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第37届中国控制会议

作者： Meiling Xu Yaguo Dong Zhan Li Min Han Tong Xing School of Control Science and Engineering Dalian University of Technology

Recently,the scale of observed time series becomes bigger and bigger,and the learning ability of conventional models is limited to depict the characteristics of large-scale time *** put forward a deep model that uses the sparse autoencoder to extract the characteristics of the time series layer by *** autoencoder can provide a simple explanation of the data by means of a reduced dimensionality of parts and extract the hidden architecture in the *** this method,on the one hand,the dimension of the matrix that describes the problem is reduced,on the other hand,a large number of data can be compressed and *** we calculate the output weights by the method of pseudo *** results on Lorenz series and PM2.5 concentration in Shanghai dataset demonstrate the effectiveness of the proposed method.

关键词： Time series prediction deep autoencoder feature extraction

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deep learning based retinal disease classification using an autoencoder and generative adversarial network

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 108卷

作者： Jeyasri, G. Karthiyayini, R. Anjalai Ammal Mahalingam Engn Coll Dept Informat Technol Kovilvenni Tamil Nadu India Anna Univ Univ Coll Engn Bharathidasan Inst Technol Campus Dept Comp Applicat Tiruchirappalli Tamilnadu India

Human eyesight relies heavily on retinal tissue, vision loss include infections of the retina and either a delay in treatment or the disease remaining untreated. Identifying retinopathy from retinal fundus image is a vital and diagnostic system performance depends on image quality and quantity. Furthermore, the diagnosis is prone to errors when a large imbalanced database is used. Hence, a fully automated retina disease prediction system is indispensable to minimize human intervention, increase the performance of the disease diagnostic system, and support ophthalmologists in conducting speedy and accurate investigations. Advancements in deep learning have remarkable results in identifying retinopathy from retinal fundus images. However, conventional deep-learning approaches struggle to learn enough in-depth features to identify aspects of mild retinal disease. To address this, integrates a deep autoencoder-based diagnostic system with a ResNet-based generative adversarial network (RGAN) to find retinal disease. This integrated model exploits a ResNet-50 structure to generate synthetic images to handle higher FAR and class imbalance-related problems and a deep autoencoder to categorize the retinal fundus pictures into benign and malicious. The proposed RGAN engenders synthetic images to train the diagnostic and real systems. The experimental outcomes have been implemented, and the recommended RGAN model increases the accuracy ratio of 95.6%, sensitivity ratio of 96.4%, specificity ratio of 97.3%, and F1-score ratio of 93.4% compared to other popular techniques.

关键词： deep autoencoder Generative adversarial network ResNet Disease prediction Retinal fundus image Retinopathy classification

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NIDS-DA: Detecting functionally preserved adversarial examples for network intrusion detection system using deep autoencoders

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 270卷

作者： Kumar, Vivek Kumar, Kamal Singh, Maheep Kumar, Neeraj Natl Inst Technol Pauri Garhwal Dept Comp Sci & Engn Srinagar 246174 Uttarakhand India IGDTUW Dept Informat Technol Delhi 110006 India Doon Univ Dept Comp Sci Dehra Dun 248001 Uttarakhand India Thapar Inst Engn & Technol Patiala Punjab India

DL has revolutionized Network Intrusion Detection Systems (NIDS) in recent years. However, these models suffer from Adversarial Examples (AEs), which are maliciously crafted to cause misclassification by a target NIDS. Unlike image recognition, AEs in a network intrusion domain are crafted for evasion and to launch attacks once they have successfully evaded detection. In recent years, several adversarial attack techniques have been developed to generate adversarial examples that retain their maliciousness even after perturbations. This is achieved by only modifying the non-functional features of a malicious data sample. In this paper, we propose a deep autoencoder (DAE) mechanism that can detect adversarial examples that are crafted by only modifying non-functional attributes. The method trains a DAE to establish a latent space relationship between non-functional attributes of feature space data samples. It then uses the inconsistency in the classification result of an AE and latent space relationship between non-functional features for adversarial detection. The paper shows that a DAE trained on only non-functional features produces fewer false positives than a DAE trained on both functional and non-functional features. We evaluate our proposed method on three data sets (CICIDS2017, NSL-KDD, and UNSW-NB15) and against five state-of-the-art AE attacks. Experimentally, our method was able to detect up to 99% AEs with very few false positives.

关键词： Adversarial example Functional/non-functional features DL Machine learning deep autoencoder

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DeMAAE: deep multiplicative attention-based autoencoder for identification of peculiarities in video sequences

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VISUAL COMPUTER 2024年第3期40卷 1729-1743页

作者： Aslam, Nazia Kolekar, Maheshkumar H. Indian Inst Technol Patna Dept Elect Engn Video Surveillance Lab Bihta 801106 India Indian Inst Technol Patna Dept Elect Engn Bihta 801106 India

In videos, anomaly detection is challenging due to its diverse nature in different application domains. Reconstruction and prediction-based methods have been widely employed to detect anomalies. Due to the generalization capability of a deep neural network, sometimes, it recreates irregular patterns along with regular ones. This paper presents a novel autoencoder-based framework called deep multiplicative attention-based autoencoder (DeMAAE) to detect anomalies in a video sequence. The global attention mechanism is used at the decoder side of DeMAAE for better feature learning during the decoding phase. An attention map is created by taking the dot product between all encoder's hidden states and the previously generated decoder's hidden state. After that, the final output of the decoder is determined by the context vector. The context vector is computed using the weighted summation of all encoder's hidden states and attention weight. DeMAAE delivers an improved runtime of 0.015 s (similar to 67 fps) for detecting anomalies during testing. Extensive experiments have been performed on the two diversified and widely used datasets (UCSD Pedestrian and CUHK Avenue) to compare the efficacy of DeMAAE with different state-of-the-art methods.

关键词： Anomaly detection Attention mechanism deep autoencoder Temporal attention

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A deep Nonnegative Matrix Factorization Approach via autoencoder for Nonlinear Fault Detection

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第8期16卷 5042-5052页

作者： Ren, Zelin Zhang, Wensheng Zhang, Zhizhong Chinese Acad Sci Inst Automat Res Ctr Precis Sensing & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China

In the era of big data, data-driven fault detection is vital for modern industrial systems. This article considers the potential complexity of fault detection and proposes a novel nonlinear method based on nonnegative matrix factorization (NMF). Motivated by an autoencoder, in this article we first utilize the input data to learn an appropriate nonlinear mapping function, which transforms the original space into a high-dimensional feature space. Then, according to the decomposition rule of NMF, we divide the learned feature space into two subspaces, and two statistics in these subspaces are designed appropriately for nonlinear fault detection. The established method, i.e., deep nonnegative matrix factorization (DNMF), is implemented by three parts: an encoder module, an NMF module, and a decoder module. Unlike conventional NMF-based nonlinear methods using implicit and predetermined kernels, DNMF provides a new nonlinear scheme applied to NMF via a deep autoencoder framework and realizes nonlinear mapping for input data automatically. Our proposed nonlinear framework can be further generalized to other linear methods. Besides, DNMF greatly expands the NMF application scope by breaking through the limitation of nonnegative input. The Tennessee Eastman process as an industrial benchmark is employed to verify the effectiveness of the proposed method.

关键词： Kernel Fault detection Feature extraction Matrix decomposition Neural networks Informatics Data-driven fault detection deep autoencoder nonlinear industrial process nonnegative matrix factorization (NMF)

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Improved convolution neural network integrating attention based deep sparse auto encoder for network intrusion detection

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APPLIED INTELLIGENCE 2025年第2期55卷 1-17页

作者： Geng, Zhiqiang Li, Xueming Ma, Bo Han, Yongming Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing 100029 Peoples R China

Network intrusion detection (NID) is seen as a pivotal technology in the network security which can detect malicious threats occurring in the network and lend stabilized services for expanding the network environments. However, Network-based Intrusion Detection Systems (NIDSs) are not sensitive to infrequent intrusions features and tend to have high misjudgment rate on imbalanced datasets, which lead to obvious defects for detecting minority classes of intrusion. Therefore, a novel neoteric NID methodology predicated upon an optimized convolutional neural network (CNN) integrating Attention based deep Sparse Auto Encoder (ADSAE) (ADSAE-CNN) is put forward. The data expanding method based on the ADSAE model integrates attention mechanisms with deep stacked autoencoders to expand intrusion records of minority classes in data preprocessing, so as to balance the data distribution of intrusion detection datasets, improve the sensitivity of the detection model to the intrusion of few categories, and enhance the monitoring of the intrusion by the system. Meanwhile, the ADSAE can encode and transform the intrusion data to ameliorate the feature extraction capability of convolutional layers of the proposed ADSAE-CNN for detecting and classifying different intrusions classes. Finally, the ADSAE-CNN methodology is devoted to the network intrusion detection of two experiments on UNSW-NB15 and CSE-CIC-IDS2018 datasets and achieves the total precision of detection 89.1% on UNSW-NB15 and 94.20% on CSE-CIC-IDS2018, which can lead to considerably elevate the detection rate of minority intrusions and means significant effectiveness on multi-class network intrusion detections.

关键词： deep autoencoder Convolution neural network Network intrusion detection Imbalance dataset Data expanding

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Blade Rub-Impact Fault Identification Using autoencoder-Based Nonlinear Function Approximation and a deep Neural Network

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SENSORS 2020年第21期20卷 6265.-6265.页

作者： Prosvirin, Alexander E. Piltan, Farzin Kim, Jong-Myon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea

A blade rub-impact fault is one of the complex and frequently appearing faults in turbines. Due to their nonlinear and nonstationary nature, complex signal analysis techniques, which are expensive in terms of computation time, are required to extract valuable fault information from the vibration signals collected from rotor systems. In this work, a novel method for diagnosing the blade rub-impact faults of different severity levels is proposed. Specifically, the deep undercomplete denoising autoencoder is first used for estimating the nonlinear function of the system under normal operating conditions. Next, the residual signals obtained as the difference between the original signals and their estimates by the autoencoder are computed. Finally, these residual signals are used as inputs to a deep neural network to determine the current state of the rotor system. The experimental results demonstrate that the amplitudes of the residual signals reflect the changes in states of the rotor system and the fault severity levels. Furthermore, these residual signals in combination with the deep neural network demonstrated promising fault identification results when applied to a complex nonlinear fault, such as a blade-rubbing fault. To test the effectiveness of the proposed nonlinear-based fault diagnosis algorithm, this technique is compared with the autoregressive with external input Laguerre proportional-integral observer that is a linear-based fault diagnosis observation technique.

关键词： data-driven fault diagnosis deep autoencoder deep neural network deep learning-based function approximation fault diagnosis rotating machinery rub-impact fault nonlinear-based fault diagnosis

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A novel method for the identification of geochemical anomalies with emphasis on addressing the problem of elemental background variation using Pb deposits in Shaoshan, central China, as a case study

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ORE GEOLOGY REVIEWS 2025年 181卷

作者： Zhang, Qinghao Lu, Jilong Dai, Weiming Wu, Hui Fan, Yuchao Gou, Zhiyi Zhao, Xinyun Jilin Univ Coll Geoexplorat Sci & Technol Changchun 130026 Peoples R China Gan Dong Univ Coll Informat Engn Fuzhou 344000 Peoples R China CAGS Key Lab Geochem Explorat Inst Geophys & Geochem Explorat Langfang 065000 Peoples R China Yantai Vocat Coll Yantai 264670 Peoples R China

Most currently available methods for identifying geochemical anomalies using machine/deep learning algorithms ignore the issue of elemental background variation. This study exemplifies the identification of Pb anomalies in regional stream sediments from Shaoshan, central China, by utilizing a deep autoencoder (DAE). The focus is on applying this algorithm to detect geochemical anomalies in areas with varying geochemical background of elements. Firstly, we grouped the stream sediment samples into seven clusters using the Expectation-Maximization (EM) clustering algorithm, effectively minimizing the influence of elemental background variation. Subsequently, elements associated with Pb mineralization in groups one to seven were determined through robust principal component analysis (RPCA): Bi-Li-Sn-Pb, Li-Pb-MgO, As-Nb-Pb, Nb-Pb-ZnAl2O3, Li-Pb-Al2O3-Fe2O3, Ag-Pb-CaO, and Ag-Bi-Li-Pb-Sb-SiO2. The elemental data for each group were then input into the DAE respectively to calculate the reconstruction error, with a threshold value of 0.24 established to delineate Pb anomalies. The identified anomalies corresponded to the known Pb deposits with an accuracy of 89%. In comparison to the DAE method, the combined approach offers a more effective means of identifying geochemical anomalies. This is primarily evident in its ability to eliminate false anomalies in areas with high background while also detecting weak anomalies in regions with low background. The integration of the EM clustering algorithm with machine/deep learning techniques for anomaly detection can significantly enhance the accuracy of geochemical anomaly identification.

关键词： Geochemical anomalies Elemental background variation The EM clustering algorithm deep autoencoder

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An adaptive mixture prior in Bayesian convolutional autoencoder for early detecting anomalous degradation behaviors in lithium-ion batteries

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 259卷

作者： Chae, Sun Geu Bae, Suk Joo Hanyang Univ Dept Ind Engn Seoul South Korea

Accurate and timely detection of anomalies in lithium-ion batteries is crucial for ensuring their reliability and safety. Complex degradation patterns and limited availability of labeled data pose significant challenges in identifying abnormal behaviors in battery usage. This paper proposes an unsupervised adaptive mixture distribution-based Bayesian convolutional autoencoder (AMDBCAE) method for detecting anomalous degradation behaviors in lithium-ion batteries at earlier cycles of reliability test. As the prior for the model parameters, we propose a mixture of the Laplace and Student's t distributions by taking uncertainties in the weights of the convolutional network and their heavy-tailed characteristics into account. Using a modified form of the Bayes by backprop algorithm, the parameter of mixture proportion is adaptively updated to capture diverse and complex degradation patterns in battery degradation data more efficiently. Extracted latent features are then processed through unsupervised clustering algorithms to identify abnormal degradation behaviors of lithium-ion batteries. The analyses of two real-world lithium-ion battery datasets demonstrate the efficiency and accuracy of the proposed unsupervised framework with limited number of testing data. The proposed method addresses the limitations of manual feature extraction and the need for extensive experimental knowledge by leveraging the adaptive BCAE model to automatically extract latent features as a virtual health indicator in sparse data environments.

关键词： deep autoencoder Latent features Reliability test Unsupervised clustering Virtual health indicator

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RAE-SVM based Webpage Tamper-Resistant Detection Algorithm

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2025年第1期21卷 1-12页

作者： Zhou, Changjian Zhang, Yutong Liang, Yunfu Xing, Jinge Northeast Agr Univ Dept Modern Educ Technol Harbin Peoples R China Northeast Agr Univ Coll Elect & Informat Harbin Peoples R China

Cybersecurity has become a key component of national strategy in recent years. Traditional cybersecurity technology such as network traffic-based intrusion detection and threatening intelligence sensing are designed to focus on the traffic features of network, which are no doubt effective defense technologies. However, these methods required decent amount of domain knowledge and massive training data, which brought a significant barrier for cybersecurity research. In this work, we propose a novel residual autoencoder and support vector machine combined approach (RAE-SVM) for webpage tamper-resistant detection using high-level webpage image features. This method, inspired by the Chinese proverb "mend the fold after the sheep have been stolen." The web crawler technology is used for website screenshot within limited domain names, and input them into autoencoder architecture and SVM for feature extraction and invaded webpage detection. This method combines the advantages of deep residual network, convolutional autoencoder and SVM, and the interdisciplinary intersection between cybersecurity and high-level image features. The experimental results demonstrate that the proposed method achieves an accuracy of 95%, significantly higher than other models, which proves the validity of the proposed method.

关键词： Cyber Security deep autoencoder Image Features Support Vector Machine Tamper-Resistant Detection

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