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Big data-driven analysis of shale gas enrichment patterns:A case study of the Wufeng–Longmaxi Formation in the Sichuan Basin and its periphery 认领 引用 被引量：1
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作者 Zongquan Hu Jin Meng +10 位作者 Wei Du Yitian Xiao Chuanxiang Sun Guanping Wang Baojian Shen Tianrui Ye Dongjun Feng Zengqin Liu Longfei Lu Ruyue Wang Qianru Wang 《Energy Geoscience》 EI CAS CSCD 2026年第1期166-178,共13页
The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoir... The Wufeng–Longmaxi Formation derives its name from the Upper Ordovician Wufeng Formation and the Lower Silurian Longmaxi Formation,found in sequence in the Sichuan Basin.This formation hosts rich shale gas reservoirs,and its shale gas enrichment patterns are examined in this study using data from 1197 shale samples collected from 14 wells.Five basic and three key parameters,eight in all,are assessed for each sample.The five basic parameters include burial depth and the contents of four mineral types—quartz,clay,carbonate,and other minerals;the three key parameters,representing shale gas enrichment,are total organic carbon(TOC)content,porosity,and gas content.The SHapley Additive exPlanations(SHAP)analysis originated in game theory is used here in an interpretable machine learning framework,to address issues of heterogeneous data structure,noisy relationships,and multi-objective optimization.An evaluation of the ranking,contribution values,and conditions of changes for these parameters offers new quantitative insights into shale gas enrichment patterns.A quantitative analysis of the relationship between data-sets identifies the primary factors controlling TOC,porosity,and gas content of shale gas reservoirs.The results show that TOC and porosity jointly influence gas content;mineral content has a significant impact on both,TOC and porosity;and the burial depth governs porosity which,in turn,affects the conditions under which shale gas is preserved.Input parameter thresholds are also determined and provide a basis for the establishment of quantitative criteria to evaluate shale gas enrichment.The predictive accuracy of the model used in this study is significantly improved by the step-wise addition of two input parameters,namely TOC and porosity,separately and together.Thus,the game theory method in big data-driven analysis uses a combination of TOC and porosity to evaluate the gas content with encouraging results—suggesting that these are the key parameters that indicate source rock and reservoir properties. 展开更多
关键词 Big data-driven analysis Primary controlling factor Shale gas enrichment pattern Wufeng–Longmaxi Formation Sichuan basin
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Adaptive-length data-driven predictive control for post-operation of space robot non-cooperative target capture with disturbances 认领 引用 被引量：1
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作者 Peiji WANG Bicheng CAI +2 位作者 Chengfei YUE Yong ZHAO Weiren WU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期485-498,共14页
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi... This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering. 展开更多
关键词 Combined control Data-driven predictive control Post operation Predictive control systems Space non-cooperative target capture
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Data-driven non-Gaussian stochastic dynamical system identification meets knowledge-based model predictive control 认领 引用
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作者 Yicheng Mao Xianbin Liu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期71-85,共15页
Randomness and nonlinearity are essential properties of the real world,and their interaction gives rise to highly complex phenomena.With the advancement of technology,merely observing data of the current system state ... Randomness and nonlinearity are essential properties of the real world,and their interaction gives rise to highly complex phenomena.With the advancement of technology,merely observing data of the current system state is no longer sufficient for prediction and application in various fields.Consequently,extracting the nonlinear evolution nature of the system from noisy data has become a prominent and challenging issue.To address this,we propose an integrated approach that combines data-driven stochastic model identification with a knowledge-based model predictive control strategy.By leveraging high-precision model identification,our data-driven control design is particularly effective for continuous target tracking problems that are difficult to address using traditional precise-model-based control theory.Furthermore,the central challenge in data science lies in maximizing the informational value of datasets while minimizing the effects of observation noise.In this study,we propose and rigorously demonstrate the stochastic Occam’s razor principle,a stochastic error estimation theory that evaluates and enhances the design of data-driven schemes to mitigate the effect of observation noise.Notably,our approach offers valuable insights for contemporary data-driven,end-to-end control challenges,particularly those involving uncertain governing equations and substantial non-Gaussian observation noise. 展开更多
关键词 Nonlinear stochastic dynamical system Data-driven system identification Non-Gaussian Lévy noise Data-driven model predictive control
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Distributed robust data-driven event-triggered control for QUAVs under stochastic disturbances 认领 引用
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作者 Chao Song Hao Li +2 位作者 Bo Li Jiacun Wang Chunwei Tian 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第1期155-171,共17页
To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance dat... To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance data-driven event-triggered fusion control method,which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm.First,the impact of random disturbances on the UAV swarm is analyzed,and a model for orientation and attitude control of QUAVs under stochastic perturbations is established,with the disturbance gain threshold determined.Second,a fault diagnosis system based on a high-gain observer is designed,constructing a fault gain criterion by integrating orientation and attitude information from QUAVs.Subsequently,a model-free dynamic linearization-based data modeling(MFDLDM)framework is developed using model-free adaptive control,which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data.On this basis,this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism,which consists of an equivalent QUAV controller and an event-triggered controller,and is able to reduce the communication conflicts while suppressing the influence of random interference.Finally,by incorporating random disturbances into the controller,comparative experiments and physical validations are conducted on the QUAV platforms,fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system. 展开更多
关键词 Data-driven QUAV control Fault diagnosis Event-triggered Non-conflicting communication
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Data-driven iterative calibration method for prior knowledge of earth-rockfilldam wetting model parameters 认领 引用
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作者 Shaolin Ding Jiajun Pan +4 位作者 Yanli Wang Lin Wang Han Xu Yiwei Lu Xudong Zhao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1621-1632,共12页
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a... Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments. 展开更多
关键词 Earth-rockfilldam Wetting deformation Prior knowledge Data-driven Bayesian inversion
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Estimation of truck mass and center of gravity using data-driven mechanics:a comparative study of TSO-SVM,CS-BP,SSA-ELM,and WOA-XGBoost in automotive dynamics 认领 引用
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作者 Wei Liu Yuanyou Fu +2 位作者 Yongjun Pan Weicheng Huang Aki Mikkola 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期160-177,共18页
Accurate estimation of a truck’s mass and center of gravity(CG)is critical for optimizing safety and performance butremains challenging due to dynamic uncertainties in weight distribution and road interactions.This s... Accurate estimation of a truck’s mass and center of gravity(CG)is critical for optimizing safety and performance butremains challenging due to dynamic uncertainties in weight distribution and road interactions.This study introduces a datadriven mechanics framework integrating four hybrid machine learning(ML)models-tuna search-optimized support vector machine,cuckoo search-optimized BP neural networks,sparrow search algorithm-optimized extreme learning machine,and whalesearch-optimized XGBoost-to enable estimation.A 17-degree-of-freedom multibody dynamics model,incorporating suspension kinematics via a semirecursive formulation,generates simulation datasets linking real-time tuck states(pitch,roll)to massand CG.Search algorithms leverage physics-derived truck state data to initialize ML hyperparameters,enhancing training efficiency.Validation against multibody benchmarks confirms accuracy,while robustness is demonstrated across driving scenariosand noise.By unifying data-driven ML with physics-based mechanics,this approach advances parameter estimation,bridgingtruck dynamics with computational intelligence for automotive design. 展开更多
关键词 Parameter estimation Data-driven mechanics Multibody dynamics Center of gravity Hybrid machine learning Real-time estimation
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Multimodal and data-driven approaches in acupuncture research:methods,applications,and challenges 认领 引用
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作者 Dehui Nie Puchen Huang +10 位作者 Dan Jin Yiming Chen Baochao Fan Shanshan Huang Yuqing Zhang Yu Zhou Bin Han Jianlong Huang Gaolei Yao Liming Lu Peijing Rong 《Acupuncture and Herbal Medicine》 CAS CSCD 2026年第2期141-154,共14页
Acupuncture research increasingly involves heterogeneous and multimodal data that are difficult to analyze using conventional methods.This review summarizes data-driven approaches in acupuncture research within a fram... Acupuncture research increasingly involves heterogeneous and multimodal data that are difficult to analyze using conventional methods.This review summarizes data-driven approaches in acupuncture research within a framework encompassing intervention,response,and contextual data.We discuss causal inference,artificial intelligence,text mining,and integrative analysis,along with their applications in efficacy evaluation,outcome prediction,mechanistic investigation,and clinical decision support.These approaches shift the focus of acupuncture research from population-level average effects toward individualized clinical decision-making by enabling the analysis of treatment heterogeneity and underlying mechanisms.However,current research remains limited by inadequate data standardization,insufficient external validation,and limited model interpretability.Despite these challenges,data-driven approaches offer substantial promise for advancing more rigorous and personalized acupuncture research. 展开更多
关键词 Acupuncture Causal inference Clinical decision support Data-driven Deep learning Machine learning Mechanistic studies Multimodal data
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Experimental data-driven deep neural network modeling and prediction for the streaks of turbulent separated shear flow 认领 引用
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作者 Xingyu Ma Jiateng Pan +1 位作者 Yihong Liu Nan Jiang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期52-58,共7页
In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a... In this paper,we report a recent experimental study of streak structures in the turbulent separated shear flow by datadriven deep neural network.By applying spanwise-aligned tetrahedron vortex generators upstream of a plane backward-facing step,spanwise-aligned high-and low-speed streaks were generated within the separated shear layer behind the step.The velocity profiles of the shear flow were measured by single-probe hot-wire anemometer in both the streamwise-vertical and the streamwise-spanwise planes in the wind tunnel.Deep neural network models are trained and verified based on the experimental datasets.The input parameter sets include the vortex generator height,spanwise spacing,and the spatial coordinates within the measurement domain,while the output parameter sets are mean and root-mean-square velocities of the shear flow.Mean squared errors between the model-predicted and experimentally measured data are used for quality evaluation of different deep neural network model designs,among which the minimum error of the optimal design descends less than 1%.For other vortex generator parameters,which are not measured in the wind tunnel or used in the training,the model prediction provides reasonable mean velocity contours with streak structures.Thus,we find that the experimental data-driven modeling approach shows reliable robustness for nonlinear fitting of complex datasets as well as considerable generalization for turbulent coherent structures. 展开更多
关键词 Streak Experimental data-driven Deep neural network Vortex generator Backward-facing step
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Industrial quality prediction via FVAE-WGAN:A data-driven approach 认领 引用
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作者 Shiwei Gao Jing Yan +1 位作者 Mengyi Chen Pengxue Yun 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第3期175-187,共13页
In industrial production,the acquisition of critical quality variables often faces significant challenges due to high costs and data scarcity,which not only limit the improvement of production efficiency but also incr... In industrial production,the acquisition of critical quality variables often faces significant challenges due to high costs and data scarcity,which not only limit the improvement of production efficiency but also increase the difficulty of quality control.With the advent of the industrial big data era,the availability and diversity of data have greatly increased,offering opportunities to address these issues.To address the problem of data scarcity,this paper proposes a novel data augmentation method for soft sensing—FVAE-WGAN,which generates high-quality synthetic data to expand the training dataset of soft sensors,thereby enhancing their prediction accuracy and generalization capability.This method integrates two stacked variational autoencoder(VAE)models with a Wasserstein generative adversarial network(WGAN),constructing a generator capable of learning from a broader data distribution.Additionally,an encoder is embedded in the discriminator,enhancing the model's ability to utilize late nt features of the data.By freezing specific layers of the discriminato r,the pro posed method reduces computational resource consumption during training and effectively mitigates overfitting.Experiments conducted on industrial process datasets show that the FVAE-WGAN model outperforms comparative models in terms of accuracy and robustness.This approach not only alleviates the impact of data scarcity,but also optimizes the efficiency and reliability of industrial processes,thereby bringing substantial economic benefits to industrial production. 展开更多
关键词 Variational autoencoder Frozen Soft sensor model Data-driven
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Data-Driven Event-Triggered Control Dealing With Noisy Data 认领 引用
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作者 Xian-Ming Zhang Qing-Long Han Xiaohua Ge 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期543-554,共12页
This paper is concerned with event-triggered control that deals with noisy data for both discrete-time and continuoustime linear systems with unknown system matrices.First,based on a sufficiently rich finite set of no... This paper is concerned with event-triggered control that deals with noisy data for both discrete-time and continuoustime linear systems with unknown system matrices.First,based on a sufficiently rich finite set of noisy data collected in an experiment,the pair of system matrices is represented as a data-based nominal matrix plus an uncertain matrix with a bounded norm.This formulation enables classical robust control techniques to be applied to tackle the robust control problem.Second,for discretetime systems,a novel event-triggering condition is proposed,by which an event is triggered if the sum of the squares of the weighted error exceeds the square of the weighted state from the previous event.For continuous-time systems,the event-triggering condition is devised as a monotonically increasing function that starts with a negative value and triggers an event when it reaches zero.This condition can exclude the so-called Zeno behaviour due to its monotonic increase property.Third,by employing a looped functional method,several criteria are derived to co-design suitable state feedback controllers and event-triggering parameters for the systems under study.Finally,the effectiveness of the proposed method is demonstrated through a case study involving a batch reactor system. 展开更多
关键词 Data-driven control event-triggered control looped functional method noisy data
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Data-Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing 认领 引用
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作者 Hong Liu Kangyan Liu +7 位作者 Biao Zhang Ziang Chen Yi Yang Qiang Sun Tao Ye Bed Poudel Kai Wang Congcong Wu 《Carbon Energy》 SCIE EI CAS CSCD 2026年第3期129-139,共11页
The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes... The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes a machine learning(ML)approach to efficiently predict and analyze perovskite film fabrication processes.By evaluating five classic ML algorithms on 130 experimental data sets from blade-coating parameters,the Random Forest(RF)model was identified as the most effective,enabling rapid prediction of over 100,000 parameter sets in just 10 min-equivalent to 3 years of manual experimentation.The RF model demonstrated strong predictive accuracy,with an R2 close to 0.8.This approach led to the identification of optimal process parameter combinations,significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold,thereby advancing the development of scalable manufacturing processes. 展开更多
关键词 Data-Driven Design of Scalable Perovskite Film Fabrication via Machine Learning Guided Processing
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Data-Driven Algorithms for Finite-Horizon and Infinite-Horizon Indefinite Linear Quadratic Stochastic Optimal Control Problems 认领 引用
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作者 Guangchen Wang Heng Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1459-1469,共11页
This paper is devoted to devising data-driven algorithms for finite-horizon and infinite-horizon linear quadratic stochastic optimal control(LQSOC)problems.In our study,the diffusion terms of system dynamics are permi... This paper is devoted to devising data-driven algorithms for finite-horizon and infinite-horizon linear quadratic stochastic optimal control(LQSOC)problems.In our study,the diffusion terms of system dynamics are permitted to hinge upon both control and state variables,and the weighting matrices of cost functionals are allowed to be indefinite.It is acknowledged that the optimal controls of finite-horizon and infinite-horizon indefinite LQSOC problems are correlated with a generalized differential Riccati equation(GDRE)and a generalized algebraic Riccati equation(GARE).Herein,we propose two data-driven algorithms to approximate the solutions of these Riccati equations,and thereby determine optimal controls,without leveraging the information of all system parameters.Additionally,we prove the convergence of these algorithms and examine the impact of computational errors.Finally,we validate the performance of these data-driven algorithms via three simulation examples. 展开更多
关键词 Data-driven algorithm generalized algebraic Riccati equation (GARE) generalized differential Riccati equation (GDRE) indefinite problem linear quadratic stochastic optimal control (LQSOC)
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A data-driven integrated optimization method of macroscopic topology and microscopic configuration for the graded functional cellular structures 认领 引用
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作者 Yu Guo Yijie Lu +2 位作者 Zhengwei Zhang Yanguo Zhou Hui Liu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期473-495,共23页
In the topology optimization of the multiscale structure,how to ensure the connectivity between adjacent microstructures,how to control the design space of microstructures,and how to reduce the amount of calculation a... In the topology optimization of the multiscale structure,how to ensure the connectivity between adjacent microstructures,how to control the design space of microstructures,and how to reduce the amount of calculation and improve calculation efficiency are three basic challenging issues currently faced.To this end,this paper proposes a data-driven approach to achieve the integrated optimization of macroscopic topology and microscopic configuration of the graded functional cellular structures.At the macro level,a topological description function is introduced to realize the topological control of the macrostructure.At the micro level,several cutting functions are used to realize the control of the configuration and size of the microstructure.The integrated optimization design of macro and micro cellular structures can be realized.Based on the computational homogenization method and numerical integration technology,an optimization problem independent offline microstructure database is established at the microscopic scale,where the relationship between the equivalent elastic parameters,relative pseudo-density,and design variables of the microstructure is stored.Based on this offline database,the entire topology optimization process is completed only on a macro scale,which greatly reduces the amount of calculation and improves calculation efficiency.In addition,implicit geometric modeling of full-scale cellular structures can be achieved using the reconstruction technique introduced in this work,which ensures smooth connection between adjacent microstructures.Finally,numerical examples are used to verify the effectiveness of the algorithm and the superiority of gradient cellular structures compared with single-scale structures. 展开更多
关键词 Graded cellular structure Integrated optimization Data-driven Computational homogenization M-VCUT Level set method
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Optimization of Thermoplastic Elastomer (TPE) Components for Aerospace Structures Using Computerized Data-Driven Design 认领 引用
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作者 Adwaa Mohammed Abdulmajeed Duaa Abdul Rida Musa +2 位作者 Ola Abdul Hussain Emad Kadum Njim Royal Madan 《Computers, Materials & Continua》 SCIE EI 2026年第6期766-794,共29页
A data-driven optimization framework that integrates machine learning surrogate models,finite element analysis(FEA),and a multi-objective optimization algorithm is used in this study for developing thermoplastic elast... A data-driven optimization framework that integrates machine learning surrogate models,finite element analysis(FEA),and a multi-objective optimization algorithm is used in this study for developing thermoplastic elastomer(TPE)parts for aerospace applications.By using FEA simulations and experiments,a database of input design parameters(e.g.,geometry and structural shape modifier)is generated.Afterwards,we train surrogate models(e.g.,Gaussian Process Regression,neural networks)to approximate mappings from design space to performance space.Finally,we propose Pareto-optimal TPE designs using the surrogate embedded in a multi-objective optimization loop(such as NSGA-Ⅱ or gradient-based methods).The novelty of this approach is demonstrated by employing highly simplified surrogate models,including an artificial neural network(ANN)with 10 hidden neurons trained on analytically generated synthetic data.The proposed methodology has been validated using an aerospace-related case study:a vibration-damping plate.Compared with the baseline configuration,Pareto-optimal designs identified by the proposed framework achieved a reduction in maximum deflection of 23%-28%and a reduction in von Mises stress of 18%-24%,depending on the selected trade-off solution,as the number of full FEA simulations required for optimization was reduced from 500 to 50.This framework enables faster design of TPE components for aerospace systems.Validation against high-fidelity ANSYS simulations showed a mean error of~1.18%and a maximum deviation of~2.6%. 展开更多
关键词 TPE aerospace structures surrogate modelling data-driven optimization multi-objective optimization method FEM
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Data-driven simulation of storm-enhanced density and tongue of ionization during the May 10–11,2024,superstorm 认领 引用
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作者 XiangYu Niu Jing Liu +4 位作者 JiaoJiao Zhang HaoNan Wu JianJun Liu YaQi Jin ShuHan Li 《Earth and Planetary Physics》 EI CSCD 2026年第2期302-314,共13页
Storm-enhanced density(SED)and the tongue of ionization(TOI)are key ionospheric storm-time structures whose rapid evolution and fine-scale variability remain challenging to capture with conventional empirical high-lat... Storm-enhanced density(SED)and the tongue of ionization(TOI)are key ionospheric storm-time structures whose rapid evolution and fine-scale variability remain challenging to capture with conventional empirical high-latitude drivers.In this study,we examine the May 10–11,2024,superstorm using the Thermosphere–Ionosphere–Electrodynamics General Circulation Model(TIEGCM)with observation-constrained high-latitude forcing.Auroral precipitation parameters(energy flux and mean energy)are assimilated from a Defense Meteorological Satellite Program(DMSP)Special Sensor Ultraviolet Spectrographic Imager(SSUSI)using a multi-resolution Gaussian process(Lattice Kriging)approach,whereas high-latitude convection potentials are derived by assimilating Super Dual Auroral Radar Network(SuperDARN)observations with the Thomas and Shepherd(2018)model(TS18).For comparison,an additional simulation is performed using empirical models for both convection and auroral forcing.The results show that during the main phase of the May 10 storm,the data-driven simulation provides a more realistic depiction of the SED source region than does the empirical model run by capturing its rapid intensification more clearly and reproducing its spatial location and structural features with higher fidelity.These improvements lead to a more accurate representation of its poleward extension into the polar cap that develops into the TOI.Above the ionospheric F2 peak over the SED source region,SuperDARN-constrained potentials generate stronger and more localized E×B drifts that dominate plasma uplift and drive its transport into the polar cap,although neutral winds and downward ambipolar diffusion partially offset these effects.Below the F2 peak,neutral winds and photochemical processes play a major role in shaping the spatial extent and intensity of the SED and TOI.These results highlight the role of observation-constrained high-latitude drivers in representing ionosphere–thermosphere responses during extreme storms and suggest their relevance for improving physical interpretation and model performance. 展开更多
关键词 data-driven simulation storm-enhanced density tongue of ionization continuity term analysis
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Data-Driven Test Case Prioritization(DD-TCP):A Machine Learning Framework for Intelligent Software Quality Assurance 认领 引用
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作者 Hafiz Arslan Ramzan Kamrul Islam +3 位作者 Md Ahbab Hussain Raiyan Muntasir Monim Sabit Md Asad Sadia Ramzan 《Computers, Materials & Continua》 SCIE EI 2026年第7期1369-1389,共21页
Regression testing of large-scale,data-intensive software systems demands efficient test-case prioritization strategies to detect faults early while minimizing computational cost.Conventional prioritization methods,su... Regression testing of large-scale,data-intensive software systems demands efficient test-case prioritization strategies to detect faults early while minimizing computational cost.Conventional prioritization methods,such as coverage-based and risk-based approaches,lack adaptability to evolving project dynamics and fail to leverage the rich test-execution data accumulated over continuous integration cycles.This study presents a Data-Driven Test-Case Prioritization(DD-TCP)Framework that incorporates statistical and machine-learning techniques to model the relationship between test-case features and historical fault detection outcomes.The framework extracts multidimensional attributes including code-change frequency,dependency metrics,execution duration,and past failure density,which are normalized and embedded into a predictive ranking model based on gradient-boosted decision trees.Test cases are then dynamically reordered using a probabilistic gain function that maximizes early fault detection probability.Comprehensive simulations on representative open-source project datasets and synthetically generated large-scale test suites reveal that the proposed Data-Driven Test-Case Prioritization(DD-TCP)framework consistently achieves superior performance,yielding a 32.4%improvement in Average Percentage of Faults Detected(APFD)and a 27.1%reduction in execution overhead relative to baseline methods.The results demonstrate the feasibility of data-centric intelligence for scalable regression testing and provide an analytical foundation for integrating machine learning into next-generation Software Quality Assurance pipelines. 展开更多
关键词 Data-driven test-case prioritization regression testing software quality assurance machine learning continuous integration fault detection efficiency intelligent software systems
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Online data-driven MPC for PWA systems with unknown parameters 认领 引用
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作者 Rui Gu Aoyun Ma +2 位作者 Dewei Li Yunwen Xu Shaoying He 《Control Theory and Technology》 EI CSCD 2026年第2期307-315,共9页
A data-driven model predictive control(MPC)algorithm based on the input-mapping method is proposed for piecewise affine(PWA)systems.These systems are characterized by unknown but constant parameters and are subject to... A data-driven model predictive control(MPC)algorithm based on the input-mapping method is proposed for piecewise affine(PWA)systems.These systems are characterized by unknown but constant parameters and are subject to disturbances,as well as state and input constraints.To support the control strategy,an offline algorithm is developed to compute a non-convex robust positively invariant set that serves as the terminal set within the MPC framework tailored for PWA systems.The online MPC algorithm directly maps the future control input and predicted state to the historical input-state data associated with the corresponding state subregion.This mapping process leverages the more accurate relationships contained in the historical input-state data to enhance the prediction accuracy of future states.A state-dependent weight embedded in the cost function enables the controller to balance prediction accuracy against convergence speed,enhancing overall performance.Moreover,conditions ensuring the recursive feasibility of the optimization problem and stability of the closed-loop system are established.The effectiveness of the proposed algorithm is demonstrated through a numerical example,which highlights its ability to handle complex system dynamics and constraints while maintaining robust performance. 展开更多
关键词 Data-driven model predictive control Input-mapping method Piecewise affine systems Unknown parameters
Two-scale topology optimization of structural fundamental eigenfrequency using a data-driven microstructure model based on M-VCUT level set 认领 引用
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作者 Zibo Wang Minjie Shao Qi Xia 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期437-447,共11页
For the optimization of fundamental eigenfrequency in vibrating structures,it has been proven that multi-scale structures have advantages over single scale structures.This study introduces a two-scale topology optimiz... For the optimization of fundamental eigenfrequency in vibrating structures,it has been proven that multi-scale structures have advantages over single scale structures.This study introduces a two-scale topology optimization method using a data-driven microstructure model based on a multiple variable cutting(M-VCUT)level set approach.This method aims to maximize the fundamental eigenfrequency of two-scale structures.The method consists of two parts:offline database construction and online topology optimization.In the process of offline database construction,many microstructures are obtained by varying the value of geometric parameters according to the M-VCUT level set approach;then,a mapping relationship between the geometric parameters and the homogenized mechanical properties of microstructures is established by compactly supported radial basis function interpolation,which gives the data-driven microstructure model.In the process of online optimization,the homogenized mechanical properties corresponding to arbitrary design variables are obtained by using the data-driven microstructure model,whose computational costs are much less than those of the homogenization.Topology optimization is carried out with this data-driven model to enhance computational efficiency.In order to adapt the method of moving asymptotes(MMA),the eigenfrequency maximization problem is converted to its reciprocal minimization problem for sensitivity calculation.The method’s effectiveness is proved through several numerical examples. 展开更多
关键词 Two-scale structure Data-driven M-VCUT level set Eigenfrequency optimization Radial basis function MMA
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AI for safety:Physics-inspired data-driven approach for cost-saving seismic fragility analysis 认领 引用
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作者 Xuetian Liu Yi Liu Xiangdi Kang 《Journal of Safety Science and Resilience》 EI CSCD 2026年第2期479-492,共14页
Seismic fragility analysis is crucial for loss assessment of earthquake disaster.This study aims to leverage the advantages of physics-inspired AI methodologies to reduce the cost of collecting datasets required for s... Seismic fragility analysis is crucial for loss assessment of earthquake disaster.This study aims to leverage the advantages of physics-inspired AI methodologies to reduce the cost of collecting datasets required for seismic fragility functions.A novel physics-inspired multi-stage data-driven approach is proposed to fit multivariate probabilistic seismic demand models(PSDMs).Inspired by physics information,this approach utilizes AI techniques to learn the increment in the predictions of the engineering demand parameter(EDP)between refined and simplified models for engineering structures of the same type under identical scenarios.Thus,it establishes a connection between the predicted results of these models under the same seismic loads.Compared with approach without physics-inspired elements,this approach significantly reduces the size of the high-cost dataset based on refined models that required to fit PSDMs and seismic fragility functions with the same accuracy,while fully utilizing a large-scale cost-saving dataset based on simplified models.Validation results from typical RC frame structures demonstrate that physics-inspired data-driven approach achieves better performance on the test set compared to approach without physics-inspired elements.The scale of the high-cost training set based on refined models is reduced by 12.98%when the mean value of R2reaches 0.99 on the test set.The seismic fragility functions developed from a PSDM fitted to 30%of the high-cost dataset closely resemble those obtained using 70%of the dataset.This study effectively reduces the time and resource costs of dataset generation,and the physics-inspired strategy provides a novel perspective on conducting cost-saving seismic fragility analysis. 展开更多
关键词 Seismic fragility analysis Cost-saving Physics-inspired Data-driven Multi-stage
Chengdu’s Real Estate Market(2019-2024):An Integrated Framework for Data-Driven Insights and Policy Analysis 认领 引用
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作者 HU Xiao WU Jing +1 位作者 WANG Yan JIANG Xinyi 《Cultural and Religious Studies》 2026年第1期26-42,共17页
This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis f... This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis framework for the Chengdu real estate market.By using the Adaptive Neuro-Fuzzy Inference System(ANFIS)prediction model,spatial GIS(Geographic Information System analysis)analysis,and interactive dashboards,this study reveals market differentiation,policy impacts,and changes in demand structure,thereby providing decision support for the government,enterprises,and homebuyers. 展开更多
关键词 Chengdu City real estate market data-driven insights policy analysis
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