Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict...Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.展开更多
Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Reg...Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,w...Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.展开更多
Algal blooms pose significant threats to marine ecosystems and human health.Accurate forecasting of chlorophyll-a(Chl-a)concentration is critical for effective control of harmful algal blooms(HABs).This study proposes...Algal blooms pose significant threats to marine ecosystems and human health.Accurate forecasting of chlorophyll-a(Chl-a)concentration is critical for effective control of harmful algal blooms(HABs).This study proposes a novel approach for enhancing Chl-a concentration forecasting by integrating the AdaBoost algorithm with long short-term memory(LSTM)neural networks.We developed a strong forecasting model by combining adaptive boosting(AdaBoost)with LSTM models in Xiamen Bay,China.This model achieved higher correlation coefficients and lower root mean square errors than individual weak models.The AdaBoost-optimized model increased the frequency of low absolute errors while decreasing the occurrence of high absolute errors,which indicated improved overall prediction accuracy and reliability.Moreover,the model effectively reduced performance fluctuations,which are frequent in deep learning models.The application of a non-uniform initial weighting scheme within the AdaBoost framework further enhanced model performance for high Chl-a concentration values,which are critical for detecting HABs.The optimization effect of AdaBoost was validated by applying it to data collected from the Ningde area.A robust framework is provided in this study to improve Chl-a concentration predictions and offer valuable insights for managing coastal ecosystems facing the challenges of algal blooms.展开更多
Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricac...Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting.To address these difficulties,this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory(LSTM)network with a Single Candidate Optimizer(SCO)algorithm.In contrast to conventional techniques that rely on random parameter initialization,the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work with a single candidate solution,thereby substantially reducing the computational overhead compared to traditional population-based metaheuristics.The performance of the model was benchmarked against various classical and deep learning models across datasets from three geographically diverse sites,using multiple evaluation metrics.Experimental findings demonstrate that the SCO-optimized model enhances prediction accuracy by up to 12.5%over standard LSTM implementations.展开更多
This study employs the Long Short-Term Memory(LSTM)rainfall-runoff model to simulate and predict runoff in typical basins of the Jiziwan Region of the Yellow River,aiming to overcome the shortcomings of traditional hy...This study employs the Long Short-Term Memory(LSTM)rainfall-runoff model to simulate and predict runoff in typical basins of the Jiziwan Region of the Yellow River,aiming to overcome the shortcomings of traditional hydrological models in complex nonlinear environments.The Jiziwan Region of the Yellow River is affected by human activities such as urbanization,agricultural development,and water resource management,leading to increasingly complex hydrological processes.Traditional hydrological models struggle to effectively capture the relationship between rainfall and runoff.The LSTM rainfall-runoff model,using deep learning techniques,automatically extracts features from data,identifies complex patterns and long-term dependency in time series,and provides more accurate and reliable runoff predictions.The results demonstrate that the LSTM rainfall-runoff model adapts well to the complex hydrological characteristics of the Jiziwan Region,showing superior performance over traditional hydrological models,especially in addressing the changing trends under the influence of climate change and human activities.By analyzing the interannual and within-year variations of runoff under different climate change scenarios,the model can predict the evolution trends of runoff under future climate conditions,providing a scientific basis for water resource management and decision-making.The results indicate that under different climate change scenarios,the runoff in several typical basins of the Jiziwan Region exhibits different variation trends.Under SSP1-2.6 and SSP2-4.5,some basins,such as the Wuding River Basin,Tuwei River Basin,and Gushanchuan Basin,show a decreasing trend in annual runoff.For example,in the Wuding River Basin,the average runoff from 2025 to 2040 is 12.48 m^(3)/s(SSP1-2.6),with an annual decrease of 0.10 m^(3)/s;in the Tuwei River Basin,the runoff from 2025 to 2040 is 12.96 m^(3)/s(SSP1-2.6),with an annual decrease of 0.10 m^(3)/s.In contrast,under SSP3-7.0 and SSP5-8.5,with climate warming and changes in precipitation patterns,runoff in some basins shows an increasing trend,particularly during the snowmelt period and with increased summer precipitation,leading to a significant rise in runoff.展开更多
Current aero-engine life prediction areas typically focus on single-scale degradation features,and the existing methods are not comprehensive enough to capture the relationship within time series data.To address this ...Current aero-engine life prediction areas typically focus on single-scale degradation features,and the existing methods are not comprehensive enough to capture the relationship within time series data.To address this problem,we propose a novel remaining useful life(RUL)estimation method based on the attention mechanism.Our approach designs a two-layer multi-scale feature extraction module that integrates degradation features at different scales.These features are then processed in parallel by a self-attention module and a three-layer long short-term memory(LSTM)network,which together capture long-term dependencies and adaptively weigh important feature.The integration of degradation patterns from both components into the attention module enhances the model’s ability to capture long-term dependencies.Visualizing the attention module’s weight matrices further improves model interpretability.Experimental results on the C-MAPSS dataset demonstrate that our approach outperforms the existing state-of-the-art methods.展开更多
Stroke survivors often face significant challenges when performing daily self-care activities due to upper limb motor impairments.Traditional surface electromyography(sEMG)analysis typically focuses on isolated hand p...Stroke survivors often face significant challenges when performing daily self-care activities due to upper limb motor impairments.Traditional surface electromyography(sEMG)analysis typically focuses on isolated hand postures,overlooking the complexity of object-interactive behaviors that are crucial for promoting patient independence.This study introduces a novel framework that combines high-density sEMG(HD-sEMG)signals with an improved Whale Optimization Algorithm(IWOA)-optimized Long Short-Term Memory(LSTM)network to address this limitation.The key contributions of this work include:(1)the creation of a specialized HD-sEMG dataset that captures nine continuous self-care behaviors,along with time and posture markers,to better reflect real-world patient interactions;(2)the development of a multi-channel feature fusion module based on Pascal’s theorem,which enables efficient signal segmentation and spatial–temporal feature extraction;and(3)the enhancement of the IWOA algorithm,which integrates optimal point set initialization,a diversity-driven pooling mechanism,and cosine-based differential evolution to optimize LSTM hyperparameters,thereby improving convergence and global search capabilities.Experimental results demonstrate superior performance,achieving 99.58%accuracy in self-care behavior recognition and 86.19%accuracy for 17 continuous gestures on the Ninapro db2 benchmark.The framework operates with low latency,meeting the real-time requirements for assistive devices.By enabling precise,context-aware recognition of daily activities,this work advances personalized rehabilitation technologies,empowering stroke patients to regain autonomy in self-care tasks.The proposed methodology offers a robust,scalable solution for clinical applications,bridging the gap between laboratory-based gesture recognition and practical,patient-centered care.展开更多
The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope ...The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope and image processing technology were employed to achieve a quantitative grain size distribution of NiTi alloys so as to provide experimental data for molecular dynamics modeling at the atomic scale.Considering the size effect of molecular dynamics model on material properties,a reasonable modeling size was provided by taking into account three characteristic dimensions from the perspective of macro,meso,and micro scales according to the Buckinghamπtheorem.Then,the corresponding MD simulation on deformation and fracture behavior was investigated to derive a parameterized traction-separation(T-S)law,and then it was embedded into cohesive elements of finite element software.Thus,the crack propagation behavior in NiTi alloys was reproduced by the finite element method(FEM).The experimental results show that the predicted initiation fracture toughness is in good agreement with experimental data.In addition,it is found that the dynamics initiation fracture toughness increases with decreasing grain size and increasing loading velocity.展开更多
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables...Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.展开更多
基金supported by the National Key Research and Development Program of China(No.2021YFB2600300).
文摘Intelligent maintenance of roads and highways requires accurate deterioration evaluation and performance prediction of asphalt pavement.To this end,we develop a time series long short-term memory(LSTM)model to predict key performance indicators(PIs)of pavement,namely the international roughness index(IRI)and rutting depth(RD).Subsequently,we propose a comprehensive performance indicator for the pavement quality index(PQI),which leverages the highway performance assessment standard method,entropy weight method,and fuzzy comprehensive evaluation method.This indicator can evaluate the overall performance condition of the pavement.The data used for the model development and analysis are extracted from tests on two full-scale accelerated test tracks,called MnRoad and RIOHTrack.Six variables are used as predictors,including temperature,precipitation,total traffic volume,asphalt surface layer thickness,pavement age,and maintenance condition.Furthermore,wavelet denoising is performed to analyze the impact of missing or abnormal data on the LSTM model accuracy.In comparison to a traditional autoregressive integrated moving average(ARIMAX)model,the proposed LSTM model performs better in terms of PI prediction and resiliency to noise.Finally,the overall prediction accuracy of our proposed performance indicator PQI is 93.8%.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd..(Grant No.H20230317)。
文摘Influenced by complex external factors,the displacement-time curve of reservoir landslides demonstrates both short-term and long-term diversity and dynamic complexity.It is difficult for existing methods,including Regression models and Neural network models,to perform multi-characteristic coupled displacement prediction because they fail to consider landslide creep characteristics.This paper integrates the creep characteristics of landslides with non-linear intelligent algorithms and proposes a dynamic intelligent landslide displacement prediction method based on a combination of the Biological Growth model(BG),Convolutional Neural Network(CNN),and Long ShortTerm Memory Network(LSTM).This prediction approach improves three different biological growth models,thereby effectively extracting landslide creep characteristic parameters.Simultaneously,it integrates external factors(rainfall and reservoir water level)to construct an internal and external comprehensive dataset for data augmentation,which is input into the improved CNN-LSTM model.Thereafter,harnessing the robust feature extraction capabilities and spatial translation invariance of CNN,the model autonomously captures short-term local fluctuation characteristics of landslide displacement,and combines LSTM's efficient handling of long-term nonlinear temporal data to improve prediction performance.An evaluation of the Liangshuijing landslide in the Three Gorges Reservoir Area indicates that BG-CNN-LSTM exhibits high prediction accuracy,excellent generalization capabilities when dealing with various types of landslides.The research provides an innovative approach to achieving the whole-process,realtime,high-precision displacement predictions for multicharacteristic coupled landslides.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
基金This work is supported by the National Key Research and Development Program of China(No.2023YFB4203000)the National Natural Science Foundation of China(No.U22A20178)
文摘Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively.
基金The National Natural Science Foundation of China under contract No.U22A20579Guangxi Key R&D Program of China under contract No.GUIKE AB25069453+2 种基金the Integrated Marine and Fishery Services Program of Fujian Provincial Department of Ocean and Fisheries under contract No.FYZF-YJYB-2025-1-2the Science and Technology Program of Fujian Province,China,under contract No.2023Y4001the Science and Technology Program of Xiamen,China,under contract No.3502Z20226021.
文摘Algal blooms pose significant threats to marine ecosystems and human health.Accurate forecasting of chlorophyll-a(Chl-a)concentration is critical for effective control of harmful algal blooms(HABs).This study proposes a novel approach for enhancing Chl-a concentration forecasting by integrating the AdaBoost algorithm with long short-term memory(LSTM)neural networks.We developed a strong forecasting model by combining adaptive boosting(AdaBoost)with LSTM models in Xiamen Bay,China.This model achieved higher correlation coefficients and lower root mean square errors than individual weak models.The AdaBoost-optimized model increased the frequency of low absolute errors while decreasing the occurrence of high absolute errors,which indicated improved overall prediction accuracy and reliability.Moreover,the model effectively reduced performance fluctuations,which are frequent in deep learning models.The application of a non-uniform initial weighting scheme within the AdaBoost framework further enhanced model performance for high Chl-a concentration values,which are critical for detecting HABs.The optimization effect of AdaBoost was validated by applying it to data collected from the Ningde area.A robust framework is provided in this study to improve Chl-a concentration predictions and offer valuable insights for managing coastal ecosystems facing the challenges of algal blooms.
文摘Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems.Nevertheless,the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting.To address these difficulties,this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory(LSTM)network with a Single Candidate Optimizer(SCO)algorithm.In contrast to conventional techniques that rely on random parameter initialization,the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work with a single candidate solution,thereby substantially reducing the computational overhead compared to traditional population-based metaheuristics.The performance of the model was benchmarked against various classical and deep learning models across datasets from three geographically diverse sites,using multiple evaluation metrics.Experimental findings demonstrate that the SCO-optimized model enhances prediction accuracy by up to 12.5%over standard LSTM implementations.
基金the National Key R&D Program of China(No.2023YFC3206504)National Natural Science Foundation of China(Nos.52121006,41961124006,51911540477)+1 种基金Young Top-Notch Talent Support Program of National High-level Talents Special Support PlanResearch Project of Ministry of Natural Resources(No.20210103)for providing financial support for this research。
文摘This study employs the Long Short-Term Memory(LSTM)rainfall-runoff model to simulate and predict runoff in typical basins of the Jiziwan Region of the Yellow River,aiming to overcome the shortcomings of traditional hydrological models in complex nonlinear environments.The Jiziwan Region of the Yellow River is affected by human activities such as urbanization,agricultural development,and water resource management,leading to increasingly complex hydrological processes.Traditional hydrological models struggle to effectively capture the relationship between rainfall and runoff.The LSTM rainfall-runoff model,using deep learning techniques,automatically extracts features from data,identifies complex patterns and long-term dependency in time series,and provides more accurate and reliable runoff predictions.The results demonstrate that the LSTM rainfall-runoff model adapts well to the complex hydrological characteristics of the Jiziwan Region,showing superior performance over traditional hydrological models,especially in addressing the changing trends under the influence of climate change and human activities.By analyzing the interannual and within-year variations of runoff under different climate change scenarios,the model can predict the evolution trends of runoff under future climate conditions,providing a scientific basis for water resource management and decision-making.The results indicate that under different climate change scenarios,the runoff in several typical basins of the Jiziwan Region exhibits different variation trends.Under SSP1-2.6 and SSP2-4.5,some basins,such as the Wuding River Basin,Tuwei River Basin,and Gushanchuan Basin,show a decreasing trend in annual runoff.For example,in the Wuding River Basin,the average runoff from 2025 to 2040 is 12.48 m^(3)/s(SSP1-2.6),with an annual decrease of 0.10 m^(3)/s;in the Tuwei River Basin,the runoff from 2025 to 2040 is 12.96 m^(3)/s(SSP1-2.6),with an annual decrease of 0.10 m^(3)/s.In contrast,under SSP3-7.0 and SSP5-8.5,with climate warming and changes in precipitation patterns,runoff in some basins shows an increasing trend,particularly during the snowmelt period and with increased summer precipitation,leading to a significant rise in runoff.
基金supported by the National Key Research and Development Program of China (2023YFB4302403)the Research and Practical Innovation Program of NUAA (xcxjh20230735)。
文摘Current aero-engine life prediction areas typically focus on single-scale degradation features,and the existing methods are not comprehensive enough to capture the relationship within time series data.To address this problem,we propose a novel remaining useful life(RUL)estimation method based on the attention mechanism.Our approach designs a two-layer multi-scale feature extraction module that integrates degradation features at different scales.These features are then processed in parallel by a self-attention module and a three-layer long short-term memory(LSTM)network,which together capture long-term dependencies and adaptively weigh important feature.The integration of degradation patterns from both components into the attention module enhances the model’s ability to capture long-term dependencies.Visualizing the attention module’s weight matrices further improves model interpretability.Experimental results on the C-MAPSS dataset demonstrate that our approach outperforms the existing state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(72061006)the research on the auxiliary diagnosis system of chronic injury of levator scapulae based on the concept of digital twin(Contract No:Qian Kehe Support[2023]General 117)Research on indoor intelligent assisted walking robot for the rehabilitation of walking ability of the elderly(Contract No:Qian kehe Support[2023]General 124).
文摘Stroke survivors often face significant challenges when performing daily self-care activities due to upper limb motor impairments.Traditional surface electromyography(sEMG)analysis typically focuses on isolated hand postures,overlooking the complexity of object-interactive behaviors that are crucial for promoting patient independence.This study introduces a novel framework that combines high-density sEMG(HD-sEMG)signals with an improved Whale Optimization Algorithm(IWOA)-optimized Long Short-Term Memory(LSTM)network to address this limitation.The key contributions of this work include:(1)the creation of a specialized HD-sEMG dataset that captures nine continuous self-care behaviors,along with time and posture markers,to better reflect real-world patient interactions;(2)the development of a multi-channel feature fusion module based on Pascal’s theorem,which enables efficient signal segmentation and spatial–temporal feature extraction;and(3)the enhancement of the IWOA algorithm,which integrates optimal point set initialization,a diversity-driven pooling mechanism,and cosine-based differential evolution to optimize LSTM hyperparameters,thereby improving convergence and global search capabilities.Experimental results demonstrate superior performance,achieving 99.58%accuracy in self-care behavior recognition and 86.19%accuracy for 17 continuous gestures on the Ninapro db2 benchmark.The framework operates with low latency,meeting the real-time requirements for assistive devices.By enabling precise,context-aware recognition of daily activities,this work advances personalized rehabilitation technologies,empowering stroke patients to regain autonomy in self-care tasks.The proposed methodology offers a robust,scalable solution for clinical applications,bridging the gap between laboratory-based gesture recognition and practical,patient-centered care.
基金Funded by the National Natural Science Foundation of China Academy of Engineering Physics and Jointly Setup"NSAF"Joint Fund(No.U1430119)。
文摘The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope and image processing technology were employed to achieve a quantitative grain size distribution of NiTi alloys so as to provide experimental data for molecular dynamics modeling at the atomic scale.Considering the size effect of molecular dynamics model on material properties,a reasonable modeling size was provided by taking into account three characteristic dimensions from the perspective of macro,meso,and micro scales according to the Buckinghamπtheorem.Then,the corresponding MD simulation on deformation and fracture behavior was investigated to derive a parameterized traction-separation(T-S)law,and then it was embedded into cohesive elements of finite element software.Thus,the crack propagation behavior in NiTi alloys was reproduced by the finite element method(FEM).The experimental results show that the predicted initiation fracture toughness is in good agreement with experimental data.In addition,it is found that the dynamics initiation fracture toughness increases with decreasing grain size and increasing loading velocity.
基金supported by the National Natural Science Foundation of China(No.42061065)the Third Xinjiang Comprehensive Scientific Expedition,China(No.2022xjkk03010102).
文摘Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.
