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
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
With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contex...With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.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
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supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.
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.
