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Graph Regularized encoder-Decoder Networks for Image Representation Learning

IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 3124-3136页

作者： Yang, Shijie Li, Liang Wang, Shuhui Zhang, Weigang Huang, Qingming Tian, Qi Univ Chinese Acad Sci UCAS Sch Comp Sci & Technol Beijing 101408 Peoples R China UCAS Key Lab Big Data Min & Knowledge Management Beijing 101408 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Beijing 100049 Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

Image representation learning with encoder-decoder networks plays a fundamental role in multimedia processing. Recent findings show that traditional encoder-decoders can be negatively affected by small visual perturbations. The learned non-smooth feature embedding cannot guarantee to capture semantic-meaningful geometric distance between visually-similar image samples. Inspired by manifold learning, we propose a graph regularized encoder-decoder network, which can preserve local geometric information of the code embedding space. More discriminative feature embedding is learnt to attain both high-level image semantic and neighbor relationship of image clusters. The proposed graph regularizer is formulated upon multi-layer perceptions. It uses the local invariance principle to explicitly reconstruct the geometric similarity graph. Theoretical analysis is provided to show the connection between our deep regularizer and traditional graph Laplacian regularizer. Practically, the network complexity is alleviated by anchor based bipartite graph, and this leverages our method into large scale scenario. Experimental evaluations show the comparable results of the proposed method with state-of-the-art models on different tasks.

关键词： Laplace equations Visualization Manifolds Image reconstruction Task analysis Decoding Semantics auto-encoder encoder-decoder graph regularizer image representation learning

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A Unified Deep Learning Anomaly Detection and Classification Approach for Smart Grid Environments

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2021年第2期18卷 1137-1151页

作者： Siniosoglou, Ilias Radoglou-Grammatikis, Panagiotis Efstathopoulos, Georgios Fouliras, Panagiotis Sarigiannidis, Panagiotis Univ Western Macedonia Dept Elect & Comp Engn Kozani 50100 Greece 0 Infin Ltd Res & Dev London E6 2JG England Univ Macedonia Dept Appl Informat Thessaloniki 54636 Greece

The interconnected and heterogeneous nature of the next-generation Electrical Grid (EG), widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can also raise domino effects against other Critical Infrastructures (CIs). In this paper, we present an Intrusion Detection System (IDS) specially designed for the SG environments that use Modbus/Transmission Control Protocol (TCP) and Distributed Network Protocol 3 (DNP3) protocols. The proposed IDS called MENSA (anoMaly dEtection aNd claSsificAtion) adopts a novel autoencoder-Generative Adversarial Network (GAN) architecture for (a) detecting operational anomalies and (b) classifying Modbus/TCP and DNP3 cyberattacks. In particular, MENSA combines the aforementioned Deep Neural Networks (DNNs) in a common architecture, taking into account the adversarial loss and the reconstruction difference. The proposed IDS is validated in four real SG evaluation environments, namely (a) SG lab, (b) substation, (c) hydropower plant and (d) power plant, solving successfully an outlier detection (i.e., anomaly detection) problem as well as a challenging multiclass classification problem consisting of 14 classes (13 Modbus/TCP cyberattacks and normal instances). Furthermore, MENSA can discriminate five cyberattacks against DNP3. The evaluation results demonstrate the efficiency of MENSA compared to other Machine Learning (ML) and Deep Learning (DL) methods in terms of Accuracy, False Positive Rate (FPR), True Positive Rate (TPR) and the F1 score.

关键词： Computer crime Protocols Intrusion detection Anomaly detection Substations Engines Decision trees Anomaly detection auto-encoder cybersecurity generative adversarial network deep learning machine learning modbus smart grid

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Brain tumor classification using deep convolutional autoencoder-based neural network: multi-task approach

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第13期80卷 19909-19929页

作者： Bashir-Gonbadi, Fatemh Khotanlou, Hassan Bu Ali Sina Univ Dept Comp Engn RIV Lab Hamadan Hamadan Iran

Diagnosis, detection and classification of tumors, in the brain MRI images, are important because misdiagnosis can lead to death. This paper proposes a method that can diagnose brain tumors in the MRI images and classify them into 5 categories using a Convolutional Neural Network (CNN). The proposed network uses a Convolutional auto-encoder Neural Network (CANN) to extract and learn deep features of input images. Extracted deep features from each level are combined to make desirable features and improve results. To classify brain tumor into three categories (Meningioma, Glioma, and Pituitary) the proposed method was applied on Cheng dataset and has reached a considerable performance accuracy of 99.3%. To diagnosis and grading Glioma tumors, the proposed method was applied on IXI and BraTS 2017 datasets, and to classify brain images into six classes including Meningioma, Pituitary, Astrocytoma, High-Grade Glioma, Low-Grade Glioma and Normal images (No tumor), the all datasets including IXI, BraTS2017, Cheng and Hazrat-e-Rassol, was used by the proposed network, and it has reached desirable performance accuracy of 99.1% and 98.5%, respectively.

关键词： auto-encoder Brain tumor Deep learning Convolutional neural network

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Deep-Learned Air-Coupled Ultrasonic Sonar Image Enhancement and Object Localization

Deep-Learned Air-Coupled Ultrasonic Sonar Image Enhancement ...

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IEEE Sensors Conference

作者： Schulte, Stefan Allevato, Gianni Haugwitz, Christoph Kupnik, Mario Tech Univ Darmstadt Measurement & Sensor Technol Grp Darmstadt Germany

ISBN: (纸本)9781665484640

Air-coupled ultrasonic phased arrays are a complement to existing lidar-, camera- and radar-based sensors for object detection and spatial imaging. These in-air sonar systems typically use conventional beamforming (CBF) for high-frame rate image formation. Consequently, in real-world multi-target environments, the unique identification of reflectors is a challenging task due to the array-specific point spread function (PSF). Therefore, we present a neural auto-encoder network based on Xception for removing the PSF characteristics from CBF images and estimating the number of reflectors. Based on this information, the reflector coordinates are extracted by Gaussian mixture model clustering. We train and test the architecture on simulated and randomized multi-target CBF images. The performance is evaluated in terms of the localization precision, reflector count error and the angular resolution obtained. The preliminary results show a low mean error for the localization (-0.61 degrees, -3mm) and an accuracy of 83% for the reflector count estimation. The angular resolution of the given array can be improved from 14 degrees to 2 degrees. Overall, we highlight the potential of state-of-the-art auto-encoder networks, typically used for optical images, for CBF image enhancement and the combination with clustering for target localization.

关键词： In-air sonar phased array deconvolution auto-encoder neural network Xception

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Deep SAR Imaging and Motion Compensation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 2232-2247页

作者： Pu, Wei UCL Dept Elect & Elect Engn London WC1E 6BT England

Compressive sensing (CS) and matrix sensing (MS) techniques have been applied to the synthetic aperture radar (SAR) imaging problem to reduce the sampling amount of SAR echo using the sparse or low-rank prior information. To further exploit the redundancy and improve sampling efficiency, we take a different approach, wherein a deep SAR imaging algorithm is proposed. The main idea is to exploit the redundancy of the backscattering coefficient using an auto-encoder structure, wherein the hidden latent layer in auto-encoder has lower dimension and less parameters than the backscattering coefficient layer. Based on the auto-encoder model, the parameters of the auto-encoder structure and the backscattering coefficient are estimated simultaneously by optimizing the reconstruction loss associated with the down-sampled SAR echo. In addition, in order to meet the practical application requirements, a deep SAR motion compensation algorithm is proposed to eliminate the effect of motion errors on imaging results. The effectiveness of the proposed algorithms is verified on both simulated and real SAR data.

关键词： Synthetic aperture radar Radar polarimetry Backscatter Radar imaging Imaging Azimuth Two dimensional displays Synthetic aperture radar (SAR) auto-encoder deep learning convolutional neural network (CNN) motion compensation

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Deep Learning-Based Intrusion Detection Systems: A Systematic Review

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IEEE ACCESS 2021年 9卷 101574-101599页

作者： Lansky, Jan Ali, Saqib Mohammadi, Mokhtar Majeed, Mohammed Kamal Karim, Sarkhel H. Taher Rashidi, Shima Hosseinzadeh, Mehdi Rahmani, Amir Masoud Univ Finance & Adm Fac Econ Studies Dept Comp Sci & Math Prague 10100 Czech Republic Sultan Qaboos Univ Coll Econ & Polit Sci Dept Informat Syst Muscat 123 Oman Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil 44001 Iraq Tishk Int Univ Dept Informat Technol Erbil 44024 Iraq Univ Halabja Coll Sci Dept Comp Halabja 46018 Iraq Univ Human Dev Dept Comp Sci Sulaymaniyah 07786 Iraq Gachon Univ Pattern Recognit & Machine Learning Lab Seongnam 13120 South Korea Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Yunlin 64002 Taiwan

Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations' data and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted.

关键词： Intrusion detection Deep learning Feature extraction Security Machine learning Anomaly detection Recurrent neural networks Intrusion detection auto-encoder recurrent neural network Boltzmann machine CNN

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Mango wine making process optimization based on artificial intelligence deep learning technology

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EXPERT SYSTEMS 2024年第6期41卷 e13032-e13032页

作者： Hua Xubin Lin Qiao Gong Fayong Cai Li Liu Junhua Xichang Univ Xichang 615000 Sichuan Peoples R China Panxi Crops Res & Utilizat Key Lab Sichuan Prov Xichang Sichuan Peoples R China Liangshan Tianmeiyi Agr Sci & Technol Co Ltd Xichang Sichuan Peoples R China

This paper combines artificial intelligence deep learning technology to optimize the wine making process of mango wine. Moreover, in view of the shortcomings of traditional electronic nose data processing methods, a deep learning method based on SSAE-BPNN is proposed for electronic nose data processing. In addition, according to the characteristics of automatic learning features, this paper uses a deep learning method based on SSAE-BPNN to simplify the process of traditional data processing methods. Finally, this paper constructs an electronic nose system that can be used to identify mango wine making characteristics, and enhances the effect of electronic nose recognition through deep learning. Through the analysis, it can be seen that the mango wine making process optimization method based on artificial intelligence deep learning technology proposed in this paper has a certain effect, and it has optimized the traditional mango wine making process.

关键词： auto-encoder deep learning electronic nose system optimization SSAE-BPNN

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Advancing spatiotemporal forecasts of CO2 plume migration using deep learning networks with transfer learning and interpretation analysis

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INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL 2024年 132卷

作者： Fan, Ming Wang, Hongsheng Zhang, Jing Hosseini, Seyyed A. Lu, Dan Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 38831 USA Univ Texas Austin Bur Econ Geol Austin TX 78758 USA Emory Univ Dept Comp Sci Atlanta GA 30322 USA

Accurate and timely forecasts of CO2 plume distribution throughout the injection and post -injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network -based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, auto -encoder (AE)-LSTM and encoder -Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO2 plumes based on 108 reservoir simulations over a 30 -year injection and a 30year post -injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO2 plume migration, achieving R2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient -based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post -injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. The methods proposed in our work enhances the performance and interpretability of CO2 plume migration forecasts, thereby facilitating informed decision -making throughout the entire lifecycle of GCS applications.

关键词： encoder-Decoder ConvLSTM networks auto-encoder Explainable machine learning Transfer learning Geologic carbon storage

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A reusable decoder network penalized by smooth group lasso and its applications to large-scale fault diagnosis of machinery

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CONTROL ENGINEERING PRACTICE 2024年 153卷

作者： Zhang, Zhiqiang He, Hongji Xu, Shuiqing Yin, Lisheng Dong, Xueping Hefei Univ Technol Sch Elect Engn & Automat Hefei 230009 Peoples R China

Representation learning approaches have achieved great success in fault diagnosis of large-scale mechanical data, among which the popular auto-encoder method has developed a series of effective variants. In the existing variants, the encoder network is re-employed to encode feature representations of the data, while the decoder network is directly discarded after training, leading to a regrettable waste of computational resources. Instead of proposing advanced variants of the auto-encoder, this paper explicitly penalizes the decoder network with group lasso, thereby transforming waste into treasure. Specifically, the group lasso constrains the column vectors of the decoder network's weight matrix at the group level, making them reusable for feature selection. Moreover, a smooth function is utilized to approximate the group lasso to prevent numerical oscillations when computing the gradients. The simulated data and experimental gear data are sequentially used to verify the effectiveness of the smooth group lasso through investigations on two representative auto-encoder variants. The results show that the decoder network penalized by smooth group lasso can be re-utilized to guide selection of a subset of key features for training a classifier, exhibiting an extraordinary feature selection capability.

关键词： Representation learning auto-encoder Large-scale fault diagnosis Smooth group lasso Feature selection

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Extracting visual features for personalized recommendation using autoencoder

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Procedia Computer Science 2024年 246卷 3634-3643页

作者： Aymen Ben Hassen Sonia Ben Ticha Anja Habacha Chaibi RIADI Laboratory National School of Computer Science University of Manouba Tunisia

In recent years, product images have garnered increasing interest within recommendation systems, as the visual appearance of products strongly influences users’ decision. The primary goal of personalized recommendation systems is to provide suggestions that reflect with the individual preferences of each user. Recently, deep learning models have demonstrated remarkable performance, revealing a strong potential in analyzing visual features. This paper present an innovative approach that utilizes item images to construct a personalized model for users. The approach involves employing autoencoder for feature extraction of visual features in item images. Subsequently, these latent features are correlated with user preferences to establish the personalized model, which is then integrated into a collaborative filtering (CF) algorithm for recommendation purposes. The effectiveness of the proposed approach is assessed through experiments on two substantial real datasets, encompassing fashion items from *** and movies from the MovieLens dataset. A comparative analysis with alternative collaborative filtering-based methods is conducted to validate our results.

关键词： Visual features Deep Learning auto-encoder Features Extraction Dimensionality reduction Recommender Systems Personalized User Modeling

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