No abstract

The specific heat of aqueous graphene+Al 2 O 3 (1:1) hybrid nanofluid was measured using the cooling method. The influence of nanoparticle mass fraction and temperature on the specific heat capacity of the hybrid nanofluids was investigated, the specific heat of the hybrid nanofluid was compared with that of aqueous graphene oxide nanofluid and Al 2 O 3 nanofluid. A fitted formula of the specific heat of the hybrid nanofluid was proposed based on the experimental data. It indicates that the specific heat reduction ratio increases with increase of nanoparticle fraction; the maximum reduction ratio is 7% at 0.15wt% at 20 °C. The mass fraction of nanoparticle affects the specific heat of hybrid nanofluid more significantly at lower temperature. Temperature impacts the specific heat more distinctly than the nanoparticle fraction. The specific heat increases with temperature and the maximum specific heat reduction ratio of the hybrid nanofluid diminishes from 7 % at 20 °C to 2% at 70 °C at the mass fraction of 0.05%.

Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms have evolved in recent years as a result of deep learning’s outstanding performance in the field of visual object tracking. There have been a number of evaluations on individual sub-problems, but none that cover the challenges, datasets, and algorithms associated with visual multi-object tracking in autonomous driving scenarios. In this research, we present an exhaustive study of algorithms in the field of visual multi-object tracking over the last ten years, based on a systematic review approach. The algorithm is broken down into three groups based on its structure: methods for tracking by detection (TBD), joint detection and tracking (JDT), and Transformer-based tracking. The research reveals that the TBD algorithm has a straightforward structure, however the correlation between its individual sub-modules is not very strong. To track multiple objects, the JDT technique combines multi-module joint learning with a deep network framework. Transformer-based algorithms have been explored over the past two years, and they have benefits in numerous assessment indicators, as well as tremendous research potential in the area of multi-object tracking. Theoretical support for algorithmic research in adjacent disciplines is provided by this paper. Additionally, the approach we discuss, which uses merely monocular cameras rather than sophisticated sensor fusion, is anticipated to pave the way for the quick creation of safe and affordable autonomous driving systems.