Study on nonlinear dynamic of ball bearing-offset disk rotor system with whirling-swing coupling vibration

A dynamic model of ball bearing-offset disk rotor system with whirling-swing coupling vibration is presented, in which the rotor disk offset position and the swing vibration of disk are concerned. In the model of ball bearing, the bearing radial clearance, nonlinear Hertzian contact force and the varying compliance (VC) vibration are considered. Numerical methods are used to obtain the nonlinear dynamic response of the system under different disk offset values for considering the disk swing vibration or not. Effects of bearing radial clearance variation on the dynamic performance of the system under different rotor offset values are investigated. It is shown that the nonlinear dynamic of the offset disk rotor system enhances obviously when rotor disk swing vibration is considered. As rotor disk offset increasing, the sensitivity of critical speed to variation of the bearing radial clearance improves

Study on nonlinear dynamic of ball bearing-offset disk rotor system with whirling-swing coupling vibration

A dynamic model of ball bearing-offset disk rotor system with whirling-swing coupling vibration is presented, in which the rotor disk offset position and the swing vibration of disk are concerned. In the model of ball bearing, the bearing radial clearance, nonlinear Hertzian contact force and the varying compliance (VC) vibration are considered. Numerical methods are used to obtain the nonlinear dynamic response of the system under different disk offset values for considering the disk swing vibration or not. Effects of bearing radial clearance variation on the dynamic performance of the system under different rotor offset values are investigated. It is shown that the nonlinear dynamic of the offset disk rotor system enhances obviously when rotor disk swing vibration is considered. As rotor disk offset increasing, the sensitivity of critical speed to variation of the bearing radial clearance improves

BIVAS: A scalable Bayesian method for bi-level variable selection with applications

In this paper, we consider a Bayesian bi-level variable selection problem in high-dimensional regressions. In many practical situations, it is natural to assign group membership to each predictor. Examples include that genetic variants can be grouped at the gene level and a covariate from different tasks naturally forms a group. Thus, it is of interest to select important groups as well as important members from those groups. The existing Markov Chain Monte Carlo (MCMC) methods are often computationally intensive and not scalable to large data sets. To address this problem, we consider variational inference for bi-level variable selection (BIVAS). In contrast to the commonly used mean-field approximation, we propose a hierarchical factorization to approximate the posterior distribution, by utilizing the structure of bi-level variable selection. Moreover, we develop a computationally efficient and fully parallelizable algorithm based on this variational approximation. We further extend the developed method to model data sets from multi-task learning. The comprehensive numerical results from both simulation studies and real data analysis demonstrate the advantages of BIVAS for variable selection, parameter estimation and computational efficiency over existing methods. The method is implemented in R package `bivas' available at https://github.com/mxcai/bivas

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper

BIVAS: A scalable Bayesian method for bi-level variable selection with applications

Journal of Computational and Graphical Statistics29140-5

Multi-Scale Sparse Network with Cross-Attention Mechanism for Image-Based Butterflies Fine-Grained Classification

Butterfly protection is critical for environmental protection, and butterfly classification study is an essential tool for doing so. We proposed a new fine-grained butterfly classification architecture to address the issues of duplicate information in some butterfly images and trouble identifying them due to their tiny inter-class variance. To begin, a Non-Local Mean Filtering and Multi-Scale Retinex-based method (NL-MSR) is employed to enhance the butterfly images in order to efficiently retain more detail information. Then, to accomplish fine-grained categorization of butterfly images, a Multi-scale Sparse Network with Cross-Attention Mechanism (CA-MSNet) is designed. In CA-MSNet, a Cross-Attention Mechanism module (CAM) that offers distinct weights in the horizontal and vertical directions based on two strategies is devised to successfully identify the spatial distribution of butterfly stripes and spots and suppress incorrect information. Then, to overcome the recognition problem of butterfly spots with small inter-class variance, a Multi-scale sparse module (MSS) with multi-scale receptive fields is constructed. Finally, a Depthwise Separable Convolution module is employed to mitigate the parameter rise induced by the MSS network. In order to validate the model\u27s feasibility and effectiveness in a complex environment, we compared it to existing methods, and our proposed method achieved an average recognition accuracy of 91.88%, with an F1 value of 92.15%, indicating that it has a good effect on the fine-grained classification of butterflies and can be applied to their recognition to realize their protection

MRDA-MGFSNet: Network based on a Multi-Rate Dilated Attention Mechanism and Multi-Granularity Feature Sharer for Image-Based Butterflies Fine-Grained Classification

Aiming at solving the problems of high background complexity of some butterfly images and the difficulty in identifying them caused by their small inter-class variance, we propose a new fine-grained butterfly classification architecture, called Network based on Multi-rate Dilated Attention Mechanism and Multi-granularity Feature Sharer (MRDA-MGFSNet). First, in this network, in order to effectively identify similar patterns between butterflies and suppress the information that is similar to the butterfly\u27s features in the background but is invalid, a Multi-rate Dilated Attention Mechanism (MRDA) with a symmetrical structure which assigns different weights to channel and spatial features is designed. Second, fusing the multi-scale receptive field module with the depthwise separable convolution module, a Multi-granularity Feature Sharer (MGFS), which can better solve the recognition problem of a small inter-class variance and reduce the increase in parameters caused by multi-scale receptive fields, is proposed. In order to verify the feasibility and effectiveness of the model in a complex environment, compared with the existing methods, our proposed method obtained a mAP of 96.64%, and an F1 value of 95.44%, which showed that the method proposed in this paper has a good effect on the fine-grained classification of butterflies

Synthesis of 1D Silica Nanostructures with Controllable Sizes Based on Short Anionic Peptide Self-Assembly

Artificial synthesis of silica under benign conditions is usually achieved by using cationic organic matrices as templates while the anionic analogues have not received enough consideration, albeit they are also functioning in biosilica formation. In this work, we report the design and self-assembly of an anionic peptide amphiphile (I₃E) and the use of its self-assemblies as templates to synthesize 1D silica nanostructures with tunable sizes. We show that short I₃E readily formed long nanofibrils in aqueous solution via a hierarchical self-assembly process. By using APTES and TEOS as silica precursors, we found that the I₃E nanofibrils templated the production of silica nanotubes with a wide size distribution, in which the silica size regulation was achieved by tuning the interactions among the peptide template and silicon species. These results clearly illustrate a facile method for generating silica nanomaterials based on anionic matrices