.01, and 0.001 s^(-1), respectively. The alloy contains three different grain structures after hot deformation: partially recrystallized tissue, completely fine recrystallized tissue, coarse-grained grains. The small-angle grain boundaries increase with increasing temperature. Increasing strain rates cause the small-angle grain boundaries to first increase and then decrease.展开更多
Acoustic scene classification(ASC)is a method of recognizing and classifying environments that employ acoustic signals.Various ASC approaches based on deep learning have been developed,with convolutional neural networ...Acoustic scene classification(ASC)is a method of recognizing and classifying environments that employ acoustic signals.Various ASC approaches based on deep learning have been developed,with convolutional neural networks(CNNs)proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models.When using ASC systems in the real world,model complexity and device robustness are essential considerations.In this paper,we propose a two-pass mobile network for low-complexity classification of the acoustic scene,named TP-MobNet.With inverse residuals and linear bottlenecks,TPMobNet is based on MobileNetV2,and following mobile blocks,coordinate attention and two-pass fusion approaches are utilized.The log-range dependencies and precise position information in feature maps can be trained via coordinate attention.By capturing more diverse feature resolutions at the network’s end sides,two-pass fusions can also train generalization.Also,the model size is reduced by applying weight quantization to the trained model.By adding weight quantization to the trained model,the model size is also lowered.The TAU Urban Acoustic Scenes 2020 Mobile development set was used for all of the experiments.It has been confirmed that the proposed model,with a model size of 219.6 kB,achieves an accuracy of 73.94%.展开更多
基金Financial support from the National Natural Science Foundation of China (No. 51471023)the Ministry of Science and Technology of the People’s Republic of China (National 973 Program, No. 2014GB120000)
文摘The change rules associated with hot deformation of FGH96 alloy were investigated by isothermal two-pass hot deformation tests in the temperature range 1050–1125°C and at strain rates ranging from 0.001 to 0.1 s^(-1) on a Gleeble 3500 thermo-simulation machine. The results showed that the softening degree of the alloy between passes decreases with increasing temperature and decreasing strain rates. The critical strain of the first-pass is greater than that of the second-pass. The true stress–true strain curves showed that single-peak dynamic recrystallization, multi-peak dynamic recrystallization, and dynamic response occur when the strain rate is 0.1, 0.01, and 0.001 s^(-1), respectively. The alloy contains three different grain structures after hot deformation: partially recrystallized tissue, completely fine recrystallized tissue, coarse-grained grains. The small-angle grain boundaries increase with increasing temperature. Increasing strain rates cause the small-angle grain boundaries to first increase and then decrease.
基金This work was supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[No.2021-0-0268,Artificial Intelligence Innovation Hub(Artificial Intelligence Institute,Seoul National University)]。
文摘Acoustic scene classification(ASC)is a method of recognizing and classifying environments that employ acoustic signals.Various ASC approaches based on deep learning have been developed,with convolutional neural networks(CNNs)proving to be the most reliable and commonly utilized in ASC systems due to their suitability for constructing lightweight models.When using ASC systems in the real world,model complexity and device robustness are essential considerations.In this paper,we propose a two-pass mobile network for low-complexity classification of the acoustic scene,named TP-MobNet.With inverse residuals and linear bottlenecks,TPMobNet is based on MobileNetV2,and following mobile blocks,coordinate attention and two-pass fusion approaches are utilized.The log-range dependencies and precise position information in feature maps can be trained via coordinate attention.By capturing more diverse feature resolutions at the network’s end sides,two-pass fusions can also train generalization.Also,the model size is reduced by applying weight quantization to the trained model.By adding weight quantization to the trained model,the model size is also lowered.The TAU Urban Acoustic Scenes 2020 Mobile development set was used for all of the experiments.It has been confirmed that the proposed model,with a model size of 219.6 kB,achieves an accuracy of 73.94%.
