Hui Li, Wei He, Huiyao An et al. · 2007
Several nonblocking packet switching architectures have been proposed for broadband network, such as Shared Bus, Shared Memory, Crossbar Matrix with Combined Input and Output Queuing, etc. Their topological demerit, such as bandwidth bottleneck and insufficient processing ability to schedule I/O matching, greatly limits their ability for large scale switching routers. This paper proposes and models a novel multi-path self-routing switching fabric by merging the bitonic sorters with the multistage interconnection network. This structure possesses the properties of complete distributing and self-routing, free of I/O matching scheduling algorithm, no internal buffer, no buffered delay and jitter, modeled with algebraic permuting group, as well as high modularity and recursive scalability. Mathematical analysis and simulations show this structure is suitable for building super large scale switching fabric to support QoS applications.
Cited by 7
Yassine Kali, Maarouf Saad, Ayoub Bouchama et al. · 2020
Multiphase machines are gaining popularity in clean, reliable and affordable energy systems for their robustness, reliability and fault-tolerant behavior. This paper addresses the problem of parameters estimation and the problem of controlling the currents of an asymmetrical six-phase induction machine with unmeasurable rotor currents. On one hand, this work proposes the use of recursive least squares estimation method that is effective and simple and allows fast convergence of the parameters to their real values. On the other hand, this paper proposes an augmented super-twisting algorithm that allows high tracking accuracy, fast finite-time convergence, matched and mismatched uncertainties rejection. Hardware-in-the-loop simulations have been conducted to demonstrate the performance and the efficiency of the estimation method and the developed nonlinear controller for the considered system.
Cited by 5
Xiao‐Yang Chen, Heng Li, Ren Zhu et al. · 2024
To ensure optimal functionality of the super-capacitor management system in practical applications, the accurate and robust state of charge (SOC) estimation is crucial, particularly to account for aging effects and varying operating conditions. This paper proposes a switched system-based approach for the co-estimation of SOC and parameters of supercapacitors coupled with balancing resistor circuits. A switched model incorporating an equivalent circuit model is developed to accommodate the activation of equalization within a series-connected supercapacitor pack. The method combines a modified recursive least squares (RLS) algorithm with a switching sliding mode observer (SMO) for real-time parameter adaptation and SOC estimation. The experimental verification under a multi-balancing charging scenario demonstrates sig-nificant enhancements in accuracy and robustness compared to traditional methods employing fixed model configurations and parameters.
Cited by 0
X. Fan, Yao Sun · Journal of Information & Knowledge Management · 2024
Although this research has made significant progress in image generation models, they still face issues such as insufficient diversity of generated images, poor quality of high-resolution images, and the need for a large amount of training data for model optimization. This paper studies poster design based on deep learning automatic image generation algorithm, using a recursive supervised image generation algorithm framework of generative adversarial networks for multi-view image generation and super-resolution generation tasks of small sample digital poster images. Various improvements have been proposed to enhance the performance of the GAN network model for poster design image generation tasks. Based on experimental research, this paper’s model uses generative adversarial networks to distinguish randomly cropped low resolution and high-resolution poster blocks, ensuring that high-resolution posters maintain their original resolution canvas texture and brush strokes, effectively improving the automatic generation effect of poster images. The evaluation results show that the quantitative evaluation of the proposed algorithm model in knowledge management is distributed in a reasonable range, which indicates that the proposed algorithm model has good performance in knowledge management. The poster design model based on deep learning automatic image generation algorithm proposed in this paper has certain effects. In subsequent practice, the automatic image generation algorithm can be combined with practical needs to improve the efficiency and design effect of poster design.
Cited by 0
Olbrich, Jannik, Ohlebusch, Enno, Büchler, Thomas · Universität Ulm, Kommunikations- und Informationszentrum (kiz) · 2024
The suffix array is arguably one of the most important data structures in sequence analysis and consequently there is a multitude of suffix sorting algorithms. However, to this date the GSACA algorithm introduced in 2015 is the only known non-recursive linear-time suffix array construction algorithm (SACA). Despite its interesting theoretical properties, there has been little effort in improving GSACA’s non-competitive real-world performance. There is a super-linear algorithm DSH, which relies on the same sorting principle and is faster than DivSufSort, the fastest SACA for over a decade. The purpose of this article is twofold: We analyse the sorting principle used in GSACA and DSH and exploit its properties to give an optimised linear-time algorithm, and we show that it can be very elegantly used to compute both the original extended Burrows-Wheeler transform (eBWT) and a bijective version of the Burrows-Wheeler transform (BBWT) in linear time. We call the algorithm “generic,” since it can be used to compute the regular suffix array and the variants used for the BBWT and eBWT. Our suffix array construction algorithm is not only significantly faster than GSACA but also outperforms DivSufSort and DSH. Our BBWT-algorithm is faster than or competitive with all other tested BBWT construction implementations on large or repetitive data, and our eBWT-algorithm is faster than all other programs on data that is not extremely repetitive.
Cited by 0Open access
Haixia Liu (764025), Mingliang Wang (1666225) · Figshare · 2025
<div> Image super-resolution reconstruction is one of the important application branches of computer vision in many fields. However, in actual complex environments, images are often subject to various interferences, leading to severe distortion in the reconstructed images. To address this issue, this study innovatively combines multi-scale feature extraction (MSFE) and attention feature fusion (AFF). After optimization, the multi-scale recursive attentional feature fusion block (MSRAFFB) and MSRAFFB Network (MSRAFFB-Net) application algorithms are proposed. Simulation experiments on standard datasets demonstrated that MSRAFFB was crucial for enhancing the overall performance of the algorithm, improving reconstruction quality over the baseline. Additionally, through reasonable network structure design (such as increasing module depth and branch complexity), MSRAFFB-Net effectively reduced reconstruction error and improved perceptual quality. The algorithm exhibited high reconstruction accuracy across different magnification factors. Furthermore, to assess the algorithm’s performance in more realistic and complex environments, the study conducted performance experiments in actual application scenarios. The results indicate that the algorithm can effectively improve the resolution and visual quality of reconstructed images while preserving the original image’s characteristic information. In summary, the proposed algorithm significantly enhances the accuracy and robustness of image reconstruction, holding positive implications for advancing the application of computer vision technology in various real-world scenarios requiring high-resolution image processing. </div>
Cited by 0Open access
Haixia Liu (764025), Mingliang Wang (1666225) · Figshare · 2025
<div> Image super-resolution reconstruction is one of the important application branches of computer vision in many fields. However, in actual complex environments, images are often subject to various interferences, leading to severe distortion in the reconstructed images. To address this issue, this study innovatively combines multi-scale feature extraction (MSFE) and attention feature fusion (AFF). After optimization, the multi-scale recursive attentional feature fusion block (MSRAFFB) and MSRAFFB Network (MSRAFFB-Net) application algorithms are proposed. Simulation experiments on standard datasets demonstrated that MSRAFFB was crucial for enhancing the overall performance of the algorithm, improving reconstruction quality over the baseline. Additionally, through reasonable network structure design (such as increasing module depth and branch complexity), MSRAFFB-Net effectively reduced reconstruction error and improved perceptual quality. The algorithm exhibited high reconstruction accuracy across different magnification factors. Furthermore, to assess the algorithm’s performance in more realistic and complex environments, the study conducted performance experiments in actual application scenarios. The results indicate that the algorithm can effectively improve the resolution and visual quality of reconstructed images while preserving the original image’s characteristic information. In summary, the proposed algorithm significantly enhances the accuracy and robustness of image reconstruction, holding positive implications for advancing the application of computer vision technology in various real-world scenarios requiring high-resolution image processing. </div>
Cited by 0Open access
Haixia Liu (764025), Mingliang Wang (1666225) · Figshare · 2025
<div> Image super-resolution reconstruction is one of the important application branches of computer vision in many fields. However, in actual complex environments, images are often subject to various interferences, leading to severe distortion in the reconstructed images. To address this issue, this study innovatively combines multi-scale feature extraction (MSFE) and attention feature fusion (AFF). After optimization, the multi-scale recursive attentional feature fusion block (MSRAFFB) and MSRAFFB Network (MSRAFFB-Net) application algorithms are proposed. Simulation experiments on standard datasets demonstrated that MSRAFFB was crucial for enhancing the overall performance of the algorithm, improving reconstruction quality over the baseline. Additionally, through reasonable network structure design (such as increasing module depth and branch complexity), MSRAFFB-Net effectively reduced reconstruction error and improved perceptual quality. The algorithm exhibited high reconstruction accuracy across different magnification factors. Furthermore, to assess the algorithm’s performance in more realistic and complex environments, the study conducted performance experiments in actual application scenarios. The results indicate that the algorithm can effectively improve the resolution and visual quality of reconstructed images while preserving the original image’s characteristic information. In summary, the proposed algorithm significantly enhances the accuracy and robustness of image reconstruction, holding positive implications for advancing the application of computer vision technology in various real-world scenarios requiring high-resolution image processing. </div>
Cited by 0Open access
Haixia Liu (764025), Mingliang Wang (1666225) · Figshare · 2025
<div> Image super-resolution reconstruction is one of the important application branches of computer vision in many fields. However, in actual complex environments, images are often subject to various interferences, leading to severe distortion in the reconstructed images. To address this issue, this study innovatively combines multi-scale feature extraction (MSFE) and attention feature fusion (AFF). After optimization, the multi-scale recursive attentional feature fusion block (MSRAFFB) and MSRAFFB Network (MSRAFFB-Net) application algorithms are proposed. Simulation experiments on standard datasets demonstrated that MSRAFFB was crucial for enhancing the overall performance of the algorithm, improving reconstruction quality over the baseline. Additionally, through reasonable network structure design (such as increasing module depth and branch complexity), MSRAFFB-Net effectively reduced reconstruction error and improved perceptual quality. The algorithm exhibited high reconstruction accuracy across different magnification factors. Furthermore, to assess the algorithm’s performance in more realistic and complex environments, the study conducted performance experiments in actual application scenarios. The results indicate that the algorithm can effectively improve the resolution and visual quality of reconstructed images while preserving the original image’s characteristic information. In summary, the proposed algorithm significantly enhances the accuracy and robustness of image reconstruction, holding positive implications for advancing the application of computer vision technology in various real-world scenarios requiring high-resolution image processing. </div>
Cited by 0Open access
Haixia Liu (764025), Mingliang Wang (1666225) · Figshare · 2025
<div> Image super-resolution reconstruction is one of the important application branches of computer vision in many fields. However, in actual complex environments, images are often subject to various interferences, leading to severe distortion in the reconstructed images. To address this issue, this study innovatively combines multi-scale feature extraction (MSFE) and attention feature fusion (AFF). After optimization, the multi-scale recursive attentional feature fusion block (MSRAFFB) and MSRAFFB Network (MSRAFFB-Net) application algorithms are proposed. Simulation experiments on standard datasets demonstrated that MSRAFFB was crucial for enhancing the overall performance of the algorithm, improving reconstruction quality over the baseline. Additionally, through reasonable network structure design (such as increasing module depth and branch complexity), MSRAFFB-Net effectively reduced reconstruction error and improved perceptual quality. The algorithm exhibited high reconstruction accuracy across different magnification factors. Furthermore, to assess the algorithm’s performance in more realistic and complex environments, the study conducted performance experiments in actual application scenarios. The results indicate that the algorithm can effectively improve the resolution and visual quality of reconstructed images while preserving the original image’s characteristic information. In summary, the proposed algorithm significantly enhances the accuracy and robustness of image reconstruction, holding positive implications for advancing the application of computer vision technology in various real-world scenarios requiring high-resolution image processing. </div>
Cited by 0Open access
Haixia Liu (764025), Mingliang Wang (1666225) · Figshare · 2025
<div> Image super-resolution reconstruction is one of the important application branches of computer vision in many fields. However, in actual complex environments, images are often subject to various interferences, leading to severe distortion in the reconstructed images. To address this issue, this study innovatively combines multi-scale feature extraction (MSFE) and attention feature fusion (AFF). After optimization, the multi-scale recursive attentional feature fusion block (MSRAFFB) and MSRAFFB Network (MSRAFFB-Net) application algorithms are proposed. Simulation experiments on standard datasets demonstrated that MSRAFFB was crucial for enhancing the overall performance of the algorithm, improving reconstruction quality over the baseline. Additionally, through reasonable network structure design (such as increasing module depth and branch complexity), MSRAFFB-Net effectively reduced reconstruction error and improved perceptual quality. The algorithm exhibited high reconstruction accuracy across different magnification factors. Furthermore, to assess the algorithm’s performance in more realistic and complex environments, the study conducted performance experiments in actual application scenarios. The results indicate that the algorithm can effectively improve the resolution and visual quality of reconstructed images while preserving the original image’s characteristic information. In summary, the proposed algorithm significantly enhances the accuracy and robustness of image reconstruction, holding positive implications for advancing the application of computer vision technology in various real-world scenarios requiring high-resolution image processing. </div>
Cited by 0Open access
Haixia Liu (764025), Mingliang Wang (1666225) · Figshare · 2025
<div> Image super-resolution reconstruction is one of the important application branches of computer vision in many fields. However, in actual complex environments, images are often subject to various interferences, leading to severe distortion in the reconstructed images. To address this issue, this study innovatively combines multi-scale feature extraction (MSFE) and attention feature fusion (AFF). After optimization, the multi-scale recursive attentional feature fusion block (MSRAFFB) and MSRAFFB Network (MSRAFFB-Net) application algorithms are proposed. Simulation experiments on standard datasets demonstrated that MSRAFFB was crucial for enhancing the overall performance of the algorithm, improving reconstruction quality over the baseline. Additionally, through reasonable network structure design (such as increasing module depth and branch complexity), MSRAFFB-Net effectively reduced reconstruction error and improved perceptual quality. The algorithm exhibited high reconstruction accuracy across different magnification factors. Furthermore, to assess the algorithm’s performance in more realistic and complex environments, the study conducted performance experiments in actual application scenarios. The results indicate that the algorithm can effectively improve the resolution and visual quality of reconstructed images while preserving the original image’s characteristic information. In summary, the proposed algorithm significantly enhances the accuracy and robustness of image reconstruction, holding positive implications for advancing the application of computer vision technology in various real-world scenarios requiring high-resolution image processing. </div>
Cited by 0Open access
Haixia Liu (764025), Mingliang Wang (1666225) · Figshare · 2025
<div> Image super-resolution reconstruction is one of the important application branches of computer vision in many fields. However, in actual complex environments, images are often subject to various interferences, leading to severe distortion in the reconstructed images. To address this issue, this study innovatively combines multi-scale feature extraction (MSFE) and attention feature fusion (AFF). After optimization, the multi-scale recursive attentional feature fusion block (MSRAFFB) and MSRAFFB Network (MSRAFFB-Net) application algorithms are proposed. Simulation experiments on standard datasets demonstrated that MSRAFFB was crucial for enhancing the overall performance of the algorithm, improving reconstruction quality over the baseline. Additionally, through reasonable network structure design (such as increasing module depth and branch complexity), MSRAFFB-Net effectively reduced reconstruction error and improved perceptual quality. The algorithm exhibited high reconstruction accuracy across different magnification factors. Furthermore, to assess the algorithm’s performance in more realistic and complex environments, the study conducted performance experiments in actual application scenarios. The results indicate that the algorithm can effectively improve the resolution and visual quality of reconstructed images while preserving the original image’s characteristic information. In summary, the proposed algorithm significantly enhances the accuracy and robustness of image reconstruction, holding positive implications for advancing the application of computer vision technology in various real-world scenarios requiring high-resolution image processing. </div>
Cited by 0Open access
Haixia Liu (764025), Mingliang Wang (1666225) · Figshare · 2025
<div> Image super-resolution reconstruction is one of the important application branches of computer vision in many fields. However, in actual complex environments, images are often subject to various interferences, leading to severe distortion in the reconstructed images. To address this issue, this study innovatively combines multi-scale feature extraction (MSFE) and attention feature fusion (AFF). After optimization, the multi-scale recursive attentional feature fusion block (MSRAFFB) and MSRAFFB Network (MSRAFFB-Net) application algorithms are proposed. Simulation experiments on standard datasets demonstrated that MSRAFFB was crucial for enhancing the overall performance of the algorithm, improving reconstruction quality over the baseline. Additionally, through reasonable network structure design (such as increasing module depth and branch complexity), MSRAFFB-Net effectively reduced reconstruction error and improved perceptual quality. The algorithm exhibited high reconstruction accuracy across different magnification factors. Furthermore, to assess the algorithm’s performance in more realistic and complex environments, the study conducted performance experiments in actual application scenarios. The results indicate that the algorithm can effectively improve the resolution and visual quality of reconstructed images while preserving the original image’s characteristic information. In summary, the proposed algorithm significantly enhances the accuracy and robustness of image reconstruction, holding positive implications for advancing the application of computer vision technology in various real-world scenarios requiring high-resolution image processing. </div>
Cited by 0Open access
Yinan Chen, Weiqi Jin, Lei Zhao et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
For the subpixel micro-scanning imaging, we propose the reconstruction algorithm based on neither interpolation nor super-resolution idea but one of the block-by-block method recursive from the boundary to centre when additional narrowband boundary view-field diaphragm whose radiation is known a prior. The aim of the predicted boundary value is to add the conditions for solving the ununiqueness ill-problem to the inverse transition matrix from the destructed process. For the non-uniform scan factor, the improved algorithm associated with certain non-uniform motion variables is proposed. Additionally, attention is focused on the case of unknown subpixel motion, when the reconstructed images are blurred by motion parameter modulation and neighbouring point aliasing because the value of micro-motion is not the correct one. Unlike other methods that the image registration is accomplished before multi-frame restoration from undersampled sequences frame by frame, in this paper, 2-D motion vector is estimated in single frame just from the blur character of reconstructed grids. We demonstrate that once the estimated motion approaches to the real one, square summation of all pixels over the unmatched image approximately descends to the minimum. The matching track based on recursive Newton secant approaching is optimized for high matching speed and precision by different strategies, including matching region hunting, matching direction choosing and convergence prejudgement. All iterative step lengths with respect to motion parameters are substituted by the suitable values derived from the statistic process and one or multi-secant solution. The simulations demonstrate the feasibility of the matching algorithm and the obvious resolution enhancement compared to the direct oversampling image.
Cited by 0
Xuan Yang, Ke Sun, Junhai Wang et al. · Journal of Nanoelectronics and Optoelectronics · 2022
The energy storage capacity configuration of high permeability photovoltaic power generation system is unreasonable and the cost is high. Taking the constant capacity of hybrid energy storage system (Hess) composed of high permeability wind frame and super capacitor as the standard, in order to ensure smooth and stable output of photovoltaic power equipment, this paper adopts the power suppression method based on empirical mode decomposition to decompose the original power into high and low frequency components, and uses super capacitor and energy storage battery to stabilize the high and low frequency components, Reduce output power fluctuation; Taking the minimum energy storage cost as the objective function, the optimal allocation of system energy storage capacity is realized by the recursive algorithm of elastic neural network. The experimental results show that when the supercapacitor capacity and photovoltaic electric field energy storage cell capacity are 0.262 mw· h and 2.889 mw· h respectively, the minimum cost is 16987 yuan, and the smoothness index is less than 5%; Compared with before the optimal configuration, the output power of the photovoltaic power generation system after the optimal configuration is smoother, the power peak valley difference is lower, and the loss can be reduced.
Cited by 3
Haoguang Liu, Yuwen Fu, Zhoujie Wang · 2024
Existing super-resolution reconstruction algorithms for remote sensing images often struggle to fully extract and utilize features in complex scenes, and the reconstruction results are not optimal due to the influence of noise. We propose a reconstruction network model that combines deep dense residual module and sub-pixel convolution. This model connects multiple dense residual modules through recursive linking and introduces a channel attention mechanism to extract multiscale features from the images. During the feature reconstruction process, a sub-pixel convolution structure is introduced to reduce the impact of noise and enhance reconstruction performance. The model is tested on the UC Merced Land Use public dataset, and the results demonstrate that the reconstruction results of our proposed model algorithm have better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at different scales compared to the current mainstream reconstruction algorithm EDSR. It is proven that the proposed model can significantly improve the reconstruction quality through comparative analysis experiment, meeting the needs for high-resolution remote sensing image processing.
Cited by 0
Hyun‐Woo Lim, Junho Song, Nolan Kurtz · Earthquake Engineering & Structural Dynamics · 2014
Summary Seismic reliability assessment of lifeline networks gives rise to various technical challenges, which are mostly caused by a large number of network components, complex network topology, and statistical dependence between component failures. For effective risk assessment and probabilistic inference based on post‐hazard observations, various non‐simulation‐based algorithms have been developed, including the selective recursive decomposition algorithm (S‐RDA). To facilitate the application of such an algorithm to large networks, a new multi‐scale approach is developed in this paper. Using spectral clustering algorithms, a network is first divided into an adequate number of clusters such that the number of inter‐cluster links is minimized while the number of the nodes in each cluster remains reasonably large. The connectivity around the identified clusters is represented by super‐links. The reduced size of the simplified network enables the S‐RDA algorithm to perform the network risk assessment efficiently. When the simplified network is still large even after a clustering, additional levels of clustering can be introduced to have a hierarchical modeling structure. The efficiency and effectiveness of the proposed multi‐scale approach are demonstrated successfully by numerical examples of a hypothetical network, a gas transmission pipeline network, and a water transmission network. Copyright © 2014 John Wiley & Sons, Ltd.
Cited by 29Open access
Zhuangzhuang Wang, Xiaochao Liu, Dong Sun et al. · IEEE/ASME Transactions on Mechatronics · 2024
This article proposes a real-time maximum tire–road friction tracking control method with disturbance compensation for aircraft antiskid braking system (ABS). Conventional aircraft ABS cannot accurately set the optimal slip ratio in real time for runways with different friction characteristics, resulting in the maximum tire–road friction not being completely utilized in real time. To address this problem, first, a runway identification method is developed to determine the optimal slip ratio of the initial runway. Then, an optimal slip ratio update algorithm based on the Burckhardt model is proposed to update the optimal slip ratio according to the tire–road friction characteristics. Adaptive forgetting factor recursive least squares is employed to handle the most important parametric uncertainty in aircraft ABS, i.e., the brake torque conversion factor. The residual uncertainties and external disturbances are observed by constructing an extended state observer (ESO). In addition, by effectively integrating modified super-twisting algorithm with ESO, a continuous and chattering-free slip ratio tracking algorithm with disturbance compensation is developed. The proposed method can eliminate the adverse effects of major parametric uncertainties and greatly reduce the burden on ESO, so it achieves accurate tracking while greatly reducing the feedback gain. Finally, the effectiveness of the proposed method is verified by comparative simulation and experimental results.
Cited by 14
Jie Zhang, Ying Rao · 2024
Summary For seismic full waveform inversion (FWI), the shot-encoding strategy greatly reduces computing cost by randomly encoding and summing all individual seismic shots into one super shot-gather. Conventional shot-encoding FWI requests sufficient randomness within shot-encoding to mitigate crosstalk effect caused by the interference among different shots. However, this inevitably destabilizes iterations of FWI even if the robust limited-memory BFGS (L-BFGS) algorithms are used. In this paper, we proposed an improved L-BFGS method that uses a compact representation (inner product) formula of direct Hessian matrix to compute gradient difference required by the L-BFGS recursive calculation. We re-designed the workflow for shot-encoded FWI and increased randomness of shot-encoding. We have demonstrated that, for shot-encoding FWI, the proposed algorithm accelerates convergence of iterative inversions, enhances the ability to suppress crosstalk noise and improves the image quality especially for intermediate and deep formations.
Cited by 1