RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving

Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show promising potential in point cloud registration. However, existing methods rely on good superpoint correspondences, which are hard to be obtained reliably and efficiently, thus resulting in less robust and accurate point cloud registration. In this paper, we propose a novel network, named RDMNet, to find dense point correspondences coarse-to-fine and improve final pose estimation based on such reliable correspondences. Our RDMNet uses a devised 3D-RoFormer mechanism to first extract distinctive superpoints and generates reliable superpoints matches between two point clouds. The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds' contextual and geometric information to generate robust superpoint correspondences. RDMNet then propagates the sparse superpoints matches to dense point matches using the neighborhood information for accurate point cloud registration. We extensively evaluate our method on multiple datasets from different environments. The experimental results demonstrate that our method outperforms existing state-of-the-art approaches in all tested datasets with a strong generalization ability.Comment: 11 pages, 9 figure

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Directivity Modeling and Simulation Analysis of a Novel Structure MEMS Piezoelectric Vector Hydrophone

In this paper, a novel dual-mass MEMS piezoelectric vector hydrophone is proposed to eliminate the transverse effect and solve the problem of directivity offset in traditional single-mass MEMS piezoelectric vector hydrophones. The reason for the directional offset of the traditional single-mass cantilever MEMS piezoelectric vector hydrophone is explained theoretically for the first time, and the angle of the directional offset is predicted successfully. Both analytical and finite element methods are employed to analyze the single-mass and dual-mass cantilever MEMS piezoelectric vector hydrophone. The results show that the directivity of the dual-mass MEMS piezoelectric vector hydrophone has no deviation, the transverse effect is basically eliminated, and the directivity (maximum concave point depth) is significantly improved, so more accurate positioning can be obtained

Thermal Diffusivity of Ti3C2Tx@C Nanocoils

Ti3C2Tx MXene is an emerging 2D material with excellent electrical and electrochemical properties. Carbon Nanocoil (CNC) is a quasi 1D material with unique helical morphology, which shows remarkable advantages in mechanical and electromagnetic properties. In this work, we designed a Ti3C2Tx@C nanocoil (CMNC) by coating Ti3C2Tx flakes on the surface of CNC for better application performance. The thermophysical properties of single CMNCs were investigated using a transient eletrothermall (TET) technique. The average room temperature thermal diffusivity and thermal conductivity of CMNCs were measured to be 8×10-6 m2/s and 15.6 W/m K, which are one order of magnitude higher than those of CNCs, due to successful coating of MXene on the surface of CNC. However, enhancement of electrical properties brought by MXene coating is much smaller than those of thermal properties. Variable temperature characterization from 298 to 334 K reveals an increasing trend of thermal diffusivity and thermal conductivity with temperature increasing, which is attributed to the interaction and heat transfer between MXene and CNCs. MXene coating provides better thermal management performance for practical applications of CNCs, such as wave absorbing

FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning

Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introduced by applying a high and fixed threshold in most state-of-the-art SSL models. However, early models prefer certain classes that are easy to learn, which results in a high-skewed class imbalance in the generated hard labels. The class imbalance will lead to less effective learning of other minority classes and slower convergence for the training model. The aim of this paper is to mitigate the performance degradation caused by class imbalance and gradually reduce the class imbalance in the unsupervised part. To achieve this objective, we propose FocalMatch, a novel SSL method that combines FixMatch and focal loss. Our contribution of FocalMatch adjusts the loss weight of various data depending on how well their predictions match up with their pseudo labels, which can accelerate system learning and model convergence and achieve state-of-the-art performance on several semi-supervised learning benchmarks. Particularly, its effectiveness is demonstrated with the dataset that has extremely limited labeled data

FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning

Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introduced by applying a high and fixed threshold in most state-of-the-art SSL models. However, early models prefer certain classes that are easy to learn, which results in a high-skewed class imbalance in the generated hard labels. The class imbalance will lead to less effective learning of other minority classes and slower convergence for the training model. The aim of this paper is to mitigate the performance degradation caused by class imbalance and gradually reduce the class imbalance in the unsupervised part. To achieve this objective, we propose FocalMatch, a novel SSL method that combines FixMatch and focal loss. Our contribution of FocalMatch adjusts the loss weight of various data depending on how well their predictions match up with their pseudo labels, which can accelerate system learning and model convergence and achieve state-of-the-art performance on several semi-supervised learning benchmarks. Particularly, its effectiveness is demonstrated with the dataset that has extremely limited labeled data

Analysis of Vibration and Noise for the Powertrain System of Electric Vehicles under Speed-Varying Operating Conditions

Whine noise from the electric powertrain system of electric vehicles, including electromagnetic noise and gear-meshing noise, significantly affects vehicle comfort and has been getting growing concern. In order to identify and avoid whine problems as early as possible in the powertrain development process, this paper presents a vibration and noise simulation methodology for the electric powertrain system of vehicles under speed-varying operating conditions. The electromagnetic forces on the stator teeth of the motor and the bearing forces on the gearbox for several constant-speed operating conditions are obtained first by electromagnetic field simulation and multi-body dynamic simulation, respectively. Order forces for the speed-varying operating condition are generated by interpolation between the obtained forces, before they are applied on the mechanical model whose natural modes have been calibrated in advance by tested modes. The whine noise radiated from the powertrain is then obtained based on acoustic boundary element analysis. The simulated bearing forces indicate that the overlooking of the motor torque ripple does not result in significant loss in simulation accuracy of electromagnetic noise. The simulation results and tested data show good consistency, with the relative frequency deviation of local peaks being less than 8% and the error of the average sound pressure level (SPL) being mostly below 10 dB (A)

Research on the Mechanical Mechanism of the Shuffle Problem of Electric Vehicles and the Sensitivity to Clearances

In order to address the shuffle problem of the electric powertrain system that occurs at the moment of torque reversing, a multibody dynamics model of the powertrain system, with the measured motor torque applied as the input loading, has been established to analyze the generating mechanism of the rotating speed ripple of the drive system which is regarded the root of shuffle. The influence on speed ripple from cumulative gap size and motor torque has been investigated. The model was validated by showing good agreement between the simulated speed response and the measured data. Perturbance on each backlash was performed in the simulation to reveal the sensitivity of the speed ripple on the size of the backlash. Much higher speed-to-gap sensitivities have been observed for the low-speed engagement pairs than the high-speed engagement pairs, indicating that compressing the backlashes of the former could achieve more NVH (noise, vibration, harshness, i.e., NVH) performance improvement