检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Ma, Wenbo Bai, Tianwen Zhu, Wei Guo, Shuaicheng School of Mechanical Engineering and Mechanics Xiangtan University Xiangtan411105 China College of Civil Engineering Xiangtan University Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan411105 China Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education Hunan University Changsha410082 China International Science Innovation Collaboration Base for Green & Advanced Civil Engineering Materials of Hunan Province Hunan University Changsha410082 China Key Laboratory for Green & Advanced Civil Engineering Materials and Application Technology of Hunan Province College of Civil Engineering Hunan University Changsha410082 China

Utilizing electrolysis manganese dioxide residue (EMDR) to prepare the supersulfated cement can achieve effective waste recycling, and its application can reduce the carbon emission caused by Portland cement. This study aims to quantitatively build the correlation between the mechanical performance and cementitious matrix composition of EMDR-based supersulfated cement. The supersulfated cement containing clinker, slag and as received EMDR is designed and prepared based on the simplex centroid design method. The compressive strength, tensile strength and flexural strength of the EMDR based supersulfated cement paste were measured at 3-day, 7-day and 28-day ages. The compressive strength of the electrolysis manganese dioxide residue (EMDR) based supersulfated cement first increases and then decreases with the mass ratio between slag and EMDR. The added cement clinker can increase the compressive strength at 3-day and 7-day ages, but the 28-day age strength first decrease and then increase with the cement clinker content. Meanwhile, the flexural and tensile performances of the EMDR based supersulfated cement generally increase with the clinker content. The measured elastic modulus results indicated the added clinker could enhance the stiffness of the stiffness development. Then the strength development mechanisms of EMDR-based supersulfated cement are analyzed based on phase and microstructure characterization. It is found the clinker can accelerate the reaction rate between the slag and EMDR, and the consumption rate of the gypsum contained in EMDR. The calcium silicate hydrate (CSH) gel phase's contribution to tensile strength is more significant than that of ettringite phase. Based on the mechanism analysis, the index of [SO3]/[Al2O3] molar ratio is proposed for the matrix design of EMDR based SSC. This study can support the design and application of EMDR-based supersulfated cement. © 2023, The Authors. All rights reserved.

关键词： Aluminum oxide

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Constrained Probabilistic Pareto Dominance for Expensive Constrained Multiobjective Optimization Problems

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IEEE Transactions on Evolutionary computation 2024年 1-1页

作者： Zhang, Zhiyao Wang, Yong Sun, Guangyong Pang, Tong Tang, Ke School of Automation Central South University Changsha China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering and the Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

This paper proposes a new parameterless constraint-handling technique, named constrained probabilistic Pareto dominance (CPPD), for expensive constrained multiobjective optimization problems (CMOPs). In CPPD, when comparing two solutions, in terms of each original objective, we design a new objective for each solution, which is the negative product of two probabilities calculated based on the predicted fitness mean values and the uncertainty information provided by Kriging models: 1) the probability that this solution satisfies all constraints, denoted as PoF, and 2) the probability that this solution is better than the other on the original objective, denoted as PoB. It is evident that for each solution, PoF and PoB indicate its feasibility and its optimality on the corresponding original objective, respectively. Then, Pareto dominance based on new objectives is executed. As a result, both competitive feasible solutions and promising infeasible solutions with good diversity can be preserved by CPPD. These two kinds of solutions can help the population to exploit the located feasible parts and to explore new feasible parts, respectively. Further, based on CPPD, we develop a Pareto-based Kriging-assisted constrained multiobjective evolutionary algorithm (called PEA) to deal with expensive CMOPs with two or three objectives. Finally, PEA is generalized to solve expensive constrained many-objective optimization problems, named PEA+. The effectiveness of CPPD, PEA, and PEA+ is verified by comprehensive experiments. IEEE

关键词： Evolutionary algorithms

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A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing

arXiv

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arXiv 2024年

作者： Feng, Yujie Yang, Yin Fan, Xiaohong Zhang, Zhengpeng Bu, Lijing Zhang, Jianping School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory for Intelligent Computing and Information Processing of the Ministry of Education Xiangtan411105 China School of Mathematics and Computational Science Xiangtan University National Center for Applied Mathematics in Hunan Hunan International Scientific and Technological Innovation Cooperation Base of Computational Science Xiangtan411105 China College of Mathematical Medicine Zhejiang Normal University Jinhua321004 China School of Automation and Electronic Information Xiangtan University Xiangtan411105 China

Recently, deep learning methods have gained remarkable achievements in the field of image restoration for remote sensing (RS). However, most existing RS image restoration methods focus mainly on conventional first-order degradation models, which may not effectively capture the imaging mechanisms of remote sensing images. Furthermore, many RS image restoration approaches that use deep learning are often criticized for their lacks of architecture transparency and model interpretability. To address these problems, we propose a novel progressive restoration network for high-order degradation imaging (HDI-PRNet), to progressively restore different image degradation. HDI-PRNet is developed based on the theoretical framework of degradation imaging, offering the benefit of mathematical interpretability within the unfolding network. The framework is composed of three main components: a module for image denoising that relies on proximal mapping prior learning, a module for image deblurring that integrates Neumann series expansion with dual-domain degradation learning, and a module for super-resolution. Extensive experiments demonstrate that our method achieves superior performance on both synthetic and real remote sensing images. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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A Novel Perturbation-Theory-Based Approach for Free Vibrations of Bi-Directional Functionally Graded Porous Beams with a New Bi-Directional Pore Distribution Pattern

SSRN

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SSRN 2024年

作者： Chen, Chen Zhou, Lilin Li, Daokui College of Aerospace Science and Engineering National University of Defense Technology Hunan Changsha410073 China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions Hunan Changsha410073 China Aviation Engineering Institute Civil Aviation Flight University of China Sichuan Guanghan618307 China

Porosity defects usually exist in the functionally graded material (FGM) due to the inherent deficiencies in the fabricating process. Omission and miscalculation of the defects possibly resulting in overpredicted load-carrying capacities. The current assumption of pore distribution ignores the influence of material gradient which is not accurate enough. A new hypothesis of a bi-directional (2D) FG porosity distribution pattern that closer to the observation is proposed to characterize the feature. Moreover, instead of the numerical method, this study pioneers a fresh thought of perturbation approach (PA) customized for solving the governing equations of the bi-directional functionally graded porous (2D FGP) beam for the first time. First, A new hypothesis of 2D porosity distribution pattern is proposed. Second, a free vibration model of the 2D FGP beam is established by the rule of mixture, Timoshenko beam theory, and Hamilton’s principle. Then, by applying the PA, the 2D FGP beam is equivalent to an even porous homogenous beam and a small material perturbation to facilitate the computation of natural frequencies. The results of high-precision show that the PA is adaptive and applicable. Finally, discussions and analyses are conducted. It is found that, the pore distribution pattern has a significant impact on the natural frequency. The frequency of the new 2D FGP beam decreases with the increase of the material gradient, the porosity coefficient, and the power-law indexes. The influence of pores can be ignored in a rough calculation once the pore distribution is observed to approach the proposed uneven pattern. © 2024, The Authors. All rights reserved.

关键词： Natural frequencies

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MVMS-RCN: A Dual-Domain Unified CT Reconstruction with Multi-sparse-view and Multi-scale Refinement-correction

arXiv

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arXiv 2024年

作者： Fan, Xiaohong Chen, Ke Yi, Huaming Yang, Yin Zhang, Jianping The School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan411105 China The Centre for Mathematical Imaging Techniques Department of Mathematical Sciences The University of Liverpool LiverpoolL6972L United Kingdom The School of Mathematics and Computational Science Xiangtan University Hunan National Applied Mathematics Center Xiangtan411105 China The School of Mathematics and Computational Science Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan411105 China

X-ray Computed Tomography (CT) is one of the most important diagnostic imaging techniques in clinical applications. Sparse-view CT imaging reduces the number of projection views to a lower radiation dose and alleviates the potential risk of radiation exposure. Most existing deep learning (DL) and deep unfolding sparse-view CT reconstruction methods: 1) do not fully use the projection data;2) do not always link their architecture designs to a mathematical theory;3) do not flexibly deal with multi-sparse-view reconstruction assignments. This paper aims to use mathematical ideas and design optimal DL imaging algorithms for sparse-view CT reconstructions. We propose a novel dual-domain unified framework that offers a great deal of flexibility for multi-sparse-view CT reconstruction through a single model. This framework combines the theoretical advantages of model-based methods with the superior reconstruction performance of DL-based methods, resulting in the expected generalizability of DL. We propose a refinement module that utilizes unfolding projection domain to refine full-sparse-view projection errors, as well as an image domain correction module that distills multi-scale geometric error corrections to reconstruct sparse-view CT. This provides us with a new way to explore the potential of projection information and a new perspective on designing network architectures. The multi-scale geometric correction module is end-to-end learnable, and our method could function as a plug-and-play reconstruction technique, adaptable to various applications. Extensive experiments demonstrate that our framework is superior to other existing state-of-the-art methods. Our source codes are available at https://***/fanxiaohong/MVMS-RCN. Copyright © 2024, The Authors. All rights reserved.

关键词： Geometry

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Gradient Recovery-Type a Posteriori Error Estimates for Steady-State Poisson-Nernst-Planck Equations

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Advances in Applied Mathematics and Mechanics 2020年第6期12卷 1353-1383页

作者： Ruigang Shen Shi Shu Ying Yang Mingjuan Fang School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtanHunan 411105China School of Mathematics and Computational Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationGuilin University of Electronic TechnologyGuilinGuangxi 541004China School of Mathematics and Computational Science Guilin University of Electronic TechnologyGuilinGuangxi 541004China

In this article,we derive the a posteriori error estimators for a class of steadystate Poisson-Nernst-Planck *** the gradient recovery operator,the upper and lower bounds of the a posteriori error estimators are established both for the electrostatic potential and *** is shown by theory and numerical experiments that the error estimators are reliable and the associated adaptive computation is efficient for the steady-state PNP systems.

关键词： Poisson-Nernst-Planck equations gradient recovery a posteriori error estimate

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VIRTUAL ELEMENT METHODS WITHOUT EXTRINSIC STABILIZATION

arXiv

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arXiv 2022年

作者： Chen, Chunyu Huang, Xuehai Wei, Huayi Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China School of Mathematics Shanghai University of Finance and Economics Shanghai200433 China

Virtual element methods (VEMs) without extrinsic stabilization in arbitrary degree of polynomial are developed for second order elliptic problems, including a nonconforming VEM and a conforming VEM in arbitrary dimension. The key is to construct local H(div)-conforming macro finite element spaces such that the associated L2 projection of the gradient of virtual element functions is computable, and the L2 projector has a uniform lower bound on the gradient of virtual element function spaces in L2 norm. Optimal error estimates are derived for these VEMs. Numerical experiments are provided to test the VEMs without extrinsic *** Codes 65N12, 65N22, 65N30 Copyright © 2022, The Authors. All rights reserved.

关键词： Stabilization

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SOLVING TIME-DEPENDENT PARAMETRIC PDES BY MULTICLASS CLASSIFICATION-BASED REDUCED ORDER MODEL

arXiv

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arXiv 2021年

作者： Cui, Chen Jiang, Kai Shu, Shi School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan Xiangtan411105 China

In this paper, we propose a network model, the multiclass classification-based reduced order model (MC-ROM), for solving time-dependent parametric partial differential equations (PPDEs). This work is inspired by the observation of applying the deep learning-based reduced order model (DL-ROM) [14] to solve diffusion-dominant PPDEs. We find that the DL-ROM has a good approximation for some special model parameters, but it cannot approximate the drastic changes of the solution as time evolves. Based on this fact, we classify the dataset according to the magnitude of the solutions and construct corresponding subnets dependent on different types of data. Then we train a classifier to integrate different subnets together to obtain the MC-ROM. When subsets have the same architecture, we can use transfer learning techniques to accelerate offline training. Numerical experiments show that the MC-ROM improves the generalization ability of the DL-ROM both for diffusion- and convection-dominant problems, and maintains the DL-ROM’s advantage of good approximation ability. We also compare the approximation accuracy and computational efficiency of the proper orthogonal decomposition (POD) which is not suitable for convection-dominant problems. For diffusion-dominant problems, the MC-ROM has better approximation accuracy than the POD in a small dimensionality reduction space, and its computational performance is more efficient than the POD’s. Copyright © 2021, The Authors. All rights reserved.

关键词： Partial differential equations

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An adaptive high-order surface finite element method for polymeric self-consistent field theory on general curved surfaces

arXiv

引用

arXiv 2021年

作者： Jiang, Kai Wang, Xin Liu, Jianggang Wei, Huayi School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan Xiangtan411105 China

In this paper, we develop an adaptive high-order surface finite element method (FEM) incorporating spectral deferred correction method for chain contour discretization to solve polymeric self-consistent field equations on general curved surfaces. The high-order surface FEM is obtained by the high-order surface geometrical approximation and the high-order function space approximation. Numerical results demonstrate that the precision order of these methods is consistent with theoretical prediction. In order to describe the sharp interface in the strongly segregated system more accurately, an adaptive FEM equipped with a new Log marking strategy is proposed. Compared with the traditional strategy, the Log marking strategy can not only label the elements that need to be refined or coarsened, but also give the refined or coarsened times, which can make full use of the information of a posterior error estimator and improve the efficiency of the adaptive algorithm. To demonstrate the power of our approach, we investigate the self-assembled patterns of diblock copolymers on several distinct curved surfaces. Numerical results illustrate the efficiency of the proposed method, especially for strongly segregated systems with economical discretization nodes. Copyright © 2021, The Authors. All rights reserved.

关键词： Molecular orbitals

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A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing

arXiv

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arXiv 2023年

作者： Feng, Yujie Yang, Yin Fan, Xiaohong Zhang, Zhengpeng Zhang, Jianping School of Mathematics and Computational Science Xiangtan University Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory for Intelligent Computing and Information Processing The Ministry of Education Xiangtan411105 China School of Mathematics and Computational Science Xiangtan University National Center for Applied Mathematics in Hunan Hunan International Scientific and Technological Innovation Cooperation Base of Computational Science Xiangtan411105 China School of Automation and Electronic Information Xiangtan University Xiangtan411105 China

MSC Codes 54H30, 68U10, 94A08Remote sensing images are essential for many applications of the earth’s sciences, but their quality can usually be degraded due to limitations in sensor technology and complex imaging environments. To address this, various remote sensing image deblurring methods have been developed to restore sharp and high-quality images from degraded observational data. However, most traditional model-based deblurring methods usually require predefined hand-crafted prior assumptions, which are difficult to handle in complex applications. On the other hand, deep learning-based deblurring methods are often considered as black boxes, lacking transparency and interpretability. In this work, we propose a new blind deblurring learning framework that utilizes alternating iterations of shrinkage thresholds. This framework involves updating blurring kernels and images, with a theoretical foundation in network design. Additionally, we propose a learnable blur kernel proximal mapping module to improve the accuracy of the blur kernel reconstruction. Furthermore, we propose a deep proximal mapping module in the image domain, which combines a generalized shrinkage threshold with a multi-scale prior feature extraction block. This module also incorporates an attention mechanism to learn adaptively the importance of prior information, improving the flexibility and robustness of prior terms, and avoiding limitations similar to hand-crafted image prior terms. Consequently, we design a novel multi-scale generalized shrinkage threshold network (MGSTNet) that focuses specifically on learning deep geometric prior features to enhance image restoration. Experimental results on real and synthetic remote sensing image datasets demonstrate the superiority of our MGSTNet framework compared to existing deblurring methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Shrinkage

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