no code implementations • 24 Feb 2024 • Yilin Zheng, Zhigong Song
The reconstruction of 3D microstructures from 2D slices is considered to hold significant value in predicting the spatial structure and physical properties of materials. The dimensional extension from 2D to 3D is viewed as a highly challenging inverse problem from the current technological perspective. Recently, methods based on generative adversarial networks have garnered widespread attention. However, they are still hampered by numerous limitations, including oversimplified models, a requirement for a substantial number of training samples, and difficulties in achieving model convergence during training. In light of this, a novel generative model that integrates the multiscale properties of U-net with and the generative capabilities of GAN has been proposed. Based on this, the innovative construction of a multi-scale channel aggregation module, a multi-scale hierarchical feature aggregation module and a convolutional block attention mechanism can better capture the properties of the material microstructure and extract the image information. The model's accuracy is further improved by combining the image regularization loss with the Wasserstein distance loss. In addition, this study utilizes the anisotropy index to accurately distinguish the nature of the image, which can clearly determine the isotropy and anisotropy of the image. It is also the first time that the generation quality of material samples from different domains is evaluated and the performance of the model itself is compared. The experimental results demonstrate that the present model not only shows a very high similarity between the generated 3D structures and real samples but is also highly consistent with real data in terms of statistical data analysis.
no code implementations • 9 Jun 2021 • Semih Cayci, Yilin Zheng, Atilla Eryilmaz
In a wide variety of applications including online advertising, contractual hiring, and wireless scheduling, the controller is constrained by a stringent budget constraint on the available resources, which are consumed in a random amount by each action, and a stochastic feasibility constraint that may impose important operational limitations on decision-making.
no code implementations • 22 Jun 2020 • Qian Zhang, Yilin Zheng, Jean Honorio
Then for the novel task, we prove that the minimization of the $\ell_1$-regularized log-determinant Bregman divergence with the additional constraint that the support is a subset of the estimated support union could reduce the sufficient sample complexity of successful support recovery to $O(\log(|S_{\text{off}}|))$ where $|S_{\text{off}}|$ is the number of off-diagonal elements in the support union and is much less than $N$ for sparse matrices.
no code implementations • 10 Apr 2018 • Zhenxin Wang, Sayan Sarcar, Jingxin Liu, Yilin Zheng, Xiangshi Ren
Image segmentation needs both local boundary position information and global object context information.