DynaMask: Dynamic Mask Selection for Instance Segmentation

The representative instance segmentation methods mostly segment different object instances with a mask of the fixed resolution, e.g., 28*28 grid. However, a low-resolution mask loses rich details, while a high-resolution mask incurs quadratic computation overhead. It is a challenging task to predict the optimal binary mask for each instance. In this paper, we propose to dynamically select suitable masks for different object proposals. First, a dual-level Feature Pyramid Network (FPN) with adaptive feature aggregation is developed to gradually increase the mask grid resolution, ensuring high-quality segmentation of objects. Specifically, an efficient region-level top-down path (r-FPN) is introduced to incorporate complementary contextual and detailed information from different stages of image-level FPN (i-FPN). Then, to alleviate the increase of computation and memory costs caused by using large masks, we develop a Mask Switch Module (MSM) with negligible computational cost to select the most suitable mask resolution for each instance, achieving high efficiency while maintaining high segmentation accuracy. Without bells and whistles, the proposed method, namely DynaMask, brings consistent and noticeable performance improvements over other state-of-the-arts at a moderate computation overhead. The source code: https://github.com/lslrh/DynaMask.Comment: Accepted by CVPR202

Challenges and Solutions for High-Speed Aviation Piston Pumps: A Review

As a core power component, aviation piston pumps are widely used in aircraft hydraulic systems. The piston pump’s power-to-weight ratio is extremely crucial in the aviation industry, and the “ceiling effect” of the PV value (product of compressive stress and linear velocity) limits the piston pump’s ability to increase working pressure. Therefore, increasing the piston pump’s speed has been a real breakthrough in terms of further enhancing the power-to-weight ratio. However, the piston pump’s design faces several challenges under the extreme operating conditions at high speeds. This study reviews several problems aviation axial piston pumps face under high-speed operating conditions, including friction loss, cavitation, cylinder overturning, flow pressure pulsation, and noise. It provides a detailed description of the research state of the art of these problems and potential solutions. The axial piston pump’s inherent sliding friction pair, according to the report, considerably restricts further increasing of its speed and power-to-weight ratio. With its mature technology and deep research base, the axial piston pump will continue to dominate the aviation pumps. Furthermore, breaking the limitation of the sliding friction pair on speed and power density, thus innovating a novel structure of the piston pump, is also crucial. Therefore, this study also elaborates on the working principle and development process of the two-dimensional (2D) piston pump, which is a representative of current high-speed pump structure innovation

Masked Surfel Prediction for Self-Supervised Point Cloud Learning

Masked auto-encoding is a popular and effective self-supervised learning approach to point cloud learning. However, most of the existing methods reconstruct only the masked points and overlook the local geometry information, which is also important to understand the point cloud data. In this work, we make the first attempt, to the best of our knowledge, to consider the local geometry information explicitly into the masked auto-encoding, and propose a novel Masked Surfel Prediction (MaskSurf) method. Specifically, given the input point cloud masked at a high ratio, we learn a transformer-based encoder-decoder network to estimate the underlying masked surfels by simultaneously predicting the surfel positions (i.e., points) and per-surfel orientations (i.e., normals). The predictions of points and normals are supervised by the Chamfer Distance and a newly introduced Position-Indexed Normal Distance in a set-to-set manner. Our MaskSurf is validated on six downstream tasks under three fine-tuning strategies. In particular, MaskSurf outperforms its closest competitor, Point-MAE, by 1.2\% on the real-world dataset of ScanObjectNN under the OBJ-BG setting, justifying the advantages of masked surfel prediction over masked point cloud reconstruction. Codes will be available at https://github.com/YBZh/MaskSurf.Comment: Codes will be available at https://github.com/YBZh/MaskSur

Analytical modeling and experimental verification of churning torque for cam guide rails of 2D piston pump

For 2D piston pump the churning torque of its cam guide rails at high speed is one of the main sources of mechanical efficiency loss. To conveniently calculate the churning torque and also provide guidance for future optimization of churning torque reduction, an accurate analytical model of constant acceleration and constant deceleration cam guide rail assembly of the stacked-rollers 2D piston pump is established, where the whole churning torque is divided into the peripheral churning torque, the pushing flow churning torque, and the end face churning torque. Then the analytical model is verified by CFD simulation preliminarily. Analytical modeling shows that at 16,000 rpm, the churning torque reaches about 0.826 N m, where the peripheral churning torque, the pushing flow churning torque, and the end face churning torque account for about 55.4%, 37.3%, and 7.3% of the total torque respectively, indicating that the structural parameters related to the pushing flow torque should be optimized with first priority at high rotational speed. Finally, the churning torque at 1000–16,000 rpm was experimentally measured and the influence of oil temperature was considered. The experimental results are in good agreement with the results of analytical modeling and CFD simulation, thus verifying the correctness of the proposed analytical model

Synthesis of Cu2O micro/nanocrystals for catalytic combustion of high-concentration CO: The crucial role of glucose

Cubic Cu2O micro/nanocrystals were successfully synthesized by liquid-phase reduction using copper salt of CuSO4 or CuCl2.2H2O, and glucose or ascorbic acid as reducing agent, respectively. The activity of the catalysts was evaluated by light-off curves of CO self-sustained catalytic combustion via temperature-programmed oxidation of CO (CO-TPO), with the results showing the activity of catalysts following the order of Cu2O-ClGLU > Cu2O-S-GLU > Cu2O-S-AA > Cu2O-Cl-AA, (Cl denotes CuCl2.2H2O, GLU denotes glucose, S denotes CuSO4 and AA denotes ascorbic acid, respectively), corresponding to the ignition temperature of 109 degrees C, 122 degrees C, 137 degrees C and 186 degrees C, respectively. The crystal structure, elemental valence, morphology and redox property of the prepared catalysts were analyzed by using various characterization techniques. Combined with in situ infrared spectrum, the CO self-sustained catalytic combustion over Cu2O catalysts mainly follows the Mars-van-Krevelen (M-v-K) mechanism: the adsorbed and activated CO reacts with lattice oxygen to yield CO2 and oxygen vacancy, and then the oxygen vacancy can be replenished by gaseous oxygen. Combined with catalytic performance of high-concentration CO, it is found that the catalysts prepared using glucose as reducing agent are more angular compared with ascorbic acid. The Cu2O-Cl-GLU synthesized with glucose and CuCl2.2H2O exhibits the best catalytic activity among all the catalysts tested, attributing to its more obvious edge and rough crystal surface. The unique structure of Cu2O-Cl-GLU leads to the high exposure rate and coordination unsaturation of atoms on the cubic Cu2O micro/nanocrystals that can improve the ability of activating gaseous O2 and low temperature reducibility, and consequently facilitating the catalytic activity

Synthesis of Cu2O micro/nanocrystals for catalytic combustion of high-concentration CO: The crucial role of glucose

Cubic Cu2O micro/nanocrystals were successfully synthesized by liquid-phase reduction using copper salt of CuSO4 or CuCl2.2H2O, and glucose or ascorbic acid as reducing agent, respectively. The activity of the catalysts was evaluated by light-off curves of CO self-sustained catalytic combustion via temperature-programmed oxidation of CO (CO-TPO), with the results showing the activity of catalysts following the order of Cu2O-ClGLU > Cu2O-S-GLU > Cu2O-S-AA > Cu2O-Cl-AA, (Cl denotes CuCl2.2H2O, GLU denotes glucose, S denotes CuSO4 and AA denotes ascorbic acid, respectively), corresponding to the ignition temperature of 109 degrees C, 122 degrees C, 137 degrees C and 186 degrees C, respectively. The crystal structure, elemental valence, morphology and redox property of the prepared catalysts were analyzed by using various characterization techniques. Combined with in situ infrared spectrum, the CO self-sustained catalytic combustion over Cu2O catalysts mainly follows the Mars-van-Krevelen (M-v-K) mechanism: the adsorbed and activated CO reacts with lattice oxygen to yield CO2 and oxygen vacancy, and then the oxygen vacancy can be replenished by gaseous oxygen. Combined with catalytic performance of high-concentration CO, it is found that the catalysts prepared using glucose as reducing agent are more angular compared with ascorbic acid. The Cu2O-Cl-GLU synthesized with glucose and CuCl2.2H2O exhibits the best catalytic activity among all the catalysts tested, attributing to its more obvious edge and rough crystal surface. The unique structure of Cu2O-Cl-GLU leads to the high exposure rate and coordination unsaturation of atoms on the cubic Cu2O micro/nanocrystals that can improve the ability of activating gaseous O2 and low temperature reducibility, and consequently facilitating the catalytic activity

Effects of Cu2O morphology on the performance of CO self-sustained catalytic combustion

Self-sustained catalytic combustion is a sustainable approach to deal with exhaust gas with high concentration CO, and revealing its reaction process is necessary and challenging. Herein, cube (Cu2O-C), octahedron (Cu2O-O) and dodecahedron (Cu2O-D) exposing different crystal planes were used to explore the catalytic combustion mechanism. The catalytic combustion can be self-sustained on the Cu2O surface and the activities decrease in the order of Cu2O-O > Cu2O-D > Cu2O-C, contributing to the different exposing planes with (1 1 1), (1 1 0) and (1 0 0), respectively. In-situ DRIFTS results prove that the catalytic combustion of CO to CO2 on Cu2O is prone to follow the MvK mechanism. Comparing with Cu2O-D and Cu2O-C, the relatively open surface of Cu2O-O plane composed of unsaturated copper and oxygen atoms facilitates the CO adsorption on Cu (I) and the mobility of lattice oxygen, leading to the highest low temperature reducibility and catalytic activity

Study on the structural evolution and heat transfer performance of Cu supported on regular morphology CeO2 in CO catalytic combustion and chemical looping combustion

Chemical looping combustion (CLC) and catalytic combustion (CC), which are potential technologies to promote CO & RARR;CO2 efficient conversion and energy conservation for the steelmaking off-gas, are investigated in reaction activity, structure evolution catalysts/oxygen carriers (OCs) and energy recovery using Cu/CeO2 materials. Activity results suggest that the rod-shaped samples with well-defined (100) crystal faces exhibit higher activity than the sphere-shaped samples with (111) crystal faces, obtaining the optimized copper content of 3 wt%. IR spectra confirmed the proposed reaction pathway that the CO adsorbed on copper sites (Cu+-CO) at the Cu-Ce interface reacts with adjacent surface lattice oxygen. The gaseous oxygen continuously migrates to the external surface of materials, thus resulting in strongly exothermic CO self-sustained combustion during CC. Such a violent reaction does not cause obvious evolution of chemical composition, crystalline phase and structure. Since the active lattice oxygen is gradually consumed but not replenished by external gaseous O2 in time, CO combustion is not self-sustained during CLC. Therefore, the reduction cycle is no longer confined to the surface of the material but penetrates deep into its body, which accelerates Cu+ enrichment at the surface and leads to irreversible sintering and agglomeration of the material