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The paper mainly studies the deformation monitoring of tunnel engineering based on 3D laser scanning. First, the principle and characteristics of 3D laser scanning technology are expounded, which lays a theoretical foundation for the research. Then, the flow and main technologies of tunnel engineering deformation monitoring are analysed. Among them, the general process includes four stages: preliminary preparation, point cloud data capture, point cloud data processing and deformation analysis. The main technologies include point and data pre-processing technology and tunnel axis fitting. Finally, combined with the case, deformation monitoring is implemented to analyse the deformation of the tunnel in detail.

The low-edge rotation, intrinsically ELM-free, and improved confinement wide-pedestal quiescent H-mode (QH-mode), discovered in DIII-D tokamak, has pedestal widths exceeding the EPED-kinetic-ballooning mode (KBM) model scaling typically by at least 25%. Ion-scale ( ${k_y}{\rho _s}$ < 1) microturbulence and its role in setting the pedestal structure is investigated using the radially local $\delta f$ gyrokinetic code CGYRO. The electromagnetic trapped electron mode (TEM) is unstable at the pedestal top, while plasma beta ( ${\beta _e})$ is ∼60% below the KBM onset threshold and the electron temperature gradient mode is found to be unstable in the peak gradient region. Nonlinear simulation reveals that the ion-scale turbulence could produce electron energy flux consistent with the flux inferred from power balance at the pedestal top, with a reasonable variation of the local $E \times B$ shearing rate; and the local neoclassical transport from NEO is dominant over the simulated turbulent transport in the ion energy flux channel. The simulated ion-scale turbulence produces much lower electron energy flux than inferred from experiment in the pedestal peak gradient region. A correction to the EPED-KBM pedestal width scaling is obtained based on the two-dimensional scan of pedestal top plasma beta ( ${\beta _e})$ and normalized electron density and temperature scale lengths, $a/{L_{{n_e}}}$ , $a/{L_{{T_e}}}$ using CGYRO linear simulations. Mode transitions among TEM, micro-tearing mode, ion-temperature gradient mode and KBM, are observed in the 2D scan at the pedestal top. A fixed normalized growth rate for these drift-type modes is taken to determine the pedestal width scaling, which shows good consistency with the QH experimental database on pedestal heights and widths. The onset of KBM instabilities and the local E × B shear suppression criterion set the lower and upper limit for the pedestal width of standard QH-mode, wide-pedestal QH-mode and type-I ELMy H mode. A potentially higher and wider pedestal is expected from the new scaling of pedestal width. This work presents an improved understanding of the ion-scale micro-turbulence of wide-pedestal QH-mode and sheds light on a promising scenario for future reactors, including ITER and beyond.

The finned surface can increase the specific surface area, which could enhance the boiling efficiency of the working medium to realize the enhancement of heat transfer performance. This research adopted a step punching method to form a radial microgroove structure on the surface of a copper plate. The scanning electron microscopy analysis indicates that when the pressed metal is in the deformation range, it will flow under extrusion and is subject to the reaction force from the metal outside the deformation range, so a micro-finned structure nearby the punching tool is formed. And the deformation area of the formed microgroove is higher than the workpiece’s primitive surface. The relationships of the punching interference length L i with θ c , a c , and α pt are analyzed; and the influence of forming parameters on the groove surface morphology as well as the relationships among these parameters are discussed. When the punching depth a c is 0.3 mm and the punching feeding angle θ c is 2°–4°, the finned surface of the radial V-shaped microgrooves can be obtained.

This study proposes a cooperative lane-changing control framework for multiple vehicles in freeway ramp merging areas, aiming to achieve safe and efficient merging. Specifically, multiple connected and automated vehicles (CAVs) form triplets to participate in cooperative lane-changing. The framework consists of two stages: Longitudinal Headway Adjustment (LHA) and Lane-Changing Execution (LCE). In the LHA stage, a centralized longitudinal controller is developed based on the vehicle's longitudinal dynamics model to optimize the longitudinal velocity of the cooperative vehicles and create suitable gaps for merging vehicles. In the LCE stage, an optimal lane-changing reference trajectory is generated using a quintic polynomial and a lateral controller is designed based on the vehicle's lateral dynamics model. Model Predictive Control (MPC) is utilized for trajectory tracking. The simulation results obtained using MATLAB/Simulink, GPOPS, and CarSim demonstrate that the proposed control strategy can be applied to different vehicle speed control scenarios. Taking a specific velocity combination as an example, the cumulative control errors in the longitudinal and lateral directions for PV (Preceding Vehicle), SV (Subject Vehicle), and FV (Following Vehicle) are 1.4014m, 0.5631m, and -0.7601m, respectively, satisfying the safety distance requirements. Compared to the Linear Quadratic Regulator (LQR) control, the proposed strategy improves control efficiency by 145.03%, 69.64%, 43.18%, and 67.61% in terms of comprehensive spacing errors, synthesized acceleration, front wheel angle, and speed fluctuation, respectively. These research findings highlight the superior performance of the proposed control strategy in terms of traffic efficiency, comfort, safety, and vehicle stability., Author(s): Zhengwu Wang [1,2]; Jian Xiang (corresponding author) [1,2]; Jie Wang [1,2]; Zhibo Gao [1,2]; Tao Chen [2]; Hao Li [2]; Rui Mao [3] 1. Introduction Lane-changing is one of [...]

The fish detection task is an essential component of marine exploration, which helps scientists monitor fish population numbers and diversity and understand changes in fish behavior and habitat. It also plays a significant role in assessing the health of marine ecosystems, formulating conservation measures, and maintaining biodiversity. However, there are two main issues with current fish detection algorithms. First, the lighting conditions underwater are significantly different from those on land. In addition, light scattering and absorption in water trigger uneven illumination, color distortion, and reduced contrast in images. The accuracy of detection algorithms can be affected by these lighting variations. Second, the wide variation of fish species in shape, color, and size brings about some challenges. As some fish have complex textures or camouflage features, it is difficult to differentiate them using current detection algorithms. To address these issues, we propose a fish detection algorithm—FishDet-YOLO—through improvement in the YOLOv8 algorithm. To tackle the complexities of underwater environments, we design an Underwater Enhancement Module network (UEM) that can be jointly trained with YOLO. The UEM enhances the details of underwater images via end-to-end training with YOLO. To address the diversity of fish species, we leverage the Mamba model's capability for long-distance dependencies without increasing computational complexity and integrate it with the C2f from YOLOv8 to create the Mamba-C2f. Through this design, the adaptability in handling complex fish detection tasks is improved. In addition, the RUOD and DUO public datasets are used to train and evaluate FishDet-YOLO. FishDet-YOLO achieves mAP scores of 89.5% and 88.8% on the test sets of RUOD and DUO, respectively, marking an improvement of 8% and 8.2% over YOLOv8. It also surpasses recent state-of-the-art general object detection and underwater fish detection algorithms. [ABSTRACT FROM AUTHOR]