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31st International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： Crivelaro, Matheus Goes Rodrigues, Douglas Gifalli, Andre Papa, Joao Paulo Gonzales, Carlos Guilherme de Souza, Andre Nunes da Silva, Gustavo Vinicius Silveira Neto, Erasmo Sao Paulo State Univ Dept Elect Engn Bauru SP Brazil Sao Paulo State Univ Dept Comp Bauru SP Brazil ISA CTEEP Specialized Maintenance Ctr Bauru SP Brazil High Voltage Equipments HVEX Dept Elect Engn Itajuba Brazil

ISBN: (纸本)9798350391893;9798350391886

Analyzing Partial Discharge (PD) signals is crucial to assessing the health of insulation in high-voltage systems. Nevertheless, noise often distorts these signals, hindering the ability to obtain precise information. This paper proposes a novel deep-learning approach using two denoising autoencoders (DAEs) to learn data representations and eliminate noise during reconstruction. By leveraging DAEs' capacity to capture essential features within the latent space, this method enhances the analysis of PD signals and yields more accurate results. This paper investigates the effectiveness of two deep-learning architectures for denoising partial discharge signals in high-voltage insulation systems. Experimental results carried out on a PD dataset demonstrated the efficiency of the Linear AE model in removing noise in sets A, B, and C suggesting that DAEs hold great promise in PD signal denoising.

关键词： partial discharge signal denoising autoencoder deep learning

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A correlative denoising autoencoder to model social influence for top-N recommender system

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Frontiers of Computer Science 2020年第3期14卷 31-43页

作者： Yiteng PAN Fazhi HE Haiping YU School of Computer Science Wuhan UniversityWuhan 430072China

In recent years,there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation *** most cases,it requires a larger number of data to train a robust deep learning model,which contains a lot of parameters to fit training ***,both data of user ratings and social networks are facing critical sparse problem,which makes it not easy to train a robust deep neural network *** this problem,we propose a novel correlative denoising autoencoder(CoDAE)method by taking correlations between users with multiple roles into account to learn robust representations from sparse inputs of ratings and social networks for *** develop the CoDAE model by utilizing three separated autoencoders to learn user features with roles of rater,truster and trustee,***,on account of that each input unit of user vectors with roles of truster and trustee is corresponding to a particular user,we propose to utilize shared parameters to learn common information of the units that corresponding to same ***,we propose a related regularization term to learn correlations between user features that learnt by the three subnetworks of CoDAE *** further conduct a series of experiments to evaluate the proposed method on two public datasets for Top-N recommendation *** experimental results demonstrate that the proposed model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP and NDCG.

关键词： social network recommender system denoising autoencoder neural network

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denoising autoencoder-based speaker feature restoration for utterances of short duration 16

Denoising autoencoder-based speaker feature restoration for ...

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16th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2015)

作者： Yamamoto, Hitoshi Koshinaka, Takafumi NEC Corp Ltd Informat & Media Proc Lab Tokyo Japan

ISBN: (纸本)9781510817906

This paper describes a speaker feature restoration method for improving text-independent speaker recognition with short utterances. The method employs a denoising autoencoder (DAE) to compensate speaker features of a short utterance which contains limited phonetic information. It first estimates phonetic distribution in the utterance as posteriors based on speech models and then transforms an i-vector of the utterance using DAE along with the phonetic posteriors. The DAE-based transformation is able to produce a reliable speaker feature with help of supervised training using pairs of long and short speech segments. Speaker recognition experiments on an NIST SRE task demonstrate a 37.9% error reduction.

关键词： speaker recognition short utterance i-vector feature compensation denoising autoencoder

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Noise reduction in the spectral domain of hyperspectral images using denoising autoencoder methods

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020年 203卷

作者： Zhang, Chu Zhou, Lei Zhao, Yiying Zhu, Susu Liu, Fei He, Yong Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Peoples R China Minist Agr & Rural Affairs Key Lab Spect Sensing Hangzhou 310058 Peoples R China

denoising of spectra has been a great challenge in hyperspectral image analysis. Near-infrared hyperspectral images of milk powder, rice flour and soybean flour were acquired and denoising in the spectral domain were studied. Noise free spectra and noises were simulated based on sample pixel-wise spectra. The noisy spectra with signal to noise ratio (SNR) around 45 dB (similar to real pixel-wise spectra) were simulated. The simulated noisy spectra were preprocessed by traditional methods as moving average smoothing (MAS), Savitzky-Golay smoothing (SGS), wavelet transform (WT) and empirical mode decomposition (EMD). The basic denoising autoencoder (DAE-1) and the stacked DAE (DAE-2) were studied for denoising. The noisy spectra with SNR around 35 dB and 55 dB were further simulated to explore the effectiveness of DAE based methods. DAE-1 and DAE-2 performed better than the other methods, with higher SNR, lower mean squared error (MSE) and mean absolute error (MAE). The developed DAE methods were applied to real-world pixel-wise spectra with good performances. The overall results proved the feasibility of using DAE based methods for noise reduction in the spectral domain of hyperspectral images, and the DAE based methods have great potential to be extended to spectral denoising of other vibrational spectroscopy techniques.

关键词： denoising autoencoder Noise reduction Hyperspectral image Wavelet transform Spectra simulation Empirical mode decomposition

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Effective network intrusion detection via representation learning: A denoising autoencoder approach

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COMPUTER COMMUNICATIONS 2022年 194卷 55-65页

作者： Lopes, Ivandro O. Zou, Deqing Abdulqadder, Ihsan H. Ruambo, Francis A. Yuan, Bin Jin, Hai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Natl Engn Res Ctr Big Data Technol & Syst Wuhan Peoples R China Cluster & Grid Comp Lab Wuhan Peoples R China Serv Comp Technol & Syst Lab Wuhan Peoples R China Nucleo Operac Soc Informacao Praia Cape Verde Big Data Secur Engn Res Ctr Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Cihan Univ Erbil Dept Informat & Software Engn Erbil Kurdistan Regio Iraq Shenzhen Huazhong Univ Sci & Technol Res Inst Shenzhen 518057 Peoples R China

The introduction of deep learning techniques in intrusion detection problems has enabled an enhanced standard of detection effectiveness. However, most of the progress has occurred in supervised learning, which required a vast amount of labeled training samples. In the real world, there is a limited amount of labeled data available to train a deep neural network, affecting the classifier's detection performance. Therefore, to address the lack of labeled network traffic required to train an effective supervised classifier, this study introduces a semi -supervised intrusion detection framework that combines the unsupervised and supervised techniques. The unsupervised pre-training approach is implemented based on a denoising autoencoder (DAE), to compress the intrusion dataset and obtain the lower-dimensional features representation. Then a portion of the compressed data is used to train the DNN classifier based on a multiclass supervised approach. The network architecture is optimized by tuning hyper-parameters using a trial-and-error approach. Comparative analysis is performed between the proposed approach and the most relevant deep learning methods available in the literature against the CICIDS2018 dataset, consisting of recent network attack traces. Our approach outperforms competitive methods while maintaining stable classification results above 99.6% on F1-score, precision, and recall metrics. Additionally, it is trained in 64 min while achieving a low false alarm rate. Furthermore, the DAE module reduces the input network traffic data to one-tenth of the size of the input dataset.

关键词： Deep learning denoising autoencoder Intrusion detection Cybersecurity

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Robust and Fast Temperature Extraction for Brillouin Optical Time-Domain Analyzer by Using denoising autoencoder-Based Deep Neural Networks

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IEEE SENSORS JOURNAL 2020年第7期20卷 3614-3620页

作者： Wang, Biwei Guo, Nan Wang, Liang Yu, Changyuan Lu, Chao Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Opt & Elect Informat Natl Engn Lab Next Generat Internet Access Syst Wuhan 430074 Peoples R China

A method of robust and fast temperature extraction for Brillouin optical time-domain analyzer (BOTDA) sensing systems using the denoising autoencoder (DAE) based deep neural networks (DNN) is demonstrated. After appropriate training, the DAE suppresses the noise on the measured Brillouin gain spectrum (BGS), and improves the signal-to-noise ratio (SNR) by 9.96dB in our experiment. To extract temperature, the DAE as a basic block is stacked to form the DNN model. Since the DNN model is based on DAE, both denoising and fast temperature extraction can be simultaneously finished using only one DNN model. Thus, it is more robust and faster than the conventional Lorentzian curve fitting (LCF) method, especially when the input data has low SNR level. In the case of 4.6dB SNR, the standard deviation (SD) of the measured temperature at the end of 40km fiber under test (FUT) is reduced from 2.4 degrees C to 1.2 degrees C by using DAE based DNN when compared with that using the LCF method, and the corresponding root-mean-square error (RMSE) is reduced from 2.4 degrees C to 1.3 degrees C. Moreover, since the temperature information can be extracted directly from the experimental BGS data without the need of time-consuming curve fitting and subsequent conversion from Brillouin frequency shift (BFS) to temperature, the speed of temperature extraction using the DAE based DNN is faster by 500 times than that using LCF. Combining the advantages of both denoising and fast processing speed, the DAE based DNN would be a practical way of temperature extraction for the BOTDA systems.

关键词： Brillouin optical time-domain analyzer denoising autoencoder deep neural networks temperature extraction

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Multiple ECG segments denoising autoencoder model

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2023年第3期68卷 275-284页

作者： Samann, Fars Schanze, Thomas TH Mittelhessen THM Inst Biomed Tech IBMT FB Life Sci Engn LSE Wiesenstr 14 D-35390 Giessen Germany Univ Duhok Dept Biomed Engn Duhok 42001 Iraq

Objectives: denoising autoencoder (DAE) with a single hidden layer of neurons can recode a signal, i.e., converting the original signal into a noise-reduced signal. The DAE approach has shown a good performance in denoising bio-signals, like electrocardiograms (ECG). In this paper, we study the effect of correlated, uncorrelated and jittered datasets on the performance of the DAE ***: Vectors of multiple concatenated ECG segments of simultaneously recorded Einthoven recordings I, II, III are considered to establish the following dataset cases: (1) correlated, (2) uncorrelated, and (3) jittered. We consider our previous work in finding the optimal number of hidden neurons receiving the input signal with respect to signal quality and computational burden by applying Akaike's information criterion. To evaluate DAE, these datasets are corrupted with six types of noise, namely mix noise (MX), motion artifact noise (MA), electrode movement (EM), baseline wander (BW), Gaussian white noise (GWN) and high-frequency noise (HFN), to simulate real case scenario. Spectral analysis is used to study the effects of noise whose power spectrum may overlap with the power spectrum of the wanted signal on DAE ***: The simulation results show (a) that the number of hidden neurons to denoise multiple correlated ECG is much lower than for jittered signals, (b) QRS-complex based ECG alignment preferable, (c) noises with slightly overlapping power spectrum, like BW and HFN, can be easily removed with sufficient number of neurons, while the noise with completely overlapping spectrum, like GWN, requires a very low-dimensional and thus coarser reduction to recover the ***: The performance of DAE model in terms of signal-to-noise ratio improvement and the required number of hidden neurons can be improved by utilizing the correlation among simultaneous Einthoven I, II, III records.

关键词： Akaike's information criterion denoising autoencoder multiple ECG segments physical noises spectral analysis

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Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network

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IET SOFTWARE 2020年第3期14卷 185-202页

作者： Zhu, Kun Zhang, Nana Ying, Shi Zhu, Dandan Wuhan Univ Sch Comp Sci 299 Bayi Rd Wuhan Peoples R China Shanghai Jiao Tong Univ Artificial Intelligence Inst 800 Dongchuan Rd Shanghai Peoples R China

Just-in-time defect prediction is an important and useful branch in software defect prediction. At present, deep learning is a research hotspot in the field of artificial intelligence, which can combine basic defect features into deep semantic features and make up for the shortcomings of machine learning algorithms. However, the mainstream deep learning techniques have not been applied yet in just-in-time defect prediction. Therefore, the authors propose a novel just-in-time defect prediction model named DAECNN-JDP based on denoising autoencoder and convolutional neural network in this study, which has three main advantages: (i) Different weights for the position vector of each dimension feature are set, which can be automatically trained by adaptive trainable vector. (ii) Through the training of denoising autoencoder, the input features that are not contaminated by noise can be obtained, thus learning more robust feature representation. (iii) The authors leverage a powerful representation-learning technique, convolution neural network, to construct the basic change features into the abstract deep semantic features. To evaluate the performance of the DAECNN-JDP model, they conduct extensive within-project and cross-project defect prediction experiments on six large open source projects. The experimental results demonstrate that the superiority of DAECNN-JDP on five evaluation metrics.

关键词： neural nets learning (artificial intelligence) denoising autoencoder software defect prediction basic defect features mainstream deep learning techniques just-in-time defect prediction model autoencoder convolutional neural network convolution neural network cross-project defect prediction experiments

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Feature learning from incomplete EEG with denoising autoencoder

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NEUROCOMPUTING 2015年 165卷 23-31页

作者： Li, Junhua Struzik, Zbigniew Zhang, Liqing Cichocki, Andrzej RIKEN Brain Sci Inst Lab Adv Brain Signal Proc Wako Saitama 3510198 Japan Shanghai Jiao Tong Univ Dept Comp Sci & Engn Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland

An alternative pathway for the human brain to communicate with the outside world is by means of a brain computer interface (BCI). A BCI can decode electroencephalogram (EEG) signals of brain activities, and then send a command or an intent to an external interactive device, such as a wheelchair. The effectiveness of the BCI depends on the performance in decoding the EEG. Usually, the EEG is contaminated by different kinds of artefacts (e.g., electromyogram (EMG), background activity), which leads to a low decoding performance. A number of filtering methods can be utilized to remove or weaken the effects of artefacts, but they generally fail when the EEG contains extreme artefacts. In such cases, the most common approach is to discard the whole data segment containing extreme artefacts. This causes the fatal drawback that the BCI cannot output decoding results during that time. In order to solve this problem, we employ the Lomb-Scargle periodogram to estimate the spectral power from incomplete EEG (after removing only parts contaminated by artefacts), and denoising autoencoder (DAE) for learning. The proposed method is evaluated with motor imagery EEG data. The results show that our method can successfully decode incomplete EEG to good effect. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Brain computer interface Spectral power estimation denoising autoencoder Motor imagery Incomplete EEG

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A swarm anomaly detection model for IoT UAVs based on a multi-modal denoising autoencoder and federated learning

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 196卷

作者： Lu, Yu Yang, Tao Zhao, Chong Chen, Wen Zeng, Rong China West Normal Univ Sch Comp Sci Nanchong Peoples R China Sichuan Univ Sch Cyber Sci & Engn Chengdu Peoples R China China West Normal Univ Sch Elect Informat Engn Nanchong Peoples R China

The widespread application of unmanned aerial vehicle (UAV) swarms has posed unique challenges for anomaly detection. Multi-modal noise from multi-source heterogeneous sensors during UAV swarm communication affects data quality, and limited data sharing between different UAV organisations restricts training a unified anomaly detection model. To address these problems, this study proposes a UAV swarm anomaly detection model based on a multi-modal denoising autoencoder and federated learning (L-MDAE). First, L-MDAE simulates noise by adding perturbations to the original data during UAV swarm communication. Second, according to the characteristics of UAV data noise, this study designs a new MSE loss function (normalised mean square error, NMSE) based on the normalised correlation coefficient. Furthermore, heterogeneous neural networks with NMSE are constructed to enhance the multi-modal noise-removal capability of the model. Finally, this study considers the UAV control node as the client and the ground control station as the server. Using a federated learning mechanism, L-MDAE is trained on a client dataset, and its parameters are integrated and distributed on the server. In this way, each UAV can effectively detect abnormal data using L-MDAE. Experimental results on five datasets, including ALFA, TLM and ITS, demonstrate that L-MDAE outperforms baseline and related models. When using ALFA, L-MDAE achieved an accuracy of 0.9919 and a swarm anomaly detection accuracy of 0.9901, approximately 2% higher than that of the baseline model.

关键词： UAV swarm Intrusion detection Federated learning denoising autoencoder

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