Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of ...Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.展开更多
The nervous system orchestrates diverse behaviors such as reproduction, sleep, feeding, and aggression, and selects a single behavior for execution at any given time. This requires neural mechanisms for behavioral sel...The nervous system orchestrates diverse behaviors such as reproduction, sleep, feeding, and aggression, and selects a single behavior for execution at any given time. This requires neural mechanisms for behavioral selection sensitive to both internal physiological states and external environmental conditions. For example, hungry animals展开更多
Sanjiao acupuncture and HuangDiSan can promote the proliferation, migration and differentiation of exogenous neural stem cells in senescence-accelerated mouse prone 8 （SAMP8） mice and can improve learning and memory...Sanjiao acupuncture and HuangDiSan can promote the proliferation, migration and differentiation of exogenous neural stem cells in senescence-accelerated mouse prone 8 （SAMP8） mice and can improve learning and memory impairment and behavioral function in dementia-model mice. Thus, we sought to determine whether Sanjiao acupuncture and HuangDiSan can elevate the effect of neural stem cell transplantation in Alzheimer’s disease model mice. Sanjiao acupuncture was used to stimulate Danzhong （CV17）, Zhongwan （CV12）,Qihai （CV6）, bilateral Xuehai （SP10） and bilateral Zusanli （ST36） 15 days before and after implantation of neural stem cells （5 × 10^5） into the hippocampal dentate gyrus of SAMP8 mice. Simultaneously, 0.2 mL HuangDiSan, containing Rehmannia Root and Chinese Angelica,was intragastrically administered. Our results demonstrated that compared with mice undergoing neural stem cell transplantation alone,learning ability was significantly improved and synaptophysin mRNA and protein levels were greatly increased in the hippocampus of mice undergoing both Sanjiao acupuncture and intragastric administration of HuangDiSan. We conclude that the combination of Sanjiao acupuncture and HuangDiSan can effectively improve dementia symptoms in mice, and the mechanism of this action might be related to the regulation of synaptophysin expression.展开更多
Aged populations have remarkable variability in recent memory and cognitive mapping. Although some individuals may have substantial age-related impairments, others perform almost as well as young individuals. This pap...Aged populations have remarkable variability in recent memory and cognitive mapping. Although some individuals may have substantial age-related impairments, others perform almost as well as young individuals. This paper reviews the relevant data on aged rats and indicates two challenges for biomarkers of aging. The first is to provide an appropriate quantitative description of these individual differences. The second is to use them effectively as markers for age-related changes in psychological functions and their neural substrates.展开更多
With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recogn...With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.展开更多
At the present time, numerical models （such as, numerical simulation based on FEM） adopted broadly in technological design and process control in forging field can not implement the realtime control of material form...At the present time, numerical models （such as, numerical simulation based on FEM） adopted broadly in technological design and process control in forging field can not implement the realtime control of material forming process. It is thus necessary to establish a dynamic model fitting for the real-time control of material deformation processing in order to increase production efficiency, improve forging qualities and increase yields. In this paper, hot deformation behaviors of FGH96 superalloy are characterized by using hot compressive simulation experiments. The artificial neural network （ANN） model of FGH96 superalloy during hot deformation is established by using back propagation （BP） network. Then according to electrical analogy theory, its analog-circuit （AC） model is obtained through mapping the ANN model into analog circuit. Testing results show that the ANN model and the AC model of FGH96 superalloy hot deformation behaviors possess high predictive precisions and can well describe the superalloy＇s dynamic flow behaviors. The ideas proposed in this paper can be applied in the real-time control of material deformation processing.展开更多
By establishing concept an transient solutions of general nonlinear systems converging to its equilibrium set, long-time behavior of solutions for cellular neural network systems is studied. A stability condition in g...By establishing concept an transient solutions of general nonlinear systems converging to its equilibrium set, long-time behavior of solutions for cellular neural network systems is studied. A stability condition in generalized sense is obtained. This result reported has an important guide to concrete neural network designs.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.51275415 and50905144)the Natural Science Basic Research Plan in Shanxi Province(No.2011JQ6004)the Program of the Ministry of Education of China for Introducing Talents of Discipline to Universities(No.B08040)
文摘Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.
文摘The nervous system orchestrates diverse behaviors such as reproduction, sleep, feeding, and aggression, and selects a single behavior for execution at any given time. This requires neural mechanisms for behavioral selection sensitive to both internal physiological states and external environmental conditions. For example, hungry animals
基金supported by the National Natural Science Foundation of China,No.81202740 and 81603686the Natural Science Foundation of Tianjin of China,No.17JCYBJC26200 and 12JCQNJC07400+1 种基金the Public Health Bureau Science and Technology Foundation of Tianjin of China,No.2014KY15the Specialized Research Foundation for the Doctoral Program of Higher Education,No.20121210120002
文摘Sanjiao acupuncture and HuangDiSan can promote the proliferation, migration and differentiation of exogenous neural stem cells in senescence-accelerated mouse prone 8 （SAMP8） mice and can improve learning and memory impairment and behavioral function in dementia-model mice. Thus, we sought to determine whether Sanjiao acupuncture and HuangDiSan can elevate the effect of neural stem cell transplantation in Alzheimer’s disease model mice. Sanjiao acupuncture was used to stimulate Danzhong （CV17）, Zhongwan （CV12）,Qihai （CV6）, bilateral Xuehai （SP10） and bilateral Zusanli （ST36） 15 days before and after implantation of neural stem cells （5 × 10^5） into the hippocampal dentate gyrus of SAMP8 mice. Simultaneously, 0.2 mL HuangDiSan, containing Rehmannia Root and Chinese Angelica,was intragastrically administered. Our results demonstrated that compared with mice undergoing neural stem cell transplantation alone,learning ability was significantly improved and synaptophysin mRNA and protein levels were greatly increased in the hippocampus of mice undergoing both Sanjiao acupuncture and intragastric administration of HuangDiSan. We conclude that the combination of Sanjiao acupuncture and HuangDiSan can effectively improve dementia symptoms in mice, and the mechanism of this action might be related to the regulation of synaptophysin expression.
文摘Aged populations have remarkable variability in recent memory and cognitive mapping. Although some individuals may have substantial age-related impairments, others perform almost as well as young individuals. This paper reviews the relevant data on aged rats and indicates two challenges for biomarkers of aging. The first is to provide an appropriate quantitative description of these individual differences. The second is to use them effectively as markers for age-related changes in psychological functions and their neural substrates.
文摘With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.
文摘At the present time, numerical models （such as, numerical simulation based on FEM） adopted broadly in technological design and process control in forging field can not implement the realtime control of material forming process. It is thus necessary to establish a dynamic model fitting for the real-time control of material deformation processing in order to increase production efficiency, improve forging qualities and increase yields. In this paper, hot deformation behaviors of FGH96 superalloy are characterized by using hot compressive simulation experiments. The artificial neural network （ANN） model of FGH96 superalloy during hot deformation is established by using back propagation （BP） network. Then according to electrical analogy theory, its analog-circuit （AC） model is obtained through mapping the ANN model into analog circuit. Testing results show that the ANN model and the AC model of FGH96 superalloy hot deformation behaviors possess high predictive precisions and can well describe the superalloy＇s dynamic flow behaviors. The ideas proposed in this paper can be applied in the real-time control of material deformation processing.
文摘By establishing concept an transient solutions of general nonlinear systems converging to its equilibrium set, long-time behavior of solutions for cellular neural network systems is studied. A stability condition in generalized sense is obtained. This result reported has an important guide to concrete neural network designs.
文摘多行为推荐(multi-behavior recommendation,MBR)在互联网平台中愈发重要,但现有方法仍面临两大挑战:a)无法刻画用户不同行为下的复杂兴趣偏好;b)难以建模不同行为间的相互关系。基于此,提出一种对比学习增强的多行为超图神经网络模型(multi-behavior hypergraph neural network model enhanced with contrastive lear-ning,MBHCL),在建模用户复杂多类型交互的同时,结合对比学习捕获行为间共性与差异,以获取更优嵌入表示,缓解冷启动与数据稀疏问题。具体地,MBHCL首先构建用户-项目多行为交互超图,以刻画用户对项目不同维度的偏好;其次设计三个对比任务整合单行为表示,通过捕捉行为间的共性与差异获取全面用户兴趣偏好。最终,MBHCL在四个真实场景数据集上进行对比实验。结果表明,在Tmall和BeiBei数据集上,HIT和NDCG指标有至少4.8%的提升,在Kuairand和Yelp数据集上,HIT和NDCG指标至少提升3.6%,并通过消融实验验证了各模块的有效性,同时显著改善了冷启动用户推荐效果。
