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V-DAFT: visual technique for texture image defect recognition with denoising autoencoder and fourier transform

SIGNAL IMAGE AND VIDEO PROCESSING 2024年第10期18卷 7405-7418页

作者： Si, Jongwook Kim, Sungyoung Kumoh Natl Inst Technol Dept Computer AI Convergence Engn Daehak Ro 61 Gumi 39177 Gyeongbuk South Korea Kumoh Natl Inst Technol Dept Comp Engn Daehak Ro 61 Gumi 39177 Gyeongbuk South Korea

Texture is the surface qualities and visual attributes of an object, determined by the arrangement, size, shape, density, and proportion of its fundamental components. In the manufacturing industry, products typically have uniform textures, allowing for automated visual inspections of the product surface to recognize defects. During this process, texture defect recognition techniques can be employed. In this paper, we propose a method that combines a convolutional autoencoder architecture with Fourier transform analysis. We employ a normal reconstructed template as defined in this study. Despite its simple structure and rapid training and inference capabilities, it offers recognition performance comparable to state-of-the-art methods. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals, which is essential for effective defect recognition as texture defects often exhibit characteristic changes in specific frequency ranges. The experiment evaluates the recognition performance using the AUC metric, with the proposed method showing a score of 93.7%. To compare with existing approaches, we present experimental results from previous research, an ablation study of the proposed method, and results based on the high-pass filter used in the Fourier mask.

关键词： Texture image Defect recognition denoising autoencoder Fourier transform Image reconstruction

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A novel denoising autoencoder hybrid network for remaining useful life estimation of lithium-ion batteries

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ENERGY SCIENCE & ENGINEERING 2024年第8期12卷 3390-3400页

作者： Xia, Wei Xu, Jinli Liu, Baolei Duan, Huiyun Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Hubei Peoples R China Jiujiang Vocat & Tech Coll Sch Mech & Elect Engn Jiujiang Jiangxi Peoples R China

Monitoring the health of lithium batteries is a crucial undertaking in ensuring the safe and dependable functioning of electric vehicles. Data-driven methods have been proved to be an effective method for identifying the complex degradation process of batteries. To augment the precision of predicting the remaining useful life (RUL), this paper introduces a pioneering architecture for a denoising autoencoder (DAE). This architecture integrates a stacked convolutional neural network with subsequent layers of bidirectional gated recurrent units within an encoder-decoder framework. The utilization of the DAE network is employed as a means to effectively capture and represent the intricate and nonlinear knowledge associated with degradation data acquired from measured sources. Simultaneously, the reconstruction loss is incorporated into the total loss to improve the accuracy and generalization of the prediction model. The efficacy of the proposed approach is substantiated through the utilization of data sets sourced from the NASA Ames Prognostics Data Repository. The comparative findings suggest that the proposed approach demonstrates an exceptional ability to achieve precise and robust estimation in predicting the RUL, surpassing other advanced methodologies. A novel denoising autoencoder hybrid network for remaining useful life estimation of lithium-ion batteries. image

关键词： CNN-BiGRU denoising autoencoder lithium-ion batteries reconstruction loss remaining useful life

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Adaptive denoising autoencoder for robust fault detection

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2024年 188卷 860-876页

作者： Li, Zixuan Zhao, Haitao East China Univ Sci & Technol Sch Informat Sci & Engn Automat Dept Shanghai 200237 Peoples R China

In real chemical processes, the collected data is often subject to interference from ambient environmental noise, resulting in a decline in the detection performance. Although denoising autoencoder can realize robust fault detection to a certain extent, its performance is limited by the addition of artificial noise, which refers to Gaussian noise or the dropout noise. Moreover, the artificial noise added is often far from the distribution of real data, making it difficult to suppress the noise in real data. To address this issue, an adaptive denoising autoencoder (ADAE) based on the diffusion model is proposed. ADAE adopts a chain -like structure, concatenating traditional DAEs by replacing the manually injected noise in each DAE block with noise generated by diffusion model at various diffusion steps. In order to better capture the key features of the original process data, a novel noise prediction network is proposed within the diffusion model, utilizing dilated and gated convolutions to capture the dynamic information of the process data. Ultimately, adaptation is achieved through a chain of DAEs, suppressing noise similar to the distribution of the original data. The superiority of ADAE in noise suppression is then verified through numerical examples and the Tennessee Eastman process (TEP), comparing it with other methods.

关键词： Process monitoring Robust fault detection denoising autoencoder Diffusion model

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Towards the Analysis of Regularized denoising autoencoder for Biosignal Processing: Lasso Versus Ridge Norms

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期134卷 319-338页

作者： Dasan, Evangelin Jeyabalan, Nelson Samuel Jebastin Unnamalai Inst Technol Dept Comp Sci & Engn Kovilpatti 628502 Tamil Nadu India Annamalai Univ Dept Zool Chidambaram 608002 Tamil Nadu India

The use of Internet of Things (IoT) that integrate smart bio sensor devices to the internet and shows individual health in real time. Healthcare organizations gain measurable insights into their most demanding problems like chronic diseases which demands long term monitoring. Various types of nano-sensors like Ingestible embedded in pills, Blood sampling sensor and tissue sensors are used in Healthcare IoT. Such implantable device collects, process and sends the vital signs called biosignals from particular organ of the human body where it has been implanted or fixed throughout the day to remote clinician. Such prolonged monitoring may weaken the battery power of nano-sensors. Since nano-sensors are miniaturized in nature and completely relies on its battery, energy awareness is incorporated in this paradigm that can help to avoid unnecessary energy consumption. This is achieved by data compression scheme. As the nano-sensors are light weight devices the designed algorithm should be low complex as well as efficient. As well, the signal acquired through this wireless sensor device are prone to be contaminated with noises because of the wearer's movements. In this study, regularized denoising autoencoder (DAE) has been employed to compress and recover the signal from its noisy version. Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge regularization concepts are used and contrasted in this article. The cost function now includes these penalty clauses to address the overfitting problem. The experimental findings demonstrate that LASSO Norm has outperformed over RIDGE in 18% for ECG, 57% for EMG & 31% for EEG signal with respect to Quality Score. The datasets used in this investigation were taken from a database that was open to the public for testing.

关键词： Internet of Things Wireless nano-sensor device denoising autoencoder Biosignals Lasso Ridge

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Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder

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INTEGRATED COMPUTER-AIDED ENGINEERING 2024年第2期31卷 157-172页

作者： Alonso, Serafin Moran, Antonio Perez, Daniel Prada, Miguel A. Fuertes, Juan J. Dominguez, Manuel Univ Leon Grp Invest Supervis Control & Automatizac Proc In Esc Ingn Ind Informat & Aeroespacial Campus Vegazana S-N Leon 24007 Spain

Technological advances in industry have made it possible to install many connected sensors, generating a great amount of observations at high rate. The advent of Industry 4.0 requires analysis capabilities of heterogeneous data in form of related multivariate time series. However, missing data can degrade processing and lead to bias and misunderstandings or even wrong decision-making. In this paper, a recurrent neural network-based denoising autoencoder is proposed for gap imputation in related multivariate time series, i.e., series that exhibit spatio-temporal correlations. The denoising autoencoder (DAE) is able to reproduce input missing data by learning to remove intentionally added gaps, while the recurrent neural network (RNN) captures temporal patterns and relationships among variables. For that reason, different unidirectional (simple RNN, GRU, LSTM) and bidirectional (BiSRNN, BiGRU, BiLSTM) architectures are compared with each other and to state-of-the-art methods using three different datasets in the experiments. The implementation with BiGRU layers outperforms the others, effectively filling gaps with a low reconstruction error. The use of this approach is appropriate for complex scenarios where several variables contain long gaps. However, extreme scenarios with very short gaps in one variable or no available data should be avoided.

关键词： Sensor observations missing data gap imputation multivariate time series denoising autoencoder recurrent neural network

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A channel estimation method using denoising autoencoder for large-scale asymmetric backscatter systems

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ICT EXPRESS 2024年第2期10卷 400-405页

作者： Jung, Chae Yoon Kang, Jae-Mo Kim, Dong In Sungkyunkwan Univ SKKU Dept Superintelligence Engn Suwon South Korea Kyungpook Natl Univ Dept Artificial Intelligence Daegu South Korea Sungkyunkwan Univ SKKU Dept Elect & Comp Engn Suwon South Korea

A novel channel estimation method based on deep learning algorithm is proposed for large-scale IoT networks. We consider asymmetric backscatter communication system to maintain low-power at sensor nodes. In order to obtain channel data, we design denoising autoencoder which consists of encoder with Feedforward Neural Network (FNN) and decoder with Convolutional Neural Network (CNN). Finally, the channel estimation error is minimized, while the pilots are optimized. Especially, we adopt beamforming technique that relies only on cascaded channel data to reduce complexity in multi-sensor system. It is shown that the accuracy is slightly degraded while the complexity is greatly reduced. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Backscatter communication Beamforming Channel estimation Deep learning denoising autoencoder

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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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SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and denoising autoencoder

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第12期27卷 5779-5790页

作者： Chen, Guanyu Shi, Tianyi Xie, Baoxing Zhao, Zhicheng Meng, Zhu Huang, Yadong Dong, Jin Beijing Acad Blockchain & Edge Comp Beijing 100080 Peoples R China Beijing Univ Posts & Telecommun Beijing 100088 Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing 100124 Peoples R China Zhoushan Acad Marine Data Sci Zhoushan 316021 Peoples R China

Objective: Electrocardiogram (ECG) signals have wide-ranging applications in various fields, and thus it is crucial to identify clean ECG signals under different sensors and collection scenarios. Despite the availability of a variety of deep learning algorithms for ECG quality assessment, these methods still lack generalization across different datasets, hindering their widespread use. Methods: In this paper, an effective model named Swin denoising autoencoder (SwinDAE) is proposed. Specifically, SwinDAE uses a DAE as the basic architecture, and incorporates a 1D Swin Transformer during the feature learning stage of the encoder and decoder. SwinDAE was first pre-trained on the public PTB-XL dataset after data augmentation, with the supervision of signal reconstruction loss and quality assessment loss. Specially, the waveform component localization loss is proposed in this paper and used for joint supervision, guiding the model to learn key information of signals. The model was then fine-tuned on the finely annotated BUT QDB dataset for quality assessment. Results: SwinDAE achieved 0.02-0.13 mean F1 score improvement on the BUT QDB dataset compared to multiple deep learning methods, and demonstrated applicability on two other datasets. Conclusion: The proposed SwinDAE shows strong generalization ability on different datasets, and surpasses other state-of-the-art deep learning methods on multiple evaluation metrics. In addition, the statistical analysis for SwinDAE prove the significance of the performance and the rationality of the prediction. Significance: SwinDAE can learn the commonality between high-quality ECG signals, exhibiting excellent performance in the application of cross-sensors and cross-collection scenarios.

关键词： Electrocardiography Quality assessment Feature extraction Deep learning Data models Sensitivity Machine learning denoising autoencoder ECG quality assessment swin Transfromer transfer learning waveform component localization

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A clinical trial termination prediction model based on denoising autoencoder and deep survival regression

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Quantitative Biology 2024年第2期12卷 205-214页

作者： Huamei Qi Wenhui Yang Wenqin Zou Yuxuan Hu School of Electronic Information Central South UniversityChangshaHunanChina School of Computer Science and Engineering Central South UniversityChangshaHunanChina

Effective clinical trials are necessary for understanding medical advances but early termination of trials can result in unnecessary waste of *** models can be used to predict survival probabilities in such ***,survival data from clinical trials are sparse,and DeepSurv cannot accurately capture their effective features,making the models weak in generalization and decreasing their prediction *** this paper,we propose a survival prediction model for clinical trial completion based on the combination of denoising autoencoder(DAE)and DeepSurv *** DAE is used to obtain a robust representation of features by breaking the loop of raw features after autoencoder training,and then the robust features are provided to DeepSurv as input for *** clinical trial dataset for training the model was obtained from the *** dataset.A study of clinical trial completion in pregnant women was conducted in response to the fact that many current clinical trials exclude pregnant *** experimental results showed that the denoising autoencoder and deep survival regression(DAE-DSR)model was able to extract meaningful and robust features for survival analysis;the C-index of the training and test datasets were 0.74 and 0.75 *** with the Cox proportional hazards model and DeepSurv model,the survival analysis curves obtained by using DAE-DSR model had more prominent features,and the model was more robust and performed better in actual prediction.

关键词： clinical trials denoising autoencoder DeepSurv experimental termination survival analysis

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RADIATION DOSE REDUCTION IN COMPUTED TOMOGRAPHY PERFUSION OF ACUTE ISCHEMIC STROKE PATIENTS USING A denoising autoencoder 21

RADIATION DOSE REDUCTION IN COMPUTED TOMOGRAPHY PERFUSION OF...

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21st IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Charatpangoon, Pattarawut Delannoy, Pauline McDougall, Connor Vega, Fernando Mohite, Aashka Barber, Philip Menon, Bijoy K. Ganesh, Aravind MacDonald, M. Ethan Univ Calgary Dept Biomed Engn Calgary AB Canada Univ Calgary Dept Elect & Software Engn Calgary AB Canada Univ Calgary Dept Radiol Calgary AB Canada Univ Calgary Dept Clin Neurosci Calgary AB Canada Univ Calgary Hotchkiss Brain Inst Calgary AB Canada Univ Paris Est Creteil Dept Engn Specialized Biomed & Hlth Ecole Publ Sante & Numer Creteil France

ISBN: (纸本)9798350313345;9798350313338

Computed Tomography Perfusion (CTP) is a widely used imaging technique for evaluating acute stroke. Compared to other imaging modalities, such as non-contrast computed tomography and X-ray, CTP involves a higher radiation exposure because of repeated volume imaging. Thus, reducing the radiation dose is an important task. However, reducing the radiation dose increases noise, which hinders the image quality. This work proposes a machine learning method using a denoising autoencoder (DAE) to remove the noise resulting from dose reduction. The study was conducted using CTP images from the PRove-IT dataset with 46 acute ischemic stroke patients. Low-dose images were simulated by introducing two types of noise, Gaussian noise and Poisson noise, for different reduction levels. These simulated noise images, along with the original images, were used to train the DAE. Additionally, the original perfusion parameter maps were compared with one from different levels of dose reduction. The findings indicate that by reducing the dose to 80%, the original and denoised images were almost identical, with an average Structural Similarity Index (SSIM) of 0.959 and 0.925 for the Gaussian and Poisson noise, respectively. Although the CTP maps had a slightly lower SSIM, they can still determine the core infarction and penumbra, with average SSIM values of four maps of 0.942 and 0.910 for the Gaussian and Poisson groups, respectively. This study demonstrates the potential to decrease the radiation dose of CTP scans by up to 80% with limited compromise on the quality of the resulting images.

关键词： computed tomography perfusion dose reduction denoising autoencoder acute ischemic stroke

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