Academy of Military Transportation

Bentonite and cerous ions were introduced to investigate their effect on the antiultraviolet aging of asphalt. In this paper, Ce-doped bentonite is obtained by mixing cerous nitrate with calcined activated bentonite. The Ce-doped bentonite and asphalt are mixed in high speed-shears to modify asphalt. Through the basic performance test and rheological properties test to study the ultraviolet (UV) aging properties of asphalt modified with Ce-doped bentonite. The results show that bentonite and cerous ions can improve the antiultraviolet aging performance of asphalt. In a certain range, the higher the cerous content, the better the modification effect. gel permeation chromatography, thermogravimetry analysis, and Fourier transform infrared spectroscopy tests analyze the changes in the microstructure of asphalt before and after modification. In addition, layered experiments are designed to prove that asphalt aging is affected by internal depth. After adding bentonite and cerous ions, the asphalt showed excellent UV resistance either on the surface or the interior. The work provides a feasible method for improving the ultraviolet aging resistance of asphalt and promoting rare-earth doped materials modified asphalt, which has a good prospect.

To address the low efficiency and poor robustness of robot plug-in charging in unstructured environments, this paper proposes a method for plug-in autonomous charging via a wheeled mobile manipulator in unstructured environments. First, a two-stage paradigm target recognition and positioning algorithm named You Only Look Once version 8 (YOLOv8) and Point Pair Features (PPF) pose estimation is constructed based on YOLOv8 and PPF. By integrating 2D image detection and 3D point cloud processing, it can quickly and accurately recognize and locate charging guns in complex environments. Then, according to the application characteristics of unstructured scenarios, an integrated system of the wheeled mobile manipulator is designed and verified through simulation. Finally, based on the custom-developed wheeled mobile manipulator platform, plug-in autonomous charging experiments are carried out in unstructured scenarios. The results show that in three typical experimental scenarios, namely normal illumination without occlusion, dim light with slight occlusion, and dark with large-area occlusion, the method proposed in this paper can achieve rapid recognition and positioning of the charging gun, meeting the precision requirements that the angle error of the charging gun pose estimation does not exceed $$10^{\circ }$$ and that the distance error does not exceed 15mm; this demonstrates the effectiveness and robustness of the designed plug-in autonomous charging method for the wheeled mobile manipulator.

Autonomous exploration of autonomous vehicles in off-road environments remains challenging due to the adverse impact on exploration efficiency and safety caused by uneven terrain. In this paper, we propose a path planning framework for autonomous exploration to obtain feasible and smooth paths for autonomous vehicles in 3D off-road environments. In our framework, we design a target selection strategy based on 3D terrain traversability analysis, and the traversability is evaluated by integrating vehicle dynamics with geometric indicators of the terrain. This strategy detects the frontiers within 3D environments and utilizes the traversability cost of frontiers as the pivotal weight within the clustering process, ensuring the accessibility of candidate points. Additionally, we introduced a more precise approach to evaluate navigation costs in off-road terrain. To obtain a smooth local path, we generate a cluster of local paths based on the global path and evaluate the optimal local path through the traversability and smoothness of the path. The method is validated in simulations and real-world environments based on representative off-road scenarios. The results demonstrate that our method reduces the exploration time by up to 36.52% and ensures the safety of the vehicle while exploring unknown 3D off-road terrain compared with state-of-the-art methods.

LiDAR point cloud-based place recognition (LPR) in unstructured natural environments remains an open challenge with limited existing research. To address the limitations of unstructured environments, such as sparse structural features, uneven point cloud density, and significant viewpoint variations, we present BSPR-Net, a dual-branch point cloud feature extraction approach for point cloud place recognition, which consists of a BEV - projection rotation - invariant convolution branch and a point cloud sparse convolution branch. This design enhances the representation capability of geometric structural features while aggregating rotation-invariant characteristics of point clouds, thereby better addressing the challenge of large viewpoint disparities in reverse-revisited unstructured environments. The proposed network was tested on multiple reverse-revisited sequences of the Wild-Places data set, a benchmark for unstructured natural environment place recognition. It achieved a maximum F1 score of 85.46 %, exceeding other classical methods by more than 4 %. The ablation experiments further confirmed the effectiveness of each module in improving place recognition performance.

This study proposes a novel parallel denoising and temperature compensation fusion algorithm for MEMS ring gyroscopes. First, the particle swarm optimization (PSO) algorithm is used to optimize the time-varying filter-based empirical mode decomposition (TVFEMD), obtaining optimal decomposition parameters. Then, TVFEMD decomposes the gyroscope output signal into a series of product function (PF) signals and a residual signal. Next, sample entropy (SE) is employed to classify the decomposed signals into three categories: noise segment, mixed segment, and feature segment. According to the parallel model structure, the noise segment is directly discarded. Meanwhile, time-frequency peak filtering (TFPF) is applied to denoise the mixed segment, while the feature segment undergoes compensation. For compensation, the football team training algorithm (FTTA) is used to optimize the parameters of the long short-term memory (LSTM) neural network, forming a novel FTTA-LSTM architecture. Both simulations and experimental results validate the effectiveness of the proposed algorithm. After processing the MEMS gyroscope output signal using the PSO-TVFEMD-SE-TFPF denoising algorithm and the FTTA-LSTM temperature drift compensation model, the angular random walk (ARW) of the MEMS gyroscope is reduced to 0.02°/√h, while the bias instability (BI) decreases to 2.23°/h. Compared to the original signal, ARW and BI are reduced by 99.43% and 97.69%, respectively. The proposed fusion-based temperature compensation method significantly enhances the temperature stability and noise performance of the gyroscope.

During cancer treatment, noise often affects signaling networks and spatiotemporal dynamics, while elevated psychological stress or anxiety in patients contributes to suboptimal treatment outcomes. In line with research findings on glioma pathogenesis, a novel hybrid multiscale agent-based model is presented, which couples stochastic differential equations, stochastic partial differential equations and a set of rules on cell phenotype switching together from micro and macro levels. The therapeutic effects of temozolomide (TMZ) alone, BKM120 alone, and the combination of TMZ with BKM120 are simulated in two dimensions. Subsequently, the impacts of different noise intensities and psychological pressure on the growth of glioma cells are separately investigated. Additionally, the impacts of different activation rates of AKT and PI3K on the size of glioma are analyzed, respectively. Ultimately, the three-dimensional (3D) tumor spheroid model is constructed to display the internal characteristics of the tumor. Results reveal that (1) the noise on the whole cuts down the growth and proliferation of glioma cells; (2) AKT, being closely related to cell cycle, greatly regulates tumor cell number; (3) the synergy of psychological intervention and drug therapy shows a significant improvement over drug therapy alone; (4) 3D simulations clearly demonstrate a treatment-proliferation game by angiogenesis. These results raise comprehension of the overall behavior of the glioma system and enable predictions of clinical treatment outcomes.

Abstract The characteristics of the welding heat source, the morphology of the molten pool, the formation mechanism of the weld, and the amount of heat input have an important and direct influence on the welding quality and performance of the joint. At present, the empirical curve that depends on the experimental results or the calculation formula of statistics cannot comprehensively evaluate the impact of welding on the mechanical properties of the whole structure. Therefore, this research is based on the finite element analysis technique of Simufact Welding software and analyzes the evolution of the temperature field, stress field, and the deformation of the residual stress in the welding process, and provides a powerful tool and a lot of support for welding technology. The aim of the study is to develop an accurate numerical simulation method for the weld temperature field and a dynamic simulation analysis system for the weld stress field and deformation.

In recent years, urban operations have emerged as the primary battleground in local wars. Most parties involved in recent local conflicts have extensively employed mini unmanned aerial vehicles (mini-UAVs) for reconnaissance, strikes, and various other purposes. Mini-UAVs, owing to their advantages of low cost, high flexibility, robust data acquisition capabilities, and innovative swarm warfare tactics, have become a significantly important force in urban operations. This article reviews the application modes, countermeasures, existing challenges, and development trends of Mini-UAVs in urban operations, offering valuable insights for the strategic deployment and future advancements of unmanned systems in this domain.

Abstract Cross‐dataset object detection methods can adapt to the needs of rapid category expansion in object detection tasks. However, these methods are prone to generate dataset‐aware errors with false alarm objects. This study is aimed to address these issues. A box‐separated multiple‐head module and box‐separated loss function based on the YOLOv8 network are devised to achieve cross‐dataset object detection. Additionally, a sameclass‐aware fusion module to avoid gradient conflicts due to cross‐category conflicts is developed. A multiple‐head fusion module is devised to reduce the number of false alarm objects caused by dataset‐aware errors. A global class‐aware sampler is also designed to adapt to the impact of the imbalanced number of categories and training samples across datasets. The effectiveness of the box‐separated multiple‐head module is verified using cross‐datasets built using the COCO, WiderFace, WiderPerson, and OpenImages V4 datasets. Extensive experiments demonstrate the efficiency and precision of the proposed method.

There are various methods to evaluate the forming limit of a sheet, and these criteria can be classified as position-dependent, time-dependent, and position-time dependent according to the basis of judgment. However, these criteria have a single function and can only find the forming limit of the sheet and cannot determine the strain distribution, strain change, or fracture location during the sheet forming process. This paper introduces a time–location-dependent method, i.e., the spatial strain rate method, which is used to detect the onset of necking of a sheet. The spatial strain rate is directly based on the strain and can not only find the forming limit of the sheet but also depict the strain distribution and strain variation during the two phases of the experimental process—distributed instability and concentrated instability—as well as predict the location of sheet fracture. The spatial strain rate of AA5083 aluminum alloy of different widths was analyzed and verified in detail via Nakazima experiments using digital image correlation techniques and compared with the guidelines published in the literature in recent years.