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Performance-Guaranteed Fault reconstruction for Mobile Robots via a Two-Dimensional Gain-Regulation Mechanism

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第1期27卷 169-179页

作者： Zhu, Jun-Wei Wu, Jun Feng, Yu Zhang, Dan Zhang, Wen-An Yu, Li Zhejiang Univ Technol Dept Automat Hangzhou 310023 Peoples R China

In mobile robots, the process or actuator faults caused by the aging of system components or misoperations may have a strong impact on the real-time control performance;however, it is still hard to identify them accurately online. This article is concerned with the fault reconstruction problem of mobile robots. To be specific, a novel intermediate estimator is proposed to reconstruct the faults, where the estimation performance is guaranteed in the sense of driving the estimation errors to a small prescribed energy bound via a two-dimensional gain-regulation mechanism. It is shown that the proposed estimator is more accurate than the existing extended state observers and nominal intermediate estimator. The experimental test verifies the effectiveness and superiority of the proposed fault reconstruction scheme.

关键词： Observers Mobile robots Estimation Matrix decomposition Scholarships Robot vision systems reconstruction algorithms Fault reconstruction intermediate estimator (IE) mobile robots prescribed energy bound two-dimensional gain-regulation mechanism (TDGRM)

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Fast reconstruction of hyperspectral images from coded acquisitions using a separability assumption

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OPTICS EXPRESS 2022年第5期30卷 8174-8185页

作者： Hemsley, Elizabeth Ardi, Ibrahim Rouvier, Tony Lacroix, Simon Carfantan, Herve Monmayrant, Antoine Univ Toulouse CNRS LAAS CNRS 7 Ave Colonel Roche F-31400 Toulouse France Univ Toulouse CNES CNRS IRAP 14 Ave Edouard Belin F-31400 Toulouse France

We present a fast reconstruction algorithm fbr hyperspectral images, utilizing a small amount of data without the need for any training. The method is implemented with a dual disperser hyperspectral imager and makes use of spatial-spectral correlations by a so-called separability assumption that assumes that the image is made of regions of homogenous spectra. The reconstruction algorithm is simple and ready-to-use and does not require any prior knowledge of the scene. A simple proof-of-principle experiment is performed, demonstrating that only a small number of acquisitions are required, and the resulting compressed data-cube is reconstructed near instantaneously. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： Anisotropic diffusion Digital micromirror devices Hyperspectral imaging Image reconstruction Neural networks reconstruction algorithms

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Application of 3D reconstruction technology based on an improved MC algorithm in a shotcreting robot

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APPLIED OPTICS 2022年第29期61卷 8649-8656页

作者： Ouyang, Qi Lin, Yanhua Zhang, Xinglan Fan, Yuexin Yang, Wejing Huang, Tao Chongqing Univ Sch Automat Chongqing 400054 Peoples R China Chongqing Univ Technol Sch Comp Sci & Engn Chongqing 400044 Peoples R China

A shotcreting robot needs to reconstruct the arch surface in three dimensions (3D) during the process of spraying a tunnel. To solve this problem, we propose an improved marching cube (MC) reconstruction method based on a point cloud splice and normal re-orient. First, we use the explosion-proof LIDAR to acquire the point cloud data of the tunnel arch, followed by the use of the iterative closest point algorithm, a PassThrough filter, and a StatisticalOutlierRemoval filter for point cloud splicing, data segmentation, and simplification, respectively. In order to improve the reconstruction accuracy, we adjusted the estimated point cloud normal for normal consis-tency and obtained the geometric features of the complex point cloud surface. Furthermore, combined with the improved MC algorithm, the 3D reconstruction of the tunnel arch is realized. The experimental results show that the proposed method can reconstruct the 3D model of the tunnel arch surface quickly and accurately, which lays a foundation for further research on a trajectory plan, spraying status monitors, and control strategies.(c) 2022 Optica Publishing Group

关键词： Clouds Feature extraction Machine vision Neural networks reconstruction algorithms Three dimensional reconstruction

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Adaptive L ^p reconstruction Method for Synthetic Aperture Interferometric Radiometer

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 3282-3291页

作者： Yang, Xiaocheng Lu, Chaodong Yan, Jingye Wu, Lin Jiang, Mingfeng Li, Lin Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China Chinese Acad Sci Sate Key Lab Space Weather Beijing 100190 Peoples R China Chinese Acad Sci Natl Space Sci Ctr Beijing 100190 Peoples R China

The brightness temperature reconstruction of synthetic aperture interferometric radiometers (SAIRs) is an underdetermined and ill-posed inverse problem. The classical Hilbert-space regularization methods have often the disadvantages of the oversmoothness effects and the oscillations. In this article, an adaptive L-p reconstruction method in Lebesgue spaces is introduced to obtain the accurate brightness temperature map in SAIRs. The proposed approach can adaptively adjust the exponent p between 1.2 and 2 according to the region of the map to be reconstructed. The experimental results show that the L-p reconstruction method can effectively reduce oversmoothness and Gibbs oscillations, and better improve the accuracy of the inverse results, compared to the traditional minimum-norm and band-limited regularization methods.

关键词： Microwave radiometry reconstruction algorithms Brightness temperature Inverse problems Earth Apertures Image reconstruction Interferometric radiometer inverse problem reconstruction method synthetic aperture

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DIOR: Deep Iterative Optimization-Based Residual-Learning for Limited-Angle CT reconstruction

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第7期41卷 1778-1790页

作者： Hu, Dianlin Zhang, Yikun Liu, Jin Luo, Shouhua Chen, Yang Southeast Univ Lab Image Sci & Technol Key Lab Comp Network & Informat Integrat Minist Educ Nanjing 210096 Peoples R China Anhui Polytech Univ Coll Comp & Informat Wuhu 241000 Peoples R China Southeast Univ Dept Biomed Engn Nanjing 210096 Peoples R China Southeast Univ Sch Comp Sci & Engn Jiangsu Prov Joint Int Res Lab Med Informat Proc Nanjing 210096 Peoples R China

Limited-angle CT is a challenging problem in real applications. Incomplete projection data will lead to severe artifacts and distortions in reconstruction images. To tackle this problem, we propose a novel reconstruction framework termed Deep Iterative Optimization-based Residual-learning (DIOR) for limited-angle CT. Instead of directly deploying the regularization term on image space, the DIOR combines iterative optimization and deep learning based on the residual domain, significantly improving the convergence property and generalization ability. Specifically, the asymmetric convolutional modules are adopted to strengthen the feature extraction capacity in smooth regions for deep priors. Besides, in our DIOR method, the information contained in low-frequency and high-frequency components is also evaluated by perceptual loss to improve the performance in tissue preservation. Both simulated and clinical datasets are performed to validate the performance of DIOR. Compared with existing competitive algorithms, quantitative and qualitative results show that the proposed method brings a promising improvement in artifact removal, detail restoration and edge preservation.

关键词： Image reconstruction Computed tomography Image edge detection TV reconstruction algorithms Optimization Deep learning Limited-angle CT reconstruction iterative optimization residual learning asymmetric convolution perceptual loss

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TRANS-Net: Transformer-Enhanced Residual-Error AlterNative Suppression Network for MRI reconstruction

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Hu, Dianlin Zhang, Yikun Zhu, Jianfeng Liu, Qiegen Chen, Yang Southeast Univ Lab Image Sci & Technol Nanjing 210096 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Nanjing 210096 Peoples R China Ctr Rech Informat Biomed Sino Francais LIA CRIBs F-35000 Rennes France Xingaoyi Med Equipment Co Ltd Yuyao 315400 Peoples R China Nanchang Univ Dept Elect Informat Engn Nanchang 330031 Jiangxi Peoples R China Southeast Univ Sch Comp Sci & Engn Jiangsu Prov Joint Int Res Lab Med Informat Proc Nanjing 210096 Peoples R China Southeast Univ Sch Comp Sci & Engn Lab Image Sci & Technol Nanjing 210096 Peoples R China

Since deep priors could exploit more intrinsic features than handcrafted prior knowledge, unrolled reconstruction methods significantly improve image quality for fast magnetic resonance imaging (MRI) reconstruction with the combination of iterative optimization and deep neural network-based regularization terms. One popular way for unrolled methods is to employ the regularization penalty on the reconstructed result within the image space. Dissimilarly, in this article, we innovatively propose a reconstruction framework termed Transformer-enhanced Residual-error AlterNative Suppression Network (TRANS-Net) for MRI reconstruction to extend the unrolled methods. Unlike some existing model-based algorithms, TRANS-Net additionally deploys the regularization term on the error maps in the residual image domain, which greatly emphasizes the high-frequency information contained in the MR images. Besides, to further explore the global spatial correlation of the reconstructed image, the transformer module is adopted to enlarge the receptive field of deep priors regularized on the reconstructed images for better tissue recovery. The quantitative and qualitative results show that the proposed TRANS-Net method has superior performance to the state-of-the-art reconstruction algorithms in anatomical structure restoration and perceptual detail preservation.

关键词： Image reconstruction Magnetic resonance imaging Neural networks Imaging reconstruction algorithms Transformers Convolutional neural networks Deep prior magnetic resonance imaging (MRI) imaging residual image domain transformer unrolled reconstruction method

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Projection-Consistent Diffusion Model-Based Image reconstruction for Limited-Angle Dual-Energy Cone-Beam CT

Projection-Consistent Diffusion Model-Based Image Reconstruc...

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2025 Conference on Medical Imaging

作者： Peng, Junbo Chang, Chih-Wei Qiu, Richard L. J. Wang, Tonghe Roper, Justin Ghavidel, Beth Tang, Xiangyang Yang, Xiaofeng Emory Univ Dept Radiat Oncol Atlanta GA 30322 USA Emory Univ Winship Canc Inst Atlanta GA 30322 USA Mem Sloan Kettering Canc Ctr Dept Med Phys New York NY 10065 USA Emory Univ Dept Radiol & Imaging Sci Atlanta GA 30322 USA

ISBN: (纸本)9781510686007;9781510686014

Limited-angle (LA) dual-energy cone-beam CT (DECBCT) is a potential approach for DE imaging on existing CBCT scanners with a comparable data acquisition time and radiation dose to the conventional single-energy scan. However, the image reconstruction for LA-DECBCT is a challenging problem since the acquired DE projections do not meet Tuy's data sufficiency condition. In this work, we develop a diffusion model-based image reconstruction for LA-DECBCT with a projection-consistent mechanism to enforce data fidelity.

关键词： Diffusion Image restoration Data modeling Model based design Data acquisition Computed tomography Process modeling Animals reconstruction algorithms Reverse modeling

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Complex "zero-shot" super-resolution reconstruction algorithm for THz imaging

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APPLIED OPTICS 2022年第20期61卷 5831-5837页

作者： Wang, Ying Qi, Feng Wang, Jinkuan Northeastern Univ Sch Commun Sci & Engn Shenyang 110819 Liaoning Peoples R China Chinese Acad Sci Key Lab Optoelect Informat Proc Shenyang 110016 Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot Shenyang 110169 Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110169 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

To deal with a terahertz (THz) super-resolution (SR) algorithm based on a convolutional neural network (CNN) without standard training datasets, a complex "zero-shot" SR (CZSSR) reconstruction algorithm is proposed according to the internal image statistics with a five-layer complex CNN model. Instead of relying on pre-training, the proposed method is of sound self-adaptability. Compared with real ZSSR, the peak SNR of CZSSR rose by about 0.94 dB, MSE decreased by 0.042, and SSIM increased by about 40% for the SR result of the measured data. The results show that the CZSSR method can solve the low-resolution problem of a THz imaging system and the shortage of datasets in THz SR based on CNN. Therefore, t his research is of great significance for application in the fields of medical imaging and non-destructive detection. (C) 2022 Optica Publishing Group

关键词： Image processing Imaging systems Medical imaging Optical imaging reconstruction algorithms Terahertz imaging

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Online reconstruction of Complex Networks From Streaming Data

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第6期52卷 5136-5147页

作者： Wu, Kai Hao, Xingxing Liu, Jing Liu, Penghui Shen, Fang Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Northwestern Univ Sch Informat Sci & Technol Xian 710127 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China

The problem of reconstructing nonlinear and complex dynamical systems from available data or time series is prominent in many fields, including engineering, physical, computer, biological, and social sciences. Many methods have been proposed to address this problem and their performance is satisfactory. However, none of them can reconstruct network structure from large-scale real-time streaming data, which leads to the failure of real-time and online analysis or control of complex systems. In this article, to overcome the limitations of current methods, we first extend the network reconstruction problem (NRP) to online settings, and then develop a follow-the-regularized-leader (FTRL)-Proximal style method to address the online complex NRP;we refer to it as Online-NR. The performance of Online-NR is validated on synthetic evolutionary game network reconstruction datasets and eight real-world networks. The experimental results demonstrate that Online-NR can effectively solve the problem of online network reconstruction with large-scale real-time streaming data. Moreover, Online-NR outperforms or matches nine state-of-the-art network reconstruction methods.

关键词： reconstruction algorithms Real-time systems Complex networks Complex systems Time series analysis Heuristic algorithms COVID-19 Complex network reconstruction data stream large-scale optimization online learning time series

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Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2024年 10卷 69-82页

作者： Lozenski, Luke Wang, Hanchen Li, Fu Anastasio, Mark Wohlberg, Brendt Lin, Youzuo Villa, Umberto Washington Univ St Louis Dept Elect & Syst Engn St Louis MO 63130 USA Los Alamos Natl Lab Energy & Nat Resources Secur Grp Los Alamos NM 87545 USA Univ Illinois Dept Bioengn Urbana IL 61801 USA Los Alamos Natl Lab Appl Math & Plasma Phys Grp Los Alamos NM 87545 USA Univ North Carolina Chapel Hill Sch Data Sci Soc Chapel Hill NC 27599 USA Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA

Ultrasound computed tomography (USCT) is an emerging imaging modality that holds great promise for breast imaging. Full-waveform inversion (FWI)-based image reconstruction methods incorporate accurate wave physics to produce high spatial resolution quantitative images of speed of sound or other acoustic properties of the breast tissues from USCT measurement data. However, the high computational cost of FWI reconstruction represents a significant burden for its widespread application in a clinical setting. The research reported here investigates the use of a convolutional neural network (CNN) to learn a mapping from USCT waveform data to speed of sound estimates. The CNN was trained using a supervised approach with a task-informed loss function aiming at preserving features of the image that are relevant to the detection of lesions. A large set of anatomically and physiologically realistic numerical breast phantoms (NBPs) and corresponding simulated USCT measurements was employed during training. Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data. The performance of the proposed method was assessed and compared against FWI using a hold-out sample of 41 NBPs and corresponding USCT data. Accuracy was measured using relative mean square error (RMSE), structural self-similarity index measure (SSIM), and lesion detection performance (DICE score). This numerical experiment demonstrates that a supervised learning model can achieve accuracy comparable to FWI in terms of RMSE and SSIM, and better performance in terms of task performance, while significantly reducing computational time.

关键词： Task analysis Imaging Image reconstruction Training Observers Feature extraction reconstruction algorithms Ultrasound computed tomography convolutional neural networks data-driven image reconstruction task informed image reconstruction computer-simulation study

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