The Mechanical Performance and Microstructure Development of Laser Beam Welds and Post Weld Heat Treatment of Ti 1023 Alloy

Titanium alloys are widely used in the aerospace industry. For example, most components of the Boeing 777 landing gear are made of the Ti-10V-2Fe-3Al (Ti1023) alloy due to its lighweight and superior mechanical strength. In addition, because Titanium is chemicaly active element, when jointing the Ti alloy components, the laser beam welding (LBW) has been selected for avoiding the eventual contaminations and narrowing the heat affected zone by the presence of its protected atmosphere and low heat input. However, there is a lack of reported research results about welded Ti1023 alloy, particularly laser beam welding. Therefore, the microstructure and mechanical properties of LBW Ti1023 were first investigated in this study.Three zones were formed on the LBW Ti1023: base material (BM), heat affected zone (HAZ), and fusion zone (FZ). The bimodal (spherical and lath) distribution of primary α phase dispersed in a matrix of β phase was observed on the BM and HAZ. However, only β phase was observed in the fusion zone (FZ). This whole β phase structure was formed due to two steps: melting and retaining. The melting temperature was higher than β-transus temperature and transformed the α phase into β phase. Then, fast cooling can avoid α martensite formed and retain the β phase. In addition to the microstructure, the experimental results from the hardness and tensile tests showed lower properties in the FZ. Moreover, the residual stress of BM and FZ were measured separately in this study.During analysis of the entire manufacturing process, post welding heat treatment (PWHT) was applied to improve mechanical properties of the welded components. Two subgroups of the heat treatment conditions were set and investigated. The experiment results of both subgroups presented a different extent of strength improvement. The heat treatment condition of one subgroup is annealing+aging. The hardness of all three zones (base material, heat affacted zone, and fusion zone) in this subgroup increased and are almost equal to each other due to their structure consisted of primary α (αp) and secondary α (αs) phase. From the hardness profile and tensile test results, the optimization heat treatment condition of this subgroup is chosen as 750oC annealing for 1 hour and following by water quenching, then through 500oC aging for 4 hours and following by air cooling. Another subgroup heat treatment condition consists only of aging. The FZ in this subgroup showed the highest hardness because only αs was observed. Also, the optimization heat treatment condition is chosen as 500oC aging for 4 hours and following by air cooling

You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking

Firstly, a new multi-object tracking framework is proposed in this paper based on multi-modal fusion. By integrating object detection and multi-object tracking into the same model, this framework avoids the complex data association process in the classical TBD paradigm, and requires no additional training. Secondly, confidence of historical trajectory regression is explored, possible states of a trajectory in the current frame (weak object or strong object) are analyzed and a confidence fusion module is designed to guide non-maximum suppression of trajectory and detection for ordered association. Finally, extensive experiments are conducted on the KITTI and Waymo datasets. The results show that the proposed method can achieve robust tracking by using only two modal detectors and it is more accurate than many of the latest TBD paradigm-based multi-modal tracking methods. The source codes of the proposed method are available at https://github.com/wangxiyang2022/YONTD-MOTComment: 10 pages, 9 figure

GPGait: Generalized Pose-based Gait Recognition

Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has received little attention but hinders the application of these methods in real-world scenarios. To improve the generalization ability of pose-based methods across datasets, we propose a \textbf{G}eneralized \textbf{P}ose-based \textbf{Gait} recognition (\textbf{GPGait}) framework. First, a Human-Oriented Transformation (HOT) and a series of Human-Oriented Descriptors (HOD) are proposed to obtain a unified pose representation with discriminative multi-features. Then, given the slight variations in the unified representation after HOT and HOD, it becomes crucial for the network to extract local-global relationships between the keypoints. To this end, a Part-Aware Graph Convolutional Network (PAGCN) is proposed to enable efficient graph partition and local-global spatial feature extraction. Experiments on four public gait recognition datasets, CASIA-B, OUMVLP-Pose, Gait3D and GREW, show that our model demonstrates better and more stable cross-domain capabilities compared to existing skeleton-based methods, achieving comparable recognition results to silhouette-based ones. Code is available at https://github.com/BNU-IVC/FastPoseGait.Comment: ICCV Camera Read

FastPoseGait: A Toolbox and Benchmark for Efficient Pose-based Gait Recognition

We present FastPoseGait, an open-source toolbox for pose-based gait recognition based on PyTorch. Our toolbox supports a set of cutting-edge pose-based gait recognition algorithms and a variety of related benchmarks. Unlike other pose-based projects that focus on a single algorithm, FastPoseGait integrates several state-of-the-art (SOTA) algorithms under a unified framework, incorporating both the latest advancements and best practices to ease the comparison of effectiveness and efficiency. In addition, to promote future research on pose-based gait recognition, we provide numerous pre-trained models and detailed benchmark results, which offer valuable insights and serve as a reference for further investigations. By leveraging the highly modular structure and diverse methods offered by FastPoseGait, researchers can quickly delve into pose-based gait recognition and promote development in the field. In this paper, we outline various features of this toolbox, aiming that our toolbox and benchmarks can further foster collaboration, facilitate reproducibility, and encourage the development of innovative algorithms for pose-based gait recognition. FastPoseGait is available at https://github.com//BNU-IVC/FastPoseGait and is actively maintained. We will continue updating this report as we add new features.Comment: 10 pages, 4 figure

SFCNeXt: a simple fully convolutional network for effective brain age estimation with small sample size

Deep neural networks (DNN) have been designed to predict the chronological age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as a valuable biomarker for the early detection of development-related or aging-related disorders. Recent DNN models for brain age estimations usually rely too much on large sample sizes and complex network structures for multi-stage feature refinement. However, in clinical application scenarios, researchers usually cannot obtain thousands or tens of thousands of MRIs in each data center for thorough training of these complex models. This paper proposes a simple fully convolutional network (SFCNeXt) for brain age estimation in small-sized cohorts with biased age distributions. The SFCNeXt consists of Single Pathway Encoded ConvNeXt (SPEC) and Hybrid Ranking Loss (HRL), aiming to estimate brain ages in a lightweight way with a sufficient exploration of MRI, age, and ranking features of each batch of subjects. Experimental results demonstrate the superiority and efficiency of our approach.Comment: This paper has been accepted by IEEE ISBI 202

Modeling Multi-wavelength Pulse Profiles of Millisecond Pulsar PSR B1821-24

PSR B1821$-$24 is a solitary millisecond pulsar (MSP) which radiates multi-wavelength pulsed photons. It has complex radio, X-ray and $\gamma$-ray pulse profiles with distinct peak phase-separations that challenge the traditional caustic emission models. Using the single-pole annular gap model with suitable magnetic inclination angle ($\alpha=40^\circ$) and viewing angle ($\zeta=75^\circ$), we managed to reproduce its pulse profiles of three wavebands. It is found that the middle radio peak is originated from the core gap region at high altitudes, and the other two radio peaks are originated from the annular gap region at relatively low altitudes. Two peaks of both X-ray and $\gamma$-ray wavebands are fundamentally originated from annular gap region, while the $\gamma$-ray emission generated from the core gap region contributes somewhat to the first $\gamma$-ray peak. Precisely reproducing the multi-wavelength pulse profiles of PSR B1821$-$24 enables us to understand emission regions of distinct wavebands and justify pulsar emission models.Comment: Accepted for publication in Ap