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RunDAE model: Running denoising autoencoder models for denoising ECG signals

COMPUTERS IN BIOLOGY AND MEDICINE 2023年 166卷 107553-107553页

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

Background: The denoising autoencoder (DAE) is commonly used to denoise bio-signals such as electrocar-diogram (ECG) signals through dimensional reduction. Typically, the DAE model needs to be trained using correlated input segments such as QRS-aligned segments or long ECG segments. However, using long ECG segments as an input can result in a complex deep DAE model that requires many hidden layers to achieve a low-dimensional representation, which is a major ***: This work proposes a novel DAE model, called running DAE (RunDAE), for denoising short ECG segments without relying on the R-peak detection algorithm for alignment. The proposed RunDAE model employs a sample-by-sample processing approach, considering the correlation between consecutive, overlapped ECG segments. The performance of both the classical DAE and RunDAE models with convolutional and dense layers, respectively, is evaluated using corrupted QRS-aligned and non-aligned ECG segments with physical noise such as motion artifacts, electrode movement, baseline wander, and simulated noise such as Gaussian white ***: The simulation results indicate that 1. QRS-aligned segments are preferable to non-aligned segments, 2. the RunDAE model outperforms the classical DAE model in denoising ECG signals, especially when using dense layers and QRS-aligned segments, 3. training the RunDAE models with normal and arrhythmic ECG signals enhance model's properties/capabilities, and 4. the RunDAE is a multistage, non-causal, nonlinear adaptive ***: A shallow learning model, which consists of a couple of hidden layers, could achieve outstanding denoising performance using only the correlation among neighboring samples.

关键词： denoising autoencoder Convolutional denoising autoencoder Running denoising autoencoder Non-aligned ECG segments QRS-aligned ECG segments Overlapping ECG segments Multistage nonlinear adaptive filter

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End-to-end recurrent denoising autoencoder embeddings for speaker identification

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NEURAL COMPUTING & APPLICATIONS 2021年第21期33卷 14429-14439页

作者： Rituerto-Gonzalez, Esther Pelaez-Moreno, Carmen Univ Carlos III Madrid Dept Signal Theory & Commun Grp Multimedia Proc Ave Univ 30 Madrid 28911 Spain

Speech 'in-the-wild' is a handicap for speaker recognition systems due to the variability induced by real-life conditions, such as environmental noise and the emotional state of the speaker. Taking advantage of the principles of representation learning, we aim to design a recurrent denoising autoencoder that extracts robust speaker embeddings from noisy spectrograms to perform speaker identification. The end-to-end proposed architecture uses a feedback loop to encode information regarding the speaker into low-dimensional representations extracted by a spectrogram denoising autoencoder. We employ data augmentation techniques by additively corrupting clean speech with real-life environmental noise in a database containing real stressed speech. Our study presents that the joint optimization of both the denoiser and speaker identification modules outperforms independent optimization of both components under stress and noise distortions as well as handcrafted features.

关键词： denoising autoencoder Speaker embeddings Noisy conditions Stress End-to-end model Speaker identification

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A transformer model blended with CNN and denoising autoencoder for inter-patient ECG arrhythmia classification

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第PartC期86卷

作者： Xia, Yong Xiong, Yueqi Wang, Kuanquan Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

Researchers have proposed numerous novel features and models under the intra-patient paradigm. However, their performance suffers when considering the inter-patient paradigm. While some state-of-the-art results have been reported in recent years under the inter-patient paradigm, many of them deviate from the standard test protocol. The performance of minority classes remains unsatisfactory for practical applications under strict test protocols. This paper presents a novel framework based on a lightweight Transformer combined with CNN and a denoising autoencoder, which enhances the performance of minority classes under the standard test protocol. The proposed model includes a new seq2seq network that extracts local features from a single heartbeat using CNN or a denoising encoder, and attends to global features from neighboring heartbeats based on a lightweight Transformer encoder. In particular, we pretrained the autoencoder on the MIT-BIH dataset and an additional dataset, considering several transfer modes for feature representation. We organized multiple continuous heartbeats into a vector sequence, where each heartbeat incorporates information from its neighbors to improve feature representation. The model evaluation was conducted using the MIT-BIH inter-patient dataset, following the AAMI standard. The Transformer with CNN embedding achieved a total accuracy of 97.66% on the test set, while the Transformer with pretrained denoising autoencoder achieved a total accuracy of 97.93%. These results demonstrate the promising performance of our models for imbalanced inter-patient ECG classification under the standard test protocol.

关键词： Seq2seq model Transformer CNN denoising autoencoder Transfer learning ECG classification

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A transfer learning framework for well placement optimization based on denoising autoencoder

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 222卷

作者： Qi, Ji Liu, Yanqing Ju, Yafeng Zhang, Kai Liu, Lu Liu, Yuanyuan Xue, Xiaoming Zhang, Liming Zhang, Huaqing Wang, Haochen Yao, Jun Zhang, Weidong China Univ Petr East China Sch Petr Engn Qingdao Peoples R China Petrochina Changqing Oilfield Co Petr Technol Res Inst Xian Peoples R China Qingdao Univ Technol Qingdao Peoples R China Dagang Oilfield Co Explorat & Dev Res Inst Tianjin Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Well placement optimization is directly related to the recovery factor of reservoir development, and at present, the mainstream solution is an evolutionary algorithm. However, time-consuming numerical simulators need to be called to evaluate each alternative well placement scheme. Since the rules of well placement problems are universal, similar reservoirs will have similar well locations. Thus, knowledge transfer across similar well placement optimization tasks can expedite searching effectively. To this end, this paper proposes a novel transfer learning framework for well placement optimization to extract the potential well placement rules based on the feature extraction capability of a single-layer denoising autoencoder. The reconstruction mapping between the previous and present tasks is established to make the randomly generated well locations inherit the knowledge from the optimal well locations of the previous task, which helps the search direction of the evolutionary al-gorithm quickly bias to the optimal solution, thus, the solving of present task can be accelerated. The simplified denoising autoencoder holds a closed-form solution after derivation of the loss function, and the corresponding reuse of knowledge will not bring much additional computational burden on the evolutionary search. In addition, a similarity measure method between well placement optimization tasks is proposed to avoid a negative transfer. At last, comprehensive experiments on benchmark functions and well placement optimization instances are presented to evaluate the effectiveness of the proposed framework.

关键词： Well placement optimization Knowledge representation denoising autoencoder Evolutionary transfer optimization Similarity measure

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Investigation of denoising autoencoder-based deep learning model in noise-riding experimental data for reliable state-of-charge estimation

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JOURNAL OF ENERGY STORAGE 2023年 72卷

作者： Hong, Seonri Kang, Moses Kim, Jonghoon Baek, Jongbok Korea Inst Energy Res Energy ICT Convergence Res Dept Energy Efficiency Res Div Daejeon 16105 South Korea Chungnam Natl Univ Dept Elect Engn Energy Storage & Convers Lab Daejeon 34134 South Korea

State-of-charge (SOC) estimation plays a crucial role in battery management systems to ensure safe and reliable operation. However, SOC estimation remains challenging due to the dynamic nature of battery systems and varying ambient conditions. Data-driven methods have emerged as effective tools for analyzing nonlinear dynamical systems, but their performance heavily relies on data quality. In actual applications, data susceptible to distortions caused by external factors such as sensor failure, circuitry, and temperature variations, leading to degraded model performance. To address the performance degradation resulting from data quality deterioration, this paper introduces a denoising autoencoder is implemented as a stacked multi-layer perceptron, which learns to reconstruct distorted data. Furthermore, we propose the ensemble method that combines the autoencoder with an estimation model for SOC estimation in lithium-ion batteries. The effectiveness of the proposed model is demonstrated through tests conducted on a dataset comprising drive cycle profile of Panasonic 18650PF cells. The model validated under two ambient temperatures scenarios: identical and different, using a distorted dataset with added randomly added noise and dropout. The experimental results reveal that the proposed model achieved a 3 % error in training the drive profile relative to the actual values at different ambient temperatures. When compared to the plain model, the proposed ensemble model showed an increased RMSE of 4 %. Additionally, the performance of different estimation models was compared, with the LSTM model achieving an RMSE 0.67 at different ambient temperatures, outperforming the Support Vector Regression (SVR) with an RMSE 1.35 and the Extended Kalman Filter (EKF) with an RMSE of 0.87.

关键词： State-of-charge estimation denoising autoencoder Data-driven method Lithium-ion batteries Long short-term memory

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Sequential application of denoising autoencoder and long-short recurrent convolutional network for noise-robust remaining-useful-life prediction framework of lithium-ion batteries

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COMPUTERS & INDUSTRIAL ENGINEERING 2023年第1期179卷

作者： Hong, Seonri Kang, Moses Kim, Jonghoon Baek, Jongbok Korea Inst Energy Res Energy ICT Convergence Res Dept Energy Efficiency Res Div Daejeon South Korea Chungnam Natl Univ Dept Elect Engn Energy Storage & Convers Lab Daejeon 34134 South Korea

Accurate prediction of the lithium-ion battery lifetime is important to maintain the performance of the battery system. Because data-driven methods are extensively used in the analysis of nonlinear dynamical systems in research, the existing literature is largely focused on the application of these methods for the prediction of battery state. Data-driven methods that are highly dependent on data are sensitive to the noise of the measurement data. Distorted data diminish the performance of data-driven models for lifetime prediction. In this study, we propose an artificial neural network-based framework, which is robust to noise, to increase the prediction accuracy of the remaining-useful-life (RUL) of lithium-ion batteries. A denoising autoencoder trained using a distorted dataset with Gaussian noise and dropout is presented to improve the robustness of the model to noise. Artificial neural network models predict RUL based on the state-of-health estimated using measurement data in the initial step. The proposed prediction model is compared with the base model and the training-noise model using distorted data to validate the robustness to noise. The results demonstrate that the proposed framework is robust to noise and has lower cycle errors compared to other methods.

关键词： Remaining -useful -life prediction denoising autoencoder Long-term recurrent convolutional network Data -driven method Lithium -ion batteries

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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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Adaptive Scheme of denoising autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment

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SENSORS 2023年第12期23卷 5544页

作者： Kim, Kyuri Lee, Jaeho Duksung Womens Univ Dept IT Media Engn Seoul 01369 South Korea Duksung Womens Univ Dept Software Seoul 01369 South Korea

In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model's performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.

关键词： RSSI indoor localization neural networks denoising autoencoder

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denoising-autoencoder-Aided Euclidean Distance Matrix Reconstruction for Connectivity-Based Localization: A Low-Rank Perspective

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APPLIED SCIENCES-BASEL 2025年第5期15卷 2656-2656页

作者： Lee, Woong-Hee Ozger, Mustafa Challita, Ursula Song, Taewon Dongguk Univ Seoul Div Elect & Elect Engn Seoul 04620 South Korea Aalborg Univ Dept Elect Syst DK-2450 Copenhagen Denmark Ericsson AB S-16440 Stockholm Sweden Soonchunhyang Univ Dept Internet Things Asan 31538 South Korea

In contrast to conventional localization methods, connectivity-based localization is a promising approach that leverages wireless links among network nodes. Here, the Euclidean distance matrix (EDM) plays a pivotal role in implementing the multidimensional scaling technique for the localization of wireless nodes based on pairwise distance measurements. This is based on the representation of complex datasets in lower-dimensional spaces, resulting from the mathematical property of an EDM being a low-rank matrix. However, EDM data are inevitably susceptible to contamination due to errors such as measurement imperfections, channel dynamics, and clock asynchronization. Motivated by the low-rank property of the EDM, we introduce a new pre-processor for connectivity-based localization, namely denoising-autoencoder-aided EDM reconstruction (DAE-EDMR). The proposed method is based on optimizing the neural network by inputting and outputting vectors of the eigenvalues of the noisy EDM and the original EDM, respectively. The optimized NN denoises the contaminated EDM, leading to an exceptional performance in connectivity-based localization. Additionally, we introduce a relaxed version of DAE-EDMR, i.e., truncated DAE-EDMR (T-DAE-EDMR), which remains operational regardless of variations in the number of nodes between the training and test phases in NN operations. The proposed algorithms show a superior performance in both EDM denoising and localization accuracy. Moreover, the method of T-DAE-EDMR notably requires a minimal number of training datasets compared to that in conventional approaches such as deep learning algorithms. Overall, our proposed algorithms reduce the required training dataset's size by approximately one-tenth while achieving more than twice the effectiveness in EDM denoising, as demonstrated through our experiments.

关键词： connectivity-based localization denoising autoencoder Euclidean distance matrix multidimensional scaling neural networks

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A Multi-Label Classification with an Adversarial-Based denoising autoencoder for Medical Image Annotation

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ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2023年第2期14卷 19-19页

作者： Chai, Yidong Liu, Hongyan Xu, Jie Samtani, Sagar Jiang, Yuanchun Liu, Haoxin Hefei Univ Technol Key Lab Proc Optimizat & Intelligence Decis Makin Minister Educ Liu Sch Management 193 Tunxi Rd Hefei 230009 Anhui Peoples R China Tsinghua Univ Res Ctr Contemporary Management Sch Econ & Management 30 Shuangqing Rd Beijing 100084 Peoples R China Capital Med Univ Beijing Tongren Hosp Beijing Inst Ophthalmol 1 Dongjiaominxiang Beijing 100005 Beijing Peoples R China Indiana Univ Kelley Sch Business 107 S Indiana Ave Bloomington IN 47405 USA

Medical image annotation aims to automatically describe the content of medical images. It helps doctors to understand the content of medical images and make better informed decisions like diagnoses. Existing methods mainly follow the approach for natural images and fail to emphasize the object abnormalities, which is the essence of medical images annotation. In light of this, we propose to transform the medical image annotation to a multi-label classification problem, where object abnormalities are focused directly. However, extant multi-label classification studies rely on arduous feature engineering, or do not solve label correlation issues well in medical images. To solve these problems, we propose a novel deep learning model where a frequent pattern mining component and an adversarial-based denoising autoencoder component are introduced. Extensive experiments are conducted on a real retinal image dataset to evaluate the performance of the proposed model. Results indicate that the proposed model significantly outperforms image captioning baselines and multi-label classification baselines.

关键词： Deep learning adversarial learning medical image annotation multi-label classification denoising autoencoder

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