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Synthesis of Synthetic Hyperspectral Images with Controllable Spectral Variability Using a Generative Adversarial Network

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REMOTE SENSING 2023年第16期15卷 3919-3919页

作者： Palsson, Burkni Ulfarsson, Magnus O. Sveinsson, Johannes R. Univ Iceland Fac Elect & Comp Engn IS-105 Reykjavik Iceland

In hyperspectral unmixing (HU), spectral variability in hyperspectral images (HSIs) is a major challenge which has received a lot of attention over the last few years. Here, we propose a method utilizing a generative adversarial network (GAN) for creating synthetic HSIs having a controllable degree of realistic spectral variability from existing HSIs with established ground truth abundance maps. Such synthetic images can be a valuable tool when developing HU methods that can deal with spectral variability. We use a variational autoencoder (VAE) to investigate how the variability in the synthesized images differs from the original images and perform blind unmixing experiments on the generated images to illustrate the effect of increasing the variability.

关键词： hyperspectral unmixing synthetic hyperspectral images generative adversarial network variational autoencoder deep learning neural network

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variational data augmentation for a learning-based granular predictive model of

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 232卷

作者： Zhao, Tianqiao Yue, Meng Jensen, Michael Endo, Satoshi Marschilok, Amy C. Nugent, Brian Cerruti, Brian Spanos, Constantine Brookhaven Natl Lab Upton NY 11973 USA Orange & Rockland Util Inc Pearl River NY 10965 USA Con Edison Co New York NY USA

As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the efforts a utility puts towards hardening the grid, storm-induced damage to the utility assets such as cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Access to a highly granular, in space and time, outage forecasting tool with long lead times (i.e., days ahead) will enhance the efficiency of service restoration efforts. In this study, we propose to develop and implement a multi -model framework as an operational tool based on a granular and multi -day outage forecasting model using operational numerical weather prediction model forecasts and detailed component outage information. An innovative two-layered recurrent neural network, i.e., a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework and a sliding window are used to address the uneven distribution of different types of weather events and make better use of the time -series data. Case studies are performed to demonstrate the performance of the new framework.

关键词： variational data augmentation Outage prediction variational autoencoder Recurrent neural networks Weather-related outages

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An automatic classification framework for identifying type of plant leaf diseases using multi-scale feature fusion-based adaptive deep network

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期95卷

作者： Nagachandrika, Bathula Prasath, R. Joe, I. R. Praveen KCG Coll Technol Dept Comp Sci & Engn Chennai 600097 Tamil Nadu India Vellore Inst Technol Comp Sci & Engn Tiruvalam Rd Vellore 632014 Tamil Nadu India

This method of identifying plant leaf disease generally involves a large team of experts with extensive knowledge of plant diseases, and it can be expensive, time-consuming, and subjective. Hence, a novel plant leaf disease classification framework is proposed to classify the plant diseases and then take preventive measures based on the classified outcomes. The plant leaf images are collected from traditional databases. The classification of leaf diseases is done with the support of the developed Multi-scale Feature Fusion-based Adaptive Deep Network (MFF-ADNet). In this developed MFF-ADNet, two processes are carried out such as feature extraction and classification. The collected images are given to the feature extraction phase, where the Visual Geometry Group (16) (VGG16), variational autoencoder (VAE), and Visual Transformer (ViT) network are used for extracting the features. The extracted features are fused and the resultant Multi-scale fused features are provided to the input of the classification process. Here, the Adaptive Convolutional Neural Network with Attention Mechanism (CNNAM) is utilized for classifying the plant leaf diseases and the parameters are optimized using the Enhanced Gannet Optimization Algorithm (EGOA) approach. From the results, the median value is obtained for a proposed method that is more than 7.18% of MAO-MFF-ADNet, 4.11% of TSO-MFF-ADNet, 8.03% of CO-MFF-ADNet and 4.07% of GOA-MFF-ADNet. Therefore, the experimental outcome of the developed plant leaf classification model is validated over various approaches to ensure the goodness of the developed scheme.

关键词： Classification of Plant Leaf Diseases Multi-scale Feature Fusion-based Adaptive Deep Network Visual Geometry Group 16 variational autoencoder Visual Transformer Adaptive Convolutional Neural Network with Attention Mechanism Enhanced Gannet Optimization Algorithm

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Online training of deep neural networks for classification

Online training of deep neural networks for classification

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作者： Tumpach, Jiří Charles University of Prague

Deep learning is usually applied to static datasets. If used for classification based on data streams, it is not easy to take into account a non-stationarity. This thesis presents work in progress on a new method for online deep classifi- cation learning in data streams with slow or moderate drift, highly relevant for the application domain of malware detection. The method uses a combination of multilayer perceptron and variational autoencoder to achieve constant mem- ory consumption by encoding past data to a generative model. This can make online learning of neural networks more accessible for independent adaptive sys- tems with limited memory. First results for real-world malware stream data are presented, and they look promising. 1

关键词： neural network variational autoencoder online learning classification

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Energy Theft Detection Model Based on VAE-GAN for Imbalanced Dataset

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ENERGIES 2023年第3期16卷 1109-1109页

作者： Sun, Youngghyu Lee, Jiyoung Kim, Soohyun Seon, Joonho Lee, Seongwoo Kyeong, Chanuk Kim, Jinyoung Kwangwoon Univ Dept Elect Convergence Engn Seoul 01897 South Korea

Energy theft causes a lot of economic losses every year. In the practical environment of energy theft detection, it is required to solve imbalanced data problem where normal user data are significantly larger than energy theft data. In this paper, a variational autoencoder-generative adversarial network (VAE-GAN)-based energy theft-detection model is proposed to overcome the imbalanced data problem. In the proposed model, the VAE-GAN generates synthetic energy theft data with the features of real energy theft data for augmenting the energy theft dataset. The obtained balanced dataset is applied to train a detector which is designed as one-dimensional convolutional neural network. The proposed model is simulated on the practical dataset for comparing with various generative models to evaluate their performance. From simulation results, it is confirmed that the proposed model outperforms the other existing models. Additionally, it is shown that the proposed model is also very useful in the environments of extreme data imbalance for a wide variety of applications by analyzing the performance of detector according to the balance rate.

关键词： energy theft imbalanced dataset data augmentation variational autoencoder generative adversarial network

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Open-set lung sound recognition model based on conditional Gaussian capsule network and variational time-frequency feature reconstruction

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartB期87卷

作者： Zhang, Yixuan Zhang, Jingye Shi, Lukui Hebei Univ Technol Sch Artificial Intelligence Tianjin 300401 Peoples R China

Lung sound auscultation is an essential method for diagnosing lung diseases;however, most existing lung sound recognition methods fail to identify classes that are unknown in training. Thus, we proposed an open-set lung sound recognition model based on the conditional Gaussian capsule network and variational time- frequency feature reconstruction. The proposed model incorporates an inference network, a cubic encoder, an attention module, a classifier, a cubic decoder, and a generative network. First, the inference network is employed to extract the time-frequency features of lung sounds at a single time step. Then, the variational distribution of lung sounds is computed using the capsule network and optimized to approximate the Gaussian model of the class to which the sample belongs according to the labels. Time-frequency synchronized feature extraction and reconstruction are performed on the entire lung sound sample using the cubic encoder and cubic decoder. Finally, we utilize the generative network to refactor the lung sound features for open-set recognition. The proposed model was evaluated experimentally on a combined dataset using two different category assignment schemes. The results demonstrate that the proposed model achieved accuracies of 82.31% and 88.47%, respectively, thereby outperforming existing methods.

关键词： Lung sound recognition Open-set recognition variational autoencoder Feature reconstruction

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Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study

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CHAOS SOLITONS & FRACTALS 2020年 140卷 110121-110121页

作者： Zeroual, Abdelhafid Harrou, Fouzi Dairi, Abdelkader Sun, Ying Univ 20 August 1955 Fac Technol Dept Elect Engn Skikda 21000 Algeria Univ 08 May 1945 LAIG Lab Guelma 24000 Algeria 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 Signal Image & Speech Lab SIMPA Lab BP 1505 Bir El Djir 31000 Oran Algeria

The novel coronavirus (COVID-19) has significantly spread over the world and comes up with new challenges to the research community. Although governments imposing numerous containment and social distancing measures, the need for the healthcare systems has dramatically increased and the effective management of infected patients becomes a challenging problem for hospitals. Thus, accurate short-term forecasting of the number of new contaminated and recovered cases is crucial for optimizing the available resources and arresting or slowing down the progression of such diseases. Recently, deep learning models demonstrated important improvements when handling time-series data in different applications. This paper presents a comparative study of five deep learning methods to forecast the number of new cases and recovered cases. Specifically, simple Recurrent Neural Network (RNN), Long short-term memory (L STM), Bidirectional L STM (BiL STM), Gated recurrent units (GRUs) and variational autoencoder (VAE) algorithms have been applied for global forecasting of COVID-19 cases based on a small volume of data. This study is based on daily confirmed and recovered cases collected from six countries namely Italy, Spain, France, China, USA, and Australia. Results demonstrate the promising potential of the deep learning model in forecasting COVID-19 cases and highlight the superior performance of the VAE compared to the other algorithms. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Data-driven Deep learning COVID-19 Forecasting Gated recurrent units Long short-term memory Recurrent neural network variational autoencoder

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variational transformer-based anomaly detection approach for multivariate time series

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MEASUREMENT 2022年 191卷 110791-110791页

作者： Wang, Xixuan Pi, Dechang Zhang, Xiangyan Liu, Hao Guo, Chang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China Beijing Inst Spacecraft Syst Engn Beijing Peoples R China

Due to the strategic importance of satellites, the safety and reliability of satellites have become more important. Sensors that monitor satellites generate lots of multivariate time series, and the abnormal patterns in the multivariate time series may imply malfunctions. The existing anomaly detection methods for multivariate time series have poor effects when processing the data with few dimensions or sparse relationships between sequences. This paper proposes an unsupervised anomaly detection model based on the variational Transformer to solve the above problems. The model uses the Transformer's self-attention mechanism to capture the potential correlations between sequences and capture the multi-scale temporal information through the improved positional encoding and up-sampling algorithm. Then, the model comprehensively considers the extracted features through the residual variational autoencoder to perform effective anomaly detection. Experimental results on a real dataset and two public datasets show that the proposed method is superior to the mainstream and state-ofthe-art methods.

关键词： Telemetry data Transformer variational autoencoder Multivariate time series Anomaly detection

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Latent Out: an unsupervised deep anomaly detection approach exploiting latent space distribution

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MACHINE LEARNING 2023年第11期112卷 4323-4349页

作者： Angiulli, Fabrizio Fassetti, Fabio Ferragina, Luca Univ Calabria DIMES I-87036 Arcavacata Di Rende Italy

Anomaly detection methods exploiting autoencoders (AE) have shown good performances. Unfortunately, deep non-linear architectures are able to perform high dimensionality reduction while keeping reconstruction error low, thus worsening outlier detecting performances of AEs. To alleviate the above problem, recently some authors have proposed to exploit variational autoencoders (VAE) and bidirectional Generative Adversarial Networks (GAN), which arise as a variant of standard AEs designed for generative purposes, both enforcing the organization of the latent space guaranteeing continuity. However, these architectures share with standard AEs the problem that they generalize so well that they can also well reconstruct anomalies. In this work we argue that the approach of selecting the worst reconstructed examples as anomalies is too simplistic if a continuous latent space autoencoder-based architecture is employed. We show that outliers tend to lie in the sparsest regions of the combined latent/error space and propose the VAEOut and LatentOut unsupervised anomaly detection algorithms, identifying outliers by performing density estimation in this augmented feature space. The proposed approach shows sensible improvements in terms of detection performances over the standard approach based on the reconstruction error.

关键词： Anomaly detection variational autoencoder Nearest neighbor density estimation

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variational co-embedding learning for attributed network clustering

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KNOWLEDGE-BASED SYSTEMS 2023年第1期270卷

作者： Yang, Shuiqiao Verma, Sunny Cai, Borui Jiang, Jiaojiao Yu, Kun Chen, Fang Yu, Shui Univ New South Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia Deakin Univ Sch Informat Technol Burwood Vic 3125 Australia Macquarie Univ Sch Engn Macquarie Pk NSW 2109 Australia Univ Technol Sydney Data Sci Inst Ultimo NSW 2007 Australia Univ Technol Sydney Sch Comp Sci Ultimo NSW 2007 Australia

Recent developments in attributed network clustering combine graph neural networks and autoencoders for unsupervised learning. Although effective, these techniques suffer from either (a) clustering-unfriendly embedding spaces or (b) limited utilization of attribute information. To address these issues, we propose a novel model called variational Co-embedding Learning Model for Attributed Network Clustering (VCLANC), which utilizes much deeper information from the network by reconstructing both the network structure and the node attributes to perform self-supervised learning. Technically, VCLANC consists of dual variational autoencoders that co-embed nodes and attributes into the same latent space, along with a trainable Gaussian mixture prior that simultaneously performs representation learning and node clustering. To optimize the variational autoencoders and infer the latent variables of embeddings and clustering assignments, we derive a new variational lower bound that maximizes the joint likelihood of the observed network structure and node attributes. Furthermore, we also adopt a mutual distance loss on the cluster centers and a clustering assignment hardening loss on the node embeddings to strengthen clustering quality. Our experimental results on four real-world datasets demonstrate the outstanding performance of VCLANC for attributed network clustering.

关键词： Attributed network clustering Graph neural network variational autoencoder

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