arXiv subjects

Junjie Niu

Publications and source records attributed to Junjie Niu.

4 recordsLinked to original sources

arXiv2025

Seasonal thermal stress analysis of defective mass concrete sidewalls based on the average forming temperature method

Thermal cracking in urban underground sidewalls is frequently observed when structures are cast in summer and enter service in winter, as seasonal temperature gradients act under structural restraint. To quantify the local stress field associated with pre-existing cracks, an orthogonal finite-element simulation matrix of 16 combinations is constructed. Distributions of maximum principal stress () at the surface crack tip and along the upper half of the crack bottom are evaluated using steady-state thermal loading and a linear-elastic constitutive model. Across all cases, pronounced tensile stress concentration occurs at both locations: the maximum ranges from 19.2 to 34.1 MPa at the crack surface end and from 17.2 to 29.4 MPa at the crack bottom. These concentrated values are consistently higher than the stress level at the same locations in an otherwise identical uncracked wall, clarifying how seasonal temperature gradients under restraint amplify local stresses around existing defects. The quantitative ranges reported here provide a basis for risk screening and for formulating practical mitigation measures (e.g., joint spacing and insulation strategies) in the design and operation of urban underground enclosure walls. In addition, three-dimensional simulations of randomly distributed internal voids show that adopting average forming temperature increases the peak tensile stress on void surfaces from 3.42 to 4.40 MPa at 10 deg C and from 5.98 to 6.96 MPa at -5 deg C, further highlighting the risk amplification effect of AFT under cold service conditions.

physics.comp-ph↗

arXiv2025

Preemptive Spatiotemporal Trajectory Adjustment for Heterogeneous Vehicles in Highway Merging Zones

Aiming at the problem of driver's perception lag and low utilization efficiency of space-time resources in expressway ramp confluence area, based on the preemptive spatiotemporal trajectory Adjustment system, from the perspective of coordinating spatiotemporal resources, the reasonable value of safe space-time distance in trajectory pre-preparation is quantitatively analyzed. The minimum safety gap required for ramp vehicles to merge into the mainline is analyzed by introducing double positioning error and spatiotemporal trajectory tracking error. A merging control strategy for autonomous driving heterogeneous vehicles is proposed, which integrates vehicle type, driving intention, and safety spatiotemporal distance. The specific confluence strategies of ramp target vehicles and mainline cooperative vehicles under different vehicle types are systematically expounded. A variety of traffic flow and speed scenarios are used for full combination simulation. By comparing the time-position-speed diagram, the vehicle operation characteristics and the dynamic difference of confluence are qualitatively analyzed, and the average speed and average delay are used as the evaluation indices to quantitatively evaluate the performance advantages of the preemptive cooperative confluence control strategy. The results show that the maximum average delay improvement rates of mainline and ramp vehicles are 90.24 % and 74.24 %, respectively. The proposed strategy can effectively avoid potential vehicle conflicts and emergency braking behaviors, improve driving safety in the confluence area, and show significant advantages in driving stability and overall traffic efficiency optimization.

eess.SY↗

arXiv2025

Autonomous Aggregate Sorting in Construction and Mining via Computer Vision-Aided Robotic Arm Systems

Traditional aggregate sorting methods, whether manual or mechanical, often suffer from low precision, limited flexibility, and poor adaptability to diverse material properties such as size, shape, and lithology. To address these limitations, this study presents a computer vision-aided robotic arm system designed for autonomous aggregate sorting in construction and mining applications. The system integrates a six-degree-of-freedom robotic arm, a binocular stereo camera for 3D perception, and a ROS-based control framework. Core techniques include an attention-augmented YOLOv8 model for aggregate detection, stereo matching for 3D localization, Denavit-Hartenberg kinematic modeling for arm motion control, minimum enclosing rectangle analysis for size estimation, and hand-eye calibration for precise coordinate alignment. Experimental validation with four aggregate types achieved an average grasping and sorting success rate of 97.5%, with comparable classification accuracy. Remaining challenges include the reliable handling of small aggregates and texture-based misclassification. Overall, the proposed system demonstrates significant potential to enhance productivity, reduce operational costs, and improve safety in aggregate handling, while providing a scalable framework for advancing smart automation in construction, mining, and recycling industries.

cs.RO↗

arXiv2025

Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching

The reliance on large labeled datasets presents a significant challenge in medical image segmentation. Few-shot learning offers a potential solution, but existing methods often still require substantial training data. This paper proposes a novel approach that leverages the Segment Anything Model 2 (SAM2), a vision foundation model with strong video segmentation capabilities. We conceptualize 3D medical image volumes as video sequences, departing from the traditional slice-by-slice paradigm. Our core innovation is a support-query matching strategy: we perform extensive data augmentation on a single labeled support image and, for each frame in the query volume, algorithmically select the most analogous augmented support image. This selected image, along with its corresponding mask, is used as a mask prompt, driving SAM2's video segmentation. This approach entirely avoids model retraining or parameter updates. We demonstrate state-of-the-art performance on benchmark few-shot medical image segmentation datasets, achieving significant improvements in accuracy and annotation efficiency. This plug-and-play method offers a powerful and generalizable solution for 3D medical image segmentation.

eess.IV↗