Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
Lithium manganese silicate(Li-Mn-Si-O)cathodes are key components of lithium-ion batteries,and their physical and mechanical properties are strongly influenced by their underlying crystal structures.In this study,a ra...Lithium manganese silicate(Li-Mn-Si-O)cathodes are key components of lithium-ion batteries,and their physical and mechanical properties are strongly influenced by their underlying crystal structures.In this study,a range of machine learning(ML)algorithms were developed and compared to predict the crystal systems of Li-Mn-Si-O cathode materials using density functional theory(DFT)data obtained from the Materials Project database.The dataset comprised 211 compositions characterized by key descriptors,including formation energy,energy above the hull,bandgap,atomic site number,density,and unit cell volume.These features were utilized to classify the materials into monoclinic(0)and triclinic(1)crystal systems.A comprehensive comparison of various classification algorithms including Decision Tree,Random Forest,XGBoost,Support VectorMachine,k-Nearest Neighbor,Stochastic Gradient Descent,Gaussian Naive Bayes,Gaussian Process,and Artificial Neural Network(ANN)was conducted.Among these,the optimized ANN architecture(6–14-14-14-1)exhibited the highest predictive performance,achieving an accuracy of 95.3%,aMatthews correlation coefficient(MCC)of 0.894,and an F-score of 0.963,demonstrating excellent consistency with DFT-predicted crystal structures.Meanwhile,RandomForest and Gaussian Processmodels also exhibited reliable and consistent predictive capability,indicating their potential as complementary approaches,particularly when data are limited or computational efficiency is required.This comparative framework provides valuable insights into model selection for crystal system classification in complex cathode materials.展开更多
Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few de...Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.展开更多
Hydrocracking technology represents a crucial position in the conversion of heavy oil and the transformation development from oil refining to the chemical industry.The properties of catalysts are one of the key factor...Hydrocracking technology represents a crucial position in the conversion of heavy oil and the transformation development from oil refining to the chemical industry.The properties of catalysts are one of the key factors in the hydrocracking process.As the main acidic component of hydrocracking catalyst,the influence of zeolite properties on the reaction performance has been the focus of research.In this study,a series of NiMo/Al_(2)O_(3)-Y catalysts were prepared using different Y zeolites as acidic components,and their performances in the hydrocracking of n-C_(10)were also evaluated.The structure-activity relationship between Y zeolite and the cracking performance of n-C_(10)was investigated with machine learning.First,a database of the physical and chemical properties of Y zeolite and their performance was established,and the correlation analysis was also conducted.Parameters such as the cell constant,acid content,acid strength,B/L ratio,mesopore volume,micropore volume of Y zeolite,and the reaction temperature were selected as independent variables.The conversion of n-C_(10)and the ratios of products C_(3)/C_(7)and i-C_(4)/n-C_(4)were selected as dependent variables.A model was established by the random forest algorithm and a new zeolite was predicted based on it.The results of model prediction were in good agreement with the experimental results.The R^(2)of the n-C_(10)conversion,C_(3)/C_(7)ratio,and i-C_(4)/n-C_(4)ratio were 0.9866,0.9845,and 0.9922,and the minimum root mean square error values were 0.0163,0.101,and 0.0211,respectively.These results can provide reference for the development of high performance hydrocracking catalyst and technology.展开更多
Gallium,an elemental metal known for its distinctive thermal and electronic characteristics,holds signif-icant importance across various industrial fields including semiconductors,biomedicine,and aerospace.When subjec...Gallium,an elemental metal known for its distinctive thermal and electronic characteristics,holds signif-icant importance across various industrial fields including semiconductors,biomedicine,and aerospace.When subjected to supercooling,gallium exhibits the ability to crystallize into multiple structures that are notably more intricate compared to those found in other metallic elements,emphasizing the complex nature of its configuration space.Despite ongoing research efforts,our comprehensive understanding of the configuration space of gallium remains incomplete.In this study,we utilize an active learning strat-egy to develop an accurate deep neural network(DNN)model with strong descriptive capabilities for gallium’s entire configuration space.By integrating this DNN model with the evolutionary crystal struc-ture prediction algorithm USPEX,we conduct an extensive exploration of gallium configurations within simulation cells containing up to 120 atoms.Through this approach,we successfully identify the experi-mentally observed phases ofα-Ga,β-Ga,γ-Ga,δ-Ga,Ga-II and Ga-III.Additionally,we predict eight ther-modynamically metastable structures,labeled as mC 20,oC 8(no.63),mC 4,oP 12,tR 18,tI 20,oC 8(no.64),and mC 12,with high potential of experimental verification.Of particular interest,we identify the true struc-ture ofβ-Ga as having orthorhombic symmetry,in contrast to the widely accepted monoclini c structure.These findings offer new insights into gallium’s configuration space,demonstrating the effectiveness of the crystal structure prediction method combined with DNN model in guiding the exploration of complex systems.展开更多
The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount ...The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.展开更多
High entropy alloys(HEAs)have recently become a popular category of alloys,composed of five or more elements.These alloys are of particular interest in the field of materials due to their unique structure and excellen...High entropy alloys(HEAs)have recently become a popular category of alloys,composed of five or more elements.These alloys are of particular interest in the field of materials due to their unique structure and excellent properties.However,the multi-component nature of these alloys poses challenges to traditional calculation methods,necessitating the development of alternative approaches for their analysis.Machine learning,a branch of artificial intelligence,has emerged as a promising solution to address the complexity inherent in the composition and structure of HEAs.The present review focuses on the fundamental definition and process of machine learning and its application in the research field of HEAs.The primary focus of this research field is the prediction of phase structure,hardness,strength,thermodynamic properties,and catalytic properties.In addition,future perspectives on the challenges in this research area are also presented.展开更多
Crystal structure prediction aims to predict stable and easily experimentally synthesized materials,which accelerates the discovery of new materials.It is worth noting that the stability of materials is the basis for ...Crystal structure prediction aims to predict stable and easily experimentally synthesized materials,which accelerates the discovery of new materials.It is worth noting that the stability of materials is the basis for ensuring high performance and reliable application of materials.Among which,the thermodynamic and molecular dynamics stability is especially important.Therefore,this paper proposes a method to predict stable crystal structures using formation energy and Lennard-Jones potential as evaluation indicators.Specifically,we use graph neural network models to predict the formation energy of crystals,and employ empirical formulas to calculate the Lennard-Jones potential.Then,we apply Bayesian optimization algorithms to search for crystal structures with low formation energy and Lennard-Jones potential approaching zero,in order to ensure the thermodynamic stability and dynamics stability of materials.In addition,considering the impact of the bonding situation between atoms in the crystal on the structural stability,this article uses contact map to analyze the atomic bonding situation of each crystal to screen out more stable materials.Finally,the experimental results show that the method we proposed can not only reduce the time for crystal structure prediction,but also ensure the stability of crystal materials.展开更多
The structured low-rank model for parallel magnetic resonance(MR)imaging can efficiently reconstruct MR images with limited auto-calibration signals.To improve the reconstruction quality of MR images,we integrate the ...The structured low-rank model for parallel magnetic resonance(MR)imaging can efficiently reconstruct MR images with limited auto-calibration signals.To improve the reconstruction quality of MR images,we integrate the joint sparsity and sparsifying transform learning(JTL)into the simultaneous auto-calibrating and k-space estimation(SAKE)structured low-rank model,named JTLSAKE.The alternate direction method of multipliers is exploited to solve the resulting optimization problem,and the optimized gradient method is used to improve the convergence speed.In addition,a graphics processing unit is used to accelerate the proposed algorithm.The experimental results on four in vivo human datasets demonstrate that the reconstruction quality of the proposed algorithm is comparable to that of JTL-based low-rank modeling of local k-space neighborhoods with parallel imaging(JTL-PLORAKS),and the proposed algorithm is 46 times faster than the JTL-PLORAKS,requiring only 4 s to reconstruct a 200×200 pixels MR image with 8 channels.展开更多
The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle...The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.展开更多
With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has ...With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency.Currently,the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs,guiding subsequent production work.This study uses well logging data from the Chang 1 reservoir,partitioning the dataset based on individual wells for model training and testing.A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function.Comparative validations with five other models,including logistic regression(LR),naive Bayes(NB),gradient boosting decision trees(GBDT),random forest(RF),and support vector machine(SVM),show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids.Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification.This model provides important references and a basis for conducting regional studies and revisiting old wells,demonstrating practical value that can be widely applied.展开更多
Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color printing.Herein,we propose a deep learning-enabled reverse design of polarization-selective ...Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color printing.Herein,we propose a deep learning-enabled reverse design of polarization-selective structural color based on coding metasurface.In this study,the long short-term memory(LSTM)neural network is presented to enable the forward and inverse mapping between coding metasurface structure and corresponding color.The results show that the method can achieve 98%accuracy for the forward prediction of color and 93%accuracy for the inverse design of the structure.Moreover,a cascaded architecture is adopted to train the inverse neural network model,which can solve the nonuniqueness problem of the polarization-selective color reverse design.This study provides a new path for the application and development of structural colors.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
Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NP...Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.展开更多
Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorith...Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.展开更多
Machine learning(ML)has emerged as a powerful tool for predicting polymer properties,including glass transition temperature(Tg),which is a critical factor influencing polymer applications.In this study,a dataset of po...Machine learning(ML)has emerged as a powerful tool for predicting polymer properties,including glass transition temperature(Tg),which is a critical factor influencing polymer applications.In this study,a dataset of polymer structures and their Tg values were created and represented as adjacency matrices based on molecular graph theory.Four key structural descriptors,flexibility,side chain occupancy length,polarity,and hydrogen bonding capacity,were extracted and used as inputs for ML models:Extra Trees(ET),Random Forest(RF),Gaussian Process Regression(GPR),and Gradient Boosting(GB).Among these,ET and GPR achieved the highest predictive performance,with R2 values of 0.97,and mean absolute errors(MAE)of approximately 7–7.5 K.The use of these extracted features significantly improved the prediction accuracy compared to previous studies.Feature importance analysis revealed that flexibility had the strongest influence on Tg,followed by side-chain occupancy length,hydrogen bonding,and polarity.This work demonstrates the potential of data-driven approaches in polymer science,providing a fast and reliable method for Tg prediction that does not require experimental inputs.展开更多
Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based s...Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.展开更多
The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera im...The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.展开更多
In view of the shortcomings of traditional Bayesian network(BN)structure learning algorithm,such as low efficiency,premature algorithm and poor learning effect,the intelligent algorithm of cuckoo search(CS)and particl...In view of the shortcomings of traditional Bayesian network(BN)structure learning algorithm,such as low efficiency,premature algorithm and poor learning effect,the intelligent algorithm of cuckoo search(CS)and particle swarm optimization(PSO)is selected.Combined with the characteristics of BN structure,a BN structure learning algorithm of CS-PSO is proposed.Firstly,the CS algorithm is improved from the following three aspects:the maximum spanning tree is used to guide the initialization direction of the CS algorithm,the fitness of the solution is used to adjust the optimization and abandoning process of the solution,and PSO algorithm is used to update the position of the CS algorithm.Secondly,according to the structure characteristics of BN,the CS-PSO algorithm is applied to the structure learning of BN.Finally,chest clinic,credit and car diagnosis classic network are utilized as the simulation model,and the modeling and simulation comparison of greedy algorithm,K2 algorithm,CS algorithm and CS-PSO algorithm are carried out.The results show that the CS-PSO algorithm has fast convergence speed,high convergence accuracy and good stability in the structure learning of BN,and it can get the accurate BN structure model faster and better.展开更多
Three-dimensional(3D)reconstruction using structured light projection has the characteristics of non-contact,high precision,easy operation,and strong real-time performance.However,for actual measurement,projection mod...Three-dimensional(3D)reconstruction using structured light projection has the characteristics of non-contact,high precision,easy operation,and strong real-time performance.However,for actual measurement,projection modulated images are disturbed by electronic noise or other interference,which reduces the precision of the measurement system.To solve this problem,a 3D measurement algorithm of structured light based on deep learning is proposed.The end-to-end multi-convolution neural network model is designed to separately extract the coarse-and fine-layer features of a 3D image.The point-cloud model is obtained by nonlinear regression.The weighting coefficient loss function is introduced to the multi-convolution neural network,and the point-cloud data are continuously optimized to obtain the 3D reconstruction model.To verify the effectiveness of the method,image datasets of different 3D gypsum models were collected,trained,and tested using the above method.Experimental results show that the algorithm effectively eliminates external light environmental interference,avoids the influence of object shape,and achieves higher stability and precision.The proposed method is proved to be effective for regular objects.展开更多
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金supported by the Learning&Academic Research Institution for Master’s,PhD students,and Postdocs LAMP Program of the National Research Foundation of Korea(NRF)grant funded by the Ministry of Education(No.RS-2023-00301974)This work was also supported by the Glocal University 30 Project fund of Gyeongsang National University in 2025.
文摘Lithium manganese silicate(Li-Mn-Si-O)cathodes are key components of lithium-ion batteries,and their physical and mechanical properties are strongly influenced by their underlying crystal structures.In this study,a range of machine learning(ML)algorithms were developed and compared to predict the crystal systems of Li-Mn-Si-O cathode materials using density functional theory(DFT)data obtained from the Materials Project database.The dataset comprised 211 compositions characterized by key descriptors,including formation energy,energy above the hull,bandgap,atomic site number,density,and unit cell volume.These features were utilized to classify the materials into monoclinic(0)and triclinic(1)crystal systems.A comprehensive comparison of various classification algorithms including Decision Tree,Random Forest,XGBoost,Support VectorMachine,k-Nearest Neighbor,Stochastic Gradient Descent,Gaussian Naive Bayes,Gaussian Process,and Artificial Neural Network(ANN)was conducted.Among these,the optimized ANN architecture(6–14-14-14-1)exhibited the highest predictive performance,achieving an accuracy of 95.3%,aMatthews correlation coefficient(MCC)of 0.894,and an F-score of 0.963,demonstrating excellent consistency with DFT-predicted crystal structures.Meanwhile,RandomForest and Gaussian Processmodels also exhibited reliable and consistent predictive capability,indicating their potential as complementary approaches,particularly when data are limited or computational efficiency is required.This comparative framework provides valuable insights into model selection for crystal system classification in complex cathode materials.
基金The authors would like to thank CNPq(Conselho Nacional de Desenvolvimento Científico e Tecnológico)—grants 407256/2022-9,303550/2025-2,402533/2023-2 and 303982/2022-5FAPEMIG(Fundação de AmparoàPesquisa do Estado de Minas Gerais)—grants APQ-00032-24 and APD-01113-25 for their financial support.
文摘Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.
文摘Hydrocracking technology represents a crucial position in the conversion of heavy oil and the transformation development from oil refining to the chemical industry.The properties of catalysts are one of the key factors in the hydrocracking process.As the main acidic component of hydrocracking catalyst,the influence of zeolite properties on the reaction performance has been the focus of research.In this study,a series of NiMo/Al_(2)O_(3)-Y catalysts were prepared using different Y zeolites as acidic components,and their performances in the hydrocracking of n-C_(10)were also evaluated.The structure-activity relationship between Y zeolite and the cracking performance of n-C_(10)was investigated with machine learning.First,a database of the physical and chemical properties of Y zeolite and their performance was established,and the correlation analysis was also conducted.Parameters such as the cell constant,acid content,acid strength,B/L ratio,mesopore volume,micropore volume of Y zeolite,and the reaction temperature were selected as independent variables.The conversion of n-C_(10)and the ratios of products C_(3)/C_(7)and i-C_(4)/n-C_(4)were selected as dependent variables.A model was established by the random forest algorithm and a new zeolite was predicted based on it.The results of model prediction were in good agreement with the experimental results.The R^(2)of the n-C_(10)conversion,C_(3)/C_(7)ratio,and i-C_(4)/n-C_(4)ratio were 0.9866,0.9845,and 0.9922,and the minimum root mean square error values were 0.0163,0.101,and 0.0211,respectively.These results can provide reference for the development of high performance hydrocracking catalyst and technology.
基金financially supported by the National Natural Science Foundation of China(No.92370118)the National Science Fund for Excellent Young Scientist Fund Program(Overseas)of China,the Science and Technology Activities Fund for Overseas Researchers of Shaanxi Province,and the Research Fund of the State Key Laboratory of Solidification Processing(NPU),China(No.2024-ZD-01)。
文摘Gallium,an elemental metal known for its distinctive thermal and electronic characteristics,holds signif-icant importance across various industrial fields including semiconductors,biomedicine,and aerospace.When subjected to supercooling,gallium exhibits the ability to crystallize into multiple structures that are notably more intricate compared to those found in other metallic elements,emphasizing the complex nature of its configuration space.Despite ongoing research efforts,our comprehensive understanding of the configuration space of gallium remains incomplete.In this study,we utilize an active learning strat-egy to develop an accurate deep neural network(DNN)model with strong descriptive capabilities for gallium’s entire configuration space.By integrating this DNN model with the evolutionary crystal struc-ture prediction algorithm USPEX,we conduct an extensive exploration of gallium configurations within simulation cells containing up to 120 atoms.Through this approach,we successfully identify the experi-mentally observed phases ofα-Ga,β-Ga,γ-Ga,δ-Ga,Ga-II and Ga-III.Additionally,we predict eight ther-modynamically metastable structures,labeled as mC 20,oC 8(no.63),mC 4,oP 12,tR 18,tI 20,oC 8(no.64),and mC 12,with high potential of experimental verification.Of particular interest,we identify the true struc-ture ofβ-Ga as having orthorhombic symmetry,in contrast to the widely accepted monoclini c structure.These findings offer new insights into gallium’s configuration space,demonstrating the effectiveness of the crystal structure prediction method combined with DNN model in guiding the exploration of complex systems.
基金supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52375073)。
文摘The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.
基金financially supported by the National Natural Science Foundation of China(Project No.52473236,62304109)Natural Science Foundation of Xinjiang Uygur Autonomous Region of China(Project No.2024D01C62).
文摘High entropy alloys(HEAs)have recently become a popular category of alloys,composed of five or more elements.These alloys are of particular interest in the field of materials due to their unique structure and excellent properties.However,the multi-component nature of these alloys poses challenges to traditional calculation methods,necessitating the development of alternative approaches for their analysis.Machine learning,a branch of artificial intelligence,has emerged as a promising solution to address the complexity inherent in the composition and structure of HEAs.The present review focuses on the fundamental definition and process of machine learning and its application in the research field of HEAs.The primary focus of this research field is the prediction of phase structure,hardness,strength,thermodynamic properties,and catalytic properties.In addition,future perspectives on the challenges in this research area are also presented.
基金supported by the Nature Science Foundation of China(Nos.61671362 and 62071366)。
文摘Crystal structure prediction aims to predict stable and easily experimentally synthesized materials,which accelerates the discovery of new materials.It is worth noting that the stability of materials is the basis for ensuring high performance and reliable application of materials.Among which,the thermodynamic and molecular dynamics stability is especially important.Therefore,this paper proposes a method to predict stable crystal structures using formation energy and Lennard-Jones potential as evaluation indicators.Specifically,we use graph neural network models to predict the formation energy of crystals,and employ empirical formulas to calculate the Lennard-Jones potential.Then,we apply Bayesian optimization algorithms to search for crystal structures with low formation energy and Lennard-Jones potential approaching zero,in order to ensure the thermodynamic stability and dynamics stability of materials.In addition,considering the impact of the bonding situation between atoms in the crystal on the structural stability,this article uses contact map to analyze the atomic bonding situation of each crystal to screen out more stable materials.Finally,the experimental results show that the method we proposed can not only reduce the time for crystal structure prediction,but also ensure the stability of crystal materials.
基金the Yunnan Fundamental Research Projects(No.202301AT070452)the National Natural Science Foundation of China(No.61861023)。
文摘The structured low-rank model for parallel magnetic resonance(MR)imaging can efficiently reconstruct MR images with limited auto-calibration signals.To improve the reconstruction quality of MR images,we integrate the joint sparsity and sparsifying transform learning(JTL)into the simultaneous auto-calibrating and k-space estimation(SAKE)structured low-rank model,named JTLSAKE.The alternate direction method of multipliers is exploited to solve the resulting optimization problem,and the optimized gradient method is used to improve the convergence speed.In addition,a graphics processing unit is used to accelerate the proposed algorithm.The experimental results on four in vivo human datasets demonstrate that the reconstruction quality of the proposed algorithm is comparable to that of JTL-based low-rank modeling of local k-space neighborhoods with parallel imaging(JTL-PLORAKS),and the proposed algorithm is 46 times faster than the JTL-PLORAKS,requiring only 4 s to reconstruct a 200×200 pixels MR image with 8 channels.
基金supported by the National Key Research and Development Plan(Grant No.2022YFB3401901)the National Natural Science Foundation of China(Grant Nos.12192210,12192214,12072295,and 12222209)+1 种基金Independent Project of State Key Laboratory of Rail Transit Vehicle System(Grant No.2023TPL-T03)Fundamental Research Funds for the Central Universities(Grant No.2682023CG004).
文摘The S38C railway axle undergoes induction hardening,resulting in a gradient-distributed microstructure and mechanical properties.The accurate identification of gradient-distributed plastic parameters for the S38C axle remains a challenging task.To tackle this challenge,the present study proposes a novel approach for identifying the gradient-distributed plastic parameters for the S38C axle by integrating nano-indentation techniques with the machine learning method.Firstly,nano-indentation tests are conducted along the radial direction of the S38C axle to obtain the gradient-distributed load-displacement curves,nano-hardness,and elastic modulus.Subsequently,the dimensionless analysis is performed to obtain the representative stress,strain,and yield stress from load-displacement curves.These parameters are then incorporated into the machine learning method as physical information to identify the gradient-distributed plastic parameters of the S38C axle.The results indicate that the proposed method based on the physics-informed neural network and multi-fidelity neural network successfully identifies the gradient-distributed plastic parameters of the S38C axles and demonstrates superior prediction accuracy and generalization compared with the purely data-driven machine learning method.
基金supported by a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918 and 202103)。
文摘With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency.Currently,the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs,guiding subsequent production work.This study uses well logging data from the Chang 1 reservoir,partitioning the dataset based on individual wells for model training and testing.A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function.Comparative validations with five other models,including logistic regression(LR),naive Bayes(NB),gradient boosting decision trees(GBDT),random forest(RF),and support vector machine(SVM),show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids.Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification.This model provides important references and a basis for conducting regional studies and revisiting old wells,demonstrating practical value that can be widely applied.
基金supported by the National Natural Science Foundation of China(Grant Nos.62375137 and 62175114).
文摘Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color printing.Herein,we propose a deep learning-enabled reverse design of polarization-selective structural color based on coding metasurface.In this study,the long short-term memory(LSTM)neural network is presented to enable the forward and inverse mapping between coding metasurface structure and corresponding color.The results show that the method can achieve 98%accuracy for the forward prediction of color and 93%accuracy for the inverse design of the structure.Moreover,a cascaded architecture is adopted to train the inverse neural network model,which can solve the nonuniqueness problem of the polarization-selective color reverse design.This study provides a new path for the application and development of structural colors.
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National Natural Science Foundation of China under Grant Nos.52208191 and 51908397Shanxi Province Science Foundation for Youths under Grant No.201901D211025China Postdoctoral Science Foundation under Grant No.2020M670695。
文摘Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.
基金supported by the National Natural Science Foundation of China(7110111671271170)+1 种基金the Program for New Century Excellent Talents in University(NCET-13-0475)the Basic Research Foundation of NPU(JC20120228)
文摘Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.
文摘Machine learning(ML)has emerged as a powerful tool for predicting polymer properties,including glass transition temperature(Tg),which is a critical factor influencing polymer applications.In this study,a dataset of polymer structures and their Tg values were created and represented as adjacency matrices based on molecular graph theory.Four key structural descriptors,flexibility,side chain occupancy length,polarity,and hydrogen bonding capacity,were extracted and used as inputs for ML models:Extra Trees(ET),Random Forest(RF),Gaussian Process Regression(GPR),and Gradient Boosting(GB).Among these,ET and GPR achieved the highest predictive performance,with R2 values of 0.97,and mean absolute errors(MAE)of approximately 7–7.5 K.The use of these extracted features significantly improved the prediction accuracy compared to previous studies.Feature importance analysis revealed that flexibility had the strongest influence on Tg,followed by side-chain occupancy length,hydrogen bonding,and polarity.This work demonstrates the potential of data-driven approaches in polymer science,providing a fast and reliable method for Tg prediction that does not require experimental inputs.
基金supported by the National Natural Science Fundation of China(61573285)the Doctoral Fundation of China(2013ZC53037)
文摘Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.
基金supported in part by the Gusu Innovation and Entrepreneurship Leading Talents in Suzhou City,grant numbers ZXL2021425 and ZXL2022476Doctor of Innovation and Entrepreneurship Program in Jiangsu Province,grant number JSSCBS20211440+6 种基金Jiangsu Province Key R&D Program,grant number BE2019682Natural Science Foundation of Jiangsu Province,grant number BK20200214National Key R&D Program of China,grant number 2017YFB0403701National Natural Science Foundation of China,grant numbers 61605210,61675226,and 62075235Youth Innovation Promotion Association of Chinese Academy of Sciences,grant number 2019320Frontier Science Research Project of the Chinese Academy of Sciences,grant number QYZDB-SSW-JSC03Strategic Priority Research Program of the Chinese Academy of Sciences,grant number XDB02060000.
文摘The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.
基金National Natural Science Foundation of China(Nos.61164010,61233003)。
文摘In view of the shortcomings of traditional Bayesian network(BN)structure learning algorithm,such as low efficiency,premature algorithm and poor learning effect,the intelligent algorithm of cuckoo search(CS)and particle swarm optimization(PSO)is selected.Combined with the characteristics of BN structure,a BN structure learning algorithm of CS-PSO is proposed.Firstly,the CS algorithm is improved from the following three aspects:the maximum spanning tree is used to guide the initialization direction of the CS algorithm,the fitness of the solution is used to adjust the optimization and abandoning process of the solution,and PSO algorithm is used to update the position of the CS algorithm.Secondly,according to the structure characteristics of BN,the CS-PSO algorithm is applied to the structure learning of BN.Finally,chest clinic,credit and car diagnosis classic network are utilized as the simulation model,and the modeling and simulation comparison of greedy algorithm,K2 algorithm,CS algorithm and CS-PSO algorithm are carried out.The results show that the CS-PSO algorithm has fast convergence speed,high convergence accuracy and good stability in the structure learning of BN,and it can get the accurate BN structure model faster and better.
基金funded by Scientific and Technological Projects of Henan Province under Grant 182102210065Key Scientific Research Projects of Henan Universities under Grant 15A413015.
文摘Three-dimensional(3D)reconstruction using structured light projection has the characteristics of non-contact,high precision,easy operation,and strong real-time performance.However,for actual measurement,projection modulated images are disturbed by electronic noise or other interference,which reduces the precision of the measurement system.To solve this problem,a 3D measurement algorithm of structured light based on deep learning is proposed.The end-to-end multi-convolution neural network model is designed to separately extract the coarse-and fine-layer features of a 3D image.The point-cloud model is obtained by nonlinear regression.The weighting coefficient loss function is introduced to the multi-convolution neural network,and the point-cloud data are continuously optimized to obtain the 3D reconstruction model.To verify the effectiveness of the method,image datasets of different 3D gypsum models were collected,trained,and tested using the above method.Experimental results show that the algorithm effectively eliminates external light environmental interference,avoids the influence of object shape,and achieves higher stability and precision.The proposed method is proved to be effective for regular objects.
