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10th International Conference on Climate Informatics (CI)

作者： Sinha, Saumya Giffard-Roisin, Sophie Karbou, Fatima Deschatres, Michael Karas, Anna Eckert, Nicolas Coleou, Cecile Monteleoni, Claire Univ Colorado Boulder CO 80309 USA Univ Grenoble Alpes ISTerre Univ Savoie Mt Blanc CNRSIRDIFSTTAR Grenoble France Meteo France CNRM GAME Grenoble France Univ Grenoble Alpes Irstea Grenoble France

ISBN: (纸本)9781450388481

This work demonstrates that deep unsupervised learning holds much promise for rare event detection, even when labeled data is limited. Rare event detection is challenging for traditional supervised learning approaches due to high class-imbalance. We demonstrate the efficacy of deep unsupervised learning, in particular a variational autoencoder (VAE), when used for anomaly detection, in an application to avalanche detection in the French Alps, from satellite SAR imagery and a limited on-the-ground survey. Remarkably, our results demonstrate that supervision (i.e., access to labeled data) is only needed in the validation phase of our training pipeline, to tune a hyper-parameter: the threshold on reconstruction error of the VAE (trained in an unsupervised fashion) that will be used to designate an observation as an anomaly, i.e., an avalanche in this context. Our method outperforms previous benchmarks in avalanche detection, including supervised learning a convolutional neural network on an artificially balanced version of the same data. To the best of our knowledge, this is the first step in exploring the potential of applying deep unsupervised learning methods to detect avalanche deposits. Our proposed semi-supervised learning pipeline is also promising for real-world settings in which the data is very unbalanced and labels are limited.

关键词： unsupervised deep learning anomaly detection variational autoencoder avalanche detection satellite imaging

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variational autoencoder for Non-Negative Matrix Factorization with Exogenous Inputs Applied to Financial Data Modelling 11

Variational Autoencoder for Non-Negative Matrix Factorizatio...

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11th International Symposium on Image and Signal Processing and Analysis (ISPA)

作者： Montesdeoca, Luis Squires, Steven Niranjan, Mahesan Univ Southampton Elect & Comp Sci Southampton England

ISBN: (纸本)9781728131405

Multi-variate time series that arise in financial data, for example, are likely to be driven by underlying lower dimensional latent variables. Extracting such latent spaces can be useful in representing the data efficiently and as a means of explaining aspects of the system from which they are generated. Here, we study an extension to the variational autoencoder model specifically cast in a probabilistic setting to deal with positive valued data to extract a non-negative matrix factorization model (NMF) in a probabilistic setting (PAE-NMF). To model financial data, where information about some underlying macroeconomic system may be observed, we extend the PAE-NMF model to include exogenous variables (PAE-XNMF). We present the learning algorithm for this model and illustrate its operation on financial data of constituents of the FTSE100 index and a set of relevant macroeconomic variables. We show an example of the latent space detecting a sharp transition around the Brexit event that is not readily apparent on any of the individual time series.

关键词： Non Negative Matrix Factorization variational autoencoder Dimensionality Reduction Deep Learning Kull-back Leibler divergence Financial Data Analysis

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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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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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variational autoencoder-Based Parameter Estimation in Beam-Space OFDM Integrated Sensing and Communication

Variational Autoencoder-Based Parameter Estimation in Beam-S...

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Dehkordi, Saeid K. Hauffen, Jan C. Jaensch, Fabian Jung, Peter Caire, Giuseppe Tech Univ Berlin Berlin Germany

ISBN: (纸本)9798350310900

In this work, we propose a framework based on Deep Neural Networks (DNNs) for radar parameter estimation in an Integrated Sensing and Communication (ISAC) system employing a realistic and hardware-efficient Hybrid Digital-Analog (HDA) architecture that uses Orthogonal Frequency Division Multiplexing (OFDM) digital modulation. This framework takes raw signals as input and utilizes a variational autoencoder (VAE) followed by a regression network to output the spatial extent and location of extended targets. Owing to the HDA setup, the co-located radar receiver uses multi-block measurements to perform parameter estimation. The proposed solution is motivated as a remedy for the increasing computational complexity associated with high-resolution extended target estimation in multi-carrier digital modulations such as OFDM. In addition, it is well known that off-grid delay-Doppler shifts which are present in the doubly-dispersive channels in the high mobility scenarios expected in ISAC applications, exhibit leakage effects that adversely affect the parameter estimation performance. Due to the data-centric nature of the proposed method, these effects can be learned by the network. We provide numerical results to showcase the effectiveness of the proposed framework for parameter estimation.(1)

关键词： integrated sensing and communication OFDM variational autoencoder parameter estimation

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variational autoencoder-based neural electrocardiogram synthesis trained by FEM-based heart simulator

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CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2024年第1期5卷 19-28页

作者： Nishikimi, Ryo Nakano, Masahiro Kashino, Kunio Tsukada, Shingo NTT Commun Sci Labs Atsugi Japan NTT Basic Res Labs Atsugi Japan NTT Commun Sci Labs 3-1Morinosato Wakamiya Atsugi Kanagawa 2430198 Japan

BACKGROUND For comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high -cost and limited environment using supercomputers owing to the massive computation. OBJECTIVE The purpose of this study is to develop an efficient method for synthesizing 12 -lead ECG signals from cardiac parameters. METHODS The proposed method is based on a variational autoencoder (VAE). The encoder and decoder of the VAE are conditioned by the cardiac parameters so that it can model the relationship between the ECG signals and the cardiac parameters. The training data are produced by a comprehensive, finite element method (FEM)-based heart simulator. New ECG signals can then be synthesized by inputting the cardiac parameters into the trained VAE decoder without relying on enormous computational resources. We used 2 metrics to evaluate the quality of ECG signals synthesized by the proposed model. RESULTS Experimental results showed that the proposed model synthesized adequate ECG signals while preserving empirically important feature points and the overall signal shapes. We also explored the optimal model by varying the number of layers and the size of latent variables in the proposed model that balances the model complexity and the simulation accuracy. CONCLUSION The proposed method has the potential to become an alternative to computationally expensive FEMbased heart simulators. It is able to synthesize ECGs from various cardiac parameters within seconds on a personal laptop computer.

关键词： variational autoencoder Electrocardiogram Synthesis Cardiac parameters Finite element method

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Modified variational autoencoder and Attention Mechanism-Based Long Short-Term Memory for Detecting Intrusions in Imbalanced Network Traffic

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SECURITY AND PRIVACY 2025年第3期8卷

作者： Oluwadamilare Harazeem Abdulganiyu Taha Ait Tchakoucht Yakub Kayode Saheed Meryam El Mouhtadi Ahmed El Hilali Alaoui School of Digital Engineering and Artificial Intelligence EuroMed University of Fes UEMF Fez Morocco Department of Information Science University of Maiduguri Maiduguri Nigeria School of IT and Computing American University of Nigeria Yola Nigeria

The internet and communication industries have grown at a very quick pace, which has caused a massive increase in the volume of data and network size. This surge has given rise to a multitude of new attacks, posing substantial challenges for network security in effectively identifying breaches. To counteract these threats, intrusion detection systems (IDS) have been created, utilizing technology to scrutinize, monitor, and analyze network traffic and ensure the conservation of availability, confidentiality, and integrity. In networks with imbalanced traffic, malicious cyber-attacks can easily go unnoticed within large volumes of regular data. This proficiency in concealing their presence poses a formidable obstacle for Network IDS in accurately and promptly detecting such threats. Despite extensive research efforts, conventional IDS proposed models are faced with persistent issues of enhancing detection accuracy and lowering false alarm rates, identifying emerging rare and zero-day intrusion types. Previous research has also emphasized the problem of uneven distribution in network traffic, potentially leading to the misclassification of attacks. As a solution to these problems, this study proposed a multi-model architecture that leverages attention mechanism-based long short-term memory (AM-LSTM) and class-wise focal loss-based variational autoencoder (CWFL-VAE), which both aimed to detect various forms of attacks, including rare or zero-day attacks, while reducing false alarm rates and computational complexity. CWFL-VAE was employed to handle imbalanced network traffic, focusing on minority classes to address the issue of misclassification; AM-LSTM was used for classification, while the Adam gradient descent technique was employed to optimize the model. The proposed system performance was assessed using two datasets: NSL-KDD, a benchmark dataset with skewed network traffic distribution, and CSE-CIC-IDS2018, featuring network traffic that is approximately 83% benign

关键词： attention mechanism focal loss imbalance network traffic intrusion detection long short-term memory (LSTM) 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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Cone beam computed tomography enhancement using feature-embedded variational autoencoder with a perceptual loss function

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2023年第5期33卷 1767-1778页

作者： Joseph, Jiffy Singh, Aparajit Pournami, P. N. Jayaraj, P. B. Puzhakkal, Niyas Natl Inst Technol Calicut Dept Comp Sci & Engn Calicut Kerala India MVR Canc Ctr & Res Inst Dept Med Phys Calicut Kerala India Natl Inst Technol Calicut Dept Comp Sci & Engn Calicut 673601 Kerala India

Image-Guided Radiation Therapy (IGRT) is a cancer treatment method. IGRT usually follows a fragmented treatment track. The weight loss of a patient causes the first treatment plan to be flawed. Medical experts can avert retaking Fan Beam Computed Tomography (FBCT) images before each session of the therapy if the Cone Beam Computed Tomography (CBCT), which is mounted on a Medical Linear Accelerator (LINAC), gives high-quality images. Rather than improvising the CBCT machine, deep-learning techniques can be used to enhance the CBCT images. This paper proposes a Feature-Embedded variational autoencoder (FE-VAE) for CBCT image enhancement. A feature embedding is incorporated using a MultiResUNet-based feature extractor to preserve structural information in the input image. A perceptual loss function, which is the product of Mean Absolute Error (MAE) and inverse Structural Similarity Index Measure (SSIM), is proposed in this work. The model is trained using Head-and-Neck (H&N) CBCT-FBCT pairs of 59 cancer patients. Our proposed architecture successfully generates images much closer to the ground truth FBCT images. The model gives an average peak signal-to-noise ratio of 32.89 dB, mean squared error of 297.36, MAE of 32.37 HU, and SSIM of 0.99. The trained model gave the least deviated SSIM and MAE, implying that the model is well-optimised. The experimentation with different losses proves the prominence of the proposed loss function. The visual quality of the results obtained is also comparable with the ground truth. The results indicate that the model produces enhanced CBCT images. Hence, the proposed FE-VAE is useful in fractionated radiotherapy to minimise the number of scanning sessions for replanning the dosimetry.

关键词： cone beam computed tomography fan beam computed tomography image-guided radiation therapy perceptual loss function variational autoencoder

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

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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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