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A Deep Auto-encoder Based Security Mechanism for Protecting Sensitive Data Using AI Based Risk Assessment 认领 引用
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作者 Lavanya M Mangayarkarasi S 《Journal of Harbin Institute of Technology(New Series)》 CAS 2025年第4期90-98,共9页
Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,b... Big data has ushered in an era of unprecedented access to vast amounts of new,unstructured data,particularly in the realm of sensitive information.It presents unique opportunities for enhancing risk alerting systems,but also poses challenges in terms of extraction and analysis due to its diverse file formats.This paper proposes the utilization of a DAE-based(Deep Auto-encoders)model for projecting risk associated with financial data.The research delves into the development of an indicator assessing the degree to which organizations successfully avoid displaying bias in handling financial information.Simulation results demonstrate the superior performance of the DAE algorithm,showcasing fewer false positives,improved overall detection rates,and a noteworthy 9%reduction in failure jitter.The optimized DAE algorithm achieves an accuracy of 99%,surpassing existing methods,thereby presenting a robust solution for sensitive data risk projection. 展开更多
关键词 data mining sensitive data deep auto-encoders
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Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder 认领 引用 被引量：10
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作者 Xiaoping Zhao Jiaxin Wu +2 位作者 Yonghong Zhang Yunqing Shi Lihua Wang 《Computers, Materials & Continua》 SCIE EI 2018年第11期223-242,共20页
With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due ... With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent. 展开更多
关键词 Big data deep learning stacked de-noising auto-encoder fourier transform
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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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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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An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder 认领 引用 被引量：1
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作者 Passent El-kafrawy Maie Aboghazalah +2 位作者 Abdelmoty M.Ahmed Hanaa Torkey Ayman El-Sayed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第2期909-926,共18页
Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice.Encryption ofmedical images is very important to secure patient information.Encrypting these images consumes a ... Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice.Encryption ofmedical images is very important to secure patient information.Encrypting these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a problem.In this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the network.On the other hand,a decoder was used to reproduce the original image back after the vector was received and decrypted.Two convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and decoding.Different hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding resolution.In this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in detail.The first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification algorithm.The second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 epochs.The third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485. 展开更多
关键词 Auto-encoder cloud image encryption IoT healthcare
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Fault Diagnosis for Rolling Bearings with Stacked Denoising Auto-encoder of Information Aggregation 认领 引用
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作者 Li Zhang Xin Gao Xiao Xu 《Journal of Harbin Institute of Technology(New Series)》 CAS 2019年第4期69-77,共9页
Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rollin... Rolling bearings are important central components in rotating machines, whose fault diagnosis is crucial in condition-based maintenance to reduce the complexity of different kinds of faults. To classify various rolling bearing faults, a prognostic algorithm consisting of four phases was proposed. Since stacked denoising auto-encoder can be filtered, noise of large numbers of mechanical vibration signals was used for deep learning structure to extract the characteristics of the noise. Unsupervised pre-training method, which can greatly simplify the traditional manual extraction approach, was utilized to process the depth of the data automatically. Furthermore, the aggregation layer of stacked denoising auto-encoder(SDA) was proposed to get rid of gradient disappearance in deeper layers of network, mix superficial nodes’ expression with deeper layers, and avoid the insufficient express ability in deeper layers. Principal component analysis(PCA) was adopted to extract different features for classification. According to the experimental data of this method and from the comparison results, the proposed method of rolling bearing fault classification reached 97.02% of correct rate, suggesting a better performance than other algorithms. 展开更多
关键词 deep learning stacked denoising auto-encoder fault diagnosis PCA classification
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Outlier Detection for Water Supply Data Based on Joint Auto-Encoder 认领 引用 被引量：2
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作者 Shu Fang Lei Huang +2 位作者 Yi Wan Weize Sun Jingxin Xu 《Computers, Materials & Continua》 SCIE EI 2020年第7期541-555,共15页
With the development of science and technology,the status of the water environment has received more and more attention.In this paper,we propose a deep learning model,named a Joint Auto-Encoder network,to solve the pr... With the development of science and technology,the status of the water environment has received more and more attention.In this paper,we propose a deep learning model,named a Joint Auto-Encoder network,to solve the problem of outlier detection in water supply data.The Joint Auto-Encoder network first expands the size of training data and extracts the useful features from the input data,and then reconstructs the input data effectively into an output.The outliers are detected based on the network’s reconstruction errors,with a larger reconstruction error indicating a higher rate to be an outlier.For water supply data,there are mainly two types of outliers:outliers with large values and those with values closed to zero.We set two separate thresholds,and,for the reconstruction errors to detect the two types of outliers respectively.The data samples with reconstruction errors exceeding the thresholds are voted to be outliers.The two thresholds can be calculated by the classification confusion matrix and the receiver operating characteristic(ROC)curve.We have also performed comparisons between the Joint Auto-Encoder and the vanilla Auto-Encoder in this paper on both the synthesis data set and the MNIST data set.As a result,our model has proved to outperform the vanilla Auto-Encoder and some other outlier detection approaches with the recall rate of 98.94 percent in water supply data. 展开更多
关键词 Water supply data outlier detection auto-encoder deep learning
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Transfer learning with deep sparse auto-encoder for speech emotion recognition 认领 引用
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作者 Liang Zhenlin Liang Ruiyu +3 位作者 Tang Manting Xie Yue Zhao Li Wang Shijia 《Journal of Southeast University(English Edition)》 EI CAS 2019年第2期160-167,共8页
In order to improve the efficiency of speech emotion recognition across corpora,a speech emotion transfer learning method based on the deep sparse auto-encoder is proposed.The algorithm first reconstructs a small amou... In order to improve the efficiency of speech emotion recognition across corpora,a speech emotion transfer learning method based on the deep sparse auto-encoder is proposed.The algorithm first reconstructs a small amount of data in the target domain by training the deep sparse auto-encoder,so that the encoder can learn the low-dimensional structural representation of the target domain data.Then,the source domain data and the target domain data are coded by the trained deep sparse auto-encoder to obtain the reconstruction data of the low-dimensional structural representation close to the target domain.Finally,a part of the reconstructed tagged target domain data is mixed with the reconstructed source domain data to jointly train the classifier.This part of the target domain data is used to guide the source domain data.Experiments on the CASIA,SoutheastLab corpus show that the model recognition rate after a small amount of data transferred reached 89.2%and 72.4%on the DNN.Compared to the training results of the complete original corpus,it only decreased by 2%in the CASIA corpus,and only 3.4%in the SoutheastLab corpus.Experiments show that the algorithm can achieve the effect of labeling all data in the extreme case that the data set has only a small amount of data tagged. 展开更多
关键词 sparse auto-encoder transfer learning speech emotion recognition
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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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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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Deep Learning-Based Stacked Auto-Encoder with Dynamic Differential Annealed Optimization for Skin Lesion Diagnosis 认领 引用
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作者 Ahmad Alassaf 《Computer Systems Science & Engineering》 SCIE EI 2023年第12期2773-2789,共17页
Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extra... Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare.Deep Learning(DL)models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models.On the other hand,skin lesionbased segregation and disintegration procedures play an essential role in earlier skin cancer detection.However,artefacts,an unclear boundary,poor contrast,and different lesion sizes make detection difficult.To address the issues in skin lesion diagnosis,this study creates the UDLS-DDOA model,an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder(UDLS)optimized by Dynamic Differential Annealed Optimization(DDOA).Pre-processing,segregation,feature removal or separation,and disintegration are part of the proposed skin lesion diagnosis model.Pre-processing of skin lesion images occurs at the initial level for noise removal in the image using the Top hat filter and painting methodology.Following that,a Fuzzy C-Means(FCM)segregation procedure is performed using a Quasi-Oppositional Elephant Herd Optimization(QOEHO)algorithm.Besides,a novel feature extraction technique using the UDLS technique is applied where the parameter tuning takes place using DDOA.In the end,the disintegration procedure would be accomplished using a SoftMax(SM)classifier.The UDLS-DDOA model is tested against the International Skin Imaging Collaboration(ISIC)dataset,and the experimental results are examined using various computational attributes.The simulation results demonstrated that the UDLS-DDOA model outperformed the compared methods significantly. 展开更多
关键词 Intelligent diagnosis stacked auto-encoder skin lesion unsupervised learning parameter selection
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Application of Improved Deep Auto-Encoder Network in Rolling Bearing Fault Diagnosis 认领 引用 被引量：1
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作者 Jian Di Leilei Wang 《Journal of Computer and Communications》 2018年第7期41-53,共13页
Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive... Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters. 展开更多
关键词 Fault Diagnosis Rolling Bearing Deep Auto-Encoder Network CAPSO Algorithm Feature Extraction
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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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Intrusion Detection through DCSYS Propagation Compared to Auto-encoders 认领 引用
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作者 Fatima Isiaka Zainab Adamu 《Journal of Computer Science Research》 2021年第3期42-49,共8页
In network settings,one of the major disadvantages that threaten the network protocols is the insecurity.In most cases,unscrupulous people or bad actors can access information through unsecured connections by planting... In network settings,one of the major disadvantages that threaten the network protocols is the insecurity.In most cases,unscrupulous people or bad actors can access information through unsecured connections by planting software or what we call malicious software otherwise anomalies.The presence of anomalies is also one of the disadvantages,internet users are constantly plagued by virus on their system and get activated when a harmless link is clicked on,this a case of true benign detected as false.Deep learning is very adept at dealing with such cases,but sometimes it has its own faults when dealing benign cases.Here we tend to adopt a dynamic control system(DCSYS)that addresses data packets based on benign scenario to truly report on false benign and exclude anomalies.Its performance is compared with artificial neural network auto-encoders to define its predictive power.Results show that though physical systems can adapt securely,it can be used for network data packets to identify true benign cases. 展开更多
关键词 Dynamic control system Deep learning Artificial neural network Auto-encoders Identify space model Benign Anomalies
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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
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
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
Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder 认领 引用
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作者 Xin HE Zhe ZHANG +1 位作者 Li XU Jiapei YU 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2022年第3期452-462,共11页
Driving behavior normalization is important for a fair evaluation of the driving style.The longitudinal control of a vehicle is investigated in this study.The normalization task can be considered as mapping of the dri... Driving behavior normalization is important for a fair evaluation of the driving style.The longitudinal control of a vehicle is investigated in this study.The normalization task can be considered as mapping of the driving behavior in a different environment to the uniform condition.Unlike the model-based approach as in previous work,where a necessary driver model is employed to conduct the driving cycle test,the approach we propose directly normalizes the driving behavior using an autoencoder(AE)when following a standard speed profile.To ensure a positive correlation between the vehicle speed and driving behavior,a gate constraint is imposed in between the encoder and decoder to form a gated AE(gAE).This approach is model-free and efficient.The proposed approach is tested for consistency with the model-based approach and for its applications to quantitative evaluation of the driving behavior and fuel consumption analysis.Simulations are conducted to verify the effectiveness of the proposed scheme. 展开更多
关键词 Driving behavior Normalization Gated auto-encoder Quantitative evaluation
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