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5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： He, Jiaxing Wang, Xiaodan Xiang, Qian Air Force Engn Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9783031189067;9783031189074

Radar high resolution range profile (HRRP) contains important structural features such as target size and scattering center distribution, which has attracted extensive attention in the field of radar target recognition. Aiming at the problems of class imbalance and insufficient data quality generated by vanilla GAN, an HRRP target recognition method based on improved conditional Wasserstein variational autoencoder Generative Adversarial Networks (CWVAEGAN) and one-dimensional convolutional neural network (CWVAEGAN-1DCNN) is proposed. CWVAEGAN class-balancing method introduces the concept of variational lower bound in VAE into GAN, and uses the encoded hidden vector instead of Gaussian noise as generator's input, which can significantly improve the fidelity of the generated samples and balance the class distribution of the dataset. The balanced dataset is used for classification through a standard 1D-CNN. The experimental results show that the samples generated by CWVAEGAN class-balancing method greatly improve the detection accuracy of 1D-CNN for minority classes, the fidelity of the samples generated by CWVAEGAN is higher than that of other class-balancing methods, and CWVAEGAN-1DCNN has strong HRRP target recognition ability.

关键词： High resolution range profile Class-balancing method Generative adversarial network variational autoencoder One-dimensional convolution neural network

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Linking Check-in Data to Users on Location-aware Social Networks 19th

Linking Check-in Data to Users on Location-aware Social Netw...

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19th Pacific Rim International Conference on Artificial Intelligence (PRICAI)

作者： Li, Yujie Sang, Yu Chen, Wei Zhao, Lei Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi Jiangsu Peoples R China

ISBN: (纸本)9783031208614;9783031208621

Linking check-in data to their owners can benefit many downstream tasks, such as POI (Point of Interest) recommendation, destination prediction, and route planning, since we can obtain redundant information for each user after linking. Consequently, we formulate and investigate the novel problem CUL (Check-in-User Linking) in this work. Notably, the main difference between CUL and the existing problem TUL (Trajectory-User Linking) is that the trajectories used in TUL are continuous, while the check-in records in CUL are discrete. To tackle the problem CUL effectively, we develop a model entitled CULVAE (Check-in-User Linking via variational autoencoder). Firstly, a well-designed grid index is applied to organize the input check-in records. Then, an encoding module is developed to embed a user with corresponding grids. Next, a decoding module is proposed to generate a low-dimensional representation of each user. Finally, a multi-class classifier is proposed to link check-in records to users based on the output of the decoding module. We conduct extensive experiments on four real-world datasets, and the results demonstrate that our proposed model CULVAE performs better than all state-of-art approaches.

关键词： Check-in Data Social networks variational autoencoder

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SEMANTIC ALIGNMENT FOR MULTI-ITEM COMPRESSION 29

SEMANTIC ALIGNMENT FOR MULTI-ITEM COMPRESSION

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IEEE International Conference on Image Processing (ICIP)

作者： Bachard, Tom Tom, Anju Jose Maugey, Thomas Univ Rennes Rennes France INRIA Paris France

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Coding algorithms usually compress independently the images of a collection, in particular when the correlation between them only resides at the semantic level, i.e., information related to the high-level image content. In this work, we propose a coding solution able to exploit this semantic redundancy to decrease the storage cost of data collections. First we introduce the multi-item compression framework. Then we derive a loss term to shape the latent space of a variational auto-encoder so that the latent vectors of semantically identical images can be aligned. Finally, we experimentally demonstrate that this alignment leads to a more compact representation of the data collection.

关键词： Multi image compression Semantic variational autoencoder Representation Learning

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Unsupervised Representation Learning-based Doppler Ultrasound Signal Quality Assessment

Unsupervised Representation Learning-based Doppler Ultrasoun...

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IEEE Global Communications Conference (GLOBECOM)

作者： Shi, Xintong Yamamoto, Kohei Ohtsuki, Tomoaki Matsui, Yutaka Owada, Kazunari Keio Univ Dept Informat & Comp Sci Yokohama Kanagawa 2238522 Japan Atom Med Co Ltd Tech Dept Res & Dev Grp Tokyo 1130021 Japan

ISBN: (纸本)9781665435406

The Doppler ultrasound (DUS) transducer has been widely used for fetal heart rate (FHR) monitoring. However, the fetal DUS signals from the transducers can be corrupted by several interference sources such as maternal and fetal movements, which makes FHR estimation using fetal DUS signals challenging. Fetal DUS signal quality assessment (SQA) can help to remove or interpolate unreliable FHRs estimated from noisy signals to improve the accuracy of FHR estimation. There are some existing approaches for fetal DUS SQA, and most of these approaches with high accuracy are based on supervised learningbased algorithms and human-defined properties. Nonetheless, the fetal DUS datasets with quality-level annotations are limited, and human-defined properties place a limitation on mining more deep information related to signal quality in fetal DUS signals. In this paper, we propose an unsupervised representation learning-based fetal DUS SQA for the improvement of FHR estimation performance. We firstly learn representations of preprocessed fetal DUS data from variational autoencoder (VAE) and then combine these representations as one signal quality index (SQI) using a self-organizing map (SOM). Finally, we apply the combined SQI and a Kalman filter (KF) to estimate fetal RR intervals (FRRI) for reducing the errors of FHR estimation. The experimental results showed that our proposed method could reduce the averaged root mean squared error (RMSE) of FRRI and averaged absolute error (AAE) of FHR.

关键词： Unsupervised representation learning Fetal doppler ultrasound signal Signal quality assessment variational autoencoder Kalman filter

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Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM

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SENSORS 2024年第10期24卷 2992页

作者： Yang, Lei Jiang, Yibo Zeng, Kang Peng, Tao Zhejiang Univ ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 311215 Peoples R China Jiaxing Shutuo Technol Co Ltd Jiaxing 314031 Peoples R China Zhejiang Loong Airlines Co Ltd Hangzhou 311243 Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power Components & Mechatron S Hangzhou Peoples R China

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time-frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods.

关键词： remaining useful life variational autoencoder bi-directional long short-term memory

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Deformation representation based convolutional mesh autoencoder for 3D hand generation

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NEUROCOMPUTING 2021年 444卷 356-365页

作者： Zheng, Xinqian Jiang, Boyi Zhang, Juyong Univ Sci & Technol China Hefei Peoples R China

Due to its flexible joints and self-occlusion, representation and reconstruction of 3D human hand is a very challenging problem. Although some parametric models have been proposed to alleviate this problem, these representation models have limited representation ability, like not being able to represent complex gestures. In this paper, we presented a new 3D hand model with powerful representation ability and applied it to high accuracy monocular RGB-D/RGB 3D hand reconstruction. To achieve this, we firstly build a large scale high-quality hand mesh data set based on MANO with a novel mesh deformation method. We train a VAE based on this data set, and get the low-dimensional representation of hand meshes. By using our HandVAE model, we can recover a 3D human hand by giving a code within this latent space. We also build a framework to recover 3D hand mesh from RGB-D/RGB data. Experimental results have demonstrated the powerfulness of our hand model in terms of the reconstruction accuracy and the application for RGB-D/RGB reconstruction. We believe that our 3D hand representation could be further used in other related human hand applications. (c) 2020 Elsevier B.V. All rights reserved.

关键词： 3D hand model Deformation representation variational autoencoder Monocular 3D hand reconstruction

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Semi-supervised fabric defect detection based on image reconstruction and density estimation

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TEXTILE RESEARCH JOURNAL 2021年第9-10期91卷 962-972页

作者： Zhou, Qihong Mei, Jun Zhang, Qian Wang, Shaozong Chen, Ge Donghua Univ Coll Mech Engn Shanghai Peoples R China Beijing Natl Innovat Inst Lightweight Ltd Beijing Peoples R China

Defective products are a major contributor toward a decline in profits in textile industries. Hence, there are compelling needs for an automated inspection system to identify and locate defects on the fabric surface. Although much effort has been made by researchers worldwide, there are still challenges with computation and accuracy in the location of defects. In this paper, we propose a hybrid semi-supervised method for fabric defect detection based on variational autoencoder (VAE) and Gaussian mixture model (GMM). The VAE model is trained for feature extraction and image reconstruction while the GMM is used to perform density estimation. By synthesizing the detection results from both image content and latent space, the method can construct defect region boundaries more accurately, which are useful in fabric quality evaluation. The proposed method is validated on AITEX and DAGM 2007 public database. Results demonstrate that the method is qualified for automated detection and outperforms other selected methods in terms of overall performance.

关键词： fabric defect detection anomaly detection variational autoencoder image reconstruction Gaussian mixture model density estimation

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Unsupervised Pre-Training of Imbalanced Data for Identification of Wafer Map Defect Patterns

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IEEE ACCESS 2021年 9卷 52352-52363页

作者： Shon, Ho Sun Batbaatar, Erdenebileg Cho, Wan-Sup Choi, Seong Gon Chungbuk Natl Univ Res Inst Comp & Informat Commun Cheongju 28644 South Korea Chungbuk Natl Univ Sch Elect Comp Engn Cheongju 28644 South Korea Chungbuk Natl Univ Dept Management Informat Syst Cheongju 28644 South Korea Chungbuk Natl Univ Sch Informat & Commun Engn Cheongju 28644 South Korea

Visual defect inspection and classification are significant steps of most manufacturing processes in the semiconductor and electronics industries. Known and unknown defects on wafer maps tend to cluster, and these spatial patterns provide valuable process information for supporting manufacturing in determining the root causes of abnormal processes. In previous studies, data augmentation-based deep learning (DL) techniques were most commonly used for the identification of wafer map defect patterns (WMDP). Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations were manually designed for the WMDP problem. In this study, we propose a DL-based method with automatic data augmentation for the WMDP task. Basically, it focuses on learning effective discriminative features, from wafer maps, through a deep network structure. The network consists of a convolution-based variational autoencoder (CVAE) sequentially. First, we pre-trained the CVAE on large training data in an unsupervised manner. Second, we fine-tuned the encoder of the CVAE, which was followed by a neural network (NN) classifier, in a supervised manner. Additionally, we describe a simple procedure for automatically searching for improved data augmentation policies. The policy mainly consists of five image processing functions: rotation, flipping, shifting, shearing range, and zooming. The effectiveness of the proposed method was demonstrated through experimental results obtained from a simulation dataset and a real-world wafer map dataset (WM-811K). This study provides guidance for the application of deep learning in semiconductor manufacturing processes to improve product quality and yield.

关键词： Feature extraction Fabrication Semiconductor device modeling Deep learning Visualization Convolutional codes Training Classification convolutional variational autoencoder deep learning imbalanced data neural network unsupervised pre-training variational autoencoder wafer map defect patterns

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Dual-Domain-Based Adversarial Defense With Conditional VAE and Bayesian Network

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第1期17卷 596-605页

作者： Zhu, Jinlin Peng, Guohao Wang, Danwei Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Adversarial examples can be imperceptible to human eyes but can easily fool deep models. Such intrigue property has raised security issues for real-world industrial deep learning systems. To combat those malicious attacks, a novel defense strategy has been proposed based on the conditional variational autoencoder (CVAE) and Bayesian network (BN). The main contribution lies in the provided systematic dual-domain-based defense framework, which covers three modules named detection, diagnosis, and recovery. Specifically, the CVAE is first introduced for latent- and residual-domain generation. Subsequently, a composite and hierarchical BN detector is proposed to conduct the adversary detection through feature validation and output justification. Afterwards, a diagnosis strategy has been constructed for residual domain and different attacks can be evaluated in the unified framework. Finally, a two-step recovery mechanism is established on the CVAE that can effectively restore the feature representations and the network predictions from various adversaries. The feasibility of the entire defense diagram has been extensively demonstrated on three real-world recognition problems.

关键词： Perturbation methods Bayes methods Detectors Informatics Manifolds Monitoring Security Adversarial examples Bayesian network (BN) defense security variational autoencoder

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Improve Regression Network on Depth Hand Pose Estimation With Auxiliary Variable

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第3期31卷 890-904页

作者： Xu, Lu Hu, Chen Tao, Jian Xue, Jianru Mei, Kuizhi Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Rocket Force Univ Engn Dept Automat Xian 710025 Peoples R China

The regression based deep neural networks have achieved state-of-the-arts performance on depth 3D hand pose estimation task. This paper focuses on improving the regression mapping between features and pose joints. Inspired by the distribution modeling ability of variational autoencoders, we introduce an auxiliary variable into the regression network. During training, the auxiliary variable is modeled by an inference distribution that learns the underlying structural kinematics of human hand. Different with other regression methods on hand poses, our network estimates the pose joints from input depth features and the learned auxiliary variable as well. We show that by introducing the auxiliary variable, the regression is benefited from 1) regularization modeled by inference distribution;and 2) prior information carried by the auxiliary model. The effectiveness of the proposed regression method is evaluated with extensively self-comparative experiments and in comparison with other regression methods on hand pose datasets. The proposed network is easy to train in an end-to-end manner and can work with various feature extraction methods. We apply the proposed regression method to an existing hand pose estimation system, and improves the estimation accuracy by 18.35% and 16.65% on public hand pose datasets.

关键词： Pose estimation Feature extraction Kinematics Three-dimensional displays Task analysis Training Hand pose estimation depth images auxiliary variable regression network variational autoencoder

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