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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Lopez-Fandino, Javier Garea, Alberto S. Heras, Dora B. Arguello, Francisco Univ Santiago de Compostela Ctr Singular Invest Tecnol Informac CiTIUS Santiago De Compostela Spain

ISBN: (纸本)9781538671504

Change detection (CD) in multitemporal datasets is a key task in remote sensing. In this paper, a scheme to perform multiclass CD for remote sensing hyperspectral datasets extracting features by means of stacked autoencoders (SAEs) is introduced. The scheme combines multiclass and binary CD to obtain an accurate multiclass changemap. The multiclass CD begins with the fusion of the multitemporal data followed by Feature Extraction (FE) by SAEs. The binary CD is based on the spectral information by calculating pixel-wise distances and thresholding, and it also incorporates spatial information through watershed segmentation. The processed image is filtered by using the binary CD map and later classified by a Support VectorMachine or an Extreme LearningMachine algorithm. The scheme was evaluated over a multitemporal hyperspectral dataset obtained fromthe Hyperion sensor. Experimental results show the effectiveness of the proposed scheme using a SAE for extracting the relevant features of the fused information when compared to other published FE methods.

关键词： Hyperspectral Change Detection Feature Extraction stacked autoencoder Support Vector Machine Extreme Learning Machine

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A Hybrid Deep Learning Approach for Advanced Persistent Threat Attack Detection 2021

A Hybrid Deep Learning Approach for Advanced Persistent Thre...

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The 5th International Conference on Future Networks & Distributed Systems

作者： Meaad Alrehaili Adel Alshamrani Ala Eshmawi University of Jeddah Saudi Arabia

ISBN: (纸本)9781450387347

Advanced Persistent Threat (APT) attack is one of the most common and costly destructive attacks on the target system. This attack has become a challenge for companies, governments, and organizations’ information security systems. In recent years, methods for detecting and preventing APT attacks that use machine learning or deep learning algorithms to analyze indications and anomalous behaviors in network traffic have become popular. However, due to a lack of typical data from attack campaigns, the APT attack detection approach that uses behavior analysis and evaluation approaches encounter many issues. Network traffic analysis to detect a common APT attack is one of the solutions for dealing with this situation. This paper develops efficient and flexible deep learning models. To analyze huge network traffic, a hybrid deep learning approach that builds two models is used: stacked autoencoder with Long Short-Term Memory (SAE-LSTM) and Convolutional Neural Networks with Long Short-Term Memory Network (CNN-LSTM) to detect indications of APT attacks. A reliable dataset ’DAPT2020’ that covers all APT stages is used to evaluate the proposed approach. The experimental results demonstrate that the hybrid deep learning approach proved to give higher performance than the individual deep learning model in detecting malicious behavior in each APT stage.

关键词： Intrusion Detection System stacked autoencoder Deep learning Advanced Persistent Threat Long Short-Term Memory Cyberattacks Convolutional Neural Networks

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Hyperspectral image classification using multi-task feature leverage with multi-variant deep learning

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EARTH SCIENCE INFORMATICS 2020年第4期13卷 1093-1102页

作者： Jayapriya, Kalyanakumar Jacob, Israel Jeena Darney, Paulraj Ebby Vin Solut Tirunelveli India GITAM Sch Technol Dept Comp Sci & Engn Bengaluru India SCAD Coll Engn & Technol Dept Elect Engn Tirunelveli India

Deep learning framework aids the researchers in learning different application areas to a greater extent. Deep learning framework is preferred over machine learning since it helps to learn the input from end to end, whereas latter one require the inputs to be cut into pieces according to the need. This paper proposes a Multi-Variant Deep Learning framework for learning and classifying the hyperspectral images. Multi-task Feature Leverage is incorporated by doing two-ordered feature extraction. The first order feature extraction was done by using Two Dimensional Empirical Wavelet Transforms (2D-EWT) and the second-order feature extraction was done by using stacked autoencoder (SAE) and Convolutional Neural Network (CNN) for the approximation image of 2D-EWT. Because of the possibility of working on prominent feature, the proposed work uses approximation image than the raw image. The classification was carried out by using Random Forest (RF), Multi- Support Vector Machine (MSVM) and Extreme learning machine (ELM).

关键词： Deep learning Random forest Multi-support vector machine Extreme learning machine stacked autoencoder Convolutional neural network

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An effective feature extraction method for fault classification and its application to industrial processes

An effective feature extraction method for fault classificat...

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第33届中国过程控制会议

作者： Adil Masud Aman Yalin Wang Chenliang Liu Xiaofeng Yuan Kai Wang Lixue Huang the School of Automation Central South University the Xinjiang Luobupo Potassium Salt Co.Ltd

Since industrial process data often presents complexity and nonlinearity,this study proposes a deep learning model based on semi-supervised Inter-Relational Mahalanobis stacked autoencoder(IRM-SAE) to learn deep fault-relevant features of process data for fault ***,the Inter-Relational Mahalanobis loss function is introduced to learn meaningful inter-relational distribution features within the ***,active time-frame preprocessing is utilized to capture dynamic features of ***,to fully utilize both labeled and unlabeled data in industrial processes,the semi-supervised strategy is introduced to learn fault-related features for better fault ***,the Tennessee Eastman process is utilized to validate the effectiveness of the proposed *** experimental results show that IRM-SAE outperforms other deep learning models with an average fault classification accuracy of 96.59%.

关键词： Deep learning feature extraction stacked autoencoder fault classification industrial processes

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Classification of hyperspectral images by deep learning of spectral-spatial features

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ARABIAN JOURNAL OF GEOSCIENCES 2020年第12期13卷 1-14页

作者： Ding, Haiyong Xu, Luming Wu, Yue Shi, Wenzhong Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomat Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Geog Sci Nanjing 210044 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China

Creating accurate land use and land cover maps using remote sensing images is one of the most important applications of remotely sensed data. Abundant spectral information in hyperspectral images (HSI) makes it possible to distinguish materials that would not be distinguishable by multi-spectral sensors. Spectral and spatial information from HSI is of primary importance for image classification. In this study, a hybrid stacked autoencoder (SAE) architecture and support vector machine (SVM) classifier was constructed to classify the HSI. The SAE architecture is constituted by stacking a multiple autoencoder (AE) deep learning network that consists in the encoder and decoder process. Spatial features in a neighbor region extracted from the principal component analysis (PCA) and the texture feature extracted from the gray-level cooccurrence matrix (GLCM) were fed into the classifier. It was found that the best result was from the combination of GLCM texture feature, PCA spatial feature, and spectral feature. Meanwhile, the representative features derived from SAE deep learning network were better than the original features. It reminded us that extracting the representative features from hyperspectral images is a key step of improving classification accuracy.

关键词： stacked autoencoder Deep learning Hyperspectral image classification Spatial features

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A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures

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SENSORS 2021年第22期21卷 7761-7761页

作者： Tuan-Khai Nguyen Ahmad, Zahoor Kim, Jong-Myon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea PD Technol Co Ulsan 44610 South Korea

In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure's failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is automatically constructed from raw AE data with a deep neural network pretrained and fine-tuned by a stacked autoencoder deep neural network (SAE-DNN). For the deep neural network structure to perform a more accurate construction of health indicator lines, a hit removal process with a one-class support vector machine (OC-SVM), which has not been investigated in previous studies, is proposed to extract only the hits which matter the most to the portrait of deterioration. The new set of hits is then harnessed as the training labels for the deep neural network. After the completion of the health indicator line construction, health indicators are forwarded to a long short-term memory recurrent neural network (LSTM-RNN) for the training and validation of the remaining useful life prediction, as this structure is capable of capturing the long-term dependencies, even with a limited set of data. Our prediction result shows a significant improvement in comparison with a similar scheme but without the hit removal process and other methods, such as the gated recurrent unit recurrent neural network (GRU-RNN) and the simple recurrent neural network.

关键词： acoustic emission deep neural network hit removal long short-term memory one-class support vector machine remaining useful life stacked autoencoder

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A deep neural network approach to QRS detection using autoencoders*,**

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115528-115528页

作者： Belkadi, Mohamed Amine Daamouche, Abdelhamid Melgani, Farid Univ MHamed Bougara Inst Elect & Elect Engn Signals & Syst Lab Boumerdes 35000 Algeria Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

Objective: In this paper, a stacked autoencoder deep neural network is proposed to extract the QRS complex from raw ECG signals without any conventional feature extraction phase. Methods: A simple architecture has been deeply trained on many datasets to ensure the generalization of the network at inference. Results: The proposed method achieved a QRS detection accuracy of 99.6% using more than 1042000 beats which is competitive with all state-of-the-art QRS detectors. Moreover, the proposed method produced only 0.82% of Detection Error Rate using six unseen datasets containing more than 1470000 beats. Thus confirms the high performance of our method to detect QRSs. Conclusion: stacked autoencoder neural networks are very effective in QRS detection. At inference, our algorithm processes 1042309 beats in less than 25.32 s. Thus, it is favorably comparable with state-of-the-art deep learning methods. Significance: The stacked autoencoder is an efficient tool for QRS detection, which could replace conventional systems to help practitioners make fast and accurate decisions.

关键词： ECG Deep learning stacked autoencoder QRS detection

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Crack detection in carbide anvil using acoustic signal and deep learning with particle swarm optimisation

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MEASUREMENT 2021年 173卷 108668-108668页

作者： Yang, Jun Chen, Bin Wang, Yanan Wang, Chunzhu Tsinghua Univ Dept Automat Beijing 10084 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China

The carbide anvil plays a significant role in producing synthetic diamond. However, it suffers from complex alternating stresses and consequently results in fatigue damage such as cracks. Accurate crack detection of the carbide anvil still faces a significant challenge. This paper develops an acoustical crack detection method of the carbide anvil using the deep learning. In the method, an online sound impulse extraction strategy is designed to construct an anvil dataset. Subsequently, the stacked autoencoder model is designed to learn a robust feature representation of the anvil states from the measured sound impulse signals. Besides, an improved particle swarm optimisation method based on classification probability is proposed for the hyper-parameter optimisation. Finally, the performance of the proposed method is evaluated using experimental data. This research can provide a potential tool for the engineers to automatically detect the crack of the carbide anvils in the diamond industry.

关键词： Crack detection Acoustic signal stacked autoencoder Particle swarm optimisation Carbide anvil

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Forecasting influenza epidemics in Hong Kong using Google search queries data: A new integrated approach

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115604-115604页

作者： Liu, Yunhao Feng, Gengzhong Tsui, Kwok-Leung Sun, Shaolong Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China City Univ Hong Kong Sch Data Sci Kowloon Tat Chee Ave Hong Kong Peoples R China Virginia Polytech Inst & State Univ Grad Dept Ind & Syst Engn Blacksburg VA 24061 USA

Forecasting influenza epidemics has important practical implications. However, the performance of traditional methods adopting in Hong Kong influenza forecasting is limited due to its particularity. This paper proposes an integrated approach for Hong Kong influenza epidemics forecasting. The novelties of our approach mainly include: firstly, we adopt a model for Google search queries data collection and selection in Hong Kong to substitute Google Correlate. Secondly, we adopt the stacked autoencoder (SAE) to reduce the dimensionality of Google search queries data. Thirdly, we adopt a signal decomposition method named variational mode decomposition (VMD) to decompose the influenza data into modes with different frequencies, which can extract the characteristic. Fourthly, we use artificial neural networks (ANN) to forecast these modes of influenza epidemics extracted by VMD respectively, then these forecasts of each mode are added to generate the final forecasting results. From the perspective of forecasting accuracy and hypothesis tests, the empirical results show that our proposed integrated approach SAE-VMD-ANN significantly outperforms some other benchmark models both in the whole period and influenza season. The performance of our proposed model during the COVID-19 pandemic is checked too.

关键词： Influenza epidemics forecasting Variational mode decomposition stacked autoencoder Artificial neural networks Google search queries data

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An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021年 289卷 112438-112438页

作者： Qiao, Weibiao Wang, Yining Zhang, Jianzhuang Tian, Wencai Tian, Yu Yang, Quan Yan Shan Univ Sch Vehicle & Energy Qinhuangdao 066004 Hebei Peoples R China North China Univ Water Resources & Elect Power Sch Environm & Municipal Engn Zhengzhou 450046 Peoples R China Beijing Univ Technol Fac Architecture Civil & Transportat Engn Beijing 100124 Peoples R China

Wavelet transform (WT) is an advanced preprocessing technique, which has been widely used in PM 10 prediction. However, this technique cannot provide stable performance due to the empirical selection of wavelet's layers. For fixing the optimal wavelet's layers in PM10 forecasting, an innovative coupled model based on WT, long short-term memory (LSTM), and SAE (stacked autoencoder) are proposed. This study designs a crossover experiment with 960 high- and low-frequency components by wavelet decomposition and predicts each component with SAE-LSTM based on 12 samples from different regions. The results indicate that the developed model outperforms other BiLSTM (Biredictional LSTM) and LSTM based on some error evaluation indicators (i.e. Nash-Sutcliffe efficiency coefficient (NSEC)), and compared with other steps, the accuracy of two-step prediction is the highest in view of root mean squares error (RMSE). In addition, for 12 samples, the prediction accuracy by using high layers is higher than that by adopting low layers for decomposing them. This paper fixes the optimal wavelet' layers in PM10 prediction, which provides a meaningful reference in other prediction scenarios based on the application of WT.

关键词： PM10 Long short-term memory stacked autoencoder Wavelet transform Prediction

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