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Multi-mode non-Gaussian variational autoencoder network with missing sources for anomaly detection of complex electromechanical equipment

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ISA TRANSACTIONS 2023年 134卷 144-158页

作者： Luo, Qinyuan Chen, Jinglong Zi, Yanyang Chang, Yuanhong Feng, Yong Xi An Jiao Tong Univ State Key Lab Mfg & Syst Engn Xian 710049 Peoples R China

Anomaly detection is crucial to the safety of complex electromechanical equipment. With the rapid accumulation of industrial data, intelligent methods without human intervention have become the mainstream of anomaly detection. Among them, variational autoencoder (VAE) performs well in anomaly detection with missing fault samples due to the self-supervised learning paradigm. However, the data from electromechanical equipment is usually non-Gaussian, making it difficult for the standard VAE based on Gaussian distribution to recognize the abnormal states. To solve the above problems, we proposed multi-mode non-Gaussian VAE (MNVAE) to detect anomalies from unknown distribution vibration signals without fault samples or prior knowledge. Firstly, the encoder maps the input to a Gaussian mixture distribution in latent space and samples a latent variable from it, after which the Householder Flow is applied to the latent variable to capture more abundant features. Finally, to describe the non-Gaussianity of the signal, Weibull distribution serves as the likelihood function of the reconstructed signal output from the decoder and as the basis for anomaly discrimination. In comparison to 6 related methods, our method yields the best results across various datasets. Through further experiments, the robustness of our method is proved and the proposed improvements are effective.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Electromechanical equipment Fault diagnosis Anomaly detection Neural network variational autoencoder

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Generating Accurate and Diverse Audio Captions Through variational autoencoder Framework

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 2520-2524页

作者： Zhang, Yiming Du, Ruoyi Tan, Zheng-Hua Wang, Wenwu Ma, Zhanyu Beijing Univ Posts & Telecommun Sch Artificial Intelligence Pattern Recognit & Intelligent Syst Lab Beijing 100876 Peoples R China Aalborg Univ Dept Elect Syst DK-9220 Aalborg Denmark Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH England

Generating both diverse and accurate descriptions is an essential goal in the audio captioning task. Traditional methods mainly focus on improving the accuracy of the generated captions but ignore their diversity. In contrast, recent methods have considered generating diverse captions for a given audio clip, but with the potential trade-off in caption accuracy. In this work, we propose a new diverse audio captioning method based on a variational autoencoder structure, dubbed AC-VAE, aiming to achieve a better trade-off between the diversity and accuracy of the generated captions. To improve diversity, AC-VAE learns the latent word distribution at each location based on contextual information. To uphold accuracy, AC-VAE incorporates an autoregressive prior module and a global constraint module, which enable precise modeling of word distribution and encourage semantic consistency of captions at the sentence level. We evaluate the proposed AC-VAE on the Clotho dataset. Experimental results show that AC-VAE achieves a better trade-off between diversity and accuracy compared to the state-of-the-art methods.

关键词： Measurement Decoding Maximum likelihood estimation Semantics Mathematical models Training Diversity reception Diverse audio captioning diverse caption generation variational autoencoder

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EXTENDING THE LEE-CARTER MODEL WITH variational autoencoder: A FUSION OF NEURAL NETWORK AND BAYESIAN APPROACH

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ASTIN BULLETIN-THE JOURNAL OF THE INTERNATIONAL ACTUARIAL ASSOCIATION 2022年第3期52卷 789-812页

作者： Miyata, Akihiro Matsuyama, Naoki Meiji Univ AMS Tokyo Japan Meiji Univ Grad Sch Adv Math Sci AMS Tokyo Japan

In this study, we propose a nonlinear Bayesian extension of the Lee-Carter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed by a time series model fitting. To address the limitations of LC, which are attributed to the two-stage estimation and insufficient model fitness to data, single-stage procedures using the Bayesian state-space (BSS) approaches and extensions of flexibility in modeling by NNs have been proposed. As a fusion of these two approaches, we propose a NN extension of LC with a variational autoencoder that performs the variational Bayesian estimation of a state-space model and dimensionality reduction by autoencoding. Despite being a NN model that performs single-stage estimation of parameters, our model has excellent interpretability and the ability to forecast with confidence intervals, as with the BSS models, without using Markov chain Monte Carlo methods.

关键词： Lee-Carter model state-space model variational autoencoder variational Bayesian inference

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ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks

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JOURNAL OF CHEMINFORMATICS 2023年第1期15卷 91-91页

作者： Hu, Chao Li, Song Yang, Chenxing Chen, Jun Xiong, Yi Fan, Guisheng Liu, Hao Hong, Liang Shanghai Jiao Tong Univ Sch Phys & Astron Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Nat Sci Shanghai 200240 Peoples R China Shanghai Matwings Technol Co Ltd Shanghai 200240 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Life Sci & Biotechnol Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Zhangjiang Inst Adv Study Shanghai 201203 Peoples R China

In recent years, drug design has been revolutionized by the application of deep learning techniques, and molecule generation is a crucial aspect of this transformation. However, most of the current deep learning approaches do not explicitly consider and apply scaffold hopping strategy when performing molecular generation. In this work, we propose ScaffoldGVAE, a variational autoencoder based on multi-view graph neural networks, for scaffold generation and scaffold hopping of drug molecules. The model integrates several important components, such as node-central and edge-central message passing, side-chain embedding, and Gaussian mixture distribution of scaffolds. To assess the efficacy of our model, we conduct a comprehensive evaluation and comparison with baseline models based on seven general generative model evaluation metrics and four scaffold hopping generative model evaluation metrics. The results demonstrate that ScaffoldGVAE can explore the unseen chemical space and generate novel molecules distinct from known compounds. Especially, the scaffold hopped molecules generated by our model are validated by the evaluation of GraphDTA, LeDock, and MM/GBSA. The case study of generating inhibitors of LRRK2 for the treatment of PD further demonstrates the effectiveness of ScaffoldGVAE in generating novel compounds through scaffold hopping. This novel approach can also be applied to other protein targets of various diseases, thereby contributing to the future development of new drugs. Source codes and data are available at https://***/ecust-hc/ScaffoldGVAE.

关键词： Drug design Molecule generation Scaffold hopping variational autoencoder Multi-view graph neural networks

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Metallogenic-Factor variational autoencoder for Geochemical Anomaly Detection by Ad-Hoc and Post-Hoc Interpretability Algorithms

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NATURAL RESOURCES RESEARCH 2023年第3期32卷 835-853页

作者： Luo, Zijing Zuo, Renguang Xiong, Yihui Zhou, Bao China Univ Geosci State Key Lab Geol Proc & Mineral Resources Wuhan 430074 Peoples R China Hubei Geol Survey Wuhan 430034 Peoples R China Hubei Geol Explorat Engn Technol Res Ctr Wuhan 430034 Peoples R China

Deep learning algorithms (DLAs) are becoming hot tools in processing geochemical survey data for mineral exploration. However, it is difficult to understand their working mechanisms and decision-making behaviors, which may lead to unreliable results. The construction of a reliable and interpretable DLA has become a focus in data-driven geoscience discovery. This study utilized a SHapley Additive exPlanations (SHAP) framework, a popular post-hoc interpretability analysis method, incorporated with the variational autoencoder (VAE) to explore the contribution of geochemical elements for multivariate geochemical anomaly recognition. The sorting of element importance obtained by SHAP tool can provide a novel view for selecting a suitable elemental association related to mineralization. Based on the metallogenic model in the southeastern Hubei Province of China, a metallogenic-factor-based VAE model was constructed using an ad-hoc interpretable modeling technique. The interpretability of the model in identifying the abnormal distribution of the element associations can be improved by constructing a hidden layer and loss function containing metallogenic regularity and key metallogenic factors. The highly anomalous areas identified by the metallogenic-factor VAE model not only contain most of the known Au deposits, but also can reasonably identify the abnormal elemental associations related to ore-forming processes under the guidance of the metallogenic regularity. According to the output visualization of the new hidden layer, and the results of receiver operating characteristic curve and success-rate curve, the metallogenic-factor VAE model exhibits satisfied interpretability and performance. The geochemical anomalies identified in this study provide critical clues for future mineral exploration.

关键词： Geochemical anomalies variational autoencoder SHapley Additive exPlanations Interpretable deep learning Metallogenic regularity Metallogenic factor

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An attempt to construct the individual model of daily facial skin temperature using variational autoencoder

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ARTIFICIAL LIFE AND ROBOTICS 2021年第4期26卷 488-493页

作者： Masaki, Ayaka Nagumo, Kent Iwashita, Yuki Oiwa, Kosuke Nozawa, Akio Aoyama Gakuin Univ Tokyo Japan

Facial skin temperature (FST) has also gained prominence as an indicator for detecting anomalies such as fever due to the COVID-19. When FST is used for engineering applications, it is enough to be able to recognize normal. We are also focusing on research to detect some anomaly in FST. In a previous study, it was confirmed that abnormal and normal conditions could be separated based on FST by using a variational autoencoder (VAE), a deep generative model. However, the simulations so far have been a far cry from reality. In this study, normal FST with a diurnal variation component was defined as a normal state, and a model of normal FST in daily life was individually reconstructed using VAE. Using the constructed model, the anomaly detection performance was evaluated by applying the Hotelling theory. As a result, the area under the curve (AUC) value in ROC analysis was confirmed to be 0.89 to 1.00 in two subjects.

关键词： Facial skin temperature Infrared thermography Deep learning Anomaly detection variational autoencoder Hotelling's theory

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Asset Pricing via the Conditional Quantile variational autoencoder

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JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2024年第2期42卷 681-694页

作者： Yang, Xuanling Zhu, Zhoufan Li, Dong Zhu, Ke Tsinghua Univ Ctr Stat Sci Dept Ind Engn Beijing Peoples R China Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Peoples R China

We propose a new asset pricing model that is applicable to the big panel of return data. The main idea of this model is to learn the conditional distribution of the return, which is approximated by a step distribution function constructed from conditional quantiles of the return. To study conditional quantiles of the return, we propose a new conditional quantile variational autoencoder (CQVAE) network. The CQVAE network specifies a factor structure for conditional quantiles with latent factors learned from a VAE network and nonlinear factor loadings learned from a "multi-head" network. Under the CQVAE network, we allow the observed covariates such as asset characteristics to guide the structure of latent factors and factor loadings. Furthermore, we provide a two-step estimation procedure for the CQVAE network. Using the learned conditional distribution of return from the CQVAE network, we propose our asset pricing model from the mean of this distribution, and additionally, we use both the mean and variance of this distribution to select portfolios. Finally, we apply our CQVAE asset pricing model to analyze a large 60-year US equity return dataset. Compared with the benchmark conditional autoencoder model, the CQVAE model not only delivers much larger values of out-of-sample total and predictive R-2's, but also earns at least 30.9% higher values of Sharpe ratios for both long-short and long-only portfolios.

关键词： Big data Conditional asset pricing model Dynamic loadings Machine learning Neural networks Nonlinear quantile factor model variational autoencoder

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Anomaly detection in facial skin temperature using variational autoencoder

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ARTIFICIAL LIFE AND ROBOTICS 2021年第1期26卷 122-128页

作者： Masaki, Ayaka Nagumo, Kent Lamsal, Bikash Oiwa, Kosuke Nozawa, Akio Aoyama Gakuin Univ Sagamihara Kanagawa Japan Kajima Corp Chofu Tokyo Japan

Facial skin temperature is a physiological index that varies with skin blood flow controlled by autonomic nervous system activity. The facial skin temperature can be remotely measured using infrared thermography, and it has recently attracted attention as a remote biomarker. For example, studies have been reported to estimate human emotions, drowsiness, and mental stress on facial skin temperature. However, it is impossible to make a machine that can discriminate all infinite physiological and psychological states. Considering the practicality of skin temperature, a machine that can determine the normal state of facial skin temperature may be sufficient. In this study, we propose a completely new approach to incorporate the concept of anomaly detection into the analysis of physiological and psychological states by facial skin temperature. In this paper, the method for separating normal and anomaly facial thermal images using an anomaly detection model was investigated to evaluate the applicability of variational autoencoder (VAE) to facial thermal images.

关键词： Facial skin temperature Thermal face image Infrared thermography Deep learning Anomaly detection variational autoencoder

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variational autoencoder based receiver for orthogonal time frequency space modulation

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DIGITAL SIGNAL PROCESSING 2021年 117卷 103170-103170页

作者： Li, Qiao Xiang, Zheng Ren, Peng Li, Wanlu Xidian Univ Sch Telecommun Engn Xian 710071 Shaanxi Peoples R China

The emerging orthogonal time frequency space (OTFS) modulation is demonstrated to offer reliable communication performance advantages over orthogonal frequency division multiplexing (OFDM) in doubly-dispersive fading channel. However, the existing embedded pilot-aided method to estimate channel impulse response (CIR) requires enormous spectral overhead to avoid the contamination of pilot symbols. In this paper, we present a variational autoencoder (VAE) based receiver for OTFS modulation that achieve a joint estimation and detection without pilot in delay-Doppler (DD) domain. The variational approach is considered to simplify the problem, and evidence lower bound (ELBO) is derived as loss function. In encoder step, an approximate posterior probability is introduced and utilized to minimize the Kullback-Leibler (KL) distance. Then we estimate CIR in decoder step and maximize the ELBO at last. From our simulation results, the proposed VAE based receiver for OTFS modulation enjoys a promising performance with other methods. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Orthogonal time frequency space modulation variational autoencoder Delay-Doppler domain Doubly dispersion

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Adversarial variational autoencoder for Top-N Recommender Systems 9

Adversarial Variational Autoencoder for Top-N Recommender Sy...

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9th IEEE International Conference on Software Engineering and Service Science (ICSESS)

作者： Zhang, Guijuan Liu, Yang Jin, Xiaoning Beijing Univ Technol Beijing Adv Innovat Ctr Future Internet Technol Beijing Peoples R China

ISBN: (纸本)9781538665657

Recommender systems play an important role in the age of mass information. They allow users to discover items that match their tastes. In this paper, we propose a novel method, called adversarial variational autoencoder, for top-N recommendation. We use generative adversarial networks to regularize variational autoencoder by imposing an arbitrary prior on the latent representation of VAE, which makes the recommendation model. We define a joint objective function as a minimization problem. Our experiments on three datasets show that the proposed model achieves high recommendation accuracy compared to other state-of-the-art models.

关键词： variational autoencoder generative adversarial networks recommender system collaborative filtering

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