MEMS 411: T-SHIRT STRIP DISPENSER

To create a fabric dispenser for Professor Mary Ruppert-Stroescu of the Sam Fox Fashion Design School

AFM characterization of physical properties in coal adsorbed with different cations induced by electric pulse fracturing

Acknowledgements This research was funded by the National Natural Science Foundation of China (grant nos. 41830427, 42130806 and 41922016), 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035) and the Fundamental Research Funds for Central Universities (grant no. 2-9-2021-067). We are very grateful to the reviewers and editors for their valuable comments and suggestions.Peer reviewedPostprin

Variation of adsorption effects in coals with different particle sizes induced by differences in microscopic adhesion

Acknowledgements This research was funded by the National Natural Science Foundation of China (grant nos. 41830427, 42130806 and 41922016), the Fundamental Research Funds for Central Universities (grant no. 2-9-2021-067) and the 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035). We are very grateful to the reviewers and editors for their valuable comments and suggestionsPeer reviewedPostprin

In-Domain GAN Inversion for Faithful Reconstruction and Editability

Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, applying well-trained GANs to real image editing remains challenging. A common solution is to find an approximate latent code that can adequately recover the input image to edit, which is also known as GAN inversion. To invert a GAN model, prior works typically focus on reconstructing the target image at the pixel level, yet few studies are conducted on whether the inverted result can well support manipulation at the semantic level. This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model. In this way, we manage to sufficiently reuse the knowledge learned by GANs for image reconstruction, facilitating a wide range of editing applications without any retraining. We further make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property. Such a trade-off sheds light on how a GAN model represents an image with various semantics encoded in the learned latent distribution. Code, models, and demo are available at the project page: https://genforce.github.io/idinvert/

Atomic force microscopy investigation of nano-scale roughness and wettability in middle to high rank coals, with samples from Qinshui Basin, China

Acknowledgements This research was funded by the National Natural Science Fund (grant nos. 41830427, 42130806 and 41922016), the Fundamental Research Funds for Central Universities (grant no. 2652018002), and financial support from China Scholarship Council ((No.202006400048).Peer reviewedPostprin

Interference mechanism in coalbed methane wells and impacts on infill adjustment for existing well patterns

This research was funded by the National Natural Science Foundation of China (grant nos. 41830427, 42130806 and 41922016), 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035) and the Fundamental Research Funds for Central Universities, China (grant no. 2-9-2021-067). We are very grateful to the reviewers and editors for their valuable comments and suggestions.Peer reviewedPublisher PD

Credit risk assessment in commercial banks based on fuzzy support vector machines

Credit risk assessment plays an important role in banks credit risk management. The objective of credit assessment is to decide credit ranks, which denote the capacity of enterprises to meet their financial commitments. Traditional "one-versusone" approach has been commonly used in the multi-classification method based on Support Vector Machine (SVM). Since SVM for pattern recognition is based on binary classification, there will be unclassifiable regions when extended to multi-classification problems. Focus on this problem, a new credit risk assessment model based on fuzzy SVM is introduced in this paper that can give a reasonable classification for unclassifiable examples. Experiment results show that the fuzzy SVM method provides a better performance in generalization ability and assessment accuracy than conventional one-versus-one multi-classification approach

Nano-CT measurement of pore-fracture evolution and diffusion transport induced by fracturing in medium-high rank coal

Acknowledgements This research was funded by the National Natural Science Foundation of China (grant nos. 42130806, 41830427 and 41922016), 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (grant no. ZD2021YC035) and the Fundamental Research Funds for Central Universities (grant no. 2-9-2021-067). We are very grateful to the reviewers and editors for their valuable comments and suggestions.Peer reviewedPostprin