The polynomial type Lagrange equation and Hamilton equation of finite dimensional constrained dynamics were considered. A new algorithm was presented for solving constraints based on Wu elimination method. The new alg...The polynomial type Lagrange equation and Hamilton equation of finite dimensional constrained dynamics were considered. A new algorithm was presented for solving constraints based on Wu elimination method. The new algorithm does not need to calculate the rank of Hessian matrix and determine the linear dependence of equations, so the steps of calculation decrease greatly. In addition, the expanding of expression occurring in the computing process is smaller. Using the symbolic computation software platform, the new algorithm can be executed in computers.展开更多
Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies ha...Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.展开更多
Depth migration can image complex structures with high accuracy,thereby stimulating the increasingly urgent demands for developing depth-domain inversions and interpretations in industry.The well-seismic calibration i...Depth migration can image complex structures with high accuracy,thereby stimulating the increasingly urgent demands for developing depth-domain inversions and interpretations in industry.The well-seismic calibration in the depth domain serves as a crucial cornerstone for these interpretations and inversions.Well data provide a partial cognition of underground media.Seismic data must be accurately calibrated with well data to expand this cognition outward.Depth-domain seismic data are non-stationary,transforming traditional,mature time-domain well calibration methods unsuitable for direct application to depth-domain seismic data.Therefore,researchers usually adopt a domain transformation strategy to complete well-seismic calibration in the time domain and then convert the calibration results into the depth domain.However,this method inevitably introduces additional error accumulation caused by domain transformation.On the basis of a comprehensive review of previous research,we propose a direct depth-domain well-seismic calibration method.This method is based on the synthesis of the depth-domain seismic records and the extraction of the depth-domain generalized seismic wavelets.We introduce constrained dynamic warping with maximum stretch depth constraint and directly match seismic data with well data in the depth domain.The actual processing results show that the method improves the efficiency of the depth-domain well-seismic calibration and produces a reliable relationship between seismic and well depths after two to four iterations.展开更多
The authors prove error estimates for the semi-implicit numerical scheme of sphere-constrained high-index saddle dynamics,which serves as a powerful instrument in finding saddle points and constructing the solution la...The authors prove error estimates for the semi-implicit numerical scheme of sphere-constrained high-index saddle dynamics,which serves as a powerful instrument in finding saddle points and constructing the solution landscapes of constrained systems on the high-dimensional sphere.Due to the semi-implicit treatment and the novel computational procedure,the orthonormality of numerical solutions at each time step could not be fully employed to simplify the derivations,and the computations of the state variable and directional vectors are coupled with the retraction,the vector transport and the orthonormalization procedure,which significantly complicates the analysis.They address these issues to prove error estimates for the proposed semi-implicit scheme and then carry out numerical experiments to substantiate the theoretical findings.展开更多
A rigid flexible coupling physical model which can represent a flexible spacecraft is investigated in this paper. By applying the mechanics theory in a non-inertial coordinate system,the rigid flexible coupling dynami...A rigid flexible coupling physical model which can represent a flexible spacecraft is investigated in this paper. By applying the mechanics theory in a non-inertial coordinate system,the rigid flexible coupling dynamic model with dynamic stiffening is established via the subsystemmodeling framework. It is clearly elucidated for the first time that,dynamic stiffening is produced by the coupling effect of the centrifugal inertial load distributed on the beamand the transverse vibration deformation of the beam. The modeling approach in this paper successfully avoids problems which are caused by other popular modeling methods nowadays： the derivation process is too complex by using only one dynamic principle; a clearly theoretical explanation for dynamic stiffening can＇t be provided. First,the continuous dynamic models of the flexible beamand the central rigid body are established via structural dynamics and angular momentumtheory respectively. Then,based on the conclusions of orthogonalization about the normal constrained modes,the finite dimensional dynamic model suitable for controller design is obtained. The numerical simulation validations showthat： dynamic stiffening is successfully incorporated into the dynamic characteristics of the first-order model established in this paper,which can indicate the dynamic responses of the rigid flexible coupling system with large overall motion accurately,and has a clear modeling mechanism,concise expressions and a good convergence.展开更多
Energy system structures are evolving toward increasing cost benefits,efficiency,and environmental sustainability.Achieving these goals is contingent upon the utilization of renewables.Energy storage is the primary ch...Energy system structures are evolving toward increasing cost benefits,efficiency,and environmental sustainability.Achieving these goals is contingent upon the utilization of renewables.Energy storage is the primary challenge associated with renewable energy.Hydrogen and fuel cells are key in addressing these issues.Iran demonstrates significant renewable-energy potential;however,only a small fraction of this potential is currently utilized.Furthermore,the country’s energy system is inefficient.Thus,a feasible plan for creating a sustainable energy system that reliably includes renewables must be developed.The household heating and cooling system is a good starting point.The required model must be dynamic and consider climatic effects,which have not been sufficiently addressed in previous studies conducted in Iran.In this study,the optimal thermodynamic variables,output power,and waste heat for different fuel-cell capacities are first determined by solving a nonlinear model.Subsequently,through a dynamic multicriteria optimization of household heating–cooling systems,the optimal system configurations for 10 years across five different case studies in various climates in Iran are determined.The objective function is to minimize the total costs,which include technology,energy,raw material,and social costs.This study demonstrates the feasibility of developing a fuel-cell technology to satisfy the energy demands of household heating and cooling systems based on case studies.However,reusing waste heat is only practical in hot and humid climates because of the low heating demand.展开更多
The fusion of experimental automation and machine learning has catalyzed a new era in materials research,prominently featuring Gaussian Process(GP)Bayesian Optimization(BO)driven autonomous experiments.Here we introdu...The fusion of experimental automation and machine learning has catalyzed a new era in materials research,prominently featuring Gaussian Process(GP)Bayesian Optimization(BO)driven autonomous experiments.Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on realtime assessments of the raw experimental data.This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training.We also incorporate a flexible,human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results.We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data.This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings,providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 10401021)the Scientific Research Foundation of Graduate University of Chinese Academy of Sciences
文摘The polynomial type Lagrange equation and Hamilton equation of finite dimensional constrained dynamics were considered. A new algorithm was presented for solving constraints based on Wu elimination method. The new algorithm does not need to calculate the rank of Hessian matrix and determine the linear dependence of equations, so the steps of calculation decrease greatly. In addition, the expanding of expression occurring in the computing process is smaller. Using the symbolic computation software platform, the new algorithm can be executed in computers.
基金supported by the Tianjin Manufacturing High Quality Development Special Foundation(No.20232185)the Roycom Foundation(No.70306901).
文摘Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.
基金supported by the National Natural Science Foundation of China(No.U23B20158)CNOOC major technology project(KJGG2022-0104)。
文摘Depth migration can image complex structures with high accuracy,thereby stimulating the increasingly urgent demands for developing depth-domain inversions and interpretations in industry.The well-seismic calibration in the depth domain serves as a crucial cornerstone for these interpretations and inversions.Well data provide a partial cognition of underground media.Seismic data must be accurately calibrated with well data to expand this cognition outward.Depth-domain seismic data are non-stationary,transforming traditional,mature time-domain well calibration methods unsuitable for direct application to depth-domain seismic data.Therefore,researchers usually adopt a domain transformation strategy to complete well-seismic calibration in the time domain and then convert the calibration results into the depth domain.However,this method inevitably introduces additional error accumulation caused by domain transformation.On the basis of a comprehensive review of previous research,we propose a direct depth-domain well-seismic calibration method.This method is based on the synthesis of the depth-domain seismic records and the extraction of the depth-domain generalized seismic wavelets.We introduce constrained dynamic warping with maximum stretch depth constraint and directly match seismic data with well data in the depth domain.The actual processing results show that the method improves the efficiency of the depth-domain well-seismic calibration and produces a reliable relationship between seismic and well depths after two to four iterations.
基金supported by the National Natural Science Foundation of China(Nos.12225102,12050002,12288101,12301555)the National Key R&D Program of China(No.2021YFF1200500)the Taishan Scholars Program of Shandong Province。
文摘The authors prove error estimates for the semi-implicit numerical scheme of sphere-constrained high-index saddle dynamics,which serves as a powerful instrument in finding saddle points and constructing the solution landscapes of constrained systems on the high-dimensional sphere.Due to the semi-implicit treatment and the novel computational procedure,the orthonormality of numerical solutions at each time step could not be fully employed to simplify the derivations,and the computations of the state variable and directional vectors are coupled with the retraction,the vector transport and the orthonormalization procedure,which significantly complicates the analysis.They address these issues to prove error estimates for the proposed semi-implicit scheme and then carry out numerical experiments to substantiate the theoretical findings.
文摘A rigid flexible coupling physical model which can represent a flexible spacecraft is investigated in this paper. By applying the mechanics theory in a non-inertial coordinate system,the rigid flexible coupling dynamic model with dynamic stiffening is established via the subsystemmodeling framework. It is clearly elucidated for the first time that,dynamic stiffening is produced by the coupling effect of the centrifugal inertial load distributed on the beamand the transverse vibration deformation of the beam. The modeling approach in this paper successfully avoids problems which are caused by other popular modeling methods nowadays： the derivation process is too complex by using only one dynamic principle; a clearly theoretical explanation for dynamic stiffening can＇t be provided. First,the continuous dynamic models of the flexible beamand the central rigid body are established via structural dynamics and angular momentumtheory respectively. Then,based on the conclusions of orthogonalization about the normal constrained modes,the finite dimensional dynamic model suitable for controller design is obtained. The numerical simulation validations showthat： dynamic stiffening is successfully incorporated into the dynamic characteristics of the first-order model established in this paper,which can indicate the dynamic responses of the rigid flexible coupling system with large overall motion accurately,and has a clear modeling mechanism,concise expressions and a good convergence.
文摘Energy system structures are evolving toward increasing cost benefits,efficiency,and environmental sustainability.Achieving these goals is contingent upon the utilization of renewables.Energy storage is the primary challenge associated with renewable energy.Hydrogen and fuel cells are key in addressing these issues.Iran demonstrates significant renewable-energy potential;however,only a small fraction of this potential is currently utilized.Furthermore,the country’s energy system is inefficient.Thus,a feasible plan for creating a sustainable energy system that reliably includes renewables must be developed.The household heating and cooling system is a good starting point.The required model must be dynamic and consider climatic effects,which have not been sufficiently addressed in previous studies conducted in Iran.In this study,the optimal thermodynamic variables,output power,and waste heat for different fuel-cell capacities are first determined by solving a nonlinear model.Subsequently,through a dynamic multicriteria optimization of household heating–cooling systems,the optimal system configurations for 10 years across five different case studies in various climates in Iran are determined.The objective function is to minimize the total costs,which include technology,energy,raw material,and social costs.This study demonstrates the feasibility of developing a fuel-cell technology to satisfy the energy demands of household heating and cooling systems based on case studies.However,reusing waste heat is only practical in hot and humid climates because of the low heating demand.
基金supported by the Center for Nanophase Materials Sciences(CNMS),which is a US Department of Energy,Office of Science User Facility at Oak Ridge National Laboratory.
文摘The fusion of experimental automation and machine learning has catalyzed a new era in materials research,prominently featuring Gaussian Process(GP)Bayesian Optimization(BO)driven autonomous experiments.Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on realtime assessments of the raw experimental data.This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training.We also incorporate a flexible,human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results.We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data.This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings,providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.
