The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
This study investigates the bifurcation dynamics underlying rhythmic transitions in a biophysical hippocampal–cortical neural network model.We specifically focus on the membrane potential dynamics of excitatory neuro...This study investigates the bifurcation dynamics underlying rhythmic transitions in a biophysical hippocampal–cortical neural network model.We specifically focus on the membrane potential dynamics of excitatory neurons in the hippocampal CA3 region and examine how strong coupling parameters modulate memory consolidation processes.Employing bifurcation analysis,we systematically characterize the model's complex dynamical behaviors.Subsequently,a characteristic waveform recognition algorithm enables precise feature extraction and automated detection of hippocampal sharp-wave ripples(SWRs).Our results demonstrate that neuronal rhythms exhibit a propensity for abrupt transitions near bifurcation points,facilitating the emergence of SWRs.Critically,temporal rhythmic analysis reveals that the occurrence of a bifurcation is not always sufficient for SWR formation.By integrating one-parameter bifurcation analysis with extremum analysis,we demonstrate that large-amplitude membrane potential oscillations near bifurcation points are highly conducive to SWR generation.This research elucidates the mechanistic link between changes in neuronal self-connection parameters and the evolution of rhythmic characteristics,providing deeper insights into the role of dynamical behavior in memory consolidation.展开更多
The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through acceler...The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.展开更多
The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster predic...The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster prediction.To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction,this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory(Bi-LSTM)network.The method involves three main modules,including microseismic spatio-temporal characteristic indicators construction,temporal prediction model,and spatial prediction model.To validate the effectiveness of the proposed method,engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia,focusing on 13 high-energy microseismic events with energy levels greater than 105 J.In terms of temporal prediction,the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions,and there is no false alarm detected throughout the entire testing period.Moreover,compared to the traditional threshold-based coal burst temporal prediction method,the accuracy of the proposed method is increased by 38.5%.In terms of spatial prediction,the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions,3 medium hazard predictions,and 4 weak hazard predictions.展开更多
Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In ...Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.展开更多
In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is es...In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.展开更多
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
A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan....A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.展开更多
Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections an...Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.展开更多
Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial featur...Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.展开更多
With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent years.In this paper,physics informed memory networks(PIM...With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent years.In this paper,physics informed memory networks(PIMNs)are proposed as a new approach to solving PDEs by using physical laws and dynamic behavior of PDEs.Unlike the fully connected structure of the PINNs,the PIMNs construct the long-term dependence of the dynamics behavior with the help of the long short-term memory network.Meanwhile,the PDEs residuals are approximated using difference schemes in the form of convolution filter,which avoids information loss at the neighborhood of the sampling points.Finally,the performance of the PIMNs is assessed by solving the Kd V equation and the nonlinear Schr?dinger equation,and the effects of difference schemes,boundary conditions,network structure and mesh size on the solutions are discussed.Experiments show that the PIMNs are insensitive to boundary conditions and have excellent solution accuracy even with only the initial conditions.展开更多
There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement an...There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.展开更多
The unloading relaxation caused by excavation for construction of high arch dams is an important factor influencing the foundation’s integrity and strength.To evaluate the degree of unloading relaxation,the long-shor...The unloading relaxation caused by excavation for construction of high arch dams is an important factor influencing the foundation’s integrity and strength.To evaluate the degree of unloading relaxation,the long-short term memory(LSTM)network was used to estimate the depth of unloading relaxation zones on the left bank foundation of the Baihetan Arch Dam.Principal component analysis indicates that rock charac-teristics,the structural plane,the protection layer,lithology,and time are the main factors.The LSTM network results demonstrate the unloading relaxation characteristics of the left bank,and the relationships with the factors were also analyzed.The structural plane has the most significant influence on the distribution of unloading relaxation zones.Compared with massive basalt,the columnar jointed basalt experiences a more significant unloading relaxation phenomenon with a clear time effect,with the average unloading relaxation period being 50 d.The protection layer can effectively reduce the unloading relaxation depth by approximately 20%.展开更多
Accurate energy consumption forecasting is essential for efficient resource management and sustainability in the building sector.Deep learning models are highly successful but struggle with limited historical data and...Accurate energy consumption forecasting is essential for efficient resource management and sustainability in the building sector.Deep learning models are highly successful but struggle with limited historical data and become unusable when historical data are unavailable,such as in newly constructed buildings.On the other hand,physics-based models,such as EnergyPlus,simulate energy consumption without relying on historical data but require extensive building parameter specifications and considerable time to model a building.This paper introduces a Physics-Guided Memory Network(PgMN),a neural network that integrates predictions from deep learning and physics-based models to address their limitations.PgMN comprises a Parallel Projection Layers to process incomplete inputs,a Memory Unit to account for persistent biases,and a Memory Experience Module to optimally extend forecasts beyond their input range and produce output.Theoretical evaluation shows that components of PgMN are mathematically valid for performing their respective tasks.The PgMN was evaluated on short-term energy forecasting at an hourly resolution,critical for operational decisionmaking in smart grid and smart building systems.Experimental validation shows accuracy and applicability of PgMN in diverse scenarios such as newly constructed buildings,missing data,sparse historical data,and dynamic infrastructure changes.This paper provides a promising solution for energy consumption forecasting in dynamic building environments,enhancing model applicability in scenarios where historical data are limited or unavailable or when physics-based models are inadequate.展开更多
To address the problem of underwater sound speed profile(SSP)inversion in underwater acoustic multipath channels,this paper combines deep learning and ray theory to propose an inversion method using a long short-term ...To address the problem of underwater sound speed profile(SSP)inversion in underwater acoustic multipath channels,this paper combines deep learning and ray theory to propose an inversion method using a long short-term memory(LSTM)network.Based on the equidistant characteristics of the horizontal line array,the proposed method takes the sensing matrix composed of multi-modal data,such as time difference of arrival and angle of arrival,as input,and utilizes the ability of the LSTM network to process timeseries data to mine the correlations between spatially ordered receiving array elements for sound speed profile inversion.On this basis,a time delay estimation method based on hard threshold estimation method and cross-correlation function is proposed to reduce the measurement errors of the sensing matrix and improve the anti-multipath performance.The feasibility and accuracy of the proposed method are verified through numerical simulations.Compared with the traditional optimization algorithm,the proposed algorithm better captures the nonlinear characteristics of SSP,with higher inversion accuracy and stronger noise resistance.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
Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with ...Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network （DMN）, its function has been associated with memory processing, and it has been referred to as the parietal memory network （PMN）. Independent component analysis （ICA） is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determi- nation in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.展开更多
Truth discovery aims to resolve conflicts among multiple sources and find the truth. Conventional methods for truth discovery mainly investigate the mutual effect between the reliability of sources and the credibility...Truth discovery aims to resolve conflicts among multiple sources and find the truth. Conventional methods for truth discovery mainly investigate the mutual effect between the reliability of sources and the credibility of statements. These methods use real numbers, which have a lower representation capability than vectors to represent the reliability. In addition, neural networks have not been used for truth discovery. In this work, we propose memory-network-based models to address truth discovery. Our proposed models use feedforward and feedback memory networks to learn the representation of the credibility of statements. Specifically, our models adopt a memory mechanism to learn the reliability of sources for truth prediction. The proposed models use categorical and continuous data during model learning by automatically assigning different weights to the loss function on the basis of their own effects. Experimental results show that our proposed models outperform state-of-the-art methods for truth discovery.展开更多
Surface EMG contains a lot of physiological information reflecting the intention of human movement.Gesture recognition by surface EMG has been widely concerned in the field of human-computer interaction and rehabilita...Surface EMG contains a lot of physiological information reflecting the intention of human movement.Gesture recognition by surface EMG has been widely concerned in the field of human-computer interaction and rehabilitation.At present,most studies on gesture recognition based on surface EMG signal are obtained by discrete separation method,ignoring continuous natural motion.A gesture recognition method of surface EMG based on improved long short-term memory network is proposed.sEMG sensors are rationally arranged according to physiological structure and muscle function.In this paper,the finger curvature is used to describe the gesture state,and the gesture at every moment can be represented by the set of different finger curvature,so as to realize continuous gesture recognition.Finally,the proposed gesture recognition model is tested on Ninapro(a large gesture recognition database).The results show that the proposed method can effectively improve the representation mining ability of surface EMG signal,and provide reference for deep learning modeling of human gesture recognition.展开更多
With breakthroughs in data processing and pattern recognition through deep learning technologies,the use of advanced algorithmic models for analyzing and interpreting soil spectral information has provided an efficien...With breakthroughs in data processing and pattern recognition through deep learning technologies,the use of advanced algorithmic models for analyzing and interpreting soil spectral information has provided an efficient and economical method for soil quality assessment.However,traditional single-output networks exhibit limitations in the prediction process,particularly in their inability to fully utilize the correlations among various elements.As a result,single-output networks tend to be optimized for a single task,neglecting the interrelationships among different soil elements,which limits prediction accuracy and model generalizability.To overcome this limitation,in this study,a multi-task learning architecture with a progressive extraction network was implemented for the simultaneous prediction of multiple indicators in soil,including nitrogen(N),organic carbon(OC),calcium carbonate(CaCO 3),cation exchange capacity(CEC),and pH.Furthermore,while incorporating the Pearson correlation coefficient,convolutional neural networks,long short-term memory networks and attention mechanisms were combined to extract local abstract features from the original spectra,thereby further improving the model.This architecture is referred to as the Relevance-sharing Progressive Layered Extraction Network.The model employs an adaptive joint loss optimization method to update the weights of individual task losses in the multi-task learning training process.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272002 and 12372061)the R&D Program of Beijing Municipal Education Commission(Grant No.KM202310009004)+1 种基金the North China University of Technology(Grant No.2023XN075-01)the Youth Research Special Project of the North China University of Technology(Grant No.2025NCUTYRSP051)。
文摘This study investigates the bifurcation dynamics underlying rhythmic transitions in a biophysical hippocampal–cortical neural network model.We specifically focus on the membrane potential dynamics of excitatory neurons in the hippocampal CA3 region and examine how strong coupling parameters modulate memory consolidation processes.Employing bifurcation analysis,we systematically characterize the model's complex dynamical behaviors.Subsequently,a characteristic waveform recognition algorithm enables precise feature extraction and automated detection of hippocampal sharp-wave ripples(SWRs).Our results demonstrate that neuronal rhythms exhibit a propensity for abrupt transitions near bifurcation points,facilitating the emergence of SWRs.Critically,temporal rhythmic analysis reveals that the occurrence of a bifurcation is not always sufficient for SWR formation.By integrating one-parameter bifurcation analysis with extremum analysis,we demonstrate that large-amplitude membrane potential oscillations near bifurcation points are highly conducive to SWR generation.This research elucidates the mechanistic link between changes in neuronal self-connection parameters and the evolution of rhythmic characteristics,providing deeper insights into the role of dynamical behavior in memory consolidation.
基金supported by the National Key Research and Development Project(Grant Number 2023YFB3709601)the National Natural Science Foundation of China(Grant Numbers 62373215,62373219,62073193)+2 种基金the Key Research and Development Plan of Shandong Province(Grant Numbers 2021CXGC010204,2022CXGC020902)the Fundamental Research Funds of Shandong University(Grant Number 2021JCG008)the Natural Science Foundation of Shandong Province(Grant Number ZR2023MF100).
文摘The remaining useful life prediction of rolling bearing is vital in safety and reliability guarantee.In engineering scenarios,only a small amount of bearing performance degradation data can be obtained through accelerated life testing.In the absence of lifetime data,the hidden long-term correlation between performance degradation data is challenging to mine effectively,which is the main factor that restricts the prediction precision and engineering application of the residual life prediction method.To address this problem,a novel method based on the multi-layer perception neural network and bidirectional long short-term memory network is proposed.Firstly,a nonlinear health indicator(HI)calculation method based on kernel principal component analysis(KPCA)and exponential weighted moving average(EWMA)is designed.Then,using the raw vibration data and HI,a multi-layer perceptron(MLP)neural network is trained to further calculate the HI of the online bearing in real time.Furthermore,The bidirectional long short-term memory model(BiLSTM)optimized by particle swarm optimization(PSO)is used to mine the time series features of HI and predict the remaining service life.Performance verification experiments and comparative experiments are carried out on the XJTU-SY bearing open dataset.The research results indicate that this method has an excellent ability to predict future HI and remaining life.
基金supported by the National Research and Development Program(2022YFC3004603)the Jiangsu Province International Collaboration Program-Key National Industrial Technology Research and Development Cooperation Projects(BZ2023050)+1 种基金the Natural Science Foundation of Jiangsu Province(BK20221109)the National Natural Science Foundation of China(52274098).
文摘The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production,and it has become a challenging task to enhance the accuracy of coal burst disaster prediction.To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction,this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory(Bi-LSTM)network.The method involves three main modules,including microseismic spatio-temporal characteristic indicators construction,temporal prediction model,and spatial prediction model.To validate the effectiveness of the proposed method,engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia,focusing on 13 high-energy microseismic events with energy levels greater than 105 J.In terms of temporal prediction,the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions,and there is no false alarm detected throughout the entire testing period.Moreover,compared to the traditional threshold-based coal burst temporal prediction method,the accuracy of the proposed method is increased by 38.5%.In terms of spatial prediction,the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions,3 medium hazard predictions,and 4 weak hazard predictions.
基金supported by the National Natural Science Foundation of China(No.62276204)Open Foundation of Science and Technology on Electronic Information Control Laboratory,Natural Science Basic Research Program of Shanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470).
文摘Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.
文摘In dense pedestrian tracking,frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.In this study,a conditional random field tracking model is established by using a visual long short term memory network in the three-dimensional(3D)space and the motion estimations jointly performed on object trajectory segments.Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation.To address the uncertainty of the length and interval of trajectory segments,a multimode long short term memory network is proposed for the object motion estimation.The tracking performance is evaluated using the PETS2009 dataset.The experimental results show that the proposed method achieves better performance than the tracking methods based on the independent motion estimation.
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National Key Research and Development Program of China (Grant No. 2022YFE0102700)National Natural Science Foundation of China (Grant No. 52102420)+2 种基金research project “Safe Da Batt” (03EMF0409A) funded by the German Federal Ministry of Digital and Transport (BMDV)China Postdoctoral Science Foundation (Grant No. 2023T160085)Sichuan Science and Technology Program (Grant No. 2024NSFSC0938)。
文摘A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.
基金supported by the US Department of Energy (DOE),the Office of Nuclear Energy,Spent Fuel and Waste Science and Technology Campaign,under Contract Number DE-AC02-05CH11231the National Energy Technology Laboratory under the award number FP00013650 at Lawrence Berkeley National Laboratory.
文摘Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault.This can be due to subsurface(geo)engineering activities such as fluid injections and geologic disposal of nuclear waste.Such activities are expected to rise in the future making it necessary to assess their short-and long-term safety.Here,a new machine learning(ML)approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed.The focus is on fault behavior near the injection borehole.To capture the temporal dependencies in the data,long short-term memory(LSTM)networks are utilized.To prevent error accumulation within the forecast window,four critical measures to train a robust LSTM model for predicting fault response are highlighted:(i)setting an appropriate value of LSTM lag,(ii)calibrating the LSTM cell dimension,(iii)learning rate reduction during weight optimization,and(iv)not adopting an independent injection cycle as a validation set.Several numerical experiments were conducted,which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection.The model also captured the decay in pressure and displacement during the injection shut-in period.Further,the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated,which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.
基金This work is supported by the National Nature Science Foundation of China(NSFC)under Grant Nos.61571106,61501169,41706103the Fundamental Research Funds for the Central Universities under Grant No.2242013K30010.
文摘Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.
文摘With the advent of physics informed neural networks(PINNs),deep learning has gained interest for solving nonlinear partial differential equations(PDEs)in recent years.In this paper,physics informed memory networks(PIMNs)are proposed as a new approach to solving PDEs by using physical laws and dynamic behavior of PDEs.Unlike the fully connected structure of the PINNs,the PIMNs construct the long-term dependence of the dynamics behavior with the help of the long short-term memory network.Meanwhile,the PDEs residuals are approximated using difference schemes in the form of convolution filter,which avoids information loss at the neighborhood of the sampling points.Finally,the performance of the PIMNs is assessed by solving the Kd V equation and the nonlinear Schr?dinger equation,and the effects of difference schemes,boundary conditions,network structure and mesh size on the solutions are discussed.Experiments show that the PIMNs are insensitive to boundary conditions and have excellent solution accuracy even with only the initial conditions.
文摘There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.
基金This work was supported by the National Key Research and Development Program of China(Grant No.2018YFC0407004)the Natural Science Foundation of China(Grants No.51939004 and 11772116).
文摘The unloading relaxation caused by excavation for construction of high arch dams is an important factor influencing the foundation’s integrity and strength.To evaluate the degree of unloading relaxation,the long-short term memory(LSTM)network was used to estimate the depth of unloading relaxation zones on the left bank foundation of the Baihetan Arch Dam.Principal component analysis indicates that rock charac-teristics,the structural plane,the protection layer,lithology,and time are the main factors.The LSTM network results demonstrate the unloading relaxation characteristics of the left bank,and the relationships with the factors were also analyzed.The structural plane has the most significant influence on the distribution of unloading relaxation zones.Compared with massive basalt,the columnar jointed basalt experiences a more significant unloading relaxation phenomenon with a clear time effect,with the average unloading relaxation period being 50 d.The protection layer can effectively reduce the unloading relaxation depth by approximately 20%.
基金supported in part by the Climate Action and Awareness Fund[EDF-CA-2021i018,Environnement Canada,K.Siddiqui and K.Grolinger]in part by the Canada Research Chairs Program[CRC-2022-00078,K.Grolinger].
文摘Accurate energy consumption forecasting is essential for efficient resource management and sustainability in the building sector.Deep learning models are highly successful but struggle with limited historical data and become unusable when historical data are unavailable,such as in newly constructed buildings.On the other hand,physics-based models,such as EnergyPlus,simulate energy consumption without relying on historical data but require extensive building parameter specifications and considerable time to model a building.This paper introduces a Physics-Guided Memory Network(PgMN),a neural network that integrates predictions from deep learning and physics-based models to address their limitations.PgMN comprises a Parallel Projection Layers to process incomplete inputs,a Memory Unit to account for persistent biases,and a Memory Experience Module to optimally extend forecasts beyond their input range and produce output.Theoretical evaluation shows that components of PgMN are mathematically valid for performing their respective tasks.The PgMN was evaluated on short-term energy forecasting at an hourly resolution,critical for operational decisionmaking in smart grid and smart building systems.Experimental validation shows accuracy and applicability of PgMN in diverse scenarios such as newly constructed buildings,missing data,sparse historical data,and dynamic infrastructure changes.This paper provides a promising solution for energy consumption forecasting in dynamic building environments,enhancing model applicability in scenarios where historical data are limited or unavailable or when physics-based models are inadequate.
基金supported by the National Natural Science Foundation of China(62371404,62271425,62071401).
文摘To address the problem of underwater sound speed profile(SSP)inversion in underwater acoustic multipath channels,this paper combines deep learning and ray theory to propose an inversion method using a long short-term memory(LSTM)network.Based on the equidistant characteristics of the horizontal line array,the proposed method takes the sensing matrix composed of multi-modal data,such as time difference of arrival and angle of arrival,as input,and utilizes the ability of the LSTM network to process timeseries data to mine the correlations between spatially ordered receiving array elements for sound speed profile inversion.On this basis,a time delay estimation method based on hard threshold estimation method and cross-correlation function is proposed to reduce the measurement errors of the sensing matrix and improve the anti-multipath performance.The feasibility and accuracy of the proposed method are verified through numerical simulations.Compared with the traditional optimization algorithm,the proposed algorithm better captures the nonlinear characteristics of SSP,with higher inversion accuracy and stronger noise resistance.
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 Basic Research(973)Program(2015CB351702)the National Natural Science Foundation of China(81571756,81270023,81278412,81171409,81000583,81471740,81220108014)+2 种基金Beijing Nova Program(XXJH2015B079 to Z.Y.)the Outstanding Young Investigator Award of Institute of Psychology,Chinese Academy of Sciences(to Z.Y.)the Key Research Program and the Hundred Talents Program of the Chinese Academy of Sciences(KSZD-EW-TZ-002 to X.N.Z)
文摘Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network （DMN）, its function has been associated with memory processing, and it has been referred to as the parietal memory network （PMN）. Independent component analysis （ICA） is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determi- nation in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.
基金supported by the National HighTech Development(863)Program of China(No.2015AA015407)the National Natural Science Foundation of China(Nos.61632011 and 61370164)
文摘Truth discovery aims to resolve conflicts among multiple sources and find the truth. Conventional methods for truth discovery mainly investigate the mutual effect between the reliability of sources and the credibility of statements. These methods use real numbers, which have a lower representation capability than vectors to represent the reliability. In addition, neural networks have not been used for truth discovery. In this work, we propose memory-network-based models to address truth discovery. Our proposed models use feedforward and feedback memory networks to learn the representation of the credibility of statements. Specifically, our models adopt a memory mechanism to learn the reliability of sources for truth prediction. The proposed models use categorical and continuous data during model learning by automatically assigning different weights to the loss function on the basis of their own effects. Experimental results show that our proposed models outperform state-of-the-art methods for truth discovery.
文摘Surface EMG contains a lot of physiological information reflecting the intention of human movement.Gesture recognition by surface EMG has been widely concerned in the field of human-computer interaction and rehabilitation.At present,most studies on gesture recognition based on surface EMG signal are obtained by discrete separation method,ignoring continuous natural motion.A gesture recognition method of surface EMG based on improved long short-term memory network is proposed.sEMG sensors are rationally arranged according to physiological structure and muscle function.In this paper,the finger curvature is used to describe the gesture state,and the gesture at every moment can be represented by the set of different finger curvature,so as to realize continuous gesture recognition.Finally,the proposed gesture recognition model is tested on Ninapro(a large gesture recognition database).The results show that the proposed method can effectively improve the representation mining ability of surface EMG signal,and provide reference for deep learning modeling of human gesture recognition.
文摘With breakthroughs in data processing and pattern recognition through deep learning technologies,the use of advanced algorithmic models for analyzing and interpreting soil spectral information has provided an efficient and economical method for soil quality assessment.However,traditional single-output networks exhibit limitations in the prediction process,particularly in their inability to fully utilize the correlations among various elements.As a result,single-output networks tend to be optimized for a single task,neglecting the interrelationships among different soil elements,which limits prediction accuracy and model generalizability.To overcome this limitation,in this study,a multi-task learning architecture with a progressive extraction network was implemented for the simultaneous prediction of multiple indicators in soil,including nitrogen(N),organic carbon(OC),calcium carbonate(CaCO 3),cation exchange capacity(CEC),and pH.Furthermore,while incorporating the Pearson correlation coefficient,convolutional neural networks,long short-term memory networks and attention mechanisms were combined to extract local abstract features from the original spectra,thereby further improving the model.This architecture is referred to as the Relevance-sharing Progressive Layered Extraction Network.The model employs an adaptive joint loss optimization method to update the weights of individual task losses in the multi-task learning training process.
