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Proceedings of the 10th International Conference on Climate Informatics

作者： Saumya Sinha Sophie Giffard-Roisin Fatima Karbou Michael Deschatres Anna Karas Nicolas Eckert Cécile Coléou Claire Monteleoni University of Colorado Boulder USA ISTerre Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS IRD IFSTTAR France CNRM-GAME Météo-France Grenoble France Irstea Université Grenoble Alpes France Irstea Université Grenoble Alpes 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 variational autoencoder anomaly detection avalanche detection satellite imaging

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Towards an Effective Intrusion Detection Model Using Focal Loss variational autoencoder for Internet of Things (IoT)

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SENSORS 2022年第15期22卷 5822页

作者： Khanam, Shapla Ahmedy, Ismail Idris, Mohd Yamani Idna Jaward, Mohamed Hisham Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Univ Malaya Fac Comp Sci & Informat Technol Ctr Mobile Cloud Comp Kuala Lumpur 50603 Malaysia Monash Univ Malaysia Sch Engn Bandar Sunway 47500 Malaysia

As the range of security attacks increases across diverse network applications, intrusion detection systems are of central interest. Such detection systems are more crucial for the Internet of Things (IoT) due to the voluminous and sensitive data it produces. However, the real-world network produces imbalanced traffic including different and unknown attack types. Due to this imbalanced nature of network traffic, the traditional learning-based detection techniques suffer from lower overall detection performance, higher false-positive rate, and lower minority-class attack detection rates. To address the issue, we propose a novel deep generative-based model called Class-wise Focal Loss variational autoencoder (CFLVAE) which overcomes the data imbalance problem by generating new samples for minority attack classes. Furthermore, we design an effective and cost-sensitive objective function called Class-wise Focal Loss (CFL) to train the traditional variational autoencoder (VAE). The CFL objective function focuses on different minority class samples and scrutinizes high-level feature representation of observed data. This leads the VAE to generate more realistic, diverse, and quality intrusion data to create a well-balanced intrusion dataset. The balanced dataset results in improving the intrusion detection accuracy of learning-based classifiers. Therefore, a Deep Neural Network (DNN) classifier with a unique architecture is then trained using the balanced intrusion dataset to enhance the detection performance. Moreover, we utilize a challenging and highly imbalanced intrusion dataset called NSL-KDD to conduct an extensive experiment with the proposed model. The results demonstrate that the proposed CFLVAE with DNN (CFLVAE-DNN) model obtains promising performance in generating realistic new intrusion data samples and achieves superior intrusion detection performance. Additionally, the proposed CFLVAE-DNN model outperforms several state-of-the-art data generation and traditi

关键词： Internet of Things variational autoencoder Class-wise Focal Loss data imbalance intrusion detection Deep Neural Network

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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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variational autoencoder-enhanced deep neural network-based detection for MIMO systems

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 6卷

作者： Omondi, Gevira Olwal, Thomas O. Department of Electrical Engineering/F'SATI Tshwane University of Technology Pretoria South Africa School of Engineering University of Nairobi Nairobi Kenya

Lately, there has been a substantial surge of interest in artificial intelligence (AI) as a promising technology to tremendously elevate the efficiency of multiple-input multiple-output (MIMO) detection within wireless communication networks. AI-aided methodologies like deep neural networks (DNNs) have empowered MIMO receivers to understand intricate channel dynamics and interference contexts, ultimately leading to noteworthy enhancements in throughput and the performance of error rates. However, the techniques in existing literature face challenges in effectively reconciling accuracy and efficiency across a spectrum of channel conditions. Therefore, this study presents a state-of-the-art DNN-centric architecture for MIMO signal detection called variational autoencoder-enhanced DNN-based detection (VAE-DNN-Det), which harnesses the power of variational autoencoders (VAEs) to efficiently capture underlying data distributions, thereby enhancing the DNN's ability to adapt to complex channel scenarios and achieve near-optimal performance. Simulations are carried out to contrast the bit error rate (BER) performance of the proposed scheme with traditional methods, where the proposed VAE-DNN-Det method attains a signal-to-noise ratio gain of nearly 1 dB compared to the traditional detection methods. © 2023 The Author(s)

关键词： Artificial intelligence Deep learning Deep neural network Emerging wireless communication MIMO detection variational autoencoder

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Motor Fault Detection and Feature Extraction Using RNN-Based variational autoencoder

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IEEE ACCESS 2019年 7卷 139086-139096页

作者： Huang, Yang Chen, Chiun-Hsun Huang, Chi-Jui Natl Chiao Tung Univ Inst Informat Management Hsinchu 30010 Taiwan Natl Chiao Tung Univ Dept Mech Engn Hsinchu 30010 Taiwan

In most of the fault detection methods, the time domain signals collected from the mechanical equipment usually need to be transformed into frequency domain or other high-level data, highly relying on professional knowledge such as signal processing and fault pattern recognition. Contrary to those existing approaches, we proposed a two-stage machine learning analysis architecture which can accurately predict the motor fault modes only by using motor vibration time-domain signals without any complicated preprocessing. In the first stage, the method RNN-based VAE was proposed which is highly suitable for dimension reduction of time series data. In addition to reducing the dimension of sequential data from 150*3 to 25 dimensions, our method furthermore improves the prediction accuracy evaluated by several classification algorithms. While other dimension reduction methods such as autoencoder and variational autoencoder cannot improve the classification accuracy effectively or even decreased. It indicates that the sequential data after dimension reduction via the RNN-based VAE still can maintain the high-dimensional data information. Furthermore, the experimental results demonstrate that it can be well applied to time series data dimension reduction and shows a significant improvement of the prediction performance, even with a simple double-layer Neural Network can reach over 99% of accuracy. In the second stage, Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are used to further perform the second dimension reduction, such that the different or unknown fault modes can be clearly visualized and detected.

关键词： Motor fault detection feature extraction recurrent neural network variational autoencoder

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Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a variational autoencoder

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ENERGIES 2022年第7期15卷 2325页

作者： Posch, Stefan Goessnitzer, Clemens Ofner, Andreas B. Pirker, Gerhard Wimmer, Andreas LEC GmbH Inffeldgasse 19 A-8010 Graz Austria Graz Univ Technol Know Ctr GmbH Inffeldgasse 13 A-8010 Graz Austria Graz Univ Technol Inst Thermodynam & Sustainable Prop Syst Inffeldgasse 19 A-8010 Graz Austria

A deeper understanding of the physical nature of cycle-to-cycle variations (CCV) in internal combustion engines (ICE) as well as reliable simulation strategies to predict these CCV are indispensable for the development of modern highly efficient combustion engines. Since the combustion process in ICE strongly depends on the turbulent flow field in the cylinder and, for spark-ignited engines, especially around the spark plug, the prediction of CCV using computational fluid dynamics (CFD) is limited to the modeling of turbulent flows. One possible way to determine CCV is by applying large eddy simulation (LES), whose potential in this field has already been shown despite its drawback of requiring considerable computational time and resources. This paper presents a novel strategy based on unsteady Reynolds-averaged Navier-Stokes (uRANS) CFD in combination with variational autoencoders (VAEs). A VAE is trained with flow field data from presimulated cycles at a specific crank angle. Then, the VAE can be used to generate artificial flow fields that serve to initialize new CFD simulations of the combustion process. With this novel approach, a high number of individual cycles can be simulated in a fraction of the time that LES needs for the same amount of cycles. Since the VAE is trained on data from presimulated cycles, the physical information of the cycles is transferred to the generated artificial cycles.

关键词： internal combustion engine combustion CFD RANS simulation cycle-to-cycle varations variational autoencoder

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Automatic fault detection in grid-connected photovoltaic systems via variational autoencoder-based monitoring

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ENERGY CONVERSION AND MANAGEMENT 2024年 314卷

作者： Harrou, Fouzi Dairi, Abdelkader Taghezouit, Bilal Khaldi, Belkacem Sun, Ying King Abdullah Univ Sci & Technol KAUST Comp Elect & Math Sci & Engn CEMSE Div Thuwal 239556900 Saudi Arabia Univ Sci & Technol Oran Mohamed Boudiaf USTO MB Comp Sci Dept BP 1505 Oran 31000 Algeria Ctr Dev Energies Renouvelables CDER BP 62Route Observ Algiers 16340 Algeria Ecole Super Informat Sidi Bel LabRI SBA Lab BP 73 Sidi Bel Abbes 22016 Algeria

Anomaly detection is indispensable for ensuring the reliable operation of grid -connected photovoltaic (PV) systems. This study introduces a semi -supervised deep learning approach for fault detection in such systems. The method leverages a variational autoencoder (VAE) to extract features and identify anomalies. By training the VAE on normal operation data, a compact latent space representation is created. Abnormal observations, indicating faults, exhibit distinct feature vectors in this latent space. Multiple anomaly detection algorithms, including Isolation Forest, Epileptic Envelope, Local Outlier Factor, and One -Class SVM, are employed to discern normal and abnormal observations. This semi -supervised approach only requires fault -free data for training, without labeled faults, making it attractive in practice. A publicly available dataset, the Gridconnected PV System Faults (GPVS-Faults) dataset, which includes data from a PV plant operating in both maximum power point tracking (MPPT) and intermediate power point tracking (IPPT) switching modes, is used for evaluation. The proposed approach is assessed across various fault scenarios, such as partial shading, inverter faults, and MPPT/IPPT controller faults in boost converters. The outcomes underscore the effectiveness of VAE-based techniques in accurately identifying these faults, with accuracy rates reaching up to 92.90% for MPPT mode and 92.99% for IPPT mode, thus contributing to the robustness of fault detection in grid -connected PV systems.

关键词： Photovoltaic systems Anomaly detection variational autoencoder Unsupervised learning

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A Semi-supervised Gaussian Mixture variational autoencoder method for few-shot fine-grained fault diagnosis

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NEURAL NETWORKS 2024年 178卷 106482页

作者： Zhao, Zhiqian Xu, Yeyin Zhang, Jiabin Zhao, Runchao Chen, Zhaobo Jiao, Yinghou Harbin Inst Technol Sch Mechatron Engn Harbin 150000 Heilongjiang Peoples R China Xi An Jiao Tong Univ Sch Sci Xian 710049 Shaanxi Peoples R China

In practical engineering, obtaining labeled high-quality fault samples poses challenges. Conventional fault diagnosis methods based on deep learning struggle to discern the underlying causes of mechanical faults from a fine-grained perspective, due to the scarcity of annotated data. To tackle those issue, we propose a novel semi-supervised Gaussian Mixed variational autoencoder method, SeGMVAE, aimed at acquiring unsupervised representations that can be transferred across fine-grained fault diagnostic tasks, enabling the identification of previously unseen faults using only the small number of labeled samples. Initially, Gaussian mixtures are introduced as a multimodal prior distribution for the variational autoencoder. This distribution is dynamically optimized for each task through an expectation-maximization (EM) algorithm, constructing a latent representation of the bridging task and unlabeled samples. Subsequently, a set variational posterior approach is presented to encode each task sample into the latent space, facilitating meta-learning. Finally, semi-supervised EM integrates the posterior of labeled data by acquiring task-specific parameters for diagnosing unseen faults. Results from two experiments demonstrate that SeGMVAE excels in identifying new fine-grained faults and exhibits outstanding performance in cross-domain fault diagnosis across different machines. Our code is available at https://***/zhiqan/SeGMVAE.

关键词： Fault diagnosis Semi-supervised Gaussian Mixture variational autoencoder Fine-grained Few-shot

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Discriminative Hamiltonian variational autoencoder for accurate tumor segmentation in data-scarce regimes

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NEUROCOMPUTING 2024年 606卷

作者： Kebaili, Aghiles Lapuyade-Lahorgue, Jerome Vera, Pierre Ruan, Su Univ Rouen Normandy LITIS UR 4108 F-76000 Rouen Normandy France CLCC Henri Becquerel F-76038 Rouen Normandy France

Deep learning has gained significant attention in medical image segmentation. However, the limited availability of annotated training data presents a challenge to achieving accurate results. In efforts to overcome this challenge, data augmentation techniques have been proposed. However, the majority of these approaches primarily focus on image generation. For segmentation tasks, providing both images and their corresponding target masks is crucial, and the generation of diverse and realistic samples remains a complex task, especially when working with limited training datasets. To this end, we propose a new end-to-end hybrid architecture based on Hamiltonian variational autoencoders (HVAE) and a discriminative regularization to improve the quality of generated images. Our method provides an accurate estimation of the joint distribution of the images and masks, resulting in the generation of realistic medical images with reduced artifacts and off-distribution instances. As generating 3D volumes requires substantial time and memory, our architecture operates on a slice- by-slice basis to segment 3D volumes, capitalizing on the richly augmented dataset. Experiments conducted on two public datasets, BRATS (MRI modality) and HECKTOR (PET modality), demonstrate the efficacy of our proposed method on different medical imaging modalities with limited data.

关键词： Deep learning Data augmentation Tumor segmentation Generative modeling variational autoencoder MRI PET Hamiltonian variational autoencoder

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Fault Detection and Diagnosis in Industrial Processes with variational autoencoder: A Comprehensive Study

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SENSORS 2022年第1期22卷 227页

作者： Zhu, Jinlin Jiang, Muyun Liu, Zhong Jiangnan Univ State Key Lab Food Sci & Technol Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Food Sci & Technol Wuxi 214122 Jiangsu Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.

关键词： process monitoring deep model variational autoencoder deep reconstruction dynamic process

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