FoundLoc: Vision-based Onboard Aerial Localization in the Wild

Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to appearance variances and stylistic dissimilarities between camera and reference imagery, or operate under the assumption of a known initial pose. In this paper, we developed a GNSS-denied localization approach for UAVs that harnesses both Visual-Inertial Odometry (VIO) and Visual Place Recognition (VPR) using a foundation model. This paper presents a novel vision-based pipeline that works exclusively with a nadir-facing camera, an Inertial Measurement Unit (IMU), and pre-existing satellite imagery for robust, accurate localization in varied environments and conditions. Our system demonstrated average localization accuracy within a $20$-meter range, with a minimum error below $1$ meter, under real-world conditions marked by drastic changes in environmental appearance and with no assumption of the vehicle's initial pose. The method is proven to be effective and robust, addressing the crucial need for reliable UAV localization in GNSS-denied environments, while also being computationally efficient enough to be deployed on resource-constrained platforms

A position-aware transformer for image captioning

Image captioning aims to generate a corresponding description of an image. In recent years, neural encoder-decoder models have been the dominant approaches, in which the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are used to translate an image into a natural language description. Among these approaches, the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. However, most conventional visual attention mechanisms are based on high-level image features, ignoring the effects of other image features, and giving insufficient consideration to the relative positions between image features. In this work, we propose a Position-Aware Transformer model with image-feature attention and position-aware attention mechanisms for the above problems. The image-feature attention firstly extracts multi-level features by using Feature Pyramid Network (FPN), then utilizes the scaled-dot-product to fuse these features, which enables our model to detect objects of different scales in the image more effectively without increasing parameters. In the position-aware attention mechanism, the relative positions between image features are obtained at first, afterwards the relative positions are incorporated into the original image features to generate captions more accurately. Experiments are carried out on the MSCOCO dataset and our approach achieves competitive BLEU-4, METEOR, ROUGE-L, CIDEr scores compared with some state-of-the-art approaches, demonstrating the effectiveness of our approach

A text classification method based on a convolutional and bidirectional long short-term memory model

Text classification refers to labelling text with specified labels, and it is widely used in public opinion supervision, spam detection, and other fields. However, due to the complex semantics of natural language and the difficulty of extracting semantic features, users of traditional methods encounter difficulties when trying to achieve better classification results. In response to this problem, a text classification method based on the CBM (Convolutional and Bi-LSTM Model) model, which can extract shallow local semantic features and deep global semantic features, is proposed. First, the text is vectorised using the Glove model in the embedding layer. Then, the vector text is sent to the Multiscale Convolutional Neural Network (MCNN) and the Bidirectional Long Short-Term Memory network (Bi-LSTM) respectively. The Bi-LSTM layer is also designed in the present work with use of mixed attention to extract deeper semantic features. Finally, the MCNN features and Bi-LSTM features are fused and sent to the softmax layer for classification. Experimental results show that the model can significantly improve the accuracy of text classification

Characterization on Crack Initiation and Early Propagation Region of Nickel-Based Alloys in Very High Cycle Fatigue

As nickel-based alloys are more and more widely used in engineering fields for bearing cyclic loadings, it is necessary to study their very-high-cycle fatigue (VHCF) properties. In this paper, the fatigue properties of nickel-based alloy 625 were investigated using an ultrasonic fatigue test apparatus. The fracture microscopy shows that around the crack initiation site there are two characteristic zones, a rough area (RA) and a fine granular area (FGA). Inclusions caused the interior fatigue crack initiation, and the coalescence of neighboring micro cracks was strongly influenced by the local microstructure, resulting in the RA morphology. Subsequently, the contact and compressing of the crack surfaces contributed to the formation of the FGA. Finally, the stress intensity factors of the RA and FGA were quantitatively evaluated for further discussion of the crack initiation and propagation processes

Research on Transfer Optimization Model of County Transit Network

County transit is an important mode that connects the county center with the surrounding countryside. This paper addresses the problem of unreasonable transit network planning, inconvenient operational optimizations, and protections in the country transit network system to build the transfer optimization model of the county transit network. The model that maximizes the synchronization reach operates in the “end-point connection”, which is the most suitable layout mode by analyzing the characteristics of county transit passenger flow and for comparing different layout modes. Typical county-level cities in three urban agglomerations in China were chosen as cases to validate the effectiveness and practicability of the proposed model. The case results are compared and analyzed in terms of the network density, departure interval, county population, and economic development level, which give theoretical support for decision-making in the planning, construction, and operation management of public transportation in China’s counties

Effects of theTiB₂-SiC Volume Ratio and Spark Plasma Sintering Temperature on the Properties and Microstructure of TiB₂-BN-SiC Composite Ceramics

TiB2-BN composite ceramics combine excellent electrical conductivity, thermal shock resistance, high-temperature resistance, corrosion resistance, and easy processing of TiB2 and BN. However, in practical applications, their high-temperature oxidation resistance is poor and the resistivity distribution is uneven and changes substantially with temperature. A TiB2-BN-SiC composite ceramic with stable and controllable resistivity was prepared by introducing SiC into the TiB2-BN composite ceramics. In this work, spark plasma sintering (SPS) technology was used to prepare TiB2-BN-SiC composite ceramics with various TiB2-SiC ratios and sintering temperatures. The samples were tested by XRD, SEM, and thermal and mechanical analysis. The results show that as the volume ratio of TiB2-SiC was increased from 3:1 to 12:1, the resistivity of the sample decreased from 8053.3 to 4923.3 μΩ·cm, the thermal conductivity increased from 24.89 to 34.15 W/(m k), and the thermal expansion rate increased from 7.49 (10−6/K) to 10.81 (10−6/K). As the sintering temperature was increased from 1650 to 1950 °C, the density of the sample increased, the mechanical properties were slightly improved, and the resistivity, thermal expansion rate, and thermal conductivity changed substantially. The volume ratio and sintering temperature are the key factors that control the resistivity and thermal characteristics of TiB2-SiC-BN composite ceramics, and the in situ from liquid phases of FeB and FeO also promotes the sintering of the TiB2-BN-SiC ceramics

How Environmental Protection Motivation Influences on Residents’ Recycled Water Reuse Behaviors: A Case Study in Xi’an City

Pro-environmental behaviors related to reclaimed water reuse are regarded as important motivations for both environmental protection and the use of reclaimed water, and these motivations could affect the citizens’ decision whether they will accept reclaimed water reuse. A hypothesis model was developed as the NAM (Norm Activation Model) has changed, and this hypothesis model was used to explore the factors that affect the citizen’s decision about the reclaimed water reuse, and obtain a better understanding of the mechanism of urban citizens in environmental protection and the related outcomes. First, 584 samples were used to verify the reliability and validity of data, and AMOS21.0 was used to test the goodness-of-fit between the sample data and the hypothesis model. Based on this, the applicability of the improved NAM was verified through the study of recycled water reuse. The hypothesis model was used to analyze its direct influences, showing that environmental motivation has positive influences on the citizens’ acceptance toward recycled water reuse. Besides, Bootstrap method was used to verify the mediation effect, proving that awareness of consequences regarding environmental pollution caused by human activities and ascription of responsibility could strengthen the citizens’ motivation to protect the environment