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35th Chinese Control and Decision Conference (CCDC)

作者： Luo, Tao Zhu, Songhao Nanjing Univ Posts & Telecommun Coll Automat & Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350334722

Cross-domain person re-recognition is very important for the applications of intelligent video surveillance. To further reduce the cross-domain differences, a new multi-branch convolutional auto-encoder based cross-domain person re-recognition network (MCAENet) is here proposed. Firstly, the reconstructed feature map is utilized as an input of the generative model module, and reconstruction loss and divergence loss are utilized to extract domain specific features and unknown domain features;then, the domain invariant feature guidance branch is introduced to enhance the robustness of domain invariant features;finally, identity loss is introduced and combined with the triple loss to constrain the distance between different pedestrians, so as to extract representative features to distinguish different pedestrians. Extensive experimental results demonstrate that the proposed cross-domain person re-recognition method based on convolutional -encoder achieves the best identification accuracy in various environments.

关键词： Person Re-Recognition convolutional auto-encoder Invariant Features Specific Feature

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Spatial Resolution Enhancement Technology of FY-3D MWRI Based on convolutional auto-encoder

Spatial Resolution Enhancement Technology of FY-3D MWRI Base...

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2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023

作者： Jiang, Han Hu, Weidong School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350358971

During data collection, the Microwave Radiation Imager (MWRI) on the FY3D satellite is affected by various degradation factors, resulting in varying degrees of reduced spatial resolution of multi-channel data. Irreversible high-frequency information degradation occurs during the degradation process. However, the existing spatial resolution enhancement methods cannot perfectly restore the high-frequency information of the original measurement. In this study, an algorithm based convolutional auto-encoder(CAE) is proposed to improve the image resolution of MWRI and restore high-frequency information. Experimental results on simulated and actual MWRI images have demonstrated the validity and effectiveness of the method. © 2023 IEEE.

关键词： convolutional auto-encoder high-frequency information MWRI spatial resolution

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A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings

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Journal of Southeast University(English Edition) 2019年第4期35卷 417-423页

作者： Ding Yunhao Jia Minping School of Mechanical Engineering Southeast UniversityNanjing 211189China

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 *** 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 *** on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed *** results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,*** 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 *** final results show that the proposed model has a better recognition effect for rolling bearing fault data.

关键词： fault diagnosis deep learning convolutional auto-encoder multi-scale convolutional kernel feature extraction

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An Unsupervised Classification Method for Flame Image of Pulverized Coal Combustion Based on convolutional auto-encoder and Hidden Markov Model

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ENERGIES 2019年第13期12卷 2585-2585页

作者： Qiu, Tian Liu, Minjian Zhou, Guiping Wang, Li Gao, Kai North China Elect Power Univ Beijing Key Lab New Technol & Syst Measuring & Co Beijing 102206 Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Shenyang 110004 Liaoning Peoples R China

Combustion condition monitoring is a fundamental and critical issue that needs to be addressed in the wide-load operation of coal-fired boilers. In this paper, an unsupervised classification framework based on the convolutional auto-encoder (CAE), the principal component analysis (PCA), and the hidden Markov model (HMM) is proposed to monitor the combustion condition with the uniformly spaced flame images, which are collected from the furnace combustion monitoring system. First, CAE is adopted to extract the features from the flame images, which obtain the sparse representations in the images. Then, PCA is applied to project the feature vectors into the orthogonal space for robustness and computation efficiency. Finally, a HMM is built to calculate the corresponding optimal states by learning the temporal behaviors in the compressed representations. A coal combustion adjustment experiment was conducted in a 660 MW opposed-firing boiler, and the sequential 14,400 flame images with three different combustion states were obtained to evaluate the effectiveness of the proposed approach. We tested six different compression dimensions of the latent variable z in the CAE model and ensured that the appropriate compress parameter was 1024. The proposed framework is compared with five other methods: the CAE + Gaussian mixture model (GMM), CAE + Kmean, the CAE + fuzzy c-mean method, CAE + HMM, and the traditional handcraft feature extraction method (TH) + HMM. The results show that the proposed framework has the highest classification accuracy (95.25% for the training samples and 97.36% for the testing samples) and has the best performance in recognizing the semi-stable state (85.67% for the training samples and 77.60% for the testing samples), indicating that the proposed framework is capable of identifying the combustion condition, changing when the combustion deteriorates as the coal feed rate falls.

关键词： flame images convolutional auto-encoder hidden Markov model unsupervised classification

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Double JPEG compression detection and localization based on convolutional auto-encoder for image content removal

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DIGITAL SIGNAL PROCESSING 2022年第0期123卷 103429-103429页

作者： Rahmati, Mohammad Razzazi, Farbod Behrad, Alireza Islamic Azad Univ Sci & Res Branch Dept Elect & Comp Engn Tehran Iran Shahed Univ Elect Engn Dept Tehran Iran

Due to the widespread prevalence of JPEG images in daily life, their authenticity has often been investigated in detecting the traces caused by double JPEG (DJPEG) compression. This paper presents a novel method of DJPEG compression detection based on deep neural networks. After removing the image con -tent using a convolutional auto-encoder (CAE), we separated the JPEG and DJPEG feature spaces into two aligned and non-aligned scenarios using a convolutional neural network (CNN) in the spatial domain. Besides detecting DJPEG compression for small-sized 64 x 64 image patches, the approach also lets us determine the manipulated regions. The proposed network was evaluated for different input images. Results on the standard raw images dataset (RAISE) demonstrated that the proposed methods outperform previous algorithms, especially in the non-aligned case. If an accurate determination of the first and second JPEG compression quality factors (QFs) is not possible, our algorithm is robust to their perturbations. Compared to one of the best existing methods, we achieved about 65% improvement in relative error reduction in some cases. (C)& nbsp;2022 Elsevier Inc. All rights reserved.

关键词： convolutional auto-encoder convolutional neural network DJPEG compression detection DJPEG localization Image content removal

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A data-driven robotic Chinese calligraphy system using convolutional auto-encoder and differential evolution

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KNOWLEDGE-BASED SYSTEMS 2019年 182卷 104802-000页

作者： Gao, Xingen Zhou, Changle Chao, Fei Yang, Longzhi Lin, Chih-Min Xu, Tao Shang, Changjing Shen, Qiang Xiamen Univ Sch Informat Sci & Engn Cognit Sci Dept Xiamen Fujian Peoples R China Aberystwyth Univ Inst Math Phys & Comp Sci Aberystwyth Dyfed Wales Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England Yuan Ze Univ Dept Elect Engn Taoyuan Taiwan

The Chinese stroke evaluation and generation systems required in an autonomous calligraphy robot play a crucial role in producing high-quality writing results with good diversity. These systems often suffer from inefficiency and non-optima despite of intensive research effort investment by the robotic community. This paper proposes a new learning system to allow a robot to automatically learn to write Chinese calligraphy effectively. In the proposed system, the writing quality evaluation subsystem assesses written strokes using a convolutional auto-encoder network (CAE), which enables the generation of aesthetic strokes with various writing styles. The trained CAE network effectively excludes poorly written strokes through stroke reconstruction, but guarantees the inheritance of information from well-written ones. With the support of the evaluation subsystem, the writing trajectory model generation subsystem is realized by multivariate normal distributions optimized by differential evolution (DE), a type of heuristic optimization search algorithm. The proposed approach was validated and evaluated using a dataset of nine stroke categories: high-quality written strokes have been resulted with good diversity which shows the robustness and efficacy of the proposed approach and its potential in autonomous action-state space exploration for other real-world applications. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Robotic Chinese calligraphy Data-driven evaluation model convolutional auto-encoder Differential evolution

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Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder

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PATTERN RECOGNITION 2022年 123卷 108403-108403页

作者： Liu, Shuo Han, Jing Puyal, Estela Laporta Kontaxis, Spyridon Sun, Shaoxiong Locatelli, Patrick Dineley, Judith Pokorny, Florian B. Dalla Costa, Gloria Leocani, Letizia Guerrero, Ana Isabel Nos, Carlos Zabalza, Ana Sorensen, Per Soelberg Buron, Mathias Magyari, Melinda Ranjan, Yatharth Rashid, Zulqarnain Conde, Pauline Stewart, Callum Folarin, Amos A. Dobson, Richard J. B. Bailon, Raquel Vairavan, Srinivasan Cummins, Nicholas Narayan, Vaibhav A. Hotopf, Matthew Comi, Giancarlo Schuller, Bjoern Univ Augsburg EIHW Chair Embedded Intelligence Hlth Care & Well Augsburg Germany Univ Cambridge Dept Comp Sci & Technol Cambridge England Univ Zaragoza Aragon Inst Engn Res I3A IIS Aragon BSICoS Grp Zaragoza Spain Biomat & Nanomed CIBER BNN CIBER Bioengn Madrid Spain Kings Coll London Inst Psychiat Psychol & Neurosci Dept Biostat & Hlth Informat London England Univ Bergamo Dept Engn & Appl Sci Bergamo Italy Med Univ Graz Div Phoniatr Graz Austria Sci Inst Hosp San Raffaele Univ Vita Salute San Raffaele & Expt Neurophysiol Inst Expt Neurol Milan Italy Univ Autonoma Barcelona Hosp Univ Vall dHebron Multiple Sclerosis Ctr Catalonia Cemcat Dept Neurol Neuroimmunol Barcelona Spain Copenhagen Univ Hosp Rigshosp Danish Multiple Sclerosis Ctr Dept Neurol Copenhagen Denmark UCL Inst Hlth Informat London England Janssen Res & Dev LLC Titusville NJ USA Kings Coll London Inst Psychiat Psychol & Neurosci Dept Psychol Med London England South London & Maudsley Natl Hlth Serv Fdn Trust London England Univ Vita Salute San Raffaele Casa Cura Privata Policlin Milan Italy Imperial Coll London GLAM Grp Language Audio & Mus London England RADAR CNS Consortium London England

This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of 95 . 3% , a sensitivity of 100% and a specificity of 90 . 6% , an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： COVID-19 Respiratory tract infection Anomaly detection Contrastive learning convolutional auto-encoder

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Residual squeeze-and-excitation convolutional auto-encoder for fault detection and diagnosis in complex industrial processes

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期136卷

作者： Yu, Jianbo Li, Shijin Liu, Xing Li, Hao Ma, Mingyan Liu, Peilun You, Lichun Tongji Univ Sch Mech Engn 4800 Caoan Rd Shanghai 201804 Peoples R China COMAC Shanghai Aircraft Mfg Co Ltd Shanghai 201324 Peoples R China State Key Lab Large Elect Drive Syst & Equipment T Tianshui Gansu Peoples R China

Recently, deep learning has attracted increasing attention in process monitoring. However, it is still a big challenge to extract effective features of process data for fault detection and diagnosis (FDD) by using deep learning-based methods. In this study, a novel deep learning-based model, residual squeeze-and-excitation convolutional auto-encoder (RSECAE) is proposed for FDD in complex industrial processes. A hybrid structure that integrates convolution calculation and auto-encoder is proposed to construct multiple convolution layers and deconvolution layers in the encoder and decoder respectively. Thus, RSECAE can learn effective features from the process signals in an unsupervised manner. At the same time, multiple residual squeeze-and-excitation networks (RSENets) are embedded in the deep network, which effectively preserves the critical feature representations of the convolution layer and transmits them to the decoder in the form of residuals. Finally, maximum mean discrepancy (MMD) term is utilized in RSECAE to reduce the distribution difference between the process signals and the learned features. This enables RSECAE to extract effective features with prior distribution information. RSECAE provides a new solution for process FDD for complicated industrial processes. Two industrial cases are adopted to validate the effectiveness of the RSECAE in process FDD. The experimental results demonstrate the effectiveness and superiority of RSECAE in process fault detection and fault diagnosis.

关键词： Complex industrial process Process FDD Deep learning Feature learning convolutional auto-encoder

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3D convolutional auto-encoder based multi-scale feature extraction for point cloud registration

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OPTICS AND LASER TECHNOLOGY 2022年 149卷 107860-107860页

作者： Ma, Hao Yin, De-Yu Liu, Jing-Bin Chen, Rui-Zhi Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Finnish Geospatial Res Inst Dept Remote Sensing & Photogrammetry Masala 02430 Finland Finnish Geospatial Res Inst Ctr Excellence Laser Scanning Res Masala 02430 Finland

Searching corresponding features between two consecutive point cloud sequences is a key step in frame-to-frame matching. Utilizing multi-resolution strategy and voxel model which reserves the original shape of input data, we propose a fully unsupervised convolutional auto-encoder (CAE) based feature matching methodology that extracts features from multi-resolution voxel model. Different resolutions can capture different information: hight resolution better reflects local details, and low resolution can describe coarse appearance in a relative wide range. CAE is further adopted to encode the voxel patch of a specific resolution to a low dimension one. The final feature descriptor is concatenated by the encoded features of different resolutions and is used to determine the initial corresponding features in terms of Euclidean distance. Refined matches are obtained through RANSAC. Experiments based on KITTI dataset show that our features outperform state-of-the-art by almost 20-30% in average matching inlier ratio.

关键词： convolutional auto-encoder Multi-scale Feature descriptor Point cloud Registration

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A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis

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MEASUREMENT 2021年 178卷 109352-109352页

作者： Qian, Quan Qin, Yi Wang, Yi Liu, Fuqiang Chongqing Univ State Key Lab Mech Transmission Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China

Deep learning has gained a great achievement in the intelligent fault diagnosis of rotating machineries. However, the labeled data is scarce in actual engineering and the marginal distribution of data is discrepant under different conditions. Transfer learning provides a feasible way to overcome these difficulties. Considering the effect of noise on the transfer fault diagnosis, this work puts forward a new deep transfer learning network based on convolutional auto-encoder(CAE-DTLN) to implement the mechanical fault diagnosis in target domain without labeled data. In the proposed framework, CAE is used as the feature extractor as it has the ability of noise removal. Moreover, both CORrelation ALignment (CORAL) loss and domain classification loss are integrated to enhance the effect of domain confusion. The proposed model is applied to the fault transfer diagnosis of planetary gearboxes under different working loads and noise levels, and it is compared with other typical fault transfer diagnosis models. The experimental results show that CAE-DTLN has higher diagnosis accuracy and stronger generalization ability. The average diagnostic accuracy of CAE-DTLN is over 99%. Moreover, the proposed transfer learning model has better anti-noise performance.

关键词： Intelligent fault diagnosis convolutional auto-encoder Deep transfer learning Anti-noise ability Domain adaption

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