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Feature-aided pose estimation approach based on variational auto-encoder structure for spacecrafts 认领 引用 被引量：2
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作者 Yanfang LIU Rui ZHOU +2 位作者 Desong DU Shuqing CAO Naiming QI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第8期329-341,共13页
Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yie... Real-time 6 Degree-of-Freedom(DoF)pose estimation is of paramount importance for various on-orbit tasks.Benefiting from the development of deep learning,Convolutional Neural Networks(CNNs)in feature extraction has yielded impressive achievements for spacecraft pose estimation.To improve the robustness and interpretability of CNNs,this paper proposes a Pose Estimation approach based on Variational Auto-Encoder structure(PE-VAE)and a Feature-Aided pose estimation approach based on Variational Auto-Encoder structure(FA-VAE),which aim to accurately estimate the 6 DoF pose of a target spacecraft.Both methods treat the pose vector as latent variables,employing an encoder-decoder network with a Variational Auto-Encoder(VAE)structure.To enhance the precision of pose estimation,PE-VAE uses the VAE structure to introduce reconstruction mechanism with the whole image.Furthermore,FA-VAE enforces feature shape constraints by exclusively reconstructing the segment of the target spacecraft with the desired shape.Comparative evaluation against leading methods on public datasets reveals similar accuracy with a threefold improvement in processing speed,showcasing the significant contribution of VAE structures to accuracy enhancement,and the additional benefit of incorporating global shape prior features. 展开更多
关键词 Pose estimation Variational auto-encoder Feature-aided Pose Estimation Approach On-orbit measurement tasks Simulated and experimental dataset
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SNP site-drug association prediction algorithm based on denoising variational auto-encoder 认领 引用 被引量：2
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作者 SONG Xiaoyu FENG Xiaobei +3 位作者 ZHU Lin LIU Tong WU Hongyang LI Yifan 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2022年第3期300-308,共9页
Single nucletide polymorphism(SNP)is an important factor for the study of genetic variation in human families and animal and plant strains.Therefore,it is widely used in the study of population genetics and disease re... Single nucletide polymorphism(SNP)is an important factor for the study of genetic variation in human families and animal and plant strains.Therefore,it is widely used in the study of population genetics and disease related gene.In pharmacogenomics research,identifying the association between SNP site and drug is the key to clinical precision medication,therefore,a predictive model of SNP site and drug association based on denoising variational auto-encoder(DVAE-SVM)is proposed.Firstly,k-mer algorithm is used to construct the initial SNP site feature vector,meanwhile,MACCS molecular fingerprint is introduced to generate the feature vector of the drug module.Then,we use the DVAE to extract the effective features of the initial feature vector of the SNP site.Finally,the effective feature vector of the SNP site and the feature vector of the drug module are fused input to the support vector machines(SVM)to predict the relationship of SNP site and drug module.The results of five-fold cross-validation experiments indicate that the proposed algorithm performs better than random forest(RF)and logistic regression(LR)classification.Further experiments show that compared with the feature extraction algorithms of principal component analysis(PCA),denoising auto-encoder(DAE)and variational auto-encode(VAE),the proposed algorithm has better prediction results. 展开更多
关键词 association prediction k-mer molecular fingerprinting support vector machine(SVM) denoising variational auto-encoder(DVAE)
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Low Frequency Residential Load Disaggregation via Improved Variational Auto-encoder and Siamese Network 认领 引用 被引量：2
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作者 Cheng Qian Zaijun Wu +2 位作者 Dongliang Xu Qinran Hu Yu Liu 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2025年第5期2137-2149,共13页
Non-intrusive load monitoring(NILM)can infer load profiles for each individual appliance from aggregated power consumption signals without installing extra sub-meters.However,performance of traditional energy disaggre... Non-intrusive load monitoring(NILM)can infer load profiles for each individual appliance from aggregated power consumption signals without installing extra sub-meters.However,performance of traditional energy disaggregation methods deteriorates in complex environments,especially susceptible to the presence of other high power consumption appliances.Practicalities are also limited by diversity of household load patterns and measurement errors.In order to address these problems,a hybrid deep learning model consisting of two steps is proposed in this paper.First,an improved variational autoencoder(VAE)structure is introduced for preliminary energy disaggregation,where the encoder and decoder layers are long short-term networks(LSTM)to extract temporal characteristics of active power signals.Afterward,a post-processing method based on Siamese one-dimensional convolutional neural network(S-1D-CNN)is adopted to remove incorrectly predicted activation segments of target appliances.Experiments are conducted on two public datasets,and results show remarkable improvements on prediction accuracy over other deep learning methods.Both transferability and stability of the proposed model are verified under different working conditions. 展开更多
关键词 Deep learning NILM post-processing Siamese network variational auto-encoder
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VMGP:A unified variational auto-encoder based multi-task model for multi-phenotype,multi-environment,and cross-population genomic selection in plants 认领 引用 被引量：1
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作者 Xiangyu Zhao Fuzhen Sun +6 位作者 Jinlong Li Dongfeng Zhang Qiusi Zhang Zhongqiang Liu Changwei Tan Hongxiang Ma Kaiyi Wang 《Artificial Intelligence in Agriculture》 SCIE EI CSCD 2025年第4期829-842,共14页
Plant breeding stands as a cornerstone for agricultural productivity and the safeguarding of food security.The advent of Genomic Selection heralds a new epoch in breeding,characterized by its capacity to harness whole... Plant breeding stands as a cornerstone for agricultural productivity and the safeguarding of food security.The advent of Genomic Selection heralds a new epoch in breeding,characterized by its capacity to harness whole-genome variation for genomic prediction.This approach transcends the need for prior knowledge of genes associated with specific traits.Nonetheless,the vast dimensionality of genomic data juxtaposed with the relatively limited number of phenotypic samples often leads to the“curse of dimensionality”,where traditional statistical,machine learning,and deep learning methods are prone to overfitting and suboptimal predictive performance.To surmount this challenge,we introduce a unified Variational auto-encoder based Multi-task Genomic Prediction model(VMGP)that integrates self-supervised genomic compression and reconstruction with multiple prediction tasks.This approach provides a robust solution,offering a formidable predictive framework that has been rigorously validated across public datasets for wheat,rice,and maize.Our model demonstrates exceptional capabilities in multi-phenotype and multi-environment genomic prediction,successfully navigating the complexities of cross-population genomic selection and underscoring its unique strengths and utility.Furthermore,by integrating VMGP with model interpretability,we can effectively triage relevant single nucleotide polymorphisms,thereby enhancing prediction performance and proposing potential cost-effective genotyping solutions.The VMGP framework,with its simplicity,stable predictive prowess,and open-source code,is exceptionally well-suited for broad dissemination within plant breeding programs.It is particularly advantageous for breeders who prioritize phenotype prediction yet may not possess extensive knowledge in deep learning or proficiency in parameter tuning. 展开更多
关键词 Genomic selection Variational auto-encoder Multi-task Deep learning Genomic prediction
Study of current distribution generation in PEMFC based on conditional variational auto-encoder 认领 引用
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作者 Chengyin Shi Cong Yin +2 位作者 Weilong Luo Hailong Liu Hao Tang 《Energy and AI》 EI CSCD 2025年第3期578-591,共14页
The Proton Exchange Membrane Fuel Cell(PEMFC)converts the chemical energy of hydrogen fuel directly into electrical energy with broad application prospects.Understanding how current density is distributed in the PEMFC... The Proton Exchange Membrane Fuel Cell(PEMFC)converts the chemical energy of hydrogen fuel directly into electrical energy with broad application prospects.Understanding how current density is distributed in the PEMFC systems is crucial as it is a key factor influencing system performance.However,direct modeling for current distribution may encounter the challenge of dimensional catastrophe owing to the high dimensionality of the data.This paper uses a high-resolution segmented measurement device with 396 points to conduct experimental tests on the current distribution of a PEMFC with reactive area of 406 cm2 during a stepwise increase in load current.The current distribution is modeled based on the test results to learn the mapping relationship between the experimental parameters and the current distribution.The proposed model utilizes a Conditional Variational Auto-Encoder(CVAE)to generate current distributions.The MSE(Mean-Square Error)of the trained CVAE model reaches 9.2×10-5,and the comparison results show that the 222.9A current distribution error has the largest MSE of 6.36×10-4 and a KL Divergence(Kullback-Leibler Divergence)of 9.55×10-4,both of which are at a low level.This model enables the direct determination of the current distribution based on the experimental parameters,thereby establishing a technical foundation for investigating the impact of experimental conditions on fuel cells.This model is also of great significance for research on fuel cell system control strategies and fault diagnosis. 展开更多
关键词 Proton exchange membrane fuel cell Segmented measurement device Current distribution Conditional variational auto-encoder
Exploring Variational Auto-encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI 认领 引用 被引量：1
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作者 Nick Bryan-Kinns Bingyuan Zhang +1 位作者 Songyan Zhao Berker Banar 《Machine Intelligence Research》 EI CSCD 2024年第1期29-45,共17页
Generative AI models for music and the arts in general are increasingly complex and hard to understand.The field of ex-plainable AI(XAI)seeks to make complex and opaque AI models such as neural networks more understan... Generative AI models for music and the arts in general are increasingly complex and hard to understand.The field of ex-plainable AI(XAI)seeks to make complex and opaque AI models such as neural networks more understandable to people.One ap-proach to making generative AI models more understandable is to impose a small number of semantically meaningful attributes on gen-erative AI models.This paper contributes a systematic examination of the impact that different combinations of variational auto-en-coder models(measureVAE and adversarialVAE),configurations of latent space in the AI model(from 4 to 256 latent dimensions),and training datasets(Irish folk,Turkish folk,classical,and pop)have on music generation performance when 2 or 4 meaningful musical at-tributes are imposed on the generative model.To date,there have been no systematic comparisons of such models at this level of com-binatorial detail.Our findings show that measureVAE has better reconstruction performance than adversarialVAE which has better musical attribute independence.Results demonstrate that measureVAE was able to generate music across music genres with inter-pretable musical dimensions of control,and performs best with low complexity music such as pop and rock.We recommend that a 32 or 64 latent dimensional space is optimal for 4 regularised dimensions when using measureVAE to generate music across genres.Our res-ults are the first detailed comparisons of configurations of state-of-the-art generative AI models for music and can be used to help select and configure AI models,musical features,and datasets for more understandable generation of music. 展开更多
关键词 Variational auto-encoder explainable AI(XAI) generative music musical features datasets
State Estimation With Model Uncertainty Using Structure Variational Bayesian and Transfer Learning 认领 引用
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作者 Shuang Gao Xiaoli Luan +2 位作者 Biao Huang Shunyi Zhao Fei Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期715-727,共13页
This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning.Assume that the source domain model is available and reliab... This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning.Assume that the source domain model is available and reliable,and the target domain model has significant model parameter uncertainties.To enhance estimation performance in the target domain,the proposed method transfers model knowledge from the source domain and adjusts it using a tuning factor before incorporating it into the target domain estimator.More specifically,this approach involves transferring the modified probability density functions of state prediction from the source domain to the target domain and determining the tuning factor via structure variational Bayesian inference using measurements in the target domain.Using numerical examples and a 1-DOF torsion system,we showcase the competitiveness of the proposed state estimator compared to the existing robust state estimation methods when dealing with parameter uncertainties.The results highlight its capability to improve estimation accuracy in practical scenarios,showcasing its potential for real-world applications. 展开更多
关键词 Markovian jump linear systems parameter uncertainty state estimation structure variational inference transfer learning
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THE VARIATIONAL PRINCIPLE FOR A BS DIMENSION OF SUBSETS FOR NON-AUTONOMOUS DYNAMICAL SYSTEMS 认领 引用
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作者 Zhongxuan YANG Xiaojun HUANG 《Acta Mathematica Scientia》 SCIE CSCD 2026年第1期311-329,共19页
In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of th... In this manuscript,we consider a non-autonomous dynamical system.Using the Carathéodory structure,we define a BS dimension on an arbitrary subset and obtain a Bowen’s equation that illustrates the relation of the BS dimension to the Pesin-Pitskel topological pressure given by Nazarian[24].Moreover,we establish a variational principle and an inverse variational principle for the BS dimension of non-autonomous dynamical systems.Finally,we also get an analogue of Billingsley’s theorem for the BS dimension of non-autonomous dynamical systems. 展开更多
关键词 non-autonomous dynamical systems BS dimension Bowen’s equation variational principle
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A novel deep learning framework with variational auto-encoder for indoor air quality prediction 认领 引用
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作者 Qiyue Wu Yun Geng +3 位作者 Xinyuan Wang Dongsheng Wang ChangKyoo Yoo Hongbin Liu 《Frontiers of Environmental Science & Engineering》 SCIE EI CSCD 2024年第1期97-109,共13页
Exposure to poor indoor air conditions poses significant risks to human health, increasing morbidity and mortality rates. Soft measurement modeling is suitable for stable and accurate monitoring of air pollutants and ... Exposure to poor indoor air conditions poses significant risks to human health, increasing morbidity and mortality rates. Soft measurement modeling is suitable for stable and accurate monitoring of air pollutants and improving air quality. Based on partial least squares (PLS), we propose an indoor air quality prediction model that utilizes variational auto-encoder regression (VAER) algorithm. To reduce the negative effects of noise, latent variables in the original data are extracted by PLS in the first step. Then, the extracted variables are used as inputs to VAER, which improve the accuracy and robustness of the model. Through comparative analysis with traditional methods, we demonstrate the superior performance of our PLS-VAER model, which exhibits improved prediction performance and stability. The root mean square error (RMSE) of PLS-VAER is reduced by 14.71%, 26.47%, and 12.50% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. Additionally, the coefficient of determination (R2) of PLS-VAER improves by 13.70%, 30.09%, and 11.25% compared to single VAER, PLS-SVR, and PLS-ANN, respectively. This research offers an innovative and environmentally-friendly approach to monitor and improve indoor air quality. 展开更多
关键词 Indoor air quality PM2.5concentration Variational auto-encoder Latent variable Soft measurement modeling
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Diffusional magnetic resonance imaging anonymizing with variational autoencoder 认领 引用
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作者 Yunheng Shen Ling Zheng +2 位作者 Ruohan Liu Haoran Feng Hairong Lv 《Quantitative Biology》 CAS CSCD 2026年第2期119-133,共15页
Anonymization is a crucial de-identification technique that protects data privacy while ensuring its utility for model building.Current generative models such as generative adversarial networks and variational autoenc... Anonymization is a crucial de-identification technique that protects data privacy while ensuring its utility for model building.Current generative models such as generative adversarial networks and variational autoencoders(VAEs)have been applied to medical image anonymization but mainly focus on general image features,lacking specificity in regions of interest such as lesions.This study proposes a novel framework for brain magnetic resonance imaging anonymization,enabling the handling of lesion region prediction while preserving patient privacy.The framework consists of three stages:pre-training VAEs to represent lesion and non-lesion regions in latent space;fine-tuning these latent representations using a diffusion model conditioned on spatial and temporal features;and generating medical image substitutions through joint decoding of lesion and nonlesion latent representations.The comparative investigation has highlighted the benefits of our proposed methods,achieving a promising privacy-utility balance.In a small number of real sample scenarios,using synthetic samples with an 86%anonymity rate still enhanced the downstream segmentation task by 4.60%and the classification task by 8.75%.Our proposed framework offers significant improvements over existing methods in preserving privacy and maintaining data utility for lesion prediction tasks,which holds potential implications for enhanced privacy practices in medical imaging. 展开更多
关键词 anonymization data privacy diffusion model medical image processing variational autoencoder
Optimal control of a rigid body based on Hamel’s variational integrators 认领 引用
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作者 Ben Niu Yuan-Yuan Li +1 位作者 Shi-Xing Liu Yong-Xin Guo 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期58-70,共13页
Based on the discrete mechanics and optimal control framework,Hamel’s variational integrators are employed to solve the fuel-optimal and time-optimal control problems for a single rigid body.The optimal control probl... Based on the discrete mechanics and optimal control framework,Hamel’s variational integrators are employed to solve the fuel-optimal and time-optimal control problems for a single rigid body.The optimal control problem is discretized via the discrete d’Alembert-Lagrange principle,and the corresponding discrete necessary conditions for optimality are derived.The sensitivity matrix is constructed and solved using the Newton-Armijo iteration method.This approach effectively linearizes the nonlinear problem,enhancing computational efficiency and ease of implementation.Finally,two numerical examples involving the optimal control of spacecraft orbital transfers are presented to verify the effectiveness and applicability of the proposed method. 展开更多
关键词 Hamel’s variational integrators Optimal control Lie group Dumbbell-shaped spacecraft
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Machine learning of chaotic characteristics in classical nonlinear dynamics using variational quantum circuit 认领 引用
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作者 Sheng-Chen Bai Shi-Ju Ran 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第2期322-328,共7页
Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of var... Replicating the chaotic characteristics inherent in nonlinear dynamical systems via machine learning(ML)is a key challenge in this rapidly advancing interdisciplinary field.In this work,we explore the potential of variational quantum circuits(VQC)for learning the stochastic properties of classical nonlinear dynamical systems.Specifically,we focus on the one-and two-dimensional logistic maps,which,while simple,remain under-explored in the context of learning dynamical characteristics.Our findings reveal that,even for such simple dynamical systems,accurately replicating longterm characteristics is hindered by a pronounced sensitivity to overfitting.While increasing the parameter complexity of the ML model typically enhances short-term prediction accuracy,it also leads to a degradation in the model’s ability to replicate long-term characteristics,primarily due to the detrimental effects of overfitting on generalization power.By comparing the VQC with two widely recognized classical ML techniques,which are long short-term memory(LSTM)networks for timeseries processing and reservoir computing,we demonstrate that VQC outperforms these methods in terms of replicating long-term characteristics.Our results suggest that for the ML of dynamics,it is demanded to develop more compact and efficient models(such as VQC)rather than more complicated and large-scale ones. 展开更多
关键词 variational quantum circuit machine learning chaos
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Variational Graph Autoencoder–Based Timing-Driven Initialization Placement 认领 引用
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作者 Ziyi Ju Ping Yu +1 位作者 Rui Song Tonglin Chen 《Computers, Materials & Continua》 SCIE EI 2026年第9期204-224,共21页
In modern high-performance chip design,achieving timing closure is essential to design success.With the increasing scale and complexity of modern chips,timing-driven placement has become increasingly important.Traditi... In modern high-performance chip design,achieving timing closure is essential to design success.With the increasing scale and complexity of modern chips,timing-driven placement has become increasingly important.Traditional placement methods primarily focus on minimizing wirelength,but lack timing optimization,making it difficult to meet the strict timing closure requirements of modern designs.Therefore,developing an efficient timing-driven placement method has become a critical challenge in modern chip design.This paper presents a novel timing-driven placement framework that integrates a variational graph autoencoder(VGAE)with a nonlinear mixed-size placement optimizer.The framework identifies timing-violation paths through static timing analysis and dynamically adjusts interconnect weights based on pin-level timing slack,enabling the VGAE to generate an initial placement that prioritizes critical-path optimization.Experimental results on the ICCAD2015 benchmarks show that the proposed method achieves a 25.4%improvement in worst negative slack and a 18.1%improvement in total negative slack compared with DREAMPlace4.0.These results demonstrate its effectiveness in improving timing quality. 展开更多
关键词 Timing optimization timing-driven placement variational graph autoencoder weight updating nonlinear optimization
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Permeability estimation using rock physics modeling and variational Bayes inversion 认领 引用
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作者 Mohammadfarid Ghasemi Abdorrazagh Javid 《Petroleum Science》 SCIE EI CAS CSCD 2026年第2期656-679,共24页
Permeability estimation is pivotal in reservoir characterization;however,prevailing methods lack a standardized approach.Traditionally reliant on core samples,permeability assessment encounters limitations across dive... Permeability estimation is pivotal in reservoir characterization;however,prevailing methods lack a standardized approach.Traditionally reliant on core samples,permeability assessment encounters limitations across diverse thicknesses and wells.An innovative core-independent two-step rock physics template(RPT)can be designed to estimate elastic and conductive properties.The suggested RPT employs the T-matrix method to leverage well-log data encompassing porosity,fluid saturation,and various textural parameters.The estimation process for textural parameters involves addressing uncertainties through the fixed form variational inference(FFVB)with the trust region reflective optimization algorithm.These uncertainties span estimated textural parameters,seismic wave propagation velocity,electrical resistivity,and hydraulic permeability.Micro and macro voids,micro-spherical pores porosity,and their semi-axis are modeled using Beta distributions for both prior and variational families.The noise in the model assumes an inverse gamma distribution for sonic travel time and true formation resistivity.Validation of the proposed method is achieved by comparing the FFVB results with Metropolis Hasting's sampling method in three depths and also through geological observations and experimental analyses on available core samples.The inverse problem,involving the determination of textural parameters through sonic travel time and resistivity,is solved.Subsequently,the forward problem is addressed to estimate permeability.The robustness of the inverse problem is underscored by minimal discrepancies between measured sonic travel times,true formation resistivity values,and the results of the forward problem.The method demonstrates its effectiveness in permeability estimation,even in regions lacking core data,thereby emphasizing its reliability and applicability in diverse geological settings. 展开更多
关键词 Variational Bayes Permeability Well log data Rock physics modelling Carbonates
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A variational approach to community detection in weighted networks 认领 引用
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作者 Lei Liu Jingnan Zhang Chengye Li 《Journal of University of Science and Technology of China》 CAS CSCD 北大核心 2026年第2期25-37,I0001,共13页
Community detection is an essential aspect in studying network structures.Most community detection methods focus on binary networks,which ignore valuable information in the edge weights.In this study,we propose a nove... Community detection is an essential aspect in studying network structures.Most community detection methods focus on binary networks,which ignore valuable information in the edge weights.In this study,we propose a novel weighted stochastic block model(WSBM).In the proposed model,we employ the variational EM algorithm to estimate the model parameters,and then extract the community structure of the weighted network.The detection accuracy and efficiency of the proposed algorithm are demonstrated through both synthetic and real examples.The theoretical results of the proposed method in terms of the asymptotic consistency of estimating the model parameters and community structure are established.The proposed method provides an effective way to detect communities in weighted networks,with promising potential for practical applications. 展开更多
关键词 weighted network variational EM yeast experimental networks asymptotic consistency
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Revival of variational method in noisy cell signaling with Fourier observer 认领 引用
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作者 Ruobing Cai Yueheng Lan 《Communications in Theoretical Physics》 SCIE CAS CSCD 2026年第1期126-138,共13页
Signal transduction in a cell is mostly mediated with biochemical reactions which are noisy and often modeled with chemical master equations or Langevin type of dynamics.Thus stochastic simulation constitutes a major ... Signal transduction in a cell is mostly mediated with biochemical reactions which are noisy and often modeled with chemical master equations or Langevin type of dynamics.Thus stochastic simulation constitutes a major part of computation in cell signaling.Nevertheless,the presence of a wide span of time scales or molecular numbers in various pathways may lead to trouble in computation efficiency or accuracy.To avoid this problem,the commonly employed variational method evolves the whole probability distribution and reduces the stochastic equations to deterministic ones of only a few parameters.However,the design of the left variational basis is essential for its successful application,especially to large networks.In this paper,we extend the conventional polynomial basis to the Fourier and further the Gaussian basis,much facilitating description of multi-peaked or localized non-Gaussian distributions and at the same time avoiding numerical instability and computational complexity frequently encountered with conventional basis.The extension here is demonstrated in several typical biochemical signaling networks and achieves similar accuracy as the benchmark Gillespie algorithm,but with much less running time,which seems to open new opportunities in the variational approach to efficient analysis of noisy dynamics. 展开更多
关键词 stochastic processes master equation variational approach signal transduction basis functions
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SUMMABILITY OF VARIATIONAL REPRESENTATIONS RELATED TO RELATIVE QUANTUM ENTROPIES 认领 引用
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作者 Mina SADEGHINEZHAD 《Acta Mathematica Scientia》 SCIE CSCD 2026年第3期1208-1218,共11页
In this study,at first,variational representations of relative quantum entropies have been obtained by q-deformed hyperbolic functions and then,using this idea,variational representations associated with finite sums o... In this study,at first,variational representations of relative quantum entropies have been obtained by q-deformed hyperbolic functions and then,using this idea,variational representations associated with finite sums of relative quantum entropies have been derived.Moreover,Gibbs variational principle has been extended by the q-deformed hyperbolic functions and finally,as an application of the obtained representations,another form of Peierls-Bogolyubov’s inequality has been presented by q-deformed hyperbolic functions. 展开更多
关键词 q-deformed hyperbolic function variational representation quantum entropy Gibbs variational principle Peierls-Bogolyubov’s inequality
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Expected Residual Minimization Method for a Class of Stochastic Tensor Variational Inequalities 认领 引用
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作者 Jian-Xun Liu Zhao-Feng Lan Sheng-Jie Li 《Journal of the Operations Research Society of China》 EI CSCD 2026年第2期565-590,共26页
This paper considers the expected residual minimization(ERM)formulation for a class of stochastic tensor variational inequalities(STVI)where the involved set contains 0.Initially,we derive some theoretical results reg... This paper considers the expected residual minimization(ERM)formulation for a class of stochastic tensor variational inequalities(STVI)where the involved set contains 0.Initially,we derive some theoretical results regarding the H-eigenvalues of tensors and formulate a class of stochastic multi-person nonoperative games as an STVI.Subsequently,we transform the STVI into an ERM problem by using the regularized gap function and explore the properties of the object function.Furthermore,we use the quasi-Monte Carlo method to address the ERM problem and conduct convergence analysis.Ultimately,we conduct numerical experiments to validate our theoretical findings. 展开更多
关键词 Stochastic tensor variational inequalities ERM formulation H-eigenvalues of tensors Convergence analysis
Deep Variational Quantum Circuits with Barren-Plateau-Free Architectures 认领 引用
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作者 Kaining Zhang Min-Hsiu Hsieh Dacheng Tao 《Artificial Intelligence Science and Engineering》 2026年第1期66-84,共19页
Variational quantum algorithms(VQAs)with random structures have poor trainability due to the exponentially vanishing gradient as the circuit depth and the qubit number increase.This result leads to a general belief th... Variational quantum algorithms(VQAs)with random structures have poor trainability due to the exponentially vanishing gradient as the circuit depth and the qubit number increase.This result leads to a general belief that a deep circuit will not be feasible.In this work,we provide a viable solution to the vanishing gradient problem for deep VQAs with theoretical guarantees.Specifically,we prove that for quantum controlled-layer and quantum residual network(QResNet),architectures,the expectation of the gradient norm can be lower bounded by a value that is independent of the qubit number and the circuit depth.Our results follow from a careful analysis of the gradient behavior on parameter space consisting of rotation angles,as employed in almost all VQAs,instead of relying on impractical 2-design assumptions.We conduct several numerical experiments as verifications,where only our circuits are trainable and converge,while hardware-efficient and random circuits with similar number of parameters in comparison cannot converge. 展开更多
关键词 variational quantum algorithms quantum machine learning barren plateaus
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Research on remaining useful life prediction of rolling bearings based on adaptive variational mode decomposition and dual-branch temporal neural network 认领 引用
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作者 Wuchu Tang Wenxin Dong +1 位作者 Jiawei Yang Guofu Wu 《Advances in Engineering Innovation》 2026年第7期12-30,共19页
To address the nonlinear and non-stationary characteristics of vibration signals during rolling bearing operation and the issue of insufficient degradation information representation,this paper designs a prediction fr... To address the nonlinear and non-stationary characteristics of vibration signals during rolling bearing operation and the issue of insufficient degradation information representation,this paper designs a prediction framework that combines Adaptive Variational Mode Decomposition(AVMD)with a SETCNBiGRU multi-head temporal attention mechanism for Remaining Useful Life(RUL)prediction.First,AVMD is used to decompose the bearing horizontal vibration signal into five Intrinsic Mode Functions(IMFs).Timedomain and frequency-domain statistics are extracted from each IMF and concatenated into a 115-dimensional degradation feature sequence.Subsequently,the model processes in parallel:a TCN-SENet branch extracts local temporal features and adaptively adjusts channel weights,while a BiGRU with multi-head temporal attention sub-network captures global bidirectional dependencies and critical degradation periods within the degradation sequence.Finally,the two types of features are fused,and the RUL prediction result is output.Experimental results demonstrate that the proposed model achieves an RMSE,MAE,and R2of 0.0582,0.0477,and 0.9483 respectively on the IEEE PHM 2012 dataset,and average values of 0.0780,0.0559,and 0.9133 on a self-built laboratory bearing dataset,indicating good prediction accuracy,robustness,and generalization ability. 展开更多
关键词 remaining useful life prediction adaptive variational mode decomposition temporal convolutional network bidirectional gated recurrent unit multi-head temporal attention mechanism
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