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Spatial-Aware Hyperspectral Nonlinear Unmixing autoencoder With Endmember Number Estimation

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 20-41页

作者： Shahid, Kazi Tanzeem Schizas, Ioannis D. Univ Texas Arlington Dept Elect Engn Arlington TX 76019 USA

In this article, we develop a novel fully unsupervised autoencoder-based scheme for nonlinear hyperspectral pixel unmixing. A unique approach is derived where high noise and unresponsive pixels are accounted for, by a unique averaging approach based on spatially aware filters built using radial basis function (RBF) kernels. A novel technique is implemented via calculating rank-equivalent kernel covariance matrices in order to estimate the unknown number of endmembers contributing to the data. Utilization of spatial information is done via RBF-based weighted averaging, which is then followed by endmember estimation via K-means clustering. The RBF distances from the cluster centers are determined to measure the position of the mixed pixels in relation to the centers, which is utilized as a preliminary estimation of the abundances. The proposed framework is robust in the presence of unresponsive pixels, while highly versatile working with different nonlinear unmixing models. Extensive numerical tests establish the superiority of the novel approach with respect to state-of-the-art methods.

关键词： Hyperspectral imaging Estimation Kernel Spatial resolution Earth Recurrent neural networks Meteorology autoencoder hyperspectral imaging neural networks nonlinear unmixing radial basis function (RBF) kernel unsupervised

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Contrastive autoencoder for anomaly detection in multivariate time series

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INFORMATION SCIENCES 2022年 610卷 266-280页

作者： Zhou, Hao Yu, Ke Zhang, Xuan Wu, Guanlin Yazidi, Anis Beijing Univ Posts & Telecommun Beijing Peoples R China Norwegian Res Ctr AS Grimstad Norway Oslo Metropolitan Univ Oslo Norway Norwegian Univ Sci & Technol Trondheim Norway Oslo Univ Hosp Oslo Norway

With the proliferation of the Internet of Things, a large amount of multivariate time series (MTS) data is being produced daily by industrial systems, corresponding in many cases to life-critical tasks. The recent anomaly detection researches focus on using deep learning methods to construct a normal profile for MTS. However, without proper constraints, these methods cannot capture the dependencies and dynamics of MTS and thus fail to model the normal pattern, resulting in unsatisfactory performance. This paper proposes CAE-AD, a novel contrastive autoencoder for anomaly detection in MTS, by introducing multi -grained contrasting methods to extract normal data pattern. First, to capture the temporal dependency of series, a projection layer is employed and a novel contextual contrasting method is applied to learn the robust temporal representation. Second, the projected series is transformed into two different views by using time-domain and frequency-domain data augmentation. Last, an instance contrasting method is proposed to learn local invariant characteristics. The experimental results show that CAE-AD achieves an F1-score ranging from 0.9119 to 0.9376 on the three public datasets, outperforming the baseline methods.(c) 2022 Published by Elsevier Inc.

关键词： Anomaly detection Multivariate time series autoencoder Contrastive learning Data augmentation

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A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第1期81卷 695-714页

作者： Patel, Heena Upla, Kishor P. Sardar Vallabhbhai Natl Inst Technol Surat 395007 India

This paper addresses an approach for the classification of hyperspectral imagery (HSI). In remote sensing, the HSI sensor acquires hundreds of images with narrow and continuous spectral width in visible and near-infrared regions of the electromagnetic (EM) spectrum. Such nature of data acquisition is very useful in the classification and/or the identification of different objects present in the HSI data. However, the low-spatial resolution and large volume of HS images make it more challenging. In the proposed approach, we use an autoencoder with convolutional neural network (AECNN) for the classification of HS images. Pre-processing with autoencoder enhances the features in the HS images which helps to obtain optimized weights in the initial layers of the CNN model. Hence, shallow CNN architecture can be utilized to extract features from the pre-processed HSI data which are used further for the classification of the same. The potential of the proposed approach has been verified by conducting many experiments on various datasets. The classification results obtained using the proposed method are compared with many state-of-the-art deep learning based methods including the winner of the geoscience and remote sensing society (GRSS) Image Fusion Contest-2018 on HSI classification held at IEEE International Geoscience and Remote Sensing Symposium (IGARSS)-2018 and it shows superiority over those methods.

关键词： autoencoder CNN ERDAS Imagine Feature extraction Hyperspectral images Classification

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Personalized recommendation with knowledge graph via dual-autoencoder

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APPLIED INTELLIGENCE 2022年第6期52卷 6196-6207页

作者： Yang, Yang Zhu, Yi Li, Yun Yangzhou Univ Sch Informat Engn Yangzhou 225009 Jiangsu Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China

In the past decades, personalized recommendation systems have attracted a vast amount of attention and researches from multiple disciplines. Recently, for the powerful ability of feature representation learning, deep neural networks have achieved sound performance in the recommendation. However, most of the existing deep recommendation approaches require a large number of labeled data, which is often expensive and labor-some in applications. Meanwhile, the side information of users and items that can extend the feature space effectively is usually scarce. To address these problems, we propose a Personalized Recommendation method, which extends items' feature representations with Knowledge Graph via dual-autoencoder (short for PRKG). More specifically, we first extract items' side information from open knowledge graph like DBpedia as items' feature extension. Secondly, we learn the low-dimensional representations of additional features collected from DBpedia via the autoencoder module and then integrate the processed features into the original feature space. Finally, the reconstructed features is incorporated into the semi-autoencoder for personalized recommendations. Extensive experiments conducted on several real-world datasets validate the effectiveness of our proposed methods compared to several state-of-the-art models.

关键词： Knowledge graph autoencoder Recommendation systems

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Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder (May, 10.1007/s00521-022-07224-2, 2022)

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NEURAL COMPUTING & APPLICATIONS 2022年第18期34卷 16155-16155页

作者： Wardana, I. Nyoman Kusuma Gardner, Julian W. Fahmy, Suhaib A. Univ Warwick Sch Engn Coventry CV4 7AL W Midlands England King Abdullah Univ Sci & Technol KAUST Comp Elect & Math Sci & Engn Thuwal 23955 Saudi Arabia Politekn Negeri Bali Dept Elect Engn Bali 80364 Indonesia

A key challenge in building machine learning models for time series prediction is the incompleteness of the datasets. Missing data can arise for a variety of reasons, including sensor failure and network outages, resulting in datasets that can be missing significant periods of measurements. Models built using these datasets can therefore be biased. Although various methods have been proposed to handle missing data in many application areas, more air quality missing data prediction requires additional investigation. This study proposes an autoencoder model with spatiotemporal considerations to estimate missing values in air quality data. The model consists of one-dimensional convolution layers, making it flexible to cover spatial and temporal behaviours of air contaminants. This model exploits data from nearby stations to enhance predictions at the target station with missing data. This method does not require additional external features, such as weather and climate data. The results show that the proposed method effectively imputes missing data for discontinuous and long-interval interrupted datasets. Compared to univariate imputation techniques (most frequent, median and mean imputations), our model achieves up to 65% RMSE improvement and 20–40% against multivariate imputation techniques (decision tree, extra-trees, k-nearest neighbours and Bayesian ridge regressors). Imputation performance degrades when neighbouring stations are negatively correlated or weakly correlated.

关键词： Air pollutant autoencoder Convolutional layer Missing data Spatiotemporal

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RffAe-S: autoencoder Based on Random Fourier Feature With Separable Loss for Unsupervised Signal Modulation Clustering

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第11期18卷 7910-7919页

作者： Bai, Jing Wang, Yiran Xiao, Zhu Alazab, Mamoun Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Guangdong Lab Artificial Intelligence & Digital E Shenzhen 518060 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Charles Darwin Univ Casuarina NT 0811 Australia

Unsupervised signal modulation clustering is becoming increasingly important due to its application in the dynamic spectrum access process of 5G wireless communication and threat detection at the physical layer of Internet of Things. The need for better clustering results makes it a challenge to avoid feature drift and improve feature separability. This article proposes a novel separable loss function to address the issue. Besides, the high-level semantic properties of modulation types make it difficult for networks to extract their features. An autoencoder structure based on the random Fourier feature (RffAe) is proposed to simulate the demodulation process of unknown signals. Combined with the separable loss of RffAe (RffAe-S), it has excellent feature extraction ability. Great experiments were carried out on RADIOML 2016.10 A and RADIOML 2016.10 B. Experimental evaluations on these datasets show that our approach RffAe-S achieves state-of-the-art results compared to classical and the most relevant deep clustering methods.

关键词： Modulation Clustering methods Feature extraction Linear programming Informatics Image reconstruction Semantics 5G wireless communication autoencoder Internet of Things (IoT) random Fourier feature (RffAe) signal modulation clustering

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LSTM-autoencoder Deep Learning Technique for PAPR Reduction in Visible Light Communication

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IEEE ACCESS 2022年 10卷 113028-113034页

作者： Mohamed, Abdelfatah Eldien, Adly S. Tag Fouda, Mostafa M. Saad, Reham S. Benha Univ Fac Engn Shoubra Dept Elect Engn Cairo 11672 Egypt Idaho State Univ Coll Sci & Engn Dept Elect & Comp Engn Pocatello ID 83209 USA

Visible light communication (VLC) is a relatively new wireless communication technology that allows for high data rate transfer. Because of its capability to enable high-speed transmission and eliminate inter-symbol interference, orthogonal frequency division multiplexing (OFDM) is widely employed in VLC. Peak to average power ratio (PAPR) is an issue that impacts the effectiveness of OFDM systems, particularly in VLC systems, because the signal is distorted by the nonlinearity of light-emitting diodes (LEDs). The proposed method Long Short Term Memory-autoencoder (LSTM-AE) uses an autoencoder as well as an LSTM to learn a compact representation of an input, allowing the model to handle variable length input sequences as well as predict or produce variable length output sequences. This study compares the suggested model with various PAPR reduction strategies to demonstrate that it offers a superior improvement in PAPR reduction of the transmitted signal while maintaining BER. Also, this model provides a flexible compromisation between PAPR and BER.

关键词： Peak to average power ratio OFDM Decoding Symbols Computer architecture Visible light communication Microprocessors Encoding Deep learning Recurrent neural networks autoencoder BER CCDF deep learning LSTM OFDM PAPR RNN VLC

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Zero-day Ransomware Attack Detection using Deep Contractive autoencoder and Voting based Ensemble Classifier

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APPLIED INTELLIGENCE 2022年第12期52卷 13941-13960页

作者： Zahoora, Umme Rajarajan, Muttukrishnan Pan, Zahoqing Khan, Asifullah Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad 45650 Pakistan City Univ London Sch Math Comp Sci & Engn London EC1V 0HB England Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Pakistan Inst Engn & Appl Sci Ctr Math Sci Islamabad 45650 Pakistan Pakistan Inst Engn & Appl Sci PIEAS Artificial Intelligence Ctr PAIC Islamabad 45650 Pakistan

Ransomware attacks are hazardous cyber-attacks that use cryptographic methods to hold victims' data until the ransom is paid. Zero-day ransomware attacks try to exploit new vulnerabilities and are considered a severe threat to existing security solutions and internet resources. In the case of zero-day attacks, training data is not available before the attack takes place. Therefore, we exploit Zero-shot Learning (ZSL) capabilities that can effectively deal with unseen classes compared to the traditional machine learning techniques. ZSL is a two-stage process comprising of: Attribute Learning (AL) and Inference Stage (IS). In this regard, this work presents a new Deep Contractive autoencoder based Attribute Learning (DCAE-ZSL) technique as well as an IS method based on Heterogeneous Voting Ensemble (DCAE-ZSL-HVE). In the proposed DCAE-ZSL approach, Contractive autoencoder (CAE) is employed to extract core features of known and unknown ransomware. The regularization term of CAE helps in penalizing the classifier's sensitivity against the small dissimilarities in the latent space. On the other hand, in case of the IS, four combination rules Global Majority (GM), Local Majority (LM), Cumulative Vote-against based Global Majority (CVAGM), Cumulative Vote-for based Global Majority (CVFGM) are utilized to find the final prediction. It is empirically shown that in comparison to conventional machine learning techniques, models trained on contractive embedding show reasonable performance against zero-day attacks. Furthermore, it is shown that the exploitation of these core features through the proposed voting based ensemble (DCAE-ZSL-HVE) has demonstrated significant improvement in detecting zero-day attacks (recall = 0.95) and reducing False Negative (FN = 6).

关键词： Zero-shot Learning Zero-day Attack Ransomware Deep Learning autoencoder Ensemble Classification

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Constrained autoencoder-Based Pulse Compressed Thermal Wave Imaging for Sub-Surface Defect Detection

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IEEE SENSORS JOURNAL 2022年第18期22卷 17335-17342页

作者： Kaur, Kirandeep Mulaveesala, Ravibabu Mishra, Priyanka Indian Inst Technol Ropar Dept Elect Engn Infrared Imaging Lab Rupnagar 140001 India

Non-destructive testing & evaluation techniques play an essential role in ensuring safety of materials in operation at various industry sectors. Pulse compressed favourable thermal wave imaging is one of the widely used non-destructive testing techniques due to its excellent noise rejection capabilities. However, the high dimensional thermal imaging data needs to be encoded into lossless compressed form to highlight the hidden defects inside the materials. This paper proposes a novel constrained and regularized autoencoder based thermography approach for sub-surface defect detection in a mild steel specimen. Certain properties such as non-correlation of encoded data, weight orthogonality, and weights with unit norm length have been highlighted which are non-existent in linear autoencoders but are responsible for better defect detection inside the materials inspected by frequency modulated thermal wave imaging. Novel constraints are formulated for autoencoder cost function to incorporate these significant properties. The proposed approach is able to provide better defect detection, in terms of signal to noise ratio of defects, than linear autoencoder as well as traditional principal component thermography approach. Also, non-correlation of encoded data is found to be the most significant factor in achieving better defect detection followed by properties ensuring weight orthogonality and weights with unit norm length.

关键词： autoencoder deep learning frequency modulated thermal wave imaging (FMTWI) non-destructive testing & evaluation (NDT&E)

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Quality-relevant feature extraction method based on teacher-student uncertainty autoencoder and its application to soft sensors

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INFORMATION SCIENCES 2022年 592卷 320-339页

作者： Lu, Yusheng Jiang, Chao Yang, Dan Peng, Xin Zhong, Weimin East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Supervised representation learning based on the teacher-student framework can extract quality-related features for soft sensors, in which the teacher network extracts representation information for the student network as supervision information. In traditional applications, the teacher network is heavy and is difficult to train, so the teacher network is conventionally pre-trained. However, the pre-training of the teacher network is unnecessary if the training process is not complicated so that it is meaningful to joint optimize the teacher-student network. In our application, the teacher-student framework is used to extract quality-related representation information for soft sensors. The objective is to maximize the mutual information of representation information and supervision information, in which the inconsistency of distributions between observed information and supervisory information is modeled as isotropic Gaussian noise. The objective is decoupled through analysis under some approximate assumptions so that the alternative iteration method can be used to update the parameters of the model. The proposed quality-related feature extraction method is applied to soft sensors combined with a traditional just-in-time learning method. Our experiments show that the prediction performance of our representation extraction method is better than other existing representation extraction algorithms. (C) 2022 Published by Elsevier Inc.

关键词： autoencoder Representation learning Soft sensor Teacher-student network

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