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43rd Chinese Control Conference (CCC)

作者： He, ShiJie Wang, Zhi-Ming Liu, Kun-Zhi Sun, Xi-Ming Dalian Univ Technol Sch Control Sci & Control Engn Dalian 116024 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

As the heart of an aircraft, it is crucial to accurately grasp the operational status of aeroengines. However, it is difficult to fully reflect the accurate fault status through a single sensor signal, and the diagnostic effect of using input signals in complex environments is not satisfactory. Therefore, this article proposes an aeroengine fault diagnosis method based on multisensor fusion and sparse denoising autoencoder, which comprehensively considers multi-sensor decision-making and reduces the difficulty of extracting high-dimensional data features, reducing the interference caused by noise. Experiments have shown that this method can effectively identify the fault status of aircraft engines, and compared to existing methods, this method can provide more accurate diagnostic results.

关键词： fault diagnosis aeroengine multi-sensor information fusion sparse denoising autoencoder

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A Double-Weighted Deterministic Extreme Learning Machine Based on sparse denoising autoencoder and Its Applications

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Journal of Computer and Communications 2022年第11期10卷 138-153页

作者： Liang Luo Bolin Liao Cheng Hua Rongbo Lu College of Computer Science and Engineering Jishou University Jishou China College of Computer and Artificial Intelligence Huaihua University Huaihua China

Extreme learning machine (ELM) is a feedforward neural network-based machine learning method that has the benefits of short training times, strong generalization capabilities, and will not fall into local minima. However, due to the traditional ELM shallow architecture, it requires a large number of hidden nodes when dealing with high-dimensional data sets to ensure its classification performance. The other aspect, it is easy to degrade the classification performance in the face of noise interference from noisy data. To improve the above problem, this paper proposes a double pseudo-inverse extreme learning machine (DPELM) based on sparse denoising autoencoder (SDAE) namely, SDAE-DPELM. The algorithm can directly determine the input weight and output weight of the network by using the pseudo-inverse method. As a result, the algorithm only requires a few hidden layer nodes to produce superior classification results when classifying data. And its combination with SDAE can effectively improve the classification performance and noise resistance. Extensive numerical experiments show that the algorithm has high classification accuracy and good robustness when dealing with high-dimensional noisy data and high-dimensional noiseless data. Furthermore, applying such an algorithm to Miao character recognition substantiates its excellent performance, which further illustrates the practicability of the algorithm.

关键词： Extreme Learning Machine sparse denoising autoencoder Pseudo-Inverse Method Miao Character Recognition

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Fault diagnosis of aircraft engines based on multi-sensor fusion sparse denoising autoencoder

Fault diagnosis of aircraft engines based on multi-sensor fu...

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第43届中国控制会议

作者： ShiJie He Zhi-Ming Wang Kun-Zhi Liu Xi-Ming Sun School of Control Science and Control Engineering Dalian University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

As the heart of an aircraft,it is crucial to accurately grasp the operational status of ***,it is difficult to fully reflect the accurate fault status through a single sensor signal,and the diagnostic effect of using input signals in complex environments is not ***,this article proposes an aeroengine fault diagnosis method based on multisensor fusion and sparse denoising autoencoder,which comprehensively considers multi-sensor decision-making and reduces the difficulty of extracting high-dimensional data features,reducing the interference caused by *** have shown that this method can effectively identify the fault status of aircraft engines,and compared to existing methods,this method can provide more accurate diagnostic results.

关键词： fault diagnosis aeroengine multi-sensor information fusion sparse denoising autoencoder

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Harnessing feature pruning with optimal deep learning based DDoS cyberattack detection on IoT environment

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Scientific Reports 2025年第1期15卷 1-15页

作者： Yang, Eunmok Jeong, Sooyong Seo, Changho Department of Financial Information Security Kookmin University Seoul 02707 South Korea Department of Convergence Science Kongju National University Gongju 32588 South Korea Basic Science Research Institution Kongju National University Gongju 32588 South Korea

The swift development of the Internet of Things (IoT) devices has created a pressing need for effective cybersecurity measures. They are vulnerable to different cyber threats that can compromise the functionality and security of urban systems. Distributed Denial of Service (DDoS) attacks are among IoT networks’ most challenging and destructive cyber threats. With the rapid growth in IoT devices and users, the vulnerability of IoT devices to such attacks has enhanced significantly, making DDoS attacks a predominant threat. This work introduces several approaches for effectively detecting IoT-based DDoS threats. Classical machine learning (ML) techniques mostly face difficulty in managing real-world traffic characteristics effectually, making them less appropriate for detecting DDoS attacks. In contrast, Artificial Intelligence (AI)-based methods have proven more effective in detecting cyber-attacks than conventional approaches. This manuscript proposes an effective Feature Pruning with Optimal Deep Learning-based DDoS Attack Detection (FPODL-DDoSAD) technique in the IoT framework. The FPODL-DDoSAD technique initially uses a min-max scalar for the data scaling into the standard layout. Besides, the feature pruning process is performed using an improved pelican optimization algorithm (IPOA), which enables the choice of an optimal subset of features. Meanwhile, DDoS attacks are recognized using a sparse denoising autoencoder (SDAE) model. Furthermore, the parameter tuning of the SDAE classifier is accomplished by utilizing the Fish Migration Optimizer (FMO) technique. The experimental values of the FPODL-DDoSAD approach are assessed on the benchmark BoT-IoT dataset. The comparison study of the FPODL-DDoSAD method demonstrates a superior accuracy value of 99.80% over existing techniques. © The Author(s) 2025.

关键词： Cyberattack detection Fish migration optimizer Min–max scalar Pelican optimization algorithm sparse denoising autoencoder

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The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2020年第11期42卷 1-13页

作者： Dong, Shaojiang He, Kun Tang, Baoping Chongqing Jiaotong Univ Sch Mech & Vehicle Engn Chongqing 400074 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

The vibration signals of rolling bearing obtained under variable working conditions do not obey the same independent distribution so that the traditional method of bearing fault diagnosis has low accuracy, a fault diagnosis method about rolling bearing based on sparse denoising autoencoder (SDAE) for deep feature extraction combining transfer learning is proposed. First, the bearing vibration signal in the time domain is transformed for frequency domain signal via Fourier transform, which is input into the SDAE for adaptive deep feature extraction. Then, the joint geometrical and statistical alignment is introduced to deal with the deep feature samples for reducing the domain discrepancy both statistically and geometrically. Finally, the k-nearest neighbor classification algorithm is used for completing the fault diagnosis of rolling bearing under variable working conditions. The experimental results show that the method presented in the paper improves the accuracy rate of fault diagnosis about rolling bearing under variable working conditions, verifies its feasibility and effectiveness.

关键词： Rolling bearing sparse denoising autoencoder Variable working conditions Fault diagnosis Transfer learning

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Sparsity-based autoencoders for denoising cluttered radar signatures

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IET RADAR SONAR AND NAVIGATION 2021年第8期15卷 915-931页

作者： Ram, Shobha Sundar Vishwakarma, Shelly Sneh, Akanksha Yasmeen, Kainat Indraprastha Inst Informat Technol New Delhi 110020 India UCL Dept Secur & Crime Sci London England

Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high-range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal-to-noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single-layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of -10 dB and label mismatch error of 50%.

关键词： wall characteristics range enhanced frontal images indoor radar images cluttered radar signatures image resolution wall clutter radar clutter high range resolution sparsity based autoencoders free space images denoised ACR signatures noisy images image coding radar imaging stacked denoising autoencoder StackedSDAE dynamic clutter target independent static clutter diverse radar frequencies sparse denoising autoencoder Doppler time spectrograms clean images image denoising image reconstruction

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An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach

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SUSTAINABLE CITIES AND SOCIETY 2021年 70卷 102923-102923页

作者： Samal, K. Krishna Rani Panda, Ankit Kumar Babu, Korra Sathya Das, Santos Kumar Natl Inst Technol Rourkela Dept Comp Sci & Engn Rourkela 769008 India Natl Inst Technol Rourkela Dept Elect & Commun Engn Rourkela 769008 India

Air pollution forecasting is a significant step for air quality pollution management to mitigate pollution's negative impact on the environment and people's health. The data-driven forecasting model can help a better understanding of environmental air quality. The existing data-driven forecasting models usually ignore missing values, the correlations between the pollutant and meteorological factors and fail to perform temporal modeling effectively, affecting prediction accuracy. In response to these issues, we present a deep learning based Convolutional LSTM-SDAE (CLS) model to forecast the particulate matter level, revealing the correlation between particulate matter and meteorological factors. In the proposed architecture, the k nearest neighbor (KNN) imputation technique is employed to recover the air quality dataset's missing values. The Convolutional Long Short Term Memory (CNN-LSTM) unit identifies the vast dataset's hidden features and performs pollutants' temporal modeling. In addition, Bidirectional Gatted Recurrent Unit (BIGRU) is implemented as both encoder and decoder in sparse denoising autoencoder, which reconstructs the CNN-LSTM model's output in the dynamic fine-tuning layer to get robust prediction results. The experimental results in Talcher, India, and Beijing, China indicate that the model can improve forecasting accuracy and outperforms the other state of art and baseline models.

关键词： Deep learning Time series forecasting Imputation Convolutional LSTM sparse denoising autoencoder PM2 5

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