Developing smart and advanced functional materials inspired by the unparalleled complexity and efficiency of biological tissues has received much attention across various *** alloys,particularly pure magnesium,have em...
详细信息
Developing smart and advanced functional materials inspired by the unparalleled complexity and efficiency of biological tissues has received much attention across various *** alloys,particularly pure magnesium,have emerged as notable candidates in orthopedics and dentistry due to their exceptional biocompatibility,degradability,and ability to promote bone regeneration[1].However,the highly reactive chemical nature of magnesium and its rapid degradation upon exposure to water.
In this note, we establish a boundary maximum principle for a class of stationary pairs of varifolds satisfying a fixed contact angle condition in any compact Riemannian manifold with smooth boundary.
In this note, we establish a boundary maximum principle for a class of stationary pairs of varifolds satisfying a fixed contact angle condition in any compact Riemannian manifold with smooth boundary.
China’s architecture,engineering,and construction(AEC)industry needs a clear and sustainable development *** inspiration from speeches delivered at a seminar hosted by the Chinese Academy of Engineering and extensive...
详细信息
China’s architecture,engineering,and construction(AEC)industry needs a clear and sustainable development *** inspiration from speeches delivered at a seminar hosted by the Chinese Academy of Engineering and extensive literature research on Chinese policies,this article presents a summary of the current trends in the AEC industry in four dimensions:industrialization as the foundation,intelligence as the enabler,lean management as the strategy,and green development as the *** four dimensions are intricately interconnected and rooted in multiple *** article provides a detailed review of the current practices,challenges,and future directions associated with each ***,ten grand challenges were proposed to stimulate discussions on the future of the AEC *** article offers an overarching understanding of the AEC industry and presents a four-dimension framework for sustainable development,which can be valuable for AEC practitioners.
Single-shot fringe projection profilometry (FPP) is widely used in the field of dynamic optical 3D reconstruction because of its high accuracy and efficiency. However, the traditional single-shot FPP methods are not s...
详细信息
Single-shot fringe projection profilometry (FPP) is widely used in the field of dynamic optical 3D reconstruction because of its high accuracy and efficiency. However, the traditional single-shot FPP methods are not satisfactory in reconstructing complex scenes with noise and discontinuous objects. Therefore, this paper proposes a Deformable Convolution-Based HINet with Attention Connection (DCAHINet), which is a dual-stage hybrid network with a deformation extraction stage and depth mapping stage. Specifically, the deformable convolution module and attention gate are introduced into DCAHINet respectively to enhance the ability of feature extraction and fusion. In addition, to solve the long-standing problem of the insufficient generalization ability of deep learning-based single-shot FPP methods on different hardware devices, DCAHINet outputs phase difference, which can be converted into 3D shapes by simple multiplication operations, rather than directly outputting 3D shapes. To the best of the author's knowledge, DCAHINet is the first network that can be applied to different hardware devices. Experiments on virtual and real datasets show that the proposed method is superior to other deep learning or traditional methods and can be used in practical application scenarios.
3D reconstruction plays a pivotal role in intelligent manufacturing, industrial inspection, and other fields. Fringe projection profilometry is a widely used 3D reconstruction method due to its high accuracy and nonco...
详细信息
3D reconstruction plays a pivotal role in intelligent manufacturing, industrial inspection, and other fields. Fringe projection profilometry is a widely used 3D reconstruction method due to its high accuracy and noncontact nature. However, when fringe projection profilometry (FPP) is applied to reconstruct highly reflective surfaces, there are not only diffuse reflections, but also undesired specular reflections. Intense specular reflection destroys the sinusoidal characteristics of the fringe pattern, leading to a decrease in 3D reconstruction accuracy. Traditional methods, such as Multiple exposures method, require multiple redundant shots to obtain high-quality fringe patterns, which makes efficiency difficult to meet industrial measurement requirements. Inspired by the application of deep learning in 3D vision, this article proposes a Y-shaped fast Fourier convolutional network (Y-FFC) for high reflection removal to provide high-quality fringe patterns. The cosine characteristics of the grayscale gradient of the sinusoidal fringe pattern make the detection of nonsinusoidal regions more accurate in the gradient domain. In addition, the undesired specular reflection component is easier to distinguish and filter out in the frequency domain. Therefore, the design of Y-FFC fully considers the gradient and frequency information. Experimental results show that the introduction of gradient and frequency information is beneficial for high reflection removal, enabling the reconstruction of industrial workpieces with highly reflective surfaces without the need for additional shots. The mean absolute error for depth measurements of an aircraft blade with a depth of 30 mm was reduced from 0.1332 to 0.0041 mm.
This article proposes a closed-form adaptive tracking control approach for linear heat equations with unknown parameters to achieve full temperature profile tracking by leveraging Fourier regularization and bi-orthogo...
详细信息
This article proposes a closed-form adaptive tracking control approach for linear heat equations with unknown parameters to achieve full temperature profile tracking by leveraging Fourier regularization and bi-orthogonal series. A state predictor which copies the plant with state partial derivatives and unknown parameters replaced by their estimates is built and an adaptive law is designed to estimate the unknown parameters. The state predictor is decomposed into two subsystems for tracking control synthesis: the first subsystem involves terms from the original heat equation, while the second subsystem is simpler and can be reformulated as a standard heat equation. Specifically, the first subsystem is regarded as an unforced PDE whose terminal states always follow the desired temperature profile such that its initial condition can be calculated by solving the backward heat equation at every time step. To address the blow-up issue in backward calculation, a Fourier regularization scheme is explored to cut off the higher-order Fourier modes and an appropriate tradeoff between approximation accuracy and robustness is achieved. Given the solutions from the first subsystem, the initial condition for the second subsystem can be subsequently calculated. We propose a numerical algorithm to calculate a set of bi-orthogonal series online and employ them to compute the boundary control function that drives the second subsystem to zero at every time step. Combining these two subsystems, it guarantees that the overall system follows the desired temperature profile. We demonstrate that the proposed closed-form adaptive tracking control algorithm achieves full temperature profile tracking with around 5% error averaged over the entire space, that is, L2 norm over time. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Methane,as the main component of natural gas,is a key transitional fuel resource due to its abundance and relatively low carbon emissions,aligning with global carbon neutrality objectives[1].Traditional natural gas st...
详细信息
Methane,as the main component of natural gas,is a key transitional fuel resource due to its abundance and relatively low carbon emissions,aligning with global carbon neutrality objectives[1].Traditional natural gas storage methods,such as liquefied and compressed natural gas,require costly infrastructure and high-pressure ***,adsorbed natural gas offers a safer,more cost-effective,and environmentally friendly solution by enhancing storage capacity at reduced pressures through the use of methane adsorbents[2].
For a function f which foliates a one-sided neighborhood of a closed hypersurface M, we give an estimate of the distance of M to a Wulff shape in terms of the Lp-norm of the traceless F-Hessian of f, where F is the su...
详细信息
For a function f which foliates a one-sided neighborhood of a closed hypersurface M, we give an estimate of the distance of M to a Wulff shape in terms of the Lp-norm of the traceless F-Hessian of f, where F is the support function of the Wulff shape. This theorem is applied to prove quantitative stability results for the anisotropic Heintze-Karcher inequality, the anisotropic Alexandrov problem, as well as for the anisotropic overdetermined boundary value problem of Serrin-type. (c) 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons .org /licenses /by /4 .0/).
We consider the 3D Boltzmann equation with the constant collision kernel. We investigate the well/ill-posedness problem using the methods from nonlinear dispersive PDEs. We construct a family of special solutions, whi...
详细信息
We consider the 3D Boltzmann equation with the constant collision kernel. We investigate the well/ill-posedness problem using the methods from nonlinear dispersive PDEs. We construct a family of special solutions, which are neither near equilibrium nor self-similar, to the equation, and prove that the well/ill-posedness threshold in H-s Sobolev space is exactly at regularity s =1, despite the fact that the equation is scale invariant at s =1/2.
The need to model data with higher dimensions, such as a tensor-variate framework where each observation is considered a three-dimensional object, increases due to rapid improvements in computational power and data st...
详细信息
The need to model data with higher dimensions, such as a tensor-variate framework where each observation is considered a three-dimensional object, increases due to rapid improvements in computational power and data storage capabilities. In this study, a finite mixture of hidden Markov model for tensor-variate time series data is developed. Simulation studies demonstrate high classification accuracy for both cluster and regime IDs. To further validate the usefulness of the proposed model, it is applied to real-life data with promising results.
暂无评论