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Convolutional BiLSTM Variational Sequence-To-Sequence Based Video Captioning for Capturing Intricate Temporal Dependencies 认领 引用
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作者 M.Gowri Shankar D.Surendran 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第5期2700-2716,共17页
In the realm of video understanding,the demand for accurate and contextually rich video captioning has surged with the increasing volume and complexity of multimedia content.This research introduces an innovative solu... In the realm of video understanding,the demand for accurate and contextually rich video captioning has surged with the increasing volume and complexity of multimedia content.This research introduces an innovative solution for video captioning by integrating a Convolutional BiLSTM Convolutional Bidirectional Long Short-Term Memory(BiLSTM)constructed Variational Sequence-to-Sequence(CBVSS)approach.The proposed framework is adept at capturing intricate temporal dependencies within video sequences,enabling a more nuanced and contextually relevant description of dynamic scenes.However,optimizing its parameters for improved performance remains a crucial challenge.In response,in this research Golden Eagle Optimization(GEO)a metaheuristic optimization technique is used to fine-tune the Convolutional BiLSTM variational sequence-to-sequence model parameters.The application of GEO aims to enhancing the CBVSS ability to produce more exact and contextually rich video captions.The proposed attains an overall higher Recall of 59.75%and Precision of 63.78%for both datasets.Additionally,the proposed CBVSS method demonstrated superior performance across both datasets,achieving the highest METEOR(25.67)and CIDER(39.87)scores on the ActivityNet dataset,and further outperforming all compared models on the YouCook2 dataset with METEOR(28.67)and CIDER(43.02),highlighting its effectiveness in generating semantically rich and contextually accurate video captions. 展开更多
关键词 Video captioning Convolutional BiLSTM Variational sequence-to-sequence model Golden eagleoptimization Intricate temporal dependencies
A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings 认领 引用 被引量：12
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作者 Ding Yunhao Jia Minping 《Journal of Southeast University(English Edition)》 EI CAS 2019年第4期417-423,共7页
Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on ... Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on the standard convolutional auto-encoder.In this model,the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal;then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier;and finally the parameters of the whole model are fine-tuned using labeled data.Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method.The results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,respectively.In addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning,convolutional neural networks and a traditional convolutional auto-encoder.The final results show that the proposed model has a better recognition effect for rolling bearing fault data. 展开更多
关键词 fault diagnosis deep learning convolutional auto-encoder multi-scale convolutional kernel feature extraction
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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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Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder 认领 引用
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作者 Xiaoxiong Feng Jianhua Liu 《Journal of Sensor Technology》 2023年第4期69-85,共17页
To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features e... To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion. 展开更多
关键词 Multi-Mode Data Fusion Coupling Convolutional Auto-Encoder Adaptive Optimization Deep Learning
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Wavelet Transform-Based Bayesian Inference Learning with Conditional Variational Autoencoder for Mitigating Injection Attack in 6G Edge Network 认领 引用 被引量：1
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作者 Binu Sudhakaran Pillai Raghavendra Kulkarni +1 位作者 Venkata Satya Suresh kumar Kondeti Surendran Rajendran 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期1141-1166,共26页
Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies... Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies. 展开更多
关键词 Bayesian inference learning automaton convolutional wavelet transform conditional variational autoencoder malicious data injection attack edge environment 6G communication
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Variational Gridded Graph Convolution Network for Node Classification 认领 引用 被引量：10
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作者 Xiaobin Hong Tong Zhang +1 位作者 Zhen Cui Jian Yang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第10期1697-1708,共12页
The existing graph convolution methods usually suffer high computational burdens,large memory requirements,and intractable batch-processing.In this paper,we propose a high-efficient variational gridded graph convoluti... The existing graph convolution methods usually suffer high computational burdens,large memory requirements,and intractable batch-processing.In this paper,we propose a high-efficient variational gridded graph convolution network(VG-GCN)to encode non-regular graph data,which overcomes all these aforementioned problems.To capture graph topology structures efficiently,in the proposed framework,we propose a hierarchically-coarsened random walk(hcr-walk)by taking advantage of the classic random walk and node/edge encapsulation.The hcr-walk greatly mitigates the problem of exponentially explosive sampling times which occur in the classic version,while preserving graph structures well.To efficiently encode local hcr-walk around one reference node,we project hcrwalk into an ordered space to form image-like grid data,which favors those conventional convolution networks.Instead of the direct 2-D convolution filtering,a variational convolution block(VCB)is designed to model the distribution of the randomsampling hcr-walk inspired by the well-formulated variational inference.We experimentally validate the efficiency and effectiveness of our proposed VG-GCN,which has high computation speed,and the comparable or even better performance when compared with baseline GCNs. 展开更多
关键词 Graph coarsening gridding node classification random walk variational convolution
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A Residual Convolutional Autoencoder-Based Structural Damage Detection Approach for Deep-Sea Mining Riser Considering Data Fusion 认领 引用
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作者 JIANG Yufeng ZHENG Zepeng +4 位作者 LIU Yu WANG Shuqing LIU Yuchi YANG Zeyun YANG Yuan 《Journal of Ocean University of China》 SCIE CAS CSCD 2025年第6期1657-1669,共13页
A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe... A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures. 展开更多
关键词 deep-sea mining riser structural damage detection residual convolutional auto-encoder data fusion principal component analysis
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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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Auto-Encoding Variational Bayesian Inference in High-Dimensional Skew-Normal Linear Mixed Models 认领 引用
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作者 YI Jieyi TANG Niansheng +1 位作者 WU Ying SU Tong 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2026年第4期1763-1794,共32页
High-dimensional linear mixed models are widely used for longitudinal data analysis,yet their reliance on normality assumptions often limits applicability in psychometric and biomedical settings.To address this,the au... High-dimensional linear mixed models are widely used for longitudinal data analysis,yet their reliance on normality assumptions often limits applicability in psychometric and biomedical settings.To address this,the authors propose a high-dimensional skew-normal linear mixed model and develop a novel variational Baysian method that integrates spike-and-slab Lasso priors for simultaneous parameter estimation and variable selection.To handle dependencies in the joint posterior,the authors propose a variational auto-encoders to extract latent features,and employ a coordinate ascent algorithm to optimize the evidence lower bound(ELBO),circumventing intractable integrals.Model comparison is conducted using the Bayes factor,approximated via the ELBO.The effectiveness of the proposed methodologies is demonstrated through simulation studies and a real-data application. 展开更多
关键词 Evidence lower bound skew normal linear mixed model spike-and-slab priors variational auto-encoder variational Bayesian inference
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Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network 认领 引用 被引量：1
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作者 Bakhtawar Aslam Ziyad Awadh Alrowaili +3 位作者 Bushra Khaliq Jaweria Manzoor Saira Raqeeb Fahad Ahmad 《Computers, Materials & Continua》 SCIE EI 2021年第8期2159-2178,共20页
The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place i... The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques. 展开更多
关键词 Deep learning image processing classification climate variation ozone layer depleted region non-depleted region UV radiation faster region-based convolutional neural network
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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
Importance of Adaptive Photometric Augmentation for Different Convolutional Neural Network 认领 引用
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作者 Saraswathi Sivamani Sun Il Chon +2 位作者 Do Yeon Choi Dong Hoon Lee Ji Hwan Park 《Computers, Materials & Continua》 SCIE EI 2022年第9期4433-4452,共20页
Existing segmentation and augmentation techniques on convolutional neural network(CNN)has produced remarkable progress in object detection.However,the nominal accuracy and performance might be downturned with the phot... Existing segmentation and augmentation techniques on convolutional neural network(CNN)has produced remarkable progress in object detection.However,the nominal accuracy and performance might be downturned with the photometric variation of images that are directly ignored in the training process,along with the context of the individual CNN algorithm.In this paper,we investigate the effect of a photometric variation like brightness and sharpness on different CNN.We observe that random augmentation of images weakens the performance unless the augmentation combines the weak limits of photometric variation.Our approach has been justified by the experimental result obtained from the PASCAL VOC 2007 dataset,with object detection CNN algorithms such as YOLOv3(You Only Look Once),Faster R-CNN(Region-based CNN),and SSD(Single Shot Multibox Detector).Each CNN model shows performance loss for varying sharpness and brightness,ranging between−80%to 80%.It was further shown that compared to random augmentation,the augmented dataset with weak photometric changes delivered high performance,but the photometric augmentation range differs for each model.Concurrently,we discuss some research questions that benefit the direction of the study.The results prove the importance of adaptive augmentation for individual CNN model,subjecting towards the robustness of object detection. 展开更多
关键词 Object detection photometric variation adaptive augmentation convolutional neural network
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Predicting the Antigenic Variant of Human Influenza A(H3N2) Virus with a Stacked Auto-Encoder Model 认领 引用
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作者 Zhiying Tan Kenli Li +1 位作者 Taijiao Jiang Yousong Peng 《国际计算机前沿大会会议论文集》 EI 2017年第2期71-73,共3页
The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic ... The influenza virus changes its antigenicity frequently due to rapid mutations, leading to immune escape and failure of vaccination. Rapid determination of the influenza antigenicity could help identify the antigenic variants in time. Here, we built a stacked auto-encoder (SAE) model for predicting the antigenic variant of human influenza A(H3N2) viruses based on the hemagglutinin (HA) protein sequences. The model achieved an accuracy of 0.95 in five-fold cross-validations, better than the logistic regression model did. Further analysis of the model shows that most of the active nodes in the hidden layer reflected the combined contribution of multiple residues to antigenic variation. Besides, some features (residues on HA protein) in the input layer were observed to take part in multiple active nodes, such as residue 189, 145 and 156, which were also reported to mostly determine the antigenic variation of influenza A(H3N2) viruses. Overall,this work is not only useful for rapidly identifying antigenic variants in influenza prevention, but also an interesting attempt in inferring the mechanisms of biological process through analysis of SAE model, which may give some insights into interpretation of the deep learning 展开更多
关键词 Stacked auto-encoder Antigenic variation nfluenza Machine learning
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Hamiltonian Reduction Using a Convolutional Auto-Encoder Coupled to a Hamiltonian Neural Network 认领 引用
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作者 Raphaël Côte Emmanuel Franck +2 位作者 Laurent Navoret Guillaume Steimer Vincent Vigon 《Communications in Computational Physics》 SCIE CSCD 2025年第2期315-352,共38页
The reduction of Hamiltonian systems aims to build smaller reduced models,valid over a certain range of time and parameters,in order to reduce computing time.By maintaining the Hamiltonian structure in the reduced mod... The reduction of Hamiltonian systems aims to build smaller reduced models,valid over a certain range of time and parameters,in order to reduce computing time.By maintaining the Hamiltonian structure in the reduced model,certain longterm stability properties can be preserved.In this paper,we propose a non-linear reduction method for models coming from the spatial discretization of partial differential equations:it is based on convolutional auto-encoders and Hamiltonian neural networks.Their training is coupled in order to learn the encoder-decoder operators and the reduced dynamics simultaneously.Several test cases on non-linear wave dynamics show that the method has better reduction properties than standard linear Hamiltonian reduction methods. 展开更多
关键词 Hamiltonian dynamics model order reduction convolutional auto-encoder Hamiltonian neural network non-linear wave equations shallow water equation
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
基于优化VMD二次分解的短期电力负荷预测 认领 引用 被引量：2
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作者 蒋建东 韩文轩 +3 位作者 赵云飞 燕跃豪 鲍薇 刘晓辉 《郑州大学学报（工学版）》 CAS 北大核心 2026年第1期124-130,共7页
针对台区配变负荷数据复杂度高、波动性强的特点,提出了一种基于二次分解和时间卷积网络的短期电力负荷预测模型。首先,使用最大互信息系数法对高维特征的负荷数据集进行特征提取;其次,采用完全自适应噪声集合经验模态分解和优化变分模... 针对台区配变负荷数据复杂度高、波动性强的特点,提出了一种基于二次分解和时间卷积网络的短期电力负荷预测模型。首先,使用最大互信息系数法对高维特征的负荷数据集进行特征提取;其次,采用完全自适应噪声集合经验模态分解和优化变分模态分解对配变负荷数据进行二次分解;再次,将两次分解得到的子序列输入时间卷积网络模型中进行预测;最后,将各子序列的预测结果叠加,得到最终的负荷预测结果。在郑州市某台区配变负荷数据上进行仿真分析,与传统时间卷积网络模型相比,所提模型MAE、MAPE和RMSE分别减少了64.29%,9.66百分点和59.00%。实验结果表明,所提组合预测模型具有更好的预测效果和更高的预测精度。 展开更多
关键词 二次分解 负荷预测 完全自适应噪声集合经验模态分解 变分模态分解 时间卷积网络
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Robust control strategy for semi-active air suspension systems based on reinforcement learning with entropy theory 认领 引用
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作者 Da Wang Guoqing Zhang +3 位作者 Chunyang Qi Chuanxue Song Feng Xiao Liqiang Jin 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期541-554,共14页
This study proposes a robust control strategy for semi-active air suspension systems(SASS)based on entropy theory.The multi-objective optimization of a system can be described as a long-term problem using entropy valu... This study proposes a robust control strategy for semi-active air suspension systems(SASS)based on entropy theory.The multi-objective optimization of a system can be described as a long-term problem using entropy values by innovatively introducing entropy theory.The state marginal probability of the SASS is incorporated into the reward function as the entropy value.This incorporation incentivizes the agent to focus on reducing the entropy value of the system state over a period of time during the exploration process,thereby reducing the degree of coupling between system states.This study also proposes an optimization strategy that introduces a state observer based on a variational auto-encoder.The observer can extract environmental features from historical states and expand the dimension of the state,thereby enhancing the generalization performance of the system under different road excitations.Bench test results show that the algorithm improves ride comfort while ensuring robustness.The root mean square(RMS)of body vertical acceleration decreased by 13.01%,while the RMS of dynamic tyre displacement only increased by 2.36%. 展开更多
关键词 Semi-active air suspension Entropy theory Robust controls Variational auto-encoder
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频域-时空协同框架下的车辆轨迹预测及意图识别模型 认领 引用 被引量：2
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作者 王庆荣 韩芳文 +1 位作者 朱昌锋 金小龙 《计算机工程与应用》 EI CSCD 北大核心 2026年第11期338-351,共14页
针对现有轨迹预测方法在高频噪声干扰下易产生误差累积,且复杂空间交互建模导致计算效率低下的问题,提出一种频域-时空协同预测框架。该方法利用多元变分模态分解算法对历史轨迹序列进行解析,并约束其异常扰动,以抑制高频噪声干扰及提... 针对现有轨迹预测方法在高频噪声干扰下易产生误差累积,且复杂空间交互建模导致计算效率低下的问题,提出一种频域-时空协同预测框架。该方法利用多元变分模态分解算法对历史轨迹序列进行解析,并约束其异常扰动,以抑制高频噪声干扰及提取频域特征;采用卷积神经网络与特征缩放的方式增强车辆与周围环境的交互感知;自适应融合多频域轨迹分量与空间交互特征,结合长短时记忆网络和驾驶意图概率向量,利用混合密度网络输出预测轨迹的概率密度分布。在下一代仿真数据集上的实验表明:相较于其他对比模型而言,所提方法均有不同程度的提升,有效实现了频域解析、时序建模及交互感知的协同优化;且在1~5 s预测范围内的最大均方误差仅为1.43 m,满足复杂交通场景下的轨迹预测需求。 展开更多
关键词 轨迹预测 频域-时空协同 多元变分模态分解(MVMD) 卷积神经网络 混合密度网络(MDN)
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基于二次分解和TSO-TCN的分时电量预测 认领 引用 被引量：1
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作者 王永利 李一鸣 +5 位作者 延子昕 白雪峰 詹祥澎 田传波 杜苁聪 周颖 《科学技术与工程》 EI 北大核心 2026年第4期1509-1517,共9页
分时电量是具有趋势性特征、季节性特征以及随机性特征的非平稳序列,使用传统方法进行预测会造成较大的误差。为提高复杂特征下分时电量的预测精度,提出了一种基于二次分解和金枪鱼群优化算法-时域卷积网络(tuna swarm optimization-tem... 分时电量是具有趋势性特征、季节性特征以及随机性特征的非平稳序列,使用传统方法进行预测会造成较大的误差。为提高复杂特征下分时电量的预测精度,提出了一种基于二次分解和金枪鱼群优化算法-时域卷积网络(tuna swarm optimization-temporal convolutional network,TSO-TCN)的电量预测模型。首先,使用变分非线性调频模态分解(variational nonlinear chirp mode decomposition,VNCMD)对原始电量数据进行分解,基于相关性分析与改进小波阈值法进行去噪处理,将去噪后数据进行辛几何模态分解(symplectic geometry mode decomposition,SGMD)得到分量序列;其次,采取K-均值聚类对分量序列进行合并,对聚类后的分量序列分别建立TSO-TCN预测模型进行预测;最后累加求和,确定最终预测结果。实验结果表明,所提出方法预测精度良好,为传统的分解-集成预测方法提供了新的思路。 展开更多
关键词 特征提取 聚类分析 变分非线性调频模态分解(VNCMD) 辛几何模态分解(SGMD) 时域卷积网络(TCN)
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