An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models...An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis(SSA) and stack long short-term memory(SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine(SVM) and auto regressive integrated moving average(ARIMA). The mean relative error(MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error(RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides.展开更多
Thermoplastic polyimides(PIs)with shape memory potential have received growing attention in recent years.In this work,highperformance thermoplastic PIs were fabricated by introducing PIs with chain rigidity(r-PI)into ...Thermoplastic polyimides(PIs)with shape memory potential have received growing attention in recent years.In this work,highperformance thermoplastic PIs were fabricated by introducing PIs with chain rigidity(r-PI)into PI with chain flexibility(f-PI).The influences of molecular chain entanglement andπ-πinteractions on their thermomechanical and shape memory properties were investigated.The degree of molecular chain entanglement was quantitively characterized based on dynamic mechanical analysis(DMA).Theπ-πinteractions were investigated in detail by X-ray diffraction(XRD)and UV-Vis spectroscopy.It was found that the entanglement density increased andπ-πinteractions became stronger with the introduction of r-PI into f-PI,leading to the improvement of shape recovery.Moreover,a broad and increased glass transition temperature(T_(g))was achieved,endowing the PIs with multiple shape memory properties.The synergistic effects of increased entanglement density and enhancedπ-πinteractions were beneficial to regulating interchain interactions and thereby achieving high shape memory performance of the PIs.展开更多
Amorphous In–Ga–Zn–O(a-IGZO)thin-film transistor(TFT)memories with novel p-SnO/n-SnO_(2) heterojunction charge trapping stacks(CTSs)are investigated comparatively under a maximum fabrication temperature of 280℃.Co...Amorphous In–Ga–Zn–O(a-IGZO)thin-film transistor(TFT)memories with novel p-SnO/n-SnO_(2) heterojunction charge trapping stacks(CTSs)are investigated comparatively under a maximum fabrication temperature of 280℃.Compared to a single p-SnO or n-SnO_(2) charge trapping layer(CTL),the heterojunction CTSs can achieve electrically programmable and erasable characteristics as well as good data retention.Of the two CTSs,the tunneling layer/p-SnO/nSnO_(2)/blocking layer architecture demonstrates much higher program efficiency,more robust data retention,and comparably superior erase characteristics.The resulting memory window is as large as 6.66 V after programming at 13 V/1 ms and erasing at-8 V/1 ms,and the ten-year memory window is extrapolated to be 4.41 V.This is attributed to shallow traps in p-SnO and deep traps in n-SnO_(2),and the formation of a built-in electric field in the heterojunction.展开更多
Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely h...Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.展开更多
The interior structural evolution accompanying reverse shape memory effect (RSMEin a Cu-Zn-Al alloy was studied by means of transmission electron microscopy. It was found that RSME is closely related to bainitic trans...The interior structural evolution accompanying reverse shape memory effect (RSMEin a Cu-Zn-Al alloy was studied by means of transmission electron microscopy. It was found that RSME is closely related to bainitic transformation in this alloy during the isothermal reaction at moderate temperatures. At a given temperature and a certain external constraint stress, the shape memory effect depends mainly on the aging time.During the early stage, the shape memory effect enhances with the increase of reactiotn time. Then it will decrease gradually apon further aging. If the alloy is overaged, the stacking faults of bainite will disappear gradually by the motion of partial dislocations through which long range diffusion of solute atoms takes place, giving rise to the deterioration of RSME. When all the bainite transforms to α phase, RSME will lose completely.展开更多
Heavy ion irradiation effects on charge trapping memory(CTM)capacitors with TiN/Al_(2)O_(3)/HfO_(2)/Al_(2)O_(3)/HfO_(2)/SiO_(2)/p-Si structure have been investigated.The ion-induced interface charges and oxide trap ch...Heavy ion irradiation effects on charge trapping memory(CTM)capacitors with TiN/Al_(2)O_(3)/HfO_(2)/Al_(2)O_(3)/HfO_(2)/SiO_(2)/p-Si structure have been investigated.The ion-induced interface charges and oxide trap charges were calculated and analyzed by capacitance-voltage(C-V)characteristics.The C-V curves shift towards the negative direction after swift heavy ion irradiation,due to the net positive charges accumulating in the trapping layer.The memory window decreases with the increase of ion fluence at high voltage,which results from heavy ion-induced structural damage in the blocking layer.The mechanism of heavy ion irradiation effects on CTM capacitors is discussed in detail with energy band diagrams.The results may help to better understand the physical mechanism of heavy ion-induced degradation of CTM capacitors.展开更多
基金supported by the Natural Science Foundation of Shaanxi Province under Grant 2019JQ206in part by the Science and Technology Department of Shaanxi Province under Grant 2020CGXNG-009in part by the Education Department of Shaanxi Province under Grant 17JK0346。
文摘An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis(SSA) and stack long short-term memory(SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine(SVM) and auto regressive integrated moving average(ARIMA). The mean relative error(MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error(RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides.
基金financially supported by the Engineering Research Center for Clean Production of Textile Printing and Dyeing,Ministry of Education(No.FZYR2021001)Shanghai Pujiang Program(No.19PJ1400400)Shanghai Key Laboratory of Lightweight Composite(No.2232019A4-04)。
文摘Thermoplastic polyimides(PIs)with shape memory potential have received growing attention in recent years.In this work,highperformance thermoplastic PIs were fabricated by introducing PIs with chain rigidity(r-PI)into PI with chain flexibility(f-PI).The influences of molecular chain entanglement andπ-πinteractions on their thermomechanical and shape memory properties were investigated.The degree of molecular chain entanglement was quantitively characterized based on dynamic mechanical analysis(DMA).Theπ-πinteractions were investigated in detail by X-ray diffraction(XRD)and UV-Vis spectroscopy.It was found that the entanglement density increased andπ-πinteractions became stronger with the introduction of r-PI into f-PI,leading to the improvement of shape recovery.Moreover,a broad and increased glass transition temperature(T_(g))was achieved,endowing the PIs with multiple shape memory properties.The synergistic effects of increased entanglement density and enhancedπ-πinteractions were beneficial to regulating interchain interactions and thereby achieving high shape memory performance of the PIs.
基金Project supported by the National Natural Science Foundation of China (Grant No.61874029)。
文摘Amorphous In–Ga–Zn–O(a-IGZO)thin-film transistor(TFT)memories with novel p-SnO/n-SnO_(2) heterojunction charge trapping stacks(CTSs)are investigated comparatively under a maximum fabrication temperature of 280℃.Compared to a single p-SnO or n-SnO_(2) charge trapping layer(CTL),the heterojunction CTSs can achieve electrically programmable and erasable characteristics as well as good data retention.Of the two CTSs,the tunneling layer/p-SnO/nSnO_(2)/blocking layer architecture demonstrates much higher program efficiency,more robust data retention,and comparably superior erase characteristics.The resulting memory window is as large as 6.66 V after programming at 13 V/1 ms and erasing at-8 V/1 ms,and the ten-year memory window is extrapolated to be 4.41 V.This is attributed to shallow traps in p-SnO and deep traps in n-SnO_(2),and the formation of a built-in electric field in the heterojunction.
文摘为了解决单个神经网络预测的局限性和时间序列的波动性,提出了一种奇异谱分析(singular spectrum analysis,SSA)和Stacking框架相结合的短期负荷预测方法。利用随机森林筛选出与历史负荷相关性强烈的特征因素,采用SSA为负荷数据降噪,简化模型计算过程;基于Stacking框架,结合长短期记忆(long and short-term memory,LSTM)-自注意力机制(self-attention mechanism,SA)、径向基(radial base functions,RBF)神经网络和线性回归方法集成新的组合模型,同时利用交叉验证方法避免模型过拟合;选取PJM和澳大利亚电力负荷数据集进行验证。仿真结果表明,与其他模型比较,所提模型预测精度高。
文摘Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.
文摘The interior structural evolution accompanying reverse shape memory effect (RSMEin a Cu-Zn-Al alloy was studied by means of transmission electron microscopy. It was found that RSME is closely related to bainitic transformation in this alloy during the isothermal reaction at moderate temperatures. At a given temperature and a certain external constraint stress, the shape memory effect depends mainly on the aging time.During the early stage, the shape memory effect enhances with the increase of reactiotn time. Then it will decrease gradually apon further aging. If the alloy is overaged, the stacking faults of bainite will disappear gradually by the motion of partial dislocations through which long range diffusion of solute atoms takes place, giving rise to the deterioration of RSME. When all the bainite transforms to α phase, RSME will lose completely.
基金the National Natural Science Foundation of China(Grant Nos.12105340,12035019,and12075290)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.2020412)。
文摘Heavy ion irradiation effects on charge trapping memory(CTM)capacitors with TiN/Al_(2)O_(3)/HfO_(2)/Al_(2)O_(3)/HfO_(2)/SiO_(2)/p-Si structure have been investigated.The ion-induced interface charges and oxide trap charges were calculated and analyzed by capacitance-voltage(C-V)characteristics.The C-V curves shift towards the negative direction after swift heavy ion irradiation,due to the net positive charges accumulating in the trapping layer.The memory window decreases with the increase of ion fluence at high voltage,which results from heavy ion-induced structural damage in the blocking layer.The mechanism of heavy ion irradiation effects on CTM capacitors is discussed in detail with energy band diagrams.The results may help to better understand the physical mechanism of heavy ion-induced degradation of CTM capacitors.
