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Transformed denoising autoencoder prior for image restoration

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2020年 72卷 102927-102927页

作者： Zhou, Jinjie He, Zhuonan Liu, Xiaodong Wang, Yuhao Wang, Shanshan Liu, Qiegen Nanchang Univ Dept Elect Informat Engn Nanchang 330031 Jiangxi Peoples R China Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Chinese Acad Sci Paul C Lauterbur Res Ctr Biomed Imaging Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Image restoration problem is generally ill-posed, which can be alleviated by learning image prior. Inspired by the considerable performance of utilizing priors in pixel domain and wavelet domain jointly, we propose a novel transformed denoising autoencoder as prior (TDAEP). The core idea behind TDAEP is to enhance the classical denoising autoencoder (DAE) via transform domain, which captures complementary information from multiple views. Specifically, 1-level nonorthogonal wavelet coefficients are used to form 4-channel feature images. Moreover, a 5-channel tensor is obtained by stacking the original image under the pixel domain and 4-channel feature images under the wavelet domain. Then we train the transformed DAE (TDAE) with the 5-channel tensor as the network input. The optimized image prior is obtained based on the trained autoencoder, and it is incorporated into an iterative restoration procedure with the aid of the auxiliary variable technique. The resulting model is affiliationed by proximal gradient descent technique. Numerous experiments demonstrated that the TDAEP outperforms a set of image restoration benchmark algorithms.

关键词： Image restoration denoising autoencoder Pixel domain Wavelet domain

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A denoising autoencoder based on U-Net and bidirectional long short-term memory for multi-level random telegraph signal analysis

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 135卷

作者： Deng, Bowen Yang, Heebong Kim, Na Young Univ Waterloo Dept Elect & Comp Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada Univ Waterloo Inst Quantum Comp 200 Univ Ave West Waterloo ON N2L 3G1 Canada Univ Waterloo Waterloo Inst Nanotechnol 200 Univ Ave West Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Chem 200 Univ Ave West Waterloo ON N2L 3G1 Canada

Random telegraph signals (RTSs) are specific time -fluctuating signal patterns marked by a series of distinctive switching events between well-defined signal levels. These signals are ubiquitous in many electronic, chemical, and biological devices and systems. Analyzing RTSs unveils associated system structures and internal operation mechanisms, offering valuable insights into performance sensitivity. Therefore, accurate parameter quantification of RTSs is essential for understanding their origin and significance. While two -level RTS analysis is straightforward, complications arise at multiple levels, especially with unwanted background fluctuations. To address this challenge, we developed a novel denoising autoencoder model with U -Net and bidirectional long short-term memory (DAE UBL) for denoising multi -level RTSs degraded by Gaussian white and pink noise. DAE UBL extracts lower -dimensional latent features with its encoder and reconstructs denoised RTS with its decoder. Trained and validated with large datasets of noisy multi -level RTSs, our DAE UBL demonstrates superior and stable denoising performance compared to four classic models with lower average median root mean squared errors by over 78% and 63% for all RTS data accompanying various strengths of white noise and pink noise. Average median signal-to-noise ratios in the DAE UBL analysis are increased by over 65% and 56% for the white noise and pink noise datasets. In the time domain, DAE UBL effectively suppresses both local and global fluctuations, thereby successfully removing background noise. Our model exhibits robust performance in denoising multi -level RTSs with strong pink noise. We envision that our DAE UBL will be an attractive denoising methodology for the complex multi -level RTS analysis.

关键词： Random telegraph signals Signal denoising denoising autoencoder Deep learning Signal processing

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The integration of knowledge graph convolution network with denoising autoencoder

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 135卷

作者： Kaur, Gurinder Liu, Fei Chen, Yi-Ping Phoebe La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic Australia

The knowledge graph convolution network (KGCN) is a recommendation model that provides a set of top recommendations based on knowledge graph developed between users, items, and their attributes. In this study, we integrate the KGCN model with denoising autoencoder (DAE) to improve its recommendation performance. A trained DAE is used to sample K-dimensional latent representation for each user, which then transforms that representation to generate a probability distribution over items. The relationship between acquired latent representation and the meta features is modelled using multivariate multiple regression (MMR) kernel. As a result, without the need for new configuration assessments, performance estimation of new data is pursued directly through MMR and the decoder of DAE. Empirically, we demonstrate that on real-world datasets, the proposed method substantially outperforms other state-of-the-art baselines. Movie-Lens 100K (ML-100K) and Movie-Lens 1M (ML-1M), two common MovieLens datasets, are used to verify the accuracy of the proposed approach. The results from experiments show significant improvement of 41.17% when the proposed method is applied on KGCN model. The proposed framework outperforms other state-of-the-art frameworks on Recall@K and normalized discounted cumulative gain (NDCG@K) metrics by achieving higher scores for Recall@5, Recall@10, NDCG@1, and NDCG@10.

关键词： Recommender system denoising autoencoder Meta feature learning Latent features representation

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Realizing spike-timing dependent plasticity learning rule in Pt/Cu:ZnO/Nb:STO memristors for implementing single spike based denoising autoencoder

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JOURNAL OF MICROMECHANICS AND MICROENGINEERING 2019年第8期29卷 085006-085006页

作者： Suresh, Bharathwaj Boppidi, Pavan Kumar Reddy Rao, B. V. V. S. N. Prabhakar Banerjee, Souri Kundu, Souvik Birla Inst Technol & Sci BITS Pilani Dept Elect & Elect Engn Hyderabad Campus Hyderabad 500078 Telangana India Birla Inst Technol & Sci BITS Pilani Dept Phys Hyderabad Campus Hyderabad 500078 Telangana India

In this work, the Cu:ZnO based memristors were fabricated and modelled and its biological synaptic characteristics were realized. Phenomenon similar to long-term potentiation and long-term depression were observed in the proposed devices and spike timing dependent plasticity learning rule was established by engineering appropriate input voltage spikes, making the devices suitable for use in artificial neural networks. In order to demonstrate learning mechanism, a denoising autoencoder network was developed by incorporating the synaptic characteristics of the device along with the concept of rank coding. To evaluate the feasibility and performance of the network, images from the MNIST database for handwritten digits were employed. The training of the proposed network was accomplished by incorporating noisy images, and it was validated with images corrupted with Gaussian, Salt & Pepper and Speckle noises. Surprisingly, the obtained results demonstrated that the shape of the digits was recovered to a great extent and almost all noise in the background were removed. The accuracy of the denoising was found to be more than 90% for most cases. The proposed network shows the efficacy of ferroelectric Cu:ZnO memristors as artificial synapses in spiking neural networks, which opens up a new path towards developing future generation biologically compatible neuromorphic systems.

关键词： denoising autoencoder Cu:ZnO memristor spiking neural network STDP learning rule

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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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Missing data imputation using an iterative denoising autoencoder (IDAE) for dissolved gas analysis

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ELECTRIC POWER SYSTEMS RESEARCH 2022年 212卷

作者： Seo, Boseong Shin, Jaekyung Kim, Taejin Youn, Byeng D. Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea OnePredict Inc Seoul 06160 South Korea Jeonbuk Natl Univ Dept Ind & Informat Syst Engn Jeonju 54896 South Korea

With the expansion of the energy market, safe and stable operation of the electrical power system has become an important issue. In an effort to achieve this goal, much research has been conducted on diagnosis approaches suitable for core components of the electrical power system. Transformers are one such core component. Most of the research on transformers has focused on developing a diagnosis model;less effort has been devoted to the data, in spite of the fact that such models require data of sufficient quantity and quality, which is not usually readily available for transformers. Thus, in this paper, we propose a way to fully exploit the valuable transformer data, using a data imputation approach called the iterative denoising autoencoder (IDAE) method. The proposed method imputes missing values of dissolved gas analysis (DGA) data, which is frequently lost, for various reasons. IDAE can help diagnose the health state of transformers accurately by estimating the missing values of DGA data. The proposed method is verified in this research through three comparative studies that examine field data provided by an electric power corporation. Specific studies provide: (1) a comparison with conventional methods on imputation performance for a single gas, (2) examination of imputation performance between multiple missing values, and (3) documentation of diagnosis accuracy before and after imputation. The results of the case studies show that the proposed method is effective for imputation of the missing DGA data.

关键词： denoising autoencoder Data imputation Dissolved gas analysis Power transformer

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State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network

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ENERGY 2021年 227卷 120451-120451页

作者： Chen, Junxiong Feng, Xiong Jiang, Lin Zhu, Qiao Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Sichuan Aerosp Syst Engn Res Inst Chengdu 610100 Peoples R China

To reduce the influence of the measurement data noise on state of charge (SOC) estimation, a novel neural network method is proposed by combining an input data processing method with the conven-tional gated recurrent unit recurrent neural network (GRU-RNN) method. First, a denoising autoencoder neural network (DAE-NN) is introduced to extract the useful data features by reducing the noise and increasing the dimensions of the battery measurement data. Then, the feature-extracted data is utilized to train the GRU-RNN, which is widely used in SOC estimation. Notice that a good input data processing method plays a key role in the SOC estimation performance and the generalization ability. Therefore, it is not trivial to combine the data processing method (DAE-NN), and the SOC estimation method (GRU-RNN), which is named DAE-GRU. Compared with the traditional GRU-RNN, the new DAE-GRU method shows a better nonlinear mapping relation between the measurement data and the SOC because of the involvement of the DAE-NN. Finally, three different driving cycles are given in the experiment to cross -train and verify the proposed DAE-GRU, GRU-RNN and RNN. Compared with the GRU-RNN and the RNN, it is demonstrated that the proposed DAE-GRU has better accuracy and robustness in the SOC estimation. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： State of charge estimation Lithium-ion battery denoising autoencoder Gated recurrent unit Recurrent neural network Electric vehicle

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Nonlinear process monitoring based on load weighted denoising autoencoder

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MEASUREMENT 2021年 171卷 108782-108782页

作者： Zhu, Jiazhen Shi, Hongbo Song, Bing Tao, Yang Tan, Shuai Zhang, Tianqing East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Shanghai Shixiang Technol Corp Ind Big Data Res Ctr Shanghai 200237 Peoples R China

Traditional monitoring methods are trained with normal data and map the process variables into latent variables directly. However, for these methods, the process variables would become intertwined in the latent variables, which results in that the fluctuations of process variables would be submerged in noise or neutralized in latent variables space. In order to address the submergence and neutralization problems, a novel algorithm load weighted denoising autoencoder (LWDAE) is proposed. According to the direction and magnitude of online data, the loading matrix of LWDAE is weighted to highlight the useful information of both training data and online data in latent variables space. In addition, to reduce the effect of noise on weighting matrix, LWDAE modifies the loss function by adding two new regularizations and revises the calculation logic of weighting matrix to consider the successive samples. Case studies of continuous stirred tank reactor demonstrate the effectiveness of LWDAE.

关键词： denoising autoencoder Nonlinear process Fault detection Online weighting strategy

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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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