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Feng J, Si Y, Zhang Y, Sun M, Yang W. A High-Performance Anti-Noise Algorithm for Arrhythmia Recognition. SENSORS (BASEL, SWITZERLAND) 2024; 24:4558. [PMID: 39065956 PMCID: PMC11280816 DOI: 10.3390/s24144558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 07/05/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024]
Abstract
In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios.
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Affiliation(s)
- Jianchao Feng
- School of Electronic and Information Engineering (SEIE), Zhuhai College of Science and Technology, Zhuhai 519041, China; (J.F.); (Y.Z.); (W.Y.)
- College of Communication Engineering, Jilin University, Changchun 130012, China;
| | - Yujuan Si
- School of Electronic and Information Engineering (SEIE), Zhuhai College of Science and Technology, Zhuhai 519041, China; (J.F.); (Y.Z.); (W.Y.)
- College of Communication Engineering, Jilin University, Changchun 130012, China;
| | - Yu Zhang
- School of Electronic and Information Engineering (SEIE), Zhuhai College of Science and Technology, Zhuhai 519041, China; (J.F.); (Y.Z.); (W.Y.)
- College of Communication Engineering, Jilin University, Changchun 130012, China;
| | - Meiqi Sun
- College of Communication Engineering, Jilin University, Changchun 130012, China;
| | - Wenke Yang
- School of Electronic and Information Engineering (SEIE), Zhuhai College of Science and Technology, Zhuhai 519041, China; (J.F.); (Y.Z.); (W.Y.)
- College of Communication Engineering, Jilin University, Changchun 130012, China;
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Shen J, Zhao H, Deng W. Broad Learning System under Label Noise: A Novel Reweighting Framework with Logarithm Kernel and Mixture Autoencoder. SENSORS (BASEL, SWITZERLAND) 2024; 24:4268. [PMID: 39001047 PMCID: PMC11244421 DOI: 10.3390/s24134268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 06/27/2024] [Accepted: 06/29/2024] [Indexed: 07/16/2024]
Abstract
The Broad Learning System (BLS) has demonstrated strong performance across a variety of problems. However, BLS based on the Minimum Mean Square Error (MMSE) criterion is highly sensitive to label noise. To enhance the robustness of BLS in environments with label noise, a function called Logarithm Kernel (LK) is designed to reweight the samples for outputting weights during the training of BLS in order to construct a Logarithm Kernel-based BLS (L-BLS) in this paper. Additionally, for image databases with numerous features, a Mixture Autoencoder (MAE) is designed to construct more representative feature nodes of BLS in complex label noise environments. For the MAE, two corresponding versions of BLS, MAEBLS, and L-MAEBLS were also developed. The extensive experiments validate the robustness and effectiveness of the proposed L-BLS, and MAE can provide more representative feature nodes for the corresponding version of BLS.
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Affiliation(s)
- Jiuru Shen
- College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
| | - Huimin Zhao
- College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
| | - Wu Deng
- College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
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Wang T, Zhang M, Zhang J, Ng WWY, Chen CLP. BASS: Broad Network Based on Localized Stochastic Sensitivity. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:1681-1695. [PMID: 35830397 DOI: 10.1109/tnnls.2022.3184846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The training of the standard broad learning system (BLS) concerns the optimization of its output weights via the minimization of both training mean square error (MSE) and a penalty term. However, it degrades the generalization capability and robustness of BLS when facing complex and noisy environments, especially when small perturbations or noise appear in input data. Therefore, this work proposes a broad network based on localized stochastic sensitivity (BASS) algorithm to tackle the issue of noise or input perturbations from a local perturbation perspective. The localized stochastic sensitivity (LSS) prompts an increase in the network's noise robustness by considering unseen samples located within a Q -neighborhood of training samples, which enhances the generalization capability of BASS with respect to noisy and perturbed data. Then, three incremental learning algorithms are derived to update BASS quickly when new samples arrive or the network is deemed to be expanded, without retraining the entire model. Due to the inherent superiorities of the LSS, extensive experimental results on 13 benchmark datasets show that BASS yields better accuracies on various regression and classification problems. For instance, BASS uses fewer parameters (12.6 million) to yield 1% higher Top-1 accuracy in comparison to AlexNet (60 million) on the large-scale ImageNet (ILSVRC2012) dataset.
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Wang F, Ni W, Liu S, Xu Z, Qiu Z, Wan Z. A 2D image 3D reconstruction function adaptive denoising algorithm. PeerJ Comput Sci 2023; 9:e1604. [PMID: 37810338 PMCID: PMC10557518 DOI: 10.7717/peerj-cs.1604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/29/2023] [Indexed: 10/10/2023]
Abstract
To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into regions based on the entropy value of each region. The background region is subject to threshold denoising, while the target region undergoes processing using an adversarial generative network. This network effectively handles 2D target images with noise and generates a 3D model of the target. The proposed algorithm aims to enhance the noise immunity of 2D images during the 3D reconstruction process and ensure that the constructed 3D target model better preserves the original image's detailed information. Through experimental testing on 2D images and real pedestrian videos contaminated with noise, our algorithm demonstrates stable preservation of image details. The reconstruction effect is evaluated in terms of noise reduction and the fidelity of the 3D model to the original target. The results show an average noise reduction exceeding 95% while effectively retaining most of the target's feature information in the original image. In summary, our proposed adaptive denoising algorithm improves the 3D reconstruction process by preserving image details that are often compromised by conventional denoising techniques. This has significant implications for enhancing image quality and maintaining target information fidelity in 3D models, providing a promising approach for addressing the challenges associated with noise reduction in 2D images during 3D reconstruction.
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Affiliation(s)
- Feng Wang
- Guangzhou Xinhua University, Dongguan, Guangdong, China
| | - Weichuan Ni
- Guangzhou Xinhua University, Dongguan, Guangdong, China
| | - Shaojiang Liu
- Guangzhou Xinhua University, Dongguan, Guangdong, China
| | - Zhiming Xu
- Guangzhou Xinhua University, Dongguan, Guangdong, China
| | - Zemin Qiu
- Guangzhou Xinhua University, Dongguan, Guangdong, China
| | - Zhiping Wan
- Guangzhou Xinhua University, Dongguan, Guangdong, China
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Ma R, Li S, Zhang B, Fang L, Li Z. Flexible and Generalized Real Photograph Denoising Exploiting Dual Meta Attention. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:6395-6407. [PMID: 35580100 DOI: 10.1109/tcyb.2022.3170472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Supervised deep learning techniques have been widely explored in real photograph denoising and achieved noticeable performances. However, being subject to specific training data, most current image denoising algorithms can easily be restricted to certain noisy types and exhibit poor generalizability across testing sets. To address this issue, we propose a novel flexible and well-generalized approach, coined as dual meta attention network (DMANet). The DMANet is mainly composed of a cascade of the self-meta attention blocks (SMABs) and collaborative-meta attention blocks (CMABs). These two blocks have two forms of advantages. First, they simultaneously take both spatial and channel attention into account, allowing our model to better exploit more informative feature interdependencies. Second, the attention blocks are embedded with the meta-subnetwork, which is based on metalearning and supports dynamic weight generation. Such a scheme can provide a beneficial means for self and collaborative updating of the attention maps on-the-fly. Instead of directly stacking the SMABs and CMABs to form a deep network architecture, we further devise a three-stage learning framework, where different blocks are utilized for each feature extraction stage according to the individual characteristics of SMAB and CMAB. On five real datasets, we demonstrate the superiority of our approach against the state of the art. Unlike most existing image denoising algorithms, our DMANet not only possesses a good generalization capability but can also be flexibly used to cope with the unknown and complex real noises, making it highly competitive for practical applications.
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Su Y, Zhu H, Wong KC, Chang Y, Li X. Hyperspectral Image Denoising via Weighted Multidirectional Low-Rank Tensor Recovery. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:2753-2766. [PMID: 36251897 DOI: 10.1109/tcyb.2022.3208095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Recently, low-rank tensor recovery methods based on subspace representation have received increased attention in the field of hyperspectral image (HSI) denoising. Unfortunately, those methods usually analyze the prior structural information within different dimensions indiscriminately, ignoring the differences between modes, leaving substantial room for improvement. In this article, we first consider the low-rank properties in the subspace and prove that the structure correlation across the nonlocal self-similarity mode is much stronger than in the spatial sparsity and spectral correlation modes. On that basis, we introduce a new multidirectional low-rank regularization, in which each mode is assigned a different weight to characterize its contribution to estimating the tensor rank. After that, integrating the proposed regularization with the subspace-based tensor recovery framework, an optimization model for HSI mixed noise removal is developed. The proposed model can be addressed efficiently via the alternating minimization algorithm. Extensive experiments implemented with synthetic and real data demonstrate that the proposed method significantly outperforms other state-of-the-art HSI denoising methods, which clearly indicates the effectiveness of the proposed approach in HSI denoising.
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Zhu R, Li L, Wu S, Lv P, Li Y, Xu M. Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Yang C, Wen H, Jiang D, Xu L, Hong S. Analysis of college students' canteen consumption by broad learning clustering: A case study in Guangdong Province, China. PLoS One 2022; 17:e0276006. [PMID: 36227952 PMCID: PMC9560066 DOI: 10.1371/journal.pone.0276006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/27/2022] [Indexed: 11/05/2022] Open
Abstract
Investigation on college students’ consumption ability help classify them as from rich or relative poor family, thus to distinguish the students who are in urgent need for government’s economic support. As canteen consumption is the main part of the expenses of the college students, we proposed the adjusted K-means clustering methods for discrimination of the college students at different economic levels. To improve the discrimination accuracy, a broad learning network architecture was built up for extracting informative features from the students’ canteen consumption records. A fuzzy transformed technique was combined in the network architecture to extend the candidate range for identifying implicit informative variables from the single type of consumption data. Then, the broad learning network model is fully trained. We specially designed to train the network parameters in an iterative tuning mode, in order to find the precise properties that reflect the consumption characteristics. The selected feature variables are further delivered to establish the adjusted K-means clustering model. For the case study, the framework of combining the broad learning network with the adjusted K-means method was applied for the discrimination of the canteen consumption data of the college students in Guangdong province, China. Results show that the most optimal broad learning architecture is structured with 14 hidden nodes, the model training and testing results are appreciating. The results indicated that the framework was feasible to classify the students into different economic levels by analyzing their canteen consumption data, so that we are able to distinguish the students who are in need for financial aid.
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Affiliation(s)
- Chun Yang
- School of Accounting, Guangzhou Huashang College, Guangzhou, China
| | - Hongwei Wen
- School of Accounting, Guangzhou Huashang College, Guangzhou, China
| | - Darui Jiang
- School of Data Science, Guangzhou Huashang College, Guangzhou, China
| | - Lijuan Xu
- School of Data Science, Guangzhou Huashang College, Guangzhou, China
| | - Shaoyong Hong
- School of Data Science, Guangzhou Huashang College, Guangzhou, China
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Zhang D, Chen CLP, Li T, Zuo Y, Duy NQ. Target tracking method of Siamese networks based on the broad learning system. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2022. [DOI: 10.1049/cit2.12134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Dan Zhang
- Navigation College Dalian Maritime University Dalian China
- Innovation and Entrepreneurship Education College Dalian Minzu University Dalian China
| | - C. L. Philip Chen
- Navigation College Dalian Maritime University Dalian China
- Computer Science and Engineering College South China University of Technology Guangzhou China
- Department of Computer and Information Science Faculty of Science and Technology University of Macau Macau China
| | - Tieshan Li
- Navigation College Dalian Maritime University Dalian China
- School of Automation Engineering University of Electronic Science and Technology of China Chengdu China
| | - Yi Zuo
- Navigation College Dalian Maritime University Dalian China
| | - Nguyen Quang Duy
- Faculty of Navigation Vietnam Maritime University Haiphong Vietnam
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Cauchy regularized broad learning system for noisy data regression. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.04.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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11
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Broad stochastic configuration network for regression. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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12
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Zhang C, Ding S, Guo L, Zhang J. Broad learning system based ensemble deep model. Soft comput 2022. [DOI: 10.1007/s00500-022-07004-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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