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Deep learning for pixel-level image fusion: Recent advances and future prospects

INFORMATION FUSION 2018年 42卷 158-173页

作者： Liu, Yu Chen, Xun Wang, Zengfu Wang, Z. Jane Ward, Rabab K. Wang, Xuesong Hefei Univ Technol Dept Biomed Engn Hefei 230009 Anhui Peoples R China Univ Sci & Technol China Dept Elect Sci & Technol Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Anhui Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China

By integrating the information contained in multiple images of the same scene into one composite image, pixel-level image fusion is recognized as having high significance in a variety of fields including medical imaging, digital photography, remote sensing, video surveillance, etc. In recent years, deep learning (DL) has achieved great success in a number of computer vision and image processing problems. The application of DL techniques in the field of pixel-level image fusion has also emerged as an active topic in the last three years. This survey paper presents a systematic review of the DL-based pixel-level image fusion literature. Specifically, we first summarize the main difficulties that exist in conventional image fusion research and discuss the advantages that DL can offer to address each of these problems. Then, the recent achievements in DL-based image fusion are reviewed in detail. More than a dozen recently proposed image fusion methods based on DL techniques including convolutional neural networks (CNNs), convolutional sparse representation (CSR) and stacked autoencoders (SAEs) are introduced. At last, by summarizing the existing DL-based image fusion methods into several generic frameworks and presenting a potential DL-based framework for developing objective evaluation metrics, we put forward some prospects for the future study on this topic. The key issues and challenges that exist in each framework are discussed.

关键词： Image fusion Deep learning Convolutional neural network Convolutional sparse representation stacked autoencoder

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Probabilistic bearing fault diagnosis using Gaussian process with tailored feature extraction

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第3-4期119卷 2059-2076页

作者： Liang, Mingxuan Zhou, Kai China Jiliang Univ Coll Mech & Elect Engn Hangzhou 310018 Peoples R China Michigan Technol Univ Dept Mech Engn Engn Mech Houghton MI 49931 USA

Deep learning methods recently have gained growing interests and are extensively applied in the data-driven bearing fault diagnosis. However, current deep learning methods perform the bearing fault diagnosis in the form of deterministic classification, which overlook the uncertainties that inevitably exist in actual practice. To tackle this issue, in this research, we develop a probabilistic fault diagnosis framework that can account for the uncertainty effect in prediction, which bears practical significance. This framework uses the Gaussian process classifier (GPC) as the mainstay, which fundamentally is built upon the Bayesian inference. To establish the high-fidelity GPC, the tailored feature extraction method can be adaptively determined through the cross validation-based grid search upon a prespecified method pool consisting of various kernel principal component analysis (KPCA) methods and stacked autoencoder. This adaptive strategy can ensure the adequate GPC model training to accurately characterize the complex nonlinear relations between the data features and respective faults. Systematic case studies using the publicly accessible experimental rolling bearing dataset, i.e., CWRU bearing dataset are carried out to validate this new framework. The results clearly illustrate the unique capability of this framework in handling uncertainties. It is also found that this framework outperforms other well-established machine learning and deep learning models in terms of accuracy and robustness. Moreover, the sensor fusion that combines the spatial vibration measurements appears to be an effective technique to further enhance the fault diagnosis performance. By fully leveraging the probabilistic feature of the framework, the future research endeavor, such as the extended fault diagnosis using limited fault labels will be facilitated.

关键词： Rolling bearings Probabilistic fault diagnosis Gaussian process classifier (GPC) Kernel principal component analysis (KPCA) stacked autoencoder Sensor fusion

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Hyperbolic Window S-Transform Aided Deep Neural Network Model-Based Power Quality Monitoring Framework in Electrical Power System

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IEEE SENSORS JOURNAL 2021年第12期21卷 13695-13703页

作者： Nandi, Kiron Das, Arup Kumar Ghosh, Riddhi Dalai, Sovan Chatterjee, Biswendu Jadavpur Univ Dept Elect Engn Kolkata 700032 India Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy

With the fast development of power grid the usage of electrical equipments is increased which led to importance of power quality disturbance sensing for reliable and smooth operation. In this paper, a deep neural network has been designed using stacked autoencoder (SAE) for deep feature extraction from time-frequency spectrum of single and combined PQ disturbances in electrical power system network. For this purpose, synthetic PQ signals are analyzed in time-frequency domain through hyperbolic window stockwell transform (HWST). Thereafter, PQ signal converted HWST time-frequency matrix has been grouped into time-frequency blocks and subsequently fed as input to 3-layer stacked autoencoder model (SAE) for deep feature learning. Finally, the extracted deep features are classified through several machine learning classifier. The results indicate that proposed framework using XGboost classifier can classify 18 different single and combined PQ event with a 99.86% accuracy. The proposed framework also yields satisfactory outcome with real life PQ data. Therefore, proposed framework can be implemented for Power quality monitoring in electrical power system.

关键词： Feature extraction Transforms Time-frequency analysis Sensors Transient analysis Reliability Power system reliability Power quality detection hyperbolic window s-transform deep neural network stacked autoencoder classification

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Palmvein Recognition Using Block-Based WLD Histogram of Gabor Feature Maps and Deep Neural Network With Bayesian Optimization

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IEEE ACCESS 2021年 9卷 97337-97353页

作者： El-Ghandour, Mohammed Obayya, Marwa Ismael Yousef, Bedir Areed, Nihal Fayez Mansoura Univ Coll Engn Dept Elect & Commun Engn Mansoura 35516 Egypt Princess Nourah Bint Abdulrahman Univ Coll Engn Dept Biomed Engn Riyadh 84428 Saudi Arabia Kafrelsheikh Univ Fac Engn Dept Elect Engn Kafr Al Sheikh 33516 Egypt Onaizah Coll Fac Engn & Informat Technol Dept Elect Engn Al Qassim 51911 Saudi Arabia

The demand on devices and systems empowered by biometric verification and identification mechanisms has been increasing in recent years as they have become a significant part of our lives. Palm vein biometric is an emerging technology that has drawn considerable attention from researchers and scientists over the last decade. In the present work, a novel feature extraction methodology named GPWLD combing the Gabor features with positional Weber's local descriptor (PWLD) features is proposed. WLD is a highly representative micro-pattern descriptor that performs well against noise and illumination changes in images. However, it lacks the ability to capture the vein pattern features at different orientations. Moreover, its descriptor packs the local information content into a single histogram that does not take the spatial locality of micro-patterns into consideration. To solve these two issues, firstly, the palm vein image is passed through Gabor filter with different orientations to capture the salient rotational features found in the output feature maps. Secondly, the spatiality is achieved by uniformly dividing each feature map into several blocks. Next, Weber's law feature descriptor (WLD) histogram is computed for each block in every feature map. Finally, these histograms are concatenated to compose the final feature vector. Due to the high dimensionality of the final feature vector, Principal component analysis (PCA) algorithm is utilized for feature size reduction. In the classification stage, a deep neural network (DNN) comprising an optimized stacked autoencoder (SAE) with Bayesian optimization and a softmax layer is used. Optimization of the SAE is carried out by using Bayesian optimization to find the optimal SAE structure and the options of the training algorithm. The Experimental results verify the discriminative power of the extracted features and the accuracy of the proposed DNN. For both Identification and verification experiments, the proposed method a

关键词： Veins Feature extraction Biometrics (access control) Optimization Histograms Gabor filters Bayes methods WLD descriptor Gabor filter deep neural network stacked autoencoder Bayesian optimization

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Dual autoencoders features for imbalance classification problem

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PATTERN RECOGNITION 2016年 60卷 875-889页

作者： Ng, Wing W. Y. Zeng, Guangjun Zhang, Jiangjun Yeung, Daniel S. Pedrycz, Witold South China Univ Technol Sch Comp Sci & Engn Machine Learning & Cybernet Res Ctr Guangzhou 510006 Guangdong Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2M7 Canada King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst Warsaw Poland

Many classification problems encountered in real-world applications exhibit a profile of imbalanced data. Current methods depend on data resampling. In fact, if the feature set provides a clear decision boundary, resampling may not be needed to solve the imbalanced classification problem. Therefore, this work proposes a feature learning method based on the autoencoder to learn a set of features with better classification capabilities of the minority and the majority classes to address the imbalanced classification problems. Two sets of features are learned by two stacked autoencoders with different activation functions to capture different characteristics of the data and they are combined to form the Dual Autoencoding Features. Samples are then classified in the new feature space learned in this manner instead of the original input space. Experimental results show that the proposed method outperforms current resampling-based methods with statistical significance for imbalanced pattern classification problems. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Imbalanced Classification Feature Learning stacked autoencoder

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Efficient adaptive deep gradient RBF network for multi-output nonlinear and nonstationary industrial processes

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JOURNAL OF PROCESS CONTROL 2023年第1期126卷 1-11页

作者： Liu, Tong Chen, Sheng Yang, Po Zhu, Yunpeng Harris, Chris J. Imperial Coll London Dept Chem Engn London SW7 2AZ England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England Ocean Univ China Fac Informat Sci & Engn Qingdao Peoples R China Univ Sheffield Dept Comp Sci Sheffield S1 4DP England Queen Mary Univ London Sch Engn & Mat Sci London E1 4NS England

Due to the complexity of process operation, industrial process data are often nonlinear and non -stationary, high dimensional, and multivariate with complex interactions between multiple outputs. To address all these issues, this paper proposes a novel industrial predictive model that integrates deep feature extraction and fast online adaptation, and can effectively deal with multiple process outputs. Specifically, a multi-output gradient radial basis function network (MGRBF) with excellent predictive capacity of nonstationary data is first used to provide preliminary prediction of target outputs. This prior quality information is combined with the original process input for deep feature learning and dimensional reduction. Through layer-wise feature extraction by the stacked autoencoder (SAE), deep quality-enhanced features can be obtained, which is further fed into a MGRBF tracker for online prediction. In order to timely capture the fast-changing process characteristics, the first two modules, namely, preliminary MGRBF predictor and SAE feature extractor are frozen after training, while the structure and parameters of the MGRBF tracker are updated online in an efficient manner. Two industrial case studies demonstrate that the proposed adaptive deep MGRBF network outperforms existing state-of-the-art online modeling approaches as well as deep learning models, in terms of both multi-output modeling accuracy and online computational complexity.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Multivariate nonlinear and nonstationary industrial process Multi-output gradient radial basis function network stacked autoencoder Quality-enhanced feature extraction Online adaptive tracking

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Deep packet: a novel approach for encrypted traffic classification using deep learning

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SOFT COMPUTING 2020年第3期24卷 1999-2012页

作者： Lotfollahi, Mohammad Siavoshani, Mahdi Jafari Zade, Ramin Shirali Hossein Saberian, Mohammdsadegh Sharif Univ Technol Tehran Iran

Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a deep learning-based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called "Deep Packet," can handle both traffic characterization in which the network traffic is categorized into major classes (e.g., FTP and P2P) and application identification in which identifying end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. The Deep Packet framework employs two deep neural network structures, namely stacked autoencoder (SAE) and convolution neural network (CNN) in order to classify network traffic. Our experiments show that the best result is achieved when Deep Packet uses CNN as its classification model where it achieves recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.

关键词： Network traffic classification Application identification Traffic characterization Deep learning Convolutional neural networks stacked autoencoder Deep Packet

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Neuron-Compressed Deep Neural Network and Its Application in Industrial Anomaly Detection

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第7期19卷 7914-7924页

作者： Wang, Kai Yan, Caoyin Mo, Yanfang Yuan, Xiaofeng Wang, Yalin Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Peoples R China City Univ Hong Kong Hong Kong Inst Data Sci Kowloon Hong Kong 999077 Peoples R China

Data modeling and online monitoring are two critical stages for data-driven anomaly detection. Regarding data modeling, deep neural networks (DNNs) can learn good decision boundaries to separate the anomaly and normal regions, due to their flexible model structures and excellent fitting ability. However, DNNs, using nonlinear activations with specific boundaries, may indirectly cause a limited anomaly detection margin, especially when there are samples far from centroids. Moreover, an anomaly detection model with a narrow detection margin is deemed insensitive to general faults. An anomaly detection model with a tight detection margin will suffer a severe performance degradation. To mitigate the intrinsic drawbacks of DNNs, we develop a new regularizer based on the maximum likelihood of complete data (i.e., observations and latent variables). The regularizer is neuronwise and mathematically acts as compressing neurons, dragging the marginal points into the centroids. Combining the regularizer with the encoding-decoding structure networks, we perform an industrial case study to verify the superiority of the proposed method.

关键词： Anomaly detection deep neural network (DNN) regularization stacked autoencoder

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Reducing smearing effect in contribution plots and improving fault detection via polynomial approximated isomap embeddings

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2023年第5期101卷 2768-2789页

作者： Alakent, Burak Bogazici Univ Dept Chem Engn TR-34342 Istanbul Turkey

Dimension reduction is an essential method used in multivariate statistical process monitoring for fault detection and diagnosis. Principal component analysis (PCA) and independent component analysis (ICA) are the most frequently used linear dimensional reduction tools, and the contribution plot is the most popular fault isolation method in the absence of any prior information on the faults. These methods, however, come with their shortcomings. The fault detection capability of linear methods may not be sufficient for non-linear processes, and smearing effect is known to deteriorate the diagnostics obtained from contribution plots. While the fault detection rate may be increased by kernelized methods or deep artificial neural network models, tuning data-dependent hyperparameter(s) and network structure with limited historical data is not an easy task. Furthermore, the resulting non-linear models often do not directly possess fault isolation capability. In the current study, we aim to devise a novel method named ICA(pIso)-PCA, which offers non-linear fault detection and isolation in a rather straightforward manner. The rationale of ICA(pIso)-PCA mainly involves building a non-linear scores matrix, composed of principal component scores and high-order polynomial approximated isomap embeddings, followed by implementation of the ICA-PCA algorithm on this matrix. Applications on a toy dataset and the Tennessee Eastman plant show that the I-2 index from ICA(pIso)-PCA yields a high fault detection rate and offers accurate contribution plots with diminished smearing effects compared to those from traditional monitoring methods. Easy implementation and the potential for future research are further advantages of the proposed method.

关键词： fault isolation independent component analysis manifold learning stacked autoencoder

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Enhancing soil organic carbon prediction of LUCAS soil database using deep learning and deep feature selection

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年第Part1期227卷

作者： Saberioon, Mohammadmehdi Gholizadeh, Asa Ghaznavi, Ali Chabrillat, Sabine Khosravi, Vahid Helmholtz Ctr Potsdam GFZ German Res Ctr Geosci Sect Remote Sensing & Geoinformat 1 4 D-14473 Potsdam Germany Flanders Res Inst Agr Fisheries & Food Technol & Food Sci Agr Engn ILVO B-9820 Merelbeke Belgium Czech Univ Life Sci Prague Fac Agrobiol Food & Nat Resources Dept Soil Sci & Soil Protect Kamycka 129 Prague 16500 Czech Republic Leibniz Univ Hannover Inst Soil Sci Herrenhauser Str 2 D-30419 Hannover Germany

The main terrestrial carbon (C) fraction is soil organic carbon (SOC), which has a considerable effect on climate change and greenhouse gas emissions through the absorption and sequestration of carbon dioxide (CO2). 2 ). This has made SOC assessment very important from both economic and environmental viewpoints. The growing count of soil spectral libraries (SSLs) from regional to global scales has brought a tremendous opportunity for the quantification of SOC through developing spectral-based prediction models. Hence, there is a need to take advantage of big data analytics for spectral data processing. The unique ability of deep learning (DL) techniques to leverage important features of high-dimensional large-scale SSLs has made them top-demanding for more sophisticated modeling. The core objective of the present study was to assess the ability of two different DL algorithms, i.e., one-dimensional convolutional neural network (1DCNN) and fully connected neural network (FCNN) coupled with stacked autoencoder (SAE) feature extraction for SOC prediction based on the data from the land use/cover area frame statistical survey (LUCAS) database. SAE extracted the high-level deep features from the visible-near-infrared-shortwave infrared (Vis-NIR-SWIR) spectra of 11441 soil samples, which were then considered as inputs to the 1DCNN and FCNN models for predicting the SOC content. Both SAE-DL feature-selected models yielded higher accuracy than those the DL developed on the entire spectra and a random forest (RF) model was constructed for comparison. The best prediction was achieved by SAE-1DCNN (R2 2 = 0.78, RMSE = 3.94%, RPD = 4.88, RPIQ = 3.91) followed by 1DCNN (R2 2 = 0.73, RMSE = 5.43%, RPD = 3.67, RPIQ = 2.84) proving the superiority of 1DCNN over FCNN in this study. These results supported the applicability of combined deep features extraction and regression methods for predicting SOC using high dimensional large-scale SSLs.

关键词： Soil organic carbon Soil spectral library Convolutional neural network Fully connected neural network Deep feature extraction Large-scale stacked autoencoder

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