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Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 138卷 265-280页

作者： Paul, Subir Kumar, D. Nagesh Indian Inst Sci Dept Civil Engn Bengaluru 560012 India Indian Inst Sci Ctr Earth Sci Bengaluru 560012 India Indian Inst Sci Interdisciplinary Ctr Water Res Bengaluru 560012 India

Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Hyperspectral remote sensing Spectral-spatial classification Mutual information autoencoder Support vector machine Random forest

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Encoder-X: Solving Unknown Coefficients Automatically in Polynomial Fitting by Using an autoencoder

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第8期33卷 3264-3276页

作者： Wang, Guojun Li, Weijun Zhang, Liping Sun, Linjun Chen, Peng Yu, Lina Ning, Xin Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China Univ Chinese Acad Sci Sch Microelect Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Beijing Key Lab Semicond Neural Network Intellige Beijing 100083 Peoples R China Shenzhen DAPU Microelect Co Ltd Shenzhen 518100 Peoples R China

Modeling, prediction, and recognition tasks depend on the proper representation of the objective curves and surfaces. Polynomial functions have been proved to be a powerful tool for representing curves and surfaces. Until now, various methods have been used for polynomial fitting. With a recent boom in neural networks, researchers have attempted to solve polynomial fitting by using this end-to-end model, which has a powerful fitting ability. However, the current neural network-based methods are poor in stability and slow in convergence speed. In this article, we develop a novel neural network-based method, called Encoder-X, for polynomial fitting, which can solve not only the explicit polynomial fitting but also the implicit polynomial fitting. The method regards polynomial coefficients as the feature value of raw data in a polynomial space expression and therefore polynomial fitting can be achieved by a special autoencoder. The entire model consists of an encoder defined by a neural network and a decoder defined by a polynomial mathematical expression. We input sampling points into an encoder to obtain polynomial coefficients and then input them into a decoder to output the predicted function value. The error between the predicted function value and the true function value can update parameters in the encoder. The results prove that this method is better than the compared methods in terms of stability, convergence, and accuracy. In addition, Encoder-X can be used for solving other mathematical modeling tasks.

关键词： Mathematical model Task analysis Convergence Taylor series Stability criteria Fitting Decoding autoencoder neural network polynomial fitting

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Regional spatio-temporal forecasting of particulate matter using autoencoder based generative adversarial network

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第5期36卷 1255-1276页

作者： Abirami, S. Chitra, P. Thiagarajar Coll Engn Dept Comp Sci Engn Madurai 625015 Tamil Nadu India

Accurate forecasting of air pollutant PM2.5(particulate matter with diameter less than 2.5 mu m) is beneficial to society. However, the non-linear spatio-temporal correlations, multi-feasible forecast values and incomplete training data due to stochasticity make it challenging for discriminative deep learning approaches to forecasting PM2.5 data. In this paper, a generative modeling approach is proposed to overcome the challenges in forecasting PM2.5 data by considering it as an ill-posed inverse problem. To strengthen its applicability, the proposed approach is theoretically validated. Furthermore, based on the proposed generative modeling, an autoencoder-based generative adversarial network (GAN) named Air-GAN is developed. Air-GAN combines a convolutional neural network- long short-term memory (CNN-LSTM) based Encoder with a conditional Wasserstein GAN (WGAN) to capture non-linear correlations in the data distribution via inverse mapping from the forecast distribution. The condition vector to conditional WGAN is the novelty in Air-GAN, which employs this inverse learning and allows the WGAN's Generator to generate accurate forecast estimates from noise distribution. The condition vector is composed of two elements: (1) the category label of the best correlated meteorological parameter with the PM2.5 data, assigned using an efficient classifier and (2) the output of the CNN-LSTM-based Encoder which is the latent representation of the forecast. The extensive evaluation of Air-GAN for predicting the real-time PM2.5 data of Delhi demonstrates its superior performance with an average inference error of 5.3 mu g/m(3), which achieves 31.7% improvement over the baseline approaches. The improved performance of Air-GAN demonstrates its efficiency to forecast stochastic PM2.5 data by generalizing to out-of-distribution data.

关键词： Spatio-temporal forecasting Particulate matter autoencoder Generative adversarial network Inverse problem CNN-LSTM

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A Secure Intrusion Detection System in Cyberphysical Systems Using a Parameter-Tuned Deep-Stacked autoencoder

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Computers, Materials & Continua 2021年第9期68卷 3915-3929页

作者： Nojood O.Aljehane College of Computer Science and Information Technology University of TabukTabukSaudi Arabia

Cyber physical systems(CPSs)are a networked system of cyber(computation,communication)and physical(sensors,actuators)elements that interact in a feedback loop with the assistance of human ***,CPSs authorize critical infrastructures and are considered to be important in the daily lives of humans because they form the basis of future smart *** utilization of CPSs,however,poses many threats,which may be of major significance for *** security issues in CPSs represent a global issue;therefore,developing a robust,secure,and effective CPS is currently a hot research *** resolve this issue,an intrusion detection system(IDS)can be designed to protect *** the IDS detects an anomaly,it instantly takes the necessary actions to avoid harming the *** this study,we introduce a new parameter-tuned deep-stacked autoencoder based on deep learning(DL),called PT-DSAE,for the IDS in *** proposed model involves preprocessing,feature extraction,parameter tuning,and ***,data preprocessing takes place to eliminate the noise present in the ***,a DL-based DSAE model is applied to detect anomalies in the *** addition,hyperparameter tuning of the DSAE takes place using a search-and-rescue optimization algorithm to tune the parameters of the DSAE,such as the number of hidden layers,batch size,epoch count,and learning *** assess the experimental outcomes of the PT-DSAE model,a series of experiments were performed using data from a sensor-based ***,a detailed comparative analysis was performed to ensure the effective detection outcome of the PT-DSAE *** experimental results obtained verified the superior performance on the applied data over the compared methods.

关键词： Cyberphysical system intrusion detection system autoencoder cybersecurity

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Seismic damage identification by graph convolutional autoencoder using adjacency matrix based on structural modes

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EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS 2024年第2期53卷 815-837页

作者： Kim, Minkyu Song, Junho Seoul Natl Univ Dept Civil & Environm Engn Seoul South Korea

Immediate post-earthquake identification of structural damage is essential to prevent the loss of structural functionality and system failure. Vibration-based damage identification methods have been widely implemented, but most tend to ignore a critical damage-sensitive feature, the spatial correlation between sensor measurements. To this end, this paper proposes near-real-time damage identification by a graph convolutional autoencoder (GCAE) based on seismic responses of the structural system. The GCAE model accurately considers the spatial correlation and structural characteristics using a weighted adjacency matrix based on the structural modes. The proposed model consists of three main parts: (1) an "encoder" that learns latent features of the input data by considering the spatial information;(2) a "graph structure decoder" that detects anomalies in spatial correlation;and (3) a "node feature decoder" that captures changes in vibration signals. The GCAE model is trained to reconstruct structural responses of the target structure and the adjacency matrix in a healthy state. The seismic damage is then identified by the structural damage index calculated based on the difference between the input and reconstructed data. As numerical investigations, the proposed method is applied to two- and three-dimensional steel frame structures. The train, validation, and test datasets are obtained by structural analyses using ground motions from the PEER-NGA strong motion database. The proposed method is verified by the near-real-time simulation using the test dataset. The results show that the proposed GCAE model can accurately identify seismic damage in near-real-time.

关键词： autoencoder damage identification graph convolutional network near-real-time seismic response structural modes

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Graph autoencoder-based unsupervised feature selection with broad and local data structure preservation

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NEUROCOMPUTING 2018年 312卷 310-323页

作者： Feng, Siwei Duarte, Marco F. Univ Massachusetts Dept Elect & Comp Engn Amherst MA 01003 USA

Feature selection is a dimensionality reduction technique that selects a subset of representative features from high-dimensional data by eliminating irrelevant and redundant features. Recently, feature selection combined with sparse learning has attracted significant attention due to its outstanding performance compared with traditional feature selection methods that ignores correlation between features. These works first map data onto a low-dimensional subspace and then select features by posing a sparsity constraint on the transformation matrix. However, they are restricted by design to linear data transformation, a potential drawback given that the underlying correlation structures of data are often non-linear. To leverage a more sophisticated embedding, we propose an autoencoder-based unsupervised feature selection approach that leverages a single-layer autoencoder for a joint framework of feature selection and manifold learning. More specifically, we enforce column sparsity on the weight matrix connecting the input layer and the hidden layer, as in previous work. Additionally, we include spectral graph analysis on the projected data into the learning process to achieve local data geometry preservation from the original data space to the low-dimensional feature space. Extensive experiments are conducted on image, audio, text, and biological data. The promising experimental results validate the superiority of the proposed method. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Unsupervised feature selection autoencoder Manifold learning Spectral graph analysis Column sparsity

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LSTMED: An uneven dynamic process monitoring method based on LSTM and autoencoder neural network

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NEURAL NETWORKS 2023年 158卷 30-41页

作者： Deng, Wenfeng Li, Yuxuan Huang, Keke Wu, Dehao Yang, Chunhua Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China

Due to the complicated production mechanism in multivariate industrial processes, different dynamic features of variables raise challenges to traditional data-driven process monitoring methods which assume the process data is static or dynamically consistent. To tackle this issue, this paper proposes a novel process monitoring method based on the long short-term memory (LSTM) and autoencoder neu-ral network (called LSTMED) for multivariate process monitoring with uneven dynamic features. First, the LSTM units are arranged in the encoder-decoder form to construct an end-to-end model. Then, the constructed model is trained in an unsupervised manner to capture long-term time dependency within variables and dominant representation of high dimensional process data. Afterward, the kernel density estimation (KDE) method is performed to determine the control limit only based on the reconstruction error from historical normal data. Finally, effective online monitoring for uneven dynamic process can be achieved. The performance and advantage of the process monitoring method proposed are explained through typical cases, including the numerical simulation and Tennessee Eastman (TE) benchmark process, and comparative experimental analysis with state-of-the-art methods.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Uneven dynamic process Long and short-term memory neural network autoencoder Process monitoring

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Person identification using autoencoder-CNN approach with multitask-based EEG biometric

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第35期83卷 83205页

作者： Bandana Das, Banee Kumar Ram, Saswat Sathya Babu, Korra Mohapatra, Ramesh Kumar Mohanty, Saraju P. SRM Univ Comp Sci & Engn Amravati Andhra Pradesh India SRM Univ Dept Elect & Commun Engn Amravati Andhra Pradesh India IIITDM Comp Sci Engn Kurnool India Natl Inst Technol Rourkela Comp Sci & Engn Rourkela India Univ North Texas Comp Sci & Engn Denton TX USA

In this research paper, we propose an unsupervised framework for feature learning based on an autoencoder to learn sparse feature representations for EEG-based person identification. autoencoder and CNN do the person identification task for signal reconstruction and recognition. Electroencephalography (EEG) based biometric system is vesting humans to recognize, identify and communicate with the outer world using brain signals for interactions. EEG-based biometrics are putting forward solutions because of their high-safety capabilities and handy transportable instruments. Motor imagery EEG (MI-EEG) is a maximum broadly centered EEG signal that exhibits a subject's motion intentions without real actions. The Proposed framework proved to be a practical approach to managing the massive volume of EEG data and identifying the person based on their different task with resting states. The experiments have been conducted on the standard publicly available motor imagery EEG dataset with 109 subjects. The highest recognition rate of 87.60% for task-based identification and 99.89% recognition rate for resting-state has been recorded using the autoencoder-CNN model. The outcomes imply that the overall performance of our proposed framework is similar or advanced to that of the state-of-the-art method. The shape is a realistic technique to control the full-size extent of EEG data and to pick out the individual based totally on their specific task.

关键词： Electroencephalography (EEG) Biometric authentication Person identification autoencoder Convolutional neural network

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Distributed denial of service attack detection using autoencoder and deep neural networks

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第3期37卷 3969-3979页

作者： Catak, Ferhat Ozgur Mustacoglu, Ahmet Fatih TUBITAK BILGEM Cyber Secur Inst TR-41400 Kocaeli Turkey Istanbul Sehir Univ Cyber Secur Engn Istanbul Turkey

Today, many companies are faced with the huge network traffics mainly consisting of the various type of network attacks due to the increased usage of the botnet, fuzzier, shellcode or network related vulnerabilities. These types of attacks are having a negative impact on the organization because they block the day-to-day operations. By using the classification models, the attacks could be identified and separated earlier. The Distributed Denial of Service Attacks (DDoS) primarily focus on preventing or reducing the availability of a service to innocent users. In this research, we focused primarily on the classification of network traffics based on the deep learning methods and technologies for network flow models. In order to increase the classification performance of a model that is based on the deep neural networks has been used. The model used in this research for the classification of network traffics evaluated and the related metrics showing the classification performance have been depicted in the figures and tables. As the results indicate, the proposed model can perform well enough for detecting DDoS attacks through deep learning technologies.

关键词： cyber security ddos deep learning autoencoder

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User-Centric Online Gossip Training for autoencoder-Based CSI Feedback

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第3期16卷 559-572页

作者： Guo, Jiajia Zuo, Yiping Wen, Chao-Kai Jin, Shi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan

Recently, the autoencoder framework has shown great potential in reducing the feedback overhead of the downlink channel state information (CSI). In this work, we further find that the user equipment in practical systems occasionally moves in a relatively stable area for a long time, and the corresponding communication environment is relatively stable. A user-centric online training strategy is proposed to further improve CSI feedback performance using the above characteristics. The key idea of the proposed method is to train a new encoder for a specific area without changes to the decoder at the base station. Given that the CSI training samples are insufficient, two data augmentation strategies, including random erasing and random phase shift, are introduced to improve the neural network generalization. In addition, the proposed user-centric online training framework is extended to the multi-user scenario for considerable performance improvement via gossip learning, which is a fully decentralized distributed learning framework and can use crowd intelligence. The simulation results show that the proposed user-centric online gossip training offers a more substantial increase in the feedback accuracy and can considerably improve autoencoder generalization.

关键词： Training Artificial neural networks Downlink Decoding Correlation Computer architecture 5G mobile communication autoencoder CSI feedback gossip learning user-centric online training

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