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Condition Monitoring Using a Latent Space of variational autoencoder Trained Only on a Healthy Machine

SENSORS 2024年第21期24卷 6825页

作者： Komorska, Iwona Puchalski, Andrzej Casimir Pulaski Radom Univ Fac Mech Engn Radom Poland

Machine learning generative models have opened up a new perspective for automated machine diagnostics. These methods improve decision-making by extracting features, classifying, and creating new observations using deep neural networks. Generative modeling aims to determine the joint distribution of input data. This contrasts traditional methods used in diagnostics based on discriminative models and the conditional probability distribution of the target variable at known feature values. In the variational autoencoder (VAE) algorithms trained by the authors, the parameters of diagnostic features are random variables, the distributions of which can be approximated based on data, and the identification of probability distributions is based on variational inference. variational inference is a tool that deals with difficult statistical problems and is usually faster than classical methods. VAEs can detect anomalies, predict failures, and optimize processes. This paper proposes an unsupervised approach to fault diagnosis using only healthy data with automatic feature extraction from the continuous probabilistic latent subspace of the VAE encoder and reduction in PCA or t-SNE. The solution, verified in the example of simulation data, is a response to a common problem related to the lack or difficulty of obtaining marked data in defected states of devices and mechanical structures.

关键词： condition monitoring diagnostic signal variational autoencoder latent space feature extraction dimensionality reduction t-SNE method

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Scale-Adaptive Asymmetric Sparse variational autoencoder for Point Cloud Compression

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IEEE TRANSACTIONS ON BROADCASTING 2024年第3期70卷 884-894页

作者： Chen, Jian Zhu, Yingtao Huang, Wei Lan, Chengdong Zhao, Tiesong Fuzhou Univ Coll Phys & Informat Engn Fujian Key Lab Intelligent Proc & Wireless Transmi Fuzhou 350108 Peoples R China Fuzhou Univ Sch Adv Mfg Fuzhou 350108 Peoples R China Fujian Prov Expressway Network Operat Co Ltd Fuzhou 350018 Peoples R China

Learning-based point cloud compression has achieved great success in Rate-Distortion (RD) efficiency. Existing methods usually utilize variational autoencoder (VAE) network, which might lead to poor detail reconstruction and high computational complexity. To address these issues, we propose a Scale-adaptive Asymmetric Sparse variational autoencoder (SAS-VAE) in this work. First, we develop an Asymmetric Multiscale Sparse Convolution (AMSC), which exploits multi-resolution branches to aggregate multiscale features at encoder, and excludes symmetric feature fusion branches to control the model complexity at decoder. Second, we design a Scale Adaptive Feature Refinement Structure (SAFRS) to adaptively adjust the number of Feature Refinement Modules (FRMs), thereby improving RD performance with an acceptable computational overhead. Third, we implement our framework with AMSC and SAFRS, and train it with an RD loss based on Fine-grained Weighted Binary Cross-Entropy (FWBCE) function. Experimental results on 8iVFB, Owlii, and MVUV datasets show that our method outperforms several popular methods, with a 90.0% time reduction and a 51.8% BD-BR saving compared with V-PCC. The code will be available soon at https://***/fancj2017/SAS-VAE.

关键词： Point cloud compression Feature extraction Decoding Convolution Three-dimensional displays Octrees Rate-distortion variational autoencoder asymmetric multiscale sparse convolution scale adaptive feature refinement structure

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Multi-Channel Multi-Scale Convolution Attention variational autoencoder (MCA-VAE): An Interpretable Anomaly Detection Algorithm Based on variational autoencoder

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SENSORS 2024年第16期24卷 5316页

作者： Liu, Jingwen Huang, Yuchen Wu, Dizhi Yang, Yuchen Chen, Yanru Chen, Liangyin Zhang, Yuanyuan Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China East China Normal Univ Coll Comp Sci Shanghai 200062 Peoples R China Sichuan Univ Institude Ind Internet Res Chengdu 610065 Peoples R China

With the rapid development of industry, the risks factories face are increasing. Therefore, the anomaly detection algorithms deployed in factories need to have high accuracy, and they need to be able to promptly discover and locate the specific equipment causing the anomaly to restore the regular operation of the abnormal equipment. However, the neural network models currently deployed in factories cannot effectively capture both temporal features within dimensions and relationship features between dimensions;some algorithms that consider both types of features lack interpretability. Therefore, we propose a high-precision, interpretable anomaly detection algorithm based on variational autoencoder (VAE). We use a multi-scale local weight-sharing convolutional neural network structure to fully extract the temporal features within each dimension of the multi-dimensional time series. Then, we model the features from various aspects through multiple attention heads, extracting the relationship features between dimensions. We map the attention output results to the latent space distribution of the VAE and propose an optimization method to improve the reconstruction performance of the VAE, detecting anomalies through reconstruction errors. Regarding anomaly interpretability, we utilize the VAE probability distribution characteristics, decompose the obtained joint probability density into conditional probabilities on each dimension, and calculate the anomaly score, which provides helpful value for technicians. Experimental results show that our algorithm performed best in terms of F1 score and AUC value. The AUC value for anomaly detection is 0.982, and the F1 score is 0.905, which is 4% higher than the best-performing baseline algorithm, Transformer with a Discriminator for Anomaly Detection (TDAD). It also provides accurate anomaly interpretation capability.

关键词： industrial control systems anomaly detection variational autoencoder anomaly interpretation

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Adversarial variational autoencoder for attributed graph embedding with high-frequency noise filtering

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APPLIED INTELLIGENCE 2023年第22期53卷 26750-26762页

作者： Zhou, Hui Liu, Xin Li, Xiangju Zhao, Zhongying Li, Chao Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

Attributed graph embedding aims to learn low-dimensional representations by fully exploiting both topological structure and rich attributes. The embeddings contain more useful information, thus yielding better performance in subsequent analysis tasks. Therefore, attributed network embedding has become an important research direction in recent years. However, most existing algorithms ignore the high-frequency noise contained in the features and do not normalize the embedding, which limits their performance in downstream tasks. To solve the above problem, we design an Adversarial Attributed Graph Embedding algorithm (AAGE) in conjunction with a Laplacian smoothing filter. In particular, the AAGE employs a variational autoencoder (VAE) to learn the embedding of nodes and attributes. Unlike most variational autoencoder-based algorithms, we first apply a carefully-designed Laplacian filter to smooth the features before performing autoencoder, secondly normalize the obtained embeddings of nodes and attributes during the autoencoder process, and finally devise a generative adversarial training method to enhance the robustness of the learned representations. To verify the potential of the proposed AAGE, we evaluate its performance on the tasks of node classification and node clustering on three real-world attributed networks. The results show that AAGE significantly outperforms state-of-the-art methods.

关键词： Attributed network variational autoencoder Normalization Generative adversarial network

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Cross-Lingual Voice Conversion With Controllable Speaker Individuality Using variational autoencoder and Star Generative Adversarial Network

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IEEE ACCESS 2021年 9卷 47503-47515页

作者： Ho, Tuan Vu Akagi, Masato Japan Adv Inst Sci & Technol Grad Sch Adv Sci & Technol Nomi 9231292 Japan

This paper proposes a non-parallel cross-lingual voice conversion (CLVC) model that can mimic voice while continuously controlling speaker individuality on the basis of the variational autoencoder (VAE) and star generative adversarial network (StarGAN). Most studies on CLVC only focused on mimicking a particular speaker voice without being able to arbitrarily modify the speaker individuality. In practice, the ability to generate speaker individuality may be more useful than just mimicking voice. Therefore, the proposed model reliably extracts the speaker embedding from different languages using a VAE. An F0 injection method is also introduced into our model to enhance the F0 modeling in the cross-lingual setting. To avoid the over-smoothing degradation problem of the conventional VAE, the adversarial training scheme of the StarGAN is adopted to improve the training-objective function of the VAE in a CLVC task. Objective and subjective measurements confirm the effectiveness of the proposed model and F0 injection method. Furthermore, speaker-similarity measurement on fictitious voices reveal a strong linear relationship between speaker individuality and interpolated speaker embedding, which indicates that speaker individuality can be controlled with our proposed model.

关键词： Training Linguistics Generative adversarial networks Gallium nitride Acoustics Decoding Task analysis Voice conversion cross-lingual controllable speaker individuality variational autoencoder generative adversarial network

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DRED: Deep REDundancy Coding of Speech Using a Rate-Distortion-Optimized variational autoencoder

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2024年第8期18卷 1441-1447页

作者： Valin, Jean-Marc Buthe, Jan Mustafa, Ahmed Klingbeil, Michael Xiph Org Fdn Jaffrey NH 03452 USA Amazon Web Serv Palo Alto CA 94303 USA

Despite recent advancements in packet loss concealment (PLC) using deep learning techniques, packet loss remains a significant challenge in real-time speech communication. Redundancy has been used in the past to recover the missing information during losses. However, conventional redundancy techniques are limited in the maximum loss duration they can cover and are often unsuitable for burst packet loss. We propose a new approach based on a rate-distortion-optimized variational autoencoder (RDO-VAE), allowing us to optimize a deep speech compression algorithm for the task of encoding large amounts of redundancy at very low bitrate. The proposed Deep REDundancy (DRED) algorithm can transmit up to 50x redundancy using less than 32 kb/s. Results show that DRED outperforms the existing Opus codec redundancy. We also demonstrate its benefits when operating in the context of WebRTC.

关键词： Audio redundancy neural speech coding variational autoencoder Audio redundancy neural speech coding variational autoencoder

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Banner layout retargeting with hierarchical reinforcement learning and variational autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第24期81卷 34417-34438页

作者： Hu, Hao Zhang, Chao Liang, Yanxue Westlake Univ Hangzhou Peoples R China Westlake Univ Intelligent Ind Res Inst Hangzhou Peoples R China

In many advertising areas, banners are often generated with different display sizes, so designers have to make huge efforts to retarget their designs to each size. Automating such retargeting process can greatly save time for designers and let them put creativity on new ads. This paper proposes a hierarchical reinforcement learning-based (HRL-based) method and a variational autoencoder-based (VAE-based) method by treating the automated banner retargeting problem as a layout retargeting task. The HRL and VAE models are trained separately to learn the scaling and positioning policy of the design elements from an original (base) layout. Hence, the proposed method can generate appropriate layouts for different target banner sizes. Meanwhile, evaluation metrics are proposed to assess the quality of generated layouts and are also reward conditions during the training process. To evaluate performances of the two models, SOTA methods such as Non-linear Inverse Optimization (NIO), Triangle Interpolation (TI), and Layout GAN (LGAN) are implemented and compared. Experimental results show that both HRL- and VAE-based methods retarget design layouts effectively, and the VAE model achieves better performance than the HRL model.

关键词： Layout retargeting Hierarchical reinforcement learning variational autoencoder SOTA methods

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Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

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Science China(Information Sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift malicious traffic identification variational autoencoder intrusion detection cyberspace security

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Advances Toward the Next Generation Fire Detection: Deep LSTM variational autoencoder for Improved Sensitivity and Reliability

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IEEE ACCESS 2021年 9卷 30636-30653页

作者： Xu, Zhaoyi Guo, Yanjie Saleh, Joseph Homer Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

Fire detection is a critical component of a building safety monitoring system and remains an important research area with weighty practical relevance. Significant advances have occurred in recent years in building automation, and the operation of buildings has become more complex and requires ever more effective monitoring systems. In this work, we develop a novel fire detection method using deep Long-Short Term Memory (LSTM) neural networks and variational autoencoder (VAE) to meet these increasingly stringent requirements and outperform existing fire detection methods. To evaluate the effectiveness of our method, we develop high-fidelity simulations, and we use datasets from real-world fire and non-fire experiments provided by NIST. We compare and discuss the performance of our proposed fire detection with alternative methods, including the standard LSTM, cumulative sum control chart (CUSUM), exponentially weighted moving average (EWMA), and two currently used fixed-temperature heat detectors. The results using the simulation-based and the real-world experiments are complementary, and they indicate that the LSTM-VAE robustly outperforms the other detection methods with, for example, statistically significant shorter alarm time lags, no missed detection, and no false alarms. The results also identify shortcomings of other detection methods and indicate a clear ranking among them (LSTM-VAE > EWMA > LSTM > CUSUM).

关键词： Fire detection machine learning anomaly detection long short-term memory variational autoencoder

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Data-driven design exploration method using conditional variational autoencoder for airfoil design

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2021年第2期64卷 613-624页

作者： Yonekura, Kazuo Suzuki, Katsuyuki Univ Tokyo Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan IHI Corp Isogo Ku 1 Shin Nakahara Cho Yokohama Kanagawa 2358501 Japan

An objective of mechanical design is to obtain a shape that satisfies specific requirements. In the present work, we achieve this goal using a conditional variational autoencoder (CVAE). The method enables us to analyze the relationship between aerodynamic performance and the shape of aerodynamic parts, and to explore new designs for the parts. In the CVAE model, a shape is fed as an input and the corresponding aerodynamic performance index is fed as a continuous label. Then, shapes are generated by specifying the continuous label and latent vector. When CVAE is applied to mechanical design, it is desired to draw shapes that reproduce the specified aerodynamic performance. In ordinal CVAE, the model is trained to minimize reconstruction loss and latent loss, and it is usually optimized considering the sum of these losses. However, the present study shows that the optimal network is not always optimal in terms of reproducing the aerodynamic performance. The proposed method is verified using two numerical examples: a two-dimensional (2D) airfoil and a turbine blade. In the airfoil example, we demonstrate the effects of latent dimension, and in the turbine design example, we demonstrate that the proposed method can be applied to a real turbine design problem and reduce the design time.

关键词： Design exploration variational autoencoder Airfoil design

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