Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial i...Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.展开更多
Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug rese...Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pK_(a) prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pK_(a) prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pK_(a) values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pK_(a) values,making it a valuable tool in the field of pK_(a) prediction.展开更多
This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by cons...This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.展开更多
Hydraulic fracturing plays a critical role in enhancing shale gas production in deep shale reservoirs.Conventional hydraulic fracturing simulation methods rely on prefabricated grids,which can be hindered by the chall...Hydraulic fracturing plays a critical role in enhancing shale gas production in deep shale reservoirs.Conventional hydraulic fracturing simulation methods rely on prefabricated grids,which can be hindered by the challenge of being computationally overpowered.This study proposes an efficient fracturing simulator to analyze fracture morphology during hydraulic fracturing processes in deep shale gas reservoirs.The simulator integrates the boundary element displacement discontinuity method and the finite volume method to model the fluid-solid coupling process by employing a pseudo-3D fracture model to calculate the fracture height.In particular,the Broyden iteration method was introduced to improve the computational efficiency and model robustness;it achieved a 46.6%reduction in computation time compared to the Newton-Raphson method.The influences of horizontal stress differences,natural fracture density,and natural fracture angle on the modified zone of the reservoir were simulated,and the following results were observed.(1)High stress difference reservoirs have smaller stimulated reservoir area than low stress difference reservoirs.(2)A higher natural fracture angle resulted in larger modification zones at low stress differences,while the effect of a natural fracture angle at high stress differences was not significant.(3)High-density and long natural fracture zones played a significant role in enhancing the stimulated reservoir area.These findings are critical for comprehending the impact of geological parameters on deep shale reservoirs.展开更多
Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbule...Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.展开更多
Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data.To date,most text mining methods are restricted to specific literature information,resulting in ...Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data.To date,most text mining methods are restricted to specific literature information,resulting in incomplete knowledge graphs.Here,we report a method that combines citation analysis with topic modeling to describe the hidden development patterns in the history of science.Leveraging this method,we construct a knowledge graph in the field of Raman spectroscopy.The traditional Latent DirichletAllocation model is chosen as the baseline model for comparison to validate the performance of our model.Our method improves the topic coherence with a minimum growth rate of 100%compared to the traditional text mining method.It outperforms the traditional text mining method on the diversity,and its growth rate ranges from 0 to 126%.The results show the effectiveness of rule-based tokenizer we designed in solving the word tokenizer problem caused by entity naming rules in the field of chemistry.It is versatile in revealing the distribution of topics,establishing the similarity and inheritance relationships,and identifying the important moments in the history of Raman spectroscopy.Our work provides a comprehensive tool for the science of science research and promises to offer new insights into the historical survey and development forecast of a research field.展开更多
The complex vibration directly affects the dynamic safety of drill string in ultra-deep wells and extra-deep wells.It is important to understand the dynamic characteristics of drill string to ensure the safety of dril...The complex vibration directly affects the dynamic safety of drill string in ultra-deep wells and extra-deep wells.It is important to understand the dynamic characteristics of drill string to ensure the safety of drill string.Due to the super slenderness ratio of drill string,strong nonlinearity implied in dynamic analysis and the complex load environment,dynamic simulation of drill string faces great challenges.At present,many simulation methods have been developed to analyze drill string dynamics,and node iteration method is one of them.The node iteration method has a unique advantage in dealing with the contact characteristics between drill string and borehole wall,but its drawback is that the calculation consumes a considerable amount of time.This paper presents a dynamic simulation method of drilling string in extra-deep well based on successive over-relaxation node iterative method(SOR node iteration method).Through theoretical analysis and numerical examples,the correctness and validity of this method were verified,and the dynamics characteristics of drill string in extra-deep wells were calculated and analyzed.The results demonstrate that,in contrast to the conventional node iteration method,the SOR node iteration method can increase the computational efficiency by 48.2%while achieving comparable results.And the whirl trajectory of the extra-deep well drill string is extremely complicated,the maximum rotational speed downhole is approximately twice the rotational speed on the ground.The dynamic torque increases rapidly at the position of the bottom stabilizer,and the lateral vibration in the middle and lower parts of drill string is relatively intense.展开更多
Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learnin...Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.展开更多
Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-sc...Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-scale defects under harsh industrial conditions with multimodal data.This paper proposes an enhanced version of You Only Look Once(YOLO)v8 for improved defect detection in automotive sealing rings.We introduce the Multi-scale Adaptive Feature Extraction(MAFE)module,which integrates Deformable ConvolutionalNetwork(DCN)and Spaceto-Depth(SPD)operations.This module effectively captures long-range dependencies,enhances spatial aggregation,and minimizes information loss of small objects during feature extraction.Furthermore,we introduce the Blur-Aware Wasserstein Distance(BAWD)loss function,which improves regression accuracy and detection capabilities for small object anchor boxes,particularly in scenarios involving defocus blur.Additionally,we have constructed a high-quality dataset of automotive sealing ring defects,providing a valuable resource for evaluating defect detection methods.Experimental results demonstrate our method’s high performance,achieving 98.30% precision,96.62% recall,and an inference speed of 20.3 ms.展开更多
High-energy gamma-ray astronomy,at frequencies of 100 MeV to 100 GeV,yields insights into the fields of compact objects,extreme processes,and particle propagation.Thousands of gamma-ray sources have been detected by t...High-energy gamma-ray astronomy,at frequencies of 100 MeV to 100 GeV,yields insights into the fields of compact objects,extreme processes,and particle propagation.Thousands of gamma-ray sources have been detected by the Fermi Gamma-ray Space Telescope,many without any known counterpart at other wavelengths or clear identification of the source.Deep learning algorithms have been successfully applied to a variety of problems in astronomy.In this paper,I give some typical examples for classifying Fermi sources with deep learning methods,to show how such techniques can improve capability to unveil the nature of high-energy gamma-ray sources.展开更多
Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Insp...Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Inspired by the great success of artificial intelligence-based algorithms,especially deep learning-based methods in the field of biology,various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization.Herein,we briefly review recent progress in deep learning-based antibody optimization,focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models.Furthermore,we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.展开更多
Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for ...Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.展开更多
To improve the accuracy of thermal response estimation and overcome the limitations of the linear regression model and Artificial Neural Network(ANN)model,this study introduces a deep learning estimation method specif...To improve the accuracy of thermal response estimation and overcome the limitations of the linear regression model and Artificial Neural Network(ANN)model,this study introduces a deep learning estimation method specifically based on the Long Short-Term Memory(LSTM)network,to predict temperature-induced girder end displacements of the Dasha Waterway Bridge,a suspension bridge in China.First,to enhance data quality and select target sensors,preprocessing based on the sigma rule and nearest neighbor interpolation is applied to the raw data.Furthermore,to eliminate the high-frequency components from the displacement signal,the wavelet transform is conducted.Subsequently,the linear regression model and ANN model are established,whose results do not meet the requirements and fail to address the time lag effect between temperature and displacements.The study proceeds to develop the LSTM network model and determine the optimal parameters through hyperparameter sensitivity analysis.Finally,the results of the LSTM network model are discussed by a comparative analysis against the linear regression model and ANN model,which indicates a higher accuracy in predicting temperatureinduced girder end displacements and the ability to mitigate the time-lag effect.To be more specific,in comparison between the linear regression model and LSTM network,the mean square error decreases from 6.5937 to 1.6808 and R^(2) increases from 0.683 to 0.930,which corresponds to a 74.51%decrease in MSE and a 36.14%improvement in R^(2).Compared to ANN,with an MSE of 4.6371 and an R^(2) of 0.807,LSTM shows a decrease in MSE of 63.75%and an increase in R^(2) of 13.23%,demonstrating a significant enhancement in predictive performance.展开更多
The accurate prediction of battery pack capacity in electric vehicles(EVs)is crucial for ensuring safety and optimizing performance.Despite extensive research on predicting cell capacity using laboratory data,predicti...The accurate prediction of battery pack capacity in electric vehicles(EVs)is crucial for ensuring safety and optimizing performance.Despite extensive research on predicting cell capacity using laboratory data,predicting the capacity of onboard battery packs from field data remains challenging due to complex operating conditions and irregular EV usage in real-world settings.Most existing methods rely on extracting health feature parameters from raw data for capacity prediction of onboard battery packs,however,selecting specific parameters often results in a loss of critical information,which reduces prediction accuracy.To this end,this paper introduces a novel framework combining deep learning and data compression techniques to accurately predict battery pack capacity onboard.The proposed data compression method converts monthly EV charging data into feature maps,which preserve essential data characteristics while reducing the volume of raw data.To address missing capacity labels in field data,a capacity labeling method is proposed,which calculates monthly battery capacity by transforming the ampere-hour integration formula and applying linear regression.Subsequently,a deep learning model is proposed to build a capacity prediction model,using feature maps from historical months to predict the battery capacity of future months,thus facilitating accurate forecasts.The proposed framework,evaluated using field data from 20 EVs,achieves a mean absolute error of 0.79 Ah,a mean absolute percentage error of 0.65%,and a root mean square error of 1.02 Ah,highlighting its potential for real-world EV applications.展开更多
The lower Yarlung Tsangpo River basin of the Qinghai-Tibet Plateau frequently experiences geo-hazardous occurrences such as landslides,ice/rock avalanches and debris flows,causing loss of human lives and damage to inf...The lower Yarlung Tsangpo River basin of the Qinghai-Tibet Plateau frequently experiences geo-hazardous occurrences such as landslides,ice/rock avalanches and debris flows,causing loss of human lives and damage to infrastructure.However,a comprehensive inventory map of geohazards is lacking for this region,due to the extreme challenges of the geomorphological and environmental conditions(i.e.,steep terrain,dense vegetation cover,and the presence of ice and snow).To this end,we propose a novel approach for mapping active geohazards in complex mountainous regions through InSAR phase gradient measurements based on a deep learning algorithm,which is then applied to the lower Yarlung Tsangpo River basin for the first time,in order to prepare an inventory map of active geohazards using ascending and descending Sentinel-1 SAR images acquired between March 2017 and July 2023.First,the InSAR phase gradient stacking method was introduced to estimate ground deformation,which offers significant advantages in minimizing the influence of InSAR decorrelation and effectively suppressing topographic residuals and atmospheric delays.InSAR phase gradient rates effectively retrieve patterns of localized ground deformation associated with geohazard activity.Then,a DeepLabv3 deep learning model was established and trained with phase gradient rate maps of manually labeled geohazards,in order to achieve the automatic identification of active geohazards.Our results show that there are 277 active geohazards within the lower Yarlung Tsangpo River basin,encompassing an area of~25600 km^(2).The DeepLabv3 model achieved good precision,recall rate and F1 scores at 92,86 and 90%,respectively.The distribution of detected geohazards is closely correlated with the topographic factors,faults and river system.Compared to the results derived from Small Baseline Subset InSAR(SBAS-InSAR)and optical images,the proposed approach can obtain high density pixels of InSAR measurement in low-coherence scenarios,thus enabling high-accuracy mapping of active geohazards in complex mountainous areas.展开更多
As one of the most seismically active regions,Sichuan Basin is a key area of seismological studies in China.This study applies a neural network model with attention mechanisms,simultaneously picking the P-wave arrival...As one of the most seismically active regions,Sichuan Basin is a key area of seismological studies in China.This study applies a neural network model with attention mechanisms,simultaneously picking the P-wave arrival times and determining the first-motion polarity.The polarity information is subsequently used to derive source focal mechanisms.The model is trained and tested using small to moderate earthquake data from June to December 2019 in Sichuan.We apply the trained model to predict first-motion polarity directions of earthquake recordings in Sichuan from January to May 2019,and then derive focal mechanism solutions using HASH algorithm with predicted results.Compared with the source mechanism solutions obtained by manual processing,the deep learning method picks more polarities from smaller events,resulting in more focal mechanism solutions.The catalog documents focal mechanism solutions of 22 events(M_(L) 2.6–4.8)from analysts during this period,whereas we obtain focal mechanism solutions of 53 events(M_(L) 1.9–4.8)through the deep learning method.The derived focal mechanism solutions for the same events are consistent with the manual solutions.This method provides an efficient way for the source mechanism inversion of small to moderate earthquakes in Sichuan region,with high stability and reliability.展开更多
Underwater wireless sensor networks(UWSNs)have emerged as a new paradigm of real-time organized systems,which are utilized in a diverse array of scenarios to manage the underwater environment surrounding them.One of t...Underwater wireless sensor networks(UWSNs)have emerged as a new paradigm of real-time organized systems,which are utilized in a diverse array of scenarios to manage the underwater environment surrounding them.One of the major challenges that these systems confront is topology control via clustering,which reduces the overload of wireless communications within a network and ensures low energy consumption and good scalability.This study aimed to present a clustering technique in which the clustering process and cluster head(CH)selection are performed based on the Markov decision process and deep reinforcement learning(DRL).DRL algorithm selects the CH by maximizing the defined reward function.Subsequently,the sensed data are collected by the CHs and then sent to the autonomous underwater vehicles.In the final phase,the consumed energy by each sensor is calculated,and its residual energy is updated.Then,the autonomous underwater vehicle performs all clustering and CH selection operations.This procedure persists until the point of cessation when the sensor’s power has been reduced to such an extent that no node can become a CH.Through analysis of the findings from this investigation and their comparison with alternative frameworks,the implementation of this method can be used to control the cluster size and the number of CHs,which ultimately augments the energy usage of nodes and prolongs the lifespan of the network.Our simulation results illustrate that the suggested methodology surpasses the conventional low-energy adaptive clustering hierarchy,the distance-and energy-constrained K-means clustering scheme,and the vector-based forward protocol and is viable for deployment in an actual operational environment.展开更多
Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalizat...Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.展开更多
This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent...This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent policy networks.The graph of welding spots distribution is encoded using the graph attention network.Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks.The policy network is used to convert the large scale welding spots allocation problem to multiple small scale singlerobot welding path planning problems,and the path planning problem is quickly solved through existing methods.Then,the model is trained through reinforcement learning.In addition,the task balancing method is used to allocate tasks to multiple stations.The proposed algorithm is compared with classical algorithms,and the results show that the algorithm based on DRL can produce higher quality solutions.展开更多
文摘Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
基金upported by the National Key Research and Development Program of China(Grant No.:2023YFF1204904)the National Natural Science Foundation of China(Grant Nos.:U23A20530 and 82173746)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission,China).
文摘Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pK_(a) prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pK_(a) prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pK_(a) values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pK_(a) values,making it a valuable tool in the field of pK_(a) prediction.
文摘This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.
文摘Hydraulic fracturing plays a critical role in enhancing shale gas production in deep shale reservoirs.Conventional hydraulic fracturing simulation methods rely on prefabricated grids,which can be hindered by the challenge of being computationally overpowered.This study proposes an efficient fracturing simulator to analyze fracture morphology during hydraulic fracturing processes in deep shale gas reservoirs.The simulator integrates the boundary element displacement discontinuity method and the finite volume method to model the fluid-solid coupling process by employing a pseudo-3D fracture model to calculate the fracture height.In particular,the Broyden iteration method was introduced to improve the computational efficiency and model robustness;it achieved a 46.6%reduction in computation time compared to the Newton-Raphson method.The influences of horizontal stress differences,natural fracture density,and natural fracture angle on the modified zone of the reservoir were simulated,and the following results were observed.(1)High stress difference reservoirs have smaller stimulated reservoir area than low stress difference reservoirs.(2)A higher natural fracture angle resulted in larger modification zones at low stress differences,while the effect of a natural fracture angle at high stress differences was not significant.(3)High-density and long natural fracture zones played a significant role in enhancing the stimulated reservoir area.These findings are critical for comprehending the impact of geological parameters on deep shale reservoirs.
基金supported by the National Natural Science Foundation of China(No.12104141).
文摘Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.
基金supported by the National Natural Science Foundation of China(T2222002,22032004)the Fundamental Research Funds for the Central Universities(Xiamen University:No.20720240053)State Key Laboratory of Vaccines for Infectious Diseases,Xiang An Biomedicine Laboratory(2023XAKJ0103074).
文摘Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data.To date,most text mining methods are restricted to specific literature information,resulting in incomplete knowledge graphs.Here,we report a method that combines citation analysis with topic modeling to describe the hidden development patterns in the history of science.Leveraging this method,we construct a knowledge graph in the field of Raman spectroscopy.The traditional Latent DirichletAllocation model is chosen as the baseline model for comparison to validate the performance of our model.Our method improves the topic coherence with a minimum growth rate of 100%compared to the traditional text mining method.It outperforms the traditional text mining method on the diversity,and its growth rate ranges from 0 to 126%.The results show the effectiveness of rule-based tokenizer we designed in solving the word tokenizer problem caused by entity naming rules in the field of chemistry.It is versatile in revealing the distribution of topics,establishing the similarity and inheritance relationships,and identifying the important moments in the history of Raman spectroscopy.Our work provides a comprehensive tool for the science of science research and promises to offer new insights into the historical survey and development forecast of a research field.
基金supported by the National Natural Science Foundation of China(52174003,52374008).
文摘The complex vibration directly affects the dynamic safety of drill string in ultra-deep wells and extra-deep wells.It is important to understand the dynamic characteristics of drill string to ensure the safety of drill string.Due to the super slenderness ratio of drill string,strong nonlinearity implied in dynamic analysis and the complex load environment,dynamic simulation of drill string faces great challenges.At present,many simulation methods have been developed to analyze drill string dynamics,and node iteration method is one of them.The node iteration method has a unique advantage in dealing with the contact characteristics between drill string and borehole wall,but its drawback is that the calculation consumes a considerable amount of time.This paper presents a dynamic simulation method of drilling string in extra-deep well based on successive over-relaxation node iterative method(SOR node iteration method).Through theoretical analysis and numerical examples,the correctness and validity of this method were verified,and the dynamics characteristics of drill string in extra-deep wells were calculated and analyzed.The results demonstrate that,in contrast to the conventional node iteration method,the SOR node iteration method can increase the computational efficiency by 48.2%while achieving comparable results.And the whirl trajectory of the extra-deep well drill string is extremely complicated,the maximum rotational speed downhole is approximately twice the rotational speed on the ground.The dynamic torque increases rapidly at the position of the bottom stabilizer,and the lateral vibration in the middle and lower parts of drill string is relatively intense.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Postdoctoral Fellowship Program of CPSF(GZC20232598)+1 种基金China Postdoctoral Science Foundation(2024M753168)National Key Scientific and Technological Infrastructure Project“Earth System Numerical Simulation Facility”(EarthLab)。
文摘Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.
文摘Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-scale defects under harsh industrial conditions with multimodal data.This paper proposes an enhanced version of You Only Look Once(YOLO)v8 for improved defect detection in automotive sealing rings.We introduce the Multi-scale Adaptive Feature Extraction(MAFE)module,which integrates Deformable ConvolutionalNetwork(DCN)and Spaceto-Depth(SPD)operations.This module effectively captures long-range dependencies,enhances spatial aggregation,and minimizes information loss of small objects during feature extraction.Furthermore,we introduce the Blur-Aware Wasserstein Distance(BAWD)loss function,which improves regression accuracy and detection capabilities for small object anchor boxes,particularly in scenarios involving defocus blur.Additionally,we have constructed a high-quality dataset of automotive sealing ring defects,providing a valuable resource for evaluating defect detection methods.Experimental results demonstrate our method’s high performance,achieving 98.30% precision,96.62% recall,and an inference speed of 20.3 ms.
基金supported by the Natural Science Foundation of China(12393853)。
文摘High-energy gamma-ray astronomy,at frequencies of 100 MeV to 100 GeV,yields insights into the fields of compact objects,extreme processes,and particle propagation.Thousands of gamma-ray sources have been detected by the Fermi Gamma-ray Space Telescope,many without any known counterpart at other wavelengths or clear identification of the source.Deep learning algorithms have been successfully applied to a variety of problems in astronomy.In this paper,I give some typical examples for classifying Fermi sources with deep learning methods,to show how such techniques can improve capability to unveil the nature of high-energy gamma-ray sources.
基金supported by the National Natural Science Foundation of China(No.12104396)the National Key R&D Program of China(Nos.2021YFF1200404 and 2021YFA1201200)+2 种基金the National Independent Innovation Demonstration Zone Shanghai Zhangjiang Major Projects(No.ZJZX2020014)the Starry Night Science Fund at Shanghai Institute for Advanced Study of Zhejiang University(No.SN-ZJU-SIAS-003)the Shanghai Artificial Intelligence Lab(No.P22KN00272),China.
文摘Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Inspired by the great success of artificial intelligence-based algorithms,especially deep learning-based methods in the field of biology,various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization.Herein,we briefly review recent progress in deep learning-based antibody optimization,focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models.Furthermore,we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.
基金the support of Research Program of Fine Exploration and Surrounding Rock Classification Technology for Deep Buried Long Tunnels Driven by Horizontal Directional Drilling and Magnetotelluric Methods Based on Deep Learning under Grant E202408010the Sichuan Science and Technology Program under Grant 2024NSFSC1984 and Grant 2024NSFSC1990。
文摘Porosity is an important attribute for evaluating the petrophysical properties of reservoirs, and has guiding significance for the exploration and development of oil and gas. The seismic inversion is a key method for comprehensively obtaining the porosity. Deep learning methods provide an intelligent approach to suppress the ambiguity of the conventional inversion method. However, under the trace-bytrace inversion strategy, there is a lack of constraints from geological structural information, resulting in poor lateral continuity of prediction results. In addition, the heterogeneity and the sedimentary variability of subsurface media also lead to uncertainty in intelligent prediction. To achieve fine prediction of porosity, we consider the lateral continuity and variability and propose an improved structural modeling deep learning porosity prediction method. First, we combine well data, waveform attributes, and structural information as constraints to model geophysical parameters, constructing a high-quality training dataset with sedimentary facies-controlled significance. Subsequently, we introduce a gated axial attention mechanism to enhance the features of dataset and design a bidirectional closed-loop network system constrained by inversion and forward processes. The constraint coefficient is adaptively adjusted by the petrophysical information contained between the porosity and impedance in the study area. We demonstrate the effectiveness of the adaptive coefficient through numerical experiments.Finally, we compare the performance differences between the proposed method and conventional deep learning methods using data from two study areas. The proposed method achieves better consistency with the logging porosity, demonstrating the superiority of the proposed method.
基金The National Key Research and Development Program of China grant No.2022YFB3706704 received by Yuan Renthe National Natural and Science Foundation of China grant No.52308150 received by Xiang Xu.
文摘To improve the accuracy of thermal response estimation and overcome the limitations of the linear regression model and Artificial Neural Network(ANN)model,this study introduces a deep learning estimation method specifically based on the Long Short-Term Memory(LSTM)network,to predict temperature-induced girder end displacements of the Dasha Waterway Bridge,a suspension bridge in China.First,to enhance data quality and select target sensors,preprocessing based on the sigma rule and nearest neighbor interpolation is applied to the raw data.Furthermore,to eliminate the high-frequency components from the displacement signal,the wavelet transform is conducted.Subsequently,the linear regression model and ANN model are established,whose results do not meet the requirements and fail to address the time lag effect between temperature and displacements.The study proceeds to develop the LSTM network model and determine the optimal parameters through hyperparameter sensitivity analysis.Finally,the results of the LSTM network model are discussed by a comparative analysis against the linear regression model and ANN model,which indicates a higher accuracy in predicting temperatureinduced girder end displacements and the ability to mitigate the time-lag effect.To be more specific,in comparison between the linear regression model and LSTM network,the mean square error decreases from 6.5937 to 1.6808 and R^(2) increases from 0.683 to 0.930,which corresponds to a 74.51%decrease in MSE and a 36.14%improvement in R^(2).Compared to ANN,with an MSE of 4.6371 and an R^(2) of 0.807,LSTM shows a decrease in MSE of 63.75%and an increase in R^(2) of 13.23%,demonstrating a significant enhancement in predictive performance.
基金supported in part by the Science and Technology Department of Sichuan Province(No.2025ZNSFSC0427,No.2024ZDZX0035)the Open Project Fund of Vehicle Measurement,Control and Safety Key Laboratory of Sichuan Province(No.QCCK2024-004)the Industrial and Educational Integration Project of Yibin(No.YB-XHU-20240001)。
文摘The accurate prediction of battery pack capacity in electric vehicles(EVs)is crucial for ensuring safety and optimizing performance.Despite extensive research on predicting cell capacity using laboratory data,predicting the capacity of onboard battery packs from field data remains challenging due to complex operating conditions and irregular EV usage in real-world settings.Most existing methods rely on extracting health feature parameters from raw data for capacity prediction of onboard battery packs,however,selecting specific parameters often results in a loss of critical information,which reduces prediction accuracy.To this end,this paper introduces a novel framework combining deep learning and data compression techniques to accurately predict battery pack capacity onboard.The proposed data compression method converts monthly EV charging data into feature maps,which preserve essential data characteristics while reducing the volume of raw data.To address missing capacity labels in field data,a capacity labeling method is proposed,which calculates monthly battery capacity by transforming the ampere-hour integration formula and applying linear regression.Subsequently,a deep learning model is proposed to build a capacity prediction model,using feature maps from historical months to predict the battery capacity of future months,thus facilitating accurate forecasts.The proposed framework,evaluated using field data from 20 EVs,achieves a mean absolute error of 0.79 Ah,a mean absolute percentage error of 0.65%,and a root mean square error of 1.02 Ah,highlighting its potential for real-world EV applications.
基金financially supported by the Joint Funds of the National Natural Science Foundation of China(Grant No.U2244226)the National Key R&D Program of China(No.2022YFC3004302)+7 种基金Chinese Geological Survey Project(No.DD20230538)the Science Foundation of Gansu Province(No.23JRRA830)the Science and Technology Major Project of Gansu Province(No.23ZDFA007)the Foundation for Innovation Groups of Basic Research in Gansu Province(24JRRA169)Power China Project(No.CD2C20230228)the Lanzhou Youth Science and Technology Talent Innovation Project(No.2024-QN-170)supported by the ESA-MOST China DRAGON-5(Grant No.59339)DRAGON-6 project(Grant No.95355).
文摘The lower Yarlung Tsangpo River basin of the Qinghai-Tibet Plateau frequently experiences geo-hazardous occurrences such as landslides,ice/rock avalanches and debris flows,causing loss of human lives and damage to infrastructure.However,a comprehensive inventory map of geohazards is lacking for this region,due to the extreme challenges of the geomorphological and environmental conditions(i.e.,steep terrain,dense vegetation cover,and the presence of ice and snow).To this end,we propose a novel approach for mapping active geohazards in complex mountainous regions through InSAR phase gradient measurements based on a deep learning algorithm,which is then applied to the lower Yarlung Tsangpo River basin for the first time,in order to prepare an inventory map of active geohazards using ascending and descending Sentinel-1 SAR images acquired between March 2017 and July 2023.First,the InSAR phase gradient stacking method was introduced to estimate ground deformation,which offers significant advantages in minimizing the influence of InSAR decorrelation and effectively suppressing topographic residuals and atmospheric delays.InSAR phase gradient rates effectively retrieve patterns of localized ground deformation associated with geohazard activity.Then,a DeepLabv3 deep learning model was established and trained with phase gradient rate maps of manually labeled geohazards,in order to achieve the automatic identification of active geohazards.Our results show that there are 277 active geohazards within the lower Yarlung Tsangpo River basin,encompassing an area of~25600 km^(2).The DeepLabv3 model achieved good precision,recall rate and F1 scores at 92,86 and 90%,respectively.The distribution of detected geohazards is closely correlated with the topographic factors,faults and river system.Compared to the results derived from Small Baseline Subset InSAR(SBAS-InSAR)and optical images,the proposed approach can obtain high density pixels of InSAR measurement in low-coherence scenarios,thus enabling high-accuracy mapping of active geohazards in complex mountainous areas.
基金the National Key R&D Program of China(2021YFC3000701)for the financial support。
文摘As one of the most seismically active regions,Sichuan Basin is a key area of seismological studies in China.This study applies a neural network model with attention mechanisms,simultaneously picking the P-wave arrival times and determining the first-motion polarity.The polarity information is subsequently used to derive source focal mechanisms.The model is trained and tested using small to moderate earthquake data from June to December 2019 in Sichuan.We apply the trained model to predict first-motion polarity directions of earthquake recordings in Sichuan from January to May 2019,and then derive focal mechanism solutions using HASH algorithm with predicted results.Compared with the source mechanism solutions obtained by manual processing,the deep learning method picks more polarities from smaller events,resulting in more focal mechanism solutions.The catalog documents focal mechanism solutions of 22 events(M_(L) 2.6–4.8)from analysts during this period,whereas we obtain focal mechanism solutions of 53 events(M_(L) 1.9–4.8)through the deep learning method.The derived focal mechanism solutions for the same events are consistent with the manual solutions.This method provides an efficient way for the source mechanism inversion of small to moderate earthquakes in Sichuan region,with high stability and reliability.
文摘Underwater wireless sensor networks(UWSNs)have emerged as a new paradigm of real-time organized systems,which are utilized in a diverse array of scenarios to manage the underwater environment surrounding them.One of the major challenges that these systems confront is topology control via clustering,which reduces the overload of wireless communications within a network and ensures low energy consumption and good scalability.This study aimed to present a clustering technique in which the clustering process and cluster head(CH)selection are performed based on the Markov decision process and deep reinforcement learning(DRL).DRL algorithm selects the CH by maximizing the defined reward function.Subsequently,the sensed data are collected by the CHs and then sent to the autonomous underwater vehicles.In the final phase,the consumed energy by each sensor is calculated,and its residual energy is updated.Then,the autonomous underwater vehicle performs all clustering and CH selection operations.This procedure persists until the point of cessation when the sensor’s power has been reduced to such an extent that no node can become a CH.Through analysis of the findings from this investigation and their comparison with alternative frameworks,the implementation of this method can be used to control the cluster size and the number of CHs,which ultimately augments the energy usage of nodes and prolongs the lifespan of the network.Our simulation results illustrate that the suggested methodology surpasses the conventional low-energy adaptive clustering hierarchy,the distance-and energy-constrained K-means clustering scheme,and the vector-based forward protocol and is viable for deployment in an actual operational environment.
基金supported in part by the Jiangsu Province Construction System Science and Technology Project(No.2024ZD056)the Research Development Fund of Xi’an Jiaotong-Liverpool University(No.RDF-24-01-097).
文摘Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.
基金National Key Research and Development Program of China,Grant/Award Number:2021YFB1714700Postdoctoral Research Foundation of China,Grant/Award Number:2024M752364Postdoctoral Fellowship Program of CPSF,Grant/Award Number:GZB20240525。
文摘This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent policy networks.The graph of welding spots distribution is encoded using the graph attention network.Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks.The policy network is used to convert the large scale welding spots allocation problem to multiple small scale singlerobot welding path planning problems,and the path planning problem is quickly solved through existing methods.Then,the model is trained through reinforcement learning.In addition,the task balancing method is used to allocate tasks to multiple stations.The proposed algorithm is compared with classical algorithms,and the results show that the algorithm based on DRL can produce higher quality solutions.
