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IC-U-Net: A U-Net-based denoising autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal

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NEUROIMAGE 2022年 263卷 119586-119586页

作者： Chuang, Chun-Hsiang Chang, Kong-Yi Huang, Chih-Sheng Jung, Tzyy-Ping Natl Tsing Hua Univ Coll Educ Res Ctr Educ & Mind Sci Hsinchu Taiwan Natl Tsing Hua Univ Inst Informat Syst & Applicat Coll Elect Engn & Comp Sci Hsinchu Taiwan Natl Tsing Hua Univ Dept Educ & Learning Technol Hsinchu Taiwan Natl Taiwan Ocean Univ Dept Comp Sci & Engn Keelung Taiwan Elan Microelect Corp Dept Artificial Intelligence Res & Dev Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Coll Artificial Intelligence & Green Energy Hsinchu Taiwan Natl Taipei Univ Technol Coll Elect Engn & Comp Sci Taipei Taiwan Univ Calif San Diego Inst Engn Med La Jolla CA USA Univ Calif San Diego Inst Neural Computat La Jolla CA USA

Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative to develop a practical and reliable artifact removal method to prevent the misinterpretation of neural signals and the underperformance of brain-computer interfaces. Based on the U-Net architecture, we developed a new artifact removal model, IC-U-Net, for removing pervasive EEG artifacts and reconstructing brain signals. IC-U-Net was trained using mixtures of brain and non-brain components decomposed by independent component analysis. It uses an ensemble of loss functions to model complex signal fluctuations in EEG recordings. The effectiveness of the proposed method in recovering brain activities and removing various artifacts (e.g., eye blinks/movements, muscle activities, and line/channel noise) was demonstrated in a simulation study and four real-world EEG experiments. IC-U-Net can reconstruct a multi-channel EEG signal and is applicable to most artifact types, offering a promising end -to-end solution for automatically removing artifacts from EEG recordings. It also meets the increasing need to image natural brain dynamics in a mobile setting. The code and pre-trained IC-U-Net model are available at https://***/roseDwayane/AIEEG .

关键词： EEG Artifact Removal Signal Reconstruction U-Net Independent Component Analysis ICLabel denoising autoencoder Deep Learning

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ProADD: Proactive battery anomaly dual detection leveraging denoising convolutional autoencoder and incremental voltage analysis

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APPLIED ENERGY 2024年 373卷

作者： Jeon, Jihun Cheon, Hojin Jung, Byungil Kim, Hongseok Sogang Univ Dept Elect Engn 35 Baekbeom Ro Seoul 04107 South Korea Doosan Enerbil Data Analyt Team 155 Jeongjail Ro Seongnam Si 13557 Gyeonggi Do South Korea

The use of lithium-ion batteries is increasing fast in many fields such as electric vehicles (EVs) and energy storage system (ESS). However, the number of accidents caused by thermal runaway of lithium-ion batteries is also increasing. Hence, it is critical to diagnose lithium-ion batteries proactively for safe operation. This paper considers both electrochemical model and deep learning model to capture the intrinsic characteristics of battery and diagnose its state from complementary perspectives. First, the denoising autoencoder (DAE) is leveraged to detect outliers in latent space clustering. Second, the traditional incremental capacity analysis (ICA) is revisited and incremental voltage analysis (IVA) is proposed to make it suitable for real-time ESS operation. Then, a method is proposed that jointly considers the DAE error and the IVA peak to proactively detect anomaly battery modules of ESS. Specifically, one-class support vector machine (OCSVM) is leveraged as well as the transformed Z-score. Our results confirm that the proposed framework named ProADD clearly identifies and quantifies anomaly modules, which provides a guideline for safe ESS operation in real fields.

关键词： Lithium-ion battery Energy storage system Anomaly detection denoising autoencoder Incremental voltage analysis

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Multivariate Time Series Missing Data Imputation Using Recurrent denoising autoencoder

Multivariate Time Series Missing Data Imputation Using Recur...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Jianye Zhang Peng Yin Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781728118680

This paper presents a novel method for imputing missing data of multivariate time series by adapting the Long Short Term-Memory (LSTM) and denoising autoencoder (DAE). Missing data are ubiquitous in many domains;proper imputation methods can improve performance on many tasks. Our method focus on multivariate time series, applying bidirectional LSTM to learn temporal information and DAE to learn correlation between variables, and we combine these two models by using LSTM as the encoder component of DAE. Several real-world datasets, including electroencephalogram (EEG), electromyogram (EMG) and electronic health records (EHRs), are extracted to test the performance of our method. Through simulation studies, we compare the proposed recurrent denoising autoencoder with several baseline imputation methods and demonstrate its effectiveness in both missing data estimation and label prediction after imputation.

关键词： missing data multivariate time series denoising autoencoder missing data Time series Multivariate Noise reduction time information

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Ameliorated Deep Learning based on Improved denoising autoencoder and GACNN

Ameliorated Deep Learning based on Improved Denoising Autoen...

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第37届中国控制会议

作者： Da Wang Xuefeng Zhou Zhihao Xu Taobo Cheng Xiaoxv Wang Haoqing Miao Shenyang University of Technology School of information science and engineering Guangdong Key Laboratory of Modern Control Technology Guangdong Institute of Intelligent Manufacturing

Improving the performance of deep learning and making it more in line with real life have always been the research direction of artificial intelligence. In this paper, a denoising autoencoder genetic algorithm convolution neural network（DGCNN） model based on deep learning is proposed. Two parts of the research work are combined to improve its performance. Firstly, the traditional autoencoder is replaced by the denoising autoencoder and improved the way of adding noise. Secondly, genetic algorithm is utilized to combine CNN at the stage of image classification. This allows DGCNN to cope with complex and volatile situations while enhancing image processing capabilities. Simulation results show that the method can enhance the ability of image processing than traditional methods. The performance of the proposed model is better than traditional method when the images of different loss levels are processed by this method. The results are verifying the feasibility and effectiveness of the model and algorithm. DGCNN shows better capacity in improving the performance of image processing and dealing with complex situations effectively.

关键词： deep learning denoising autoencoder genetic algorithm convolution neural network

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Mood detection from daily conversational speech using denoising autoencoder and LSTM

Mood detection from daily conversational speech using denois...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kun-Yi Huang Chung-Hsien Wu Ming-Hsiang Su Hsiang-Chi Fu Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781509041183

In current studies, an extended subjective self-report method is generally used for measuring emotions. Even though it is commonly accepted that speech emotion perceived by the listener is close to the intended emotion conveyed by the speaker, research has indicated that there still remains a mismatch between them. In addition, the individuals with different personalities generally have different emotion expressions. Based on the investigation, in this study, a support vector machine (SVM)-based emotion model is first developed to detect perceived emotion from daily conversational speech. Then, a denoising autoencoder (DAE) is used to construct an emotion conversion model to characterize the relationship between the perceived emotion and the expressed emotion of the subject for a specific personality. Finally, a long short-term memory (LSTM)-based mood model is constructed to model the temporal fluctuation of speech emotions for mood detection. Experimental results show that the proposed method achieved a detection accuracy of 64.5%, improving by 5.0% compared to the HMM-based method.

关键词： Long-term emotion tracking mood detection denoising autoencoder long short-term memory

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Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions

Unsupervised adaptation of a denoising autoencoder by Bayesi...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： J. Heymann R. Haeb-Umbach P. Golik R. Schluter Dept. of Commun. Eng. Univ. of Paderborn Paderborn Germany

ISBN: (纸本)9781467369985

The parametric Bayesian Feature Enhancement (BFE) and a datadriven denoising autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different conditions by an appropriate parameter setting, while the latter needs to be trained on conditions similar to the ones expected at decoding time, making it vulnerable to a mismatch between training and test conditions. We use a DNN backend and study reverberant ASR under three types of mismatch conditions: different room reverberation times, different speaker to microphone distances and the difference between artificially reverberated data and the recordings in a reverberant environment. We show that for these mismatch conditions BFE can provide the targets for a DA. This unsupervised adaptation provides a performance gain over the direct use of BFE and even enables to compensate for the mismatch of real and simulated reverberant data.

关键词： deep neuronal networks denoising autoencoder feature enhancement robust speech recognition BFE Speech recognition parameter setting Noise reduction MISMATCH reverberation chambers Bayesian inference Refractive surgery enhancement

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Adaptive Noise Level for Stacked denoising autoencoder

Adaptive Noise Level for Stacked Denoising Autoencoder

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第三届机电一体化与工业信息学国际学术研讨会(ISMII 2017)

作者： Qianjun Zhang Lei Zhang Machine Intelligence Laboratory College of Computer ScienceSichuan University

The stacked denoising autoencoder(SDAE) is a slight modification of stacked autoencoder,which is trained to reconstruct a clean version of an input from its corrupted *** has been successfully used to learn new representations for an unsupervised ***,the noise level in the SDAE is determined by experience and remains fixed throughout the training *** address this limitation,we present an adaptive stacked denoising autoencoder based on the principle of annealing(Adaptive SDAE),a novel method of adaptively obtaining the noise *** is achieved by first computing the average noise level for each epoch using a linear average noise level function based on the principle of annealing;and second calculating the noise level for each input neuron based on the average noise level,and the contribution of the input neuron to the activation of hidden neurons(which depend on the input neuron's value and the weights).Thus,the network includes a combination of features at multiple *** experimental results show that our proposed Adaptive SDAE performed better than SDAE and other unsupervised feature learning methods.

关键词： Machine learning denoising autoencoder Adaptive noise level Annealing Feature combination

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Ball Screw System Performance Evaluation based on denoising autoencoder

Ball Screw System Performance Evaluation based on Denoising ...

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The 29th International Congress on Condition Monitoring and Diagnostic Engineering Management

作者： Liang Guo Hongli Gao Haifeng Huang School of Mechanical Engineering Southwest Jiaotong University

Ball screw is widely used in many precision *** its performance is valuable to assure safe *** it is important to study on condition-based maintenance technique of ball *** this paper,we introduce a new ball screw performance evaluation based on deep learning ***,the ball screw system acceleration performance degradation experiment is conducted to acquisition the experiment ***,the time,frequency and time-frequency domain features are extracted to represent the characteristic of vibration *** last,the denosied autoencoder deep neural network is applied to learn the deeper features and recognition the ball screw *** contrast experiments are performed,the results demonstrates the proposed method is effective in ball screw system performance evaluation.

关键词： Ball screw system performance evaluation denoising autoencoder Deep neural network Introduction

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Wi-Fi DSAR: Wi-Fi based Indoor Localization using denoising Supervised autoencoder 30

Wi-Fi DSAR: Wi-Fi based Indoor Localization using Denoising ...

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30th Wireless and Optical Communications Conference (WOCC)

作者： Wang, Yun-Hao Yang, Ta-Wei Chou, Cheng-Fu Chang, Ing-Chau Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Natl Taiwan Univ Informat Technol Off Taipei Taiwan Natl Changhua Univ Educ Dept Comp Sci & Informat Engn Changhua Taiwan

ISBN: (纸本)9781665427722

In recent years, the demand for Wi-Fi has grown exponentially, which has led to the rapid development of indoor positioning services based on Wi-Fi fingerprints. Due to the Received Signal Strength Indicator (RSSI) variation over time, the device heterogeneity, and dynamic changes in the environment, the accuracy and robustness of traditional Wi-Fi fingerprint-based methods usually degrade. To alleviate these issues, we propose an approach based on the denoising supervised autoencoder for regression tasks that we named Wi-Fi DSAR. The idea of Wi-Fi DSAR design is to (a) has strong resistance to the noise in the received Wi-Fi signal, and (b) prevent overfitting the fingerprint database to achieve robustness. We do a performance study by comparing other fingerprint-based works with our Wi-Fi DSAR. All works are evaluated in three open datasets, i.e., DSI, IPIN2016, and IPIN2020, with different area sizes and access point densities. Experimental results demonstrate that compared with existing fingerprint-based methods, our Wi-Fi DSAR is able to reduce the average positioning error by 20% to 50% This is because our Wi-Fi DSAR has both noise immunity and generalization performance.

关键词： indoor localization Wi-Fi fingerprint positioning machine learning RSSI denoising autoencoder

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Exploring multi-channel features for denoising-autoencoder-based speech enhancement 40

Exploring multi-channel features for denoising-autoencoder-b...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Araki, Shoko Hayashi, Tomoki Delcroix, Marc Fujimoto, Masakiyo Takeda, Kazuya Nakatani, Tomohiro NTT Communication Science Laboratories NTT Corporation 2-4 Hikaridai Soraku-gun Kyoto Japan Department of Media Science Nagoya University 1 Furo-cho Nagoya-shi Aichi Japan

ISBN: (纸本)9781467369978

This paper investigates a multi-channel denoising autoencoder (DAE)-based speech enhancement approach. In recent years, deep neural network (DNN)-based monaural speech enhancement and robust automatic speech recognition (ASR) approaches have attracted much attention due to their high performance. Although multi-channel speech enhancement usually outperforms single channel approaches, there has been little research on the use of multi-channel processing in the context of DAE. In this paper, we explore the use of several multi-channel features as DAE input to confirm whether multi-channel information can improve performance. Experimental results show that certain multi-channel features outperform both a monaural DAE and a conventional time-frequency-mask-based speech enhancement method. © 2015 IEEE.

关键词： Deep learning denoising autoencoder multi-channel noise suppression PASCAL 'CHiME' challenge

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