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An Efficient Convolutional denoising autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

SENSORS 2024年第6期24卷 1959页

作者： Qin, Yahang Li, Zhenni Xie, Shengli Zhao, Haoli Wang, Qianming Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong HongKong Macao Joint Lab Smart Discrete Guangzhou 510006 Guangdong Peoples R China 111 Ctr Intelligent Batch Mfg Based IoT Technol G Guangzhou 510006 Peoples R China Minist Educ Key Lab Intelligent Informat Proc & Syst Integrat Guangzhou 510006 Peoples R China Guangdong Key Lab IoT Informat Technol GDUT Guangzhou 510006 Peoples R China Taidou Microelect Technol Co Ltd Guangzhou 510663 Peoples R China

The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model's robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.

关键词： denoising autoencoder BDS NLOS time series features urban forest

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RunDAE model: Running denoising autoencoder models for denoising ECG signals

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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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Transformed denoising autoencoder prior for image restoration

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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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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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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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