Vision-based localization methods under GPS-denied conditions

This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss the broad application of optical flow in feature extraction-based Visual Odometry (VO) solutions and introduce advanced optical flow estimation methods. For AVL, we review recent advances in Visual Simultaneous Localization and Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman Filter (EKF) based methods. We also introduce the application of offline map registration and lane vision detection schemes to achieve Absolute Visual Localization. This paper compares the performance and applications of mainstream methods for visual localization and provides suggestions for future studies.Comment: 32 pages, 15 figure

A GPT-Based Approach for Scientometric Analysis: Exploring the Landscape of Artificial Intelligence Research

This study presents a comprehensive approach that addresses the challenges of scientometric analysis in the rapidly evolving field of Artificial Intelligence (AI). By combining search terms related to AI with the advanced language processing capabilities of generative pre-trained transformers (GPT), we developed a highly accurate method for identifying and analyzing AI-related articles in the Web of Science (WoS) database. Our multi-step approach included filtering articles based on WoS citation topics, category, keyword screening, and GPT classification. We evaluated the effectiveness of our method through precision and recall calculations, finding that our combined approach captured around 94% of AI-related articles in the entire WoS corpus with a precision of 90%. Following this, we analyzed the publication volume trends, revealing a continuous growth pattern from 2013 to 2022 and an increasing degree of interdisciplinarity. We conducted citation analysis on the top countries and institutions and identified common research themes using keyword analysis and GPT. This study demonstrates the potential of our approach to facilitate accurate scientometric analysis, by providing insights into the growth, interdisciplinary nature, and key players in the field.Comment: 29 pages, 10 figures, 5 table

Flow measurements in microporous media using micro-particle image velocimetry

An experimental study focused on the identification of the flow regimes and quantification of the velocity field at pore scale in microporous media is presented and discussed. Transparent porous media are fabricated by removing a pore forming agent in slightly sintered glass beads of size 50 μ m between two glass slides, leaving typical pores with a size of 500 μ m . Pressure-drop measurements and particle image velocimetry measurements are conducted simultaneously in order to evaluate the flow regimes and flow behaviors at pore size based Reynolds numbers from 0.1 to 140. Four different regimes, pre-Darcy, Darcy, Forchheimer, and turbulent, are found and presented. Spatial distribution and characteristics of the time-averaged velocity in all regimes and fluctuation intensity in transitional and turbulent regimes are investigated. Critical Reynolds numbers are identified using both velocity and pressure-drop measurements and the results agree very well, providing direct evidence underpinning the transition. The effects of porosity on these flow properties are also studied, and finally the flow regime boundaries are compared with the literature. These data provide an insight into the flow properties in microporous media with various porosities and an improved understanding that could be further utilized to enhance the flow and heat transfer performance of microporous media. It also demonstrates that velocity and pressure measurements used in combination can be an effective method for studying microporous media

Fluid flow characterisation in randomly packed microscale porous beds with different sphere sizes using micro-particle image velocimetry

An experimental study using pressure-drop and μ-PIV measurements was undertaken to better understand the fluid flow characteristics in different flow regimes within randomly packed microscale porous beds with different sized spheres. Three sintered glass samples were made with glass spheres having a mean diameter of 170 μm, 430 μm and 710 μm by slightly sintering them between two glass slides, forming a sample with a sandwich structure. Four different regimes, pre-Darcy, Darcy, Forchheimer and turbulent were identified in each sintered glass sample using the pressure-drop measurements. The permeability increases with glass sphere size and so does the Reynolds number corresponding to each flow regime boundary. It was found that for a given Re, the pressure drop in the sample with 170 μm diameter spheres can be ten times higher than the pressure drop in the sample with 710 μm diameter spheres. Four different pore geometries were identified to be the focus of the measurement zones of the μ-PIV, which were taken across a range of Re spanning all the flow regimes identified in the pressure-drop measurements. The non-dimensional time-averaged velocity distribution was found to be similar in each flow regime for the samples with 170 μm and 430 μm diameter spheres, whereas it changed dramatically for the sample with 710 μm diameter spheres. In general the velocity profiles through the channels within the porous media were found to be near-parabolic, especially in the Darcy and Forchheimer regimes, but in the turbulent regime inertial effects such as localised jets were observed. Detailed observational and statistical analysis of the velocity distributions highlights their very strong dependency on the local geometry with highly localised regions of flow apparently in a different flow regime to that of the bulk flow. However, the global average of the fluctuations throughout the measurement zone does align well with the pressure drop measurements

Facial expression recognition based on multi branch structure

Facial expression recognition (FER) is an important means for machines to perceive human emotions and interact with human beings. Most of the existing facial expression recognition methods only use a single convolutional neural network to extract the global features of the face. Some insignificant details and features with low frequency are easy to be ignored, and part of the facial features are lost. This paper proposes a facial expression recognition method based on multi branch structure, which extracts the global and detailed features of the face from the global and local aspects respectively, so as to make a more detailed representation of the facial expression and further improve the accuracy of facial expression recognition. Specifically, we first design a multi branch network, which takes Resnet-50 as the backbone network. The network structure after Conv Block3 is divided into three branches. The first branch is used to extract the global features of the face, and the second and third branches are used to cut the face into two parts and three parts after Conv Block5 to extract the detailed features of the face. Finally, the global features and detail features are fused in the full connection layer and input into the classifier for classification. The experimental results show that the accuracy of this method is 73.7%, which is 4% higher than that of traditional Resnet-50, which fully verifies the effectiveness of this method

Oxygen Vacancies Lead to Loss of Domain Order, Particle Fracture, and Rapid Capacity Fade in Lithium Manganospinel (LiMn₂O₄) Batteries

Spinel-structured lithium manganese oxide (LiMn₂O₄) has attracted much attention because of its high energy density, low cost, and environmental impact. In this article, structural analysis methods such as powder neutron diffraction (PND), X-ray diffraction (XRD), and high-resolution transmission and scanning electron microscopies (TEM & SEM) reveal the capacity fading mechanism of LiMn₂O₄ as it relates to the mechanical degradation of the material. Micro-fractures form after the first charge (to 4.45 V vs. Li^+/0) of a commercial lithium manganese oxide phase, best represented by the formula LiMn₂O_3.88. Diffraction methods show that the grain size decreases and multiple phases form after 850 electrochemical cycles at 0.2 <i>C</i> current. The microfractures are directly observed through microscopy studies as particle cracks propagate along the (1 1 1) planes, with clear lattice twisting observed along this direction. Long-term galvanostatic cycling results in increased charge-transfer resistance and capacity loss. Upon preparing samples with controlled oxygen contents, LiMn₂O_4.03 and LiMn₂O_3.87, the mechanical failure of the lithium manganese oxide can be correlated to the oxygen vacancies in the materials, providing guidance for better synthesis methods