Effect of Surface Roughness on Elastohydrodynamic Lubrication Performance of Cylindrical Roller Bearing

In order to study the effect of surface roughness on the Elastohydrodynamic Lubrication (EHL) performance of cylindrical roller bearing, an EHL model of cylindrical roller bearing with three dimensional surface cosine roughness based on finite length line contact theory is established. The EHL performance of cylindrical roller bearing is calculated by the Finite Difference Method (FDM) program, with which the effects of surface cosine roughness amplitude, wavelength and texture angle on EHL performance of cylindrical roller bearing are analyzed. The numerical results show that the roughness amplitude, wavelength and texture angle have great influence on the EHL performance in the contact area. The increase of roughness amplitude and wavelength in a reasonable range is beneficial to the enhancement of EHL performance of the cylindrical roller bearing, and the transverse roughness is more favorable to enhance the bearing capacity and reduce the friction coefficient

BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation

Cross-modal Unsupervised Domain Adaptation (UDA) aims to exploit the complementarity of 2D-3D data to overcome the lack of annotation in a new domain. However, UDA methods rely on access to the target domain during training, meaning the trained model only works in a specific target domain. In light of this, we propose cross-modal learning under bird's-eye view for Domain Generalization (DG) of 3D semantic segmentation, called BEV-DG. DG is more challenging because the model cannot access the target domain during training, meaning it needs to rely on cross-modal learning to alleviate the domain gap. Since 3D semantic segmentation requires the classification of each point, existing cross-modal learning is directly conducted point-to-point, which is sensitive to the misalignment in projections between pixels and points. To this end, our approach aims to optimize domain-irrelevant representation modeling with the aid of cross-modal learning under bird's-eye view. We propose BEV-based Area-to-area Fusion (BAF) to conduct cross-modal learning under bird's-eye view, which has a higher fault tolerance for point-level misalignment. Furthermore, to model domain-irrelevant representations, we propose BEV-driven Domain Contrastive Learning (BDCL) with the help of cross-modal learning under bird's-eye view. We design three domain generalization settings based on three 3D datasets, and BEV-DG significantly outperforms state-of-the-art competitors with tremendous margins in all settings.Comment: Accepted by ICCV 202

Weakly Supervised Semantic Segmentation for Large-Scale Point Cloud

Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}

PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-temporal Satellite Imagery

Satellite imagery analysis plays a vital role in remote sensing, but the information loss caused by cloud cover seriously hinders its application. This study presents a high-performance cloud removal architecture called Progressive Multi-scale Attention Autoencoder (PMAA), which simultaneously leverages global and local information. It mainly consists of a cloud detection backbone and a cloud removal module. The cloud detection backbone uses cloud masks to reinforce cloudy areas to prompt the cloud removal module. The cloud removal module mainly comprises a novel Multi-scale Attention Module (MAM) and a Local Interaction Module (LIM). PMAA establishes the long-range dependency of multi-scale features using MAM and modulates the reconstruction of the fine-grained details using LIM, allowing for the simultaneous representation of fine- and coarse-grained features at the same level. With the help of diverse and multi-scale feature representation, PMAA outperforms the previous state-of-the-art model CTGAN consistently on the Sen2_MTC_Old and Sen2_MTC_New datasets. Furthermore, PMAA has a considerable efficiency advantage, with only 0.5% and 14.6% of the parameters and computational complexity of CTGAN, respectively. These extensive results highlight the potential of PMAA as a lightweight cloud removal network suitable for deployment on edge devices. We will release the code and trained models to facilitate the study in this direction.Comment: 8 pages, 5 figure

Three-phase AC-AC hexagonal chopper system with heterodyne modulation for power flow control enhancement

This paper proposes a three-phase AC chopper system for the interconnection of various distributed generation (DG) farms or main utilities to enhance the active and reactive power flow control. The absence of large energy storage component in direct AC-AC converter makes the system footprint small and reliable. As the interface for different AC sources, the presented converter can be configured as star or delta. However, delta connection is preferred as it can trap the potential zero-sequence current and reduce the current rating of the switching devices. In this way, the proposed converter resembles the hexagonal chopper, and it offers an inherent degree of freedom for output voltage phase-shifting. Considering the scalability in high voltage applications, a new version of the hexagonal chopper with half-bridge cell modular multilevel structure is developed. The modular multilevel AC hexagonal chopper (M2AHC) is operated in quasi-2-level mode to suppress the electro-magnetic interference (EMI) caused by high voltage switching. Quasi-2-level operation divides the voltage level transition into multi-steps, diminishing the voltage rising and falling rates (dv/dt) in high voltage condition. Then, heterodyne modulation is adopted for the presented chopper system, supplying a new degree of freedom to decouple the phase and amplitude regulation. Based on this idea, system control strategy is developed in synchronous reference frame (SRF). Simulations and experimentations have confirmed the validity of the proposed approaches

MIMO-Based Forward-Looking SAR Imaging Algorithm and Simulation

Multiple-input multiple-output (MIMO) radar imaging can provide higher resolution and better sensitivity and thus can be applied to targets detection, recognition, and tracking. Missile-borne forward-looking SAR (MFL-SAR) is a new and special MIMO radar mode. It has advantage of two-dimensional (2D) imaging capability in forward direction over monostatic missile-borne SAR and airborne SAR. However, it is difficult to obtain accurate 2D frequency spectrum of the target echo signal due to the high velocity and descending height of this platform, which brings a lot of obstacles to imaging algorithm design. Therefore, a new imaging algorithm for MFL-SAR configuration based on the method of series reversion is proposed in this paper. This imaging method can implement range compression, secondary range compression (SRC), and range cell migration correction (RCMC) effectively. Finally, some simulations of point targets and comparison results confirm the efficiency of our proposed algorithm

FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary

This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high density field-programmable gate array (FPGA) for hardware acceleration. Traditionally, CS can be implemented using a high-level computer language in a personal computer (PC) or multicore platforms, such as graphics processing units (GPUs) and Digital Signal Processors (DSPs). However, reconstruction algorithms are computing demanding and software implementation of these algorithms is extremely slow and power consuming. In this paper, the orthogonal matching pursuit (OMP) algorithm is refined to solve the sparse decomposition optimization for partial Fourier dictionary, which is always adopted in radar imaging and detection application. OMP reconstruction can be divided into two main stages: optimization which finds the closely correlated vectors and least square problem. For large scale dictionary, the implementation of correlation is time consuming since it often requires a large number of matrix multiplications. Also solving the least square problem always needs a scalable matrix decomposition operation. To solve these problems efficiently, the correlation optimization is implemented by fast Fourier transform (FFT) and the large scale least square problem is implemented by Conjugate Gradient (CG) technique, respectively. The proposed method is verified by FPGA (Xilinx Virtex-7 XC7VX690T) realization, revealing its effectiveness in real-time applications

Fracture mechanism of air percussive rotary bit matrix based on impact stress wave theory

An air percussive rotary bit is a key component of air percussive rotary drilling technology, and its fracture failure seriously affects the safe operation and economic efficiency of drilling. This paper presents (1) theoretical analysis of the impact stress wave propagating in the air percussive rotary bit and effect of the stress wave on bit fracture and (2) finite element simulation study based on the stress wave theory which builds a model of the air hammer piston, drill and rock, defines material parameters, meshes and defines boundary conditions, clarifies propagation characteristics of the impact stress wave, analyzes stress characteristics of the bit matrix under different conditions (same drilling pressure and same piston speed, different drilling pressure and same piston speed and same drilling pressure and different piston speed) and determines the main factors of bit matrix fracture. The correctness of the theoretical analysis was verified with simulation results and fundamental ways of preventing bit fracture failure were proposed to provide a theoretical basis for the structural optimization design of a new bit. The results show that a bit section mutation is the root cause for the shock of the impact wave and the change in nature of the wave during propagation. The tensile wave is the root cause for bit matrix fracture, and a breakage is the most serious at stomatal interchanges. With increasing drilling pressure and piston speed, the rate of increase in the peak stress of the bit matrix increases, leading to early fatigue fracture of the bit matrix. The fracture of the bit matrix can be reduced, and the bit life can be extended by rationally designing the bit sectional structure parameters, ensuring that the bit withstands the effects of the compression wave so as to reduce the formation of a tensile wave, and rationally choosing drilling process parameters (such as drilling pressure and air pressure)

UniHead: Unifying Multi-Perception for Detection Heads

The detection head constitutes a pivotal component within object detectors, tasked with executing both classification and localization functions. Regrettably, the commonly used parallel head often lacks omni perceptual capabilities, such as deformation perception, global perception and cross-task perception. Despite numerous methods attempt to enhance these abilities from a single aspect, achieving a comprehensive and unified solution remains a significant challenge. In response to this challenge, we have developed an innovative detection head, termed UniHead, to unify three perceptual abilities simultaneously. More precisely, our approach (1) introduces deformation perception, enabling the model to adaptively sample object features; (2) proposes a Dual-axial Aggregation Transformer (DAT) to adeptly model long-range dependencies, thereby achieving global perception; and (3) devises a Cross-task Interaction Transformer (CIT) that facilitates interaction between the classification and localization branches, thus aligning the two tasks. As a plug-and-play method, the proposed UniHead can be conveniently integrated with existing detectors. Extensive experiments on the COCO dataset demonstrate that our UniHead can bring significant improvements to many detectors. For instance, the UniHead can obtain +2.7 AP gains in RetinaNet, +2.9 AP gains in FreeAnchor, and +2.1 AP gains in GFL. The code will be publicly available. Code Url: https://github.com/zht8506/UniHead.Comment: 10 pages, 5 figure