Browsing by Author "Yan X"

Now showing 1 - 5 of 5
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    A highway vehicle routing dataset during the 2019 Kincade Fire evacuation.
    (Springer Nature Limited, 2022-10-07) Xu Y; Zhao X; Lovreglio R; Kuligowski E; Nilsson D; Cova TJ; Yan X
    As the threat of wildfire increases, it is imperative to enhance the understanding of household evacuation behavior and movements. Mobile GPS data provide a unique opportunity for studying evacuation routing behavior with high ecological validity, but there are little publicly available data. We generated a highway vehicle routing dataset derived from GPS trajectories generated by mobile devices (e.g., smartphones) in Sonoma County, California during the 2019 Kincade Fire that started on October 23, 2019. This dataset contains 21,160 highway vehicle routing records within Sonoma County from October 16, 2019 to November 13, 2019. The quality of the dataset is validated by checking trajectories and average travel speeds. The potential use of this dataset lies in analyzing and modeling evacuee route choice behavior, estimating traffic conditions during the evacuation, and validating wildfire evacuation simulation models.
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    Analyzing Risk Perception, Evacuation Decision and Delay Time: A Case Study of the 2021 Marshall Fire in Colorado
    (Elsevier B.V., 2023-12-11) Forrister A; Kuligowski ED; Sun Y; Yan X; Lovreglio R; Cova TJ; Zhao X
    Climate change is increasing the threat of wildfires to populated areas, especially those within the wildland-urban interface (WUI). The 2021 Marshall fire forced the evacuation of over 30,000 people in Boulder, Jefferson and Adams Counties in Colorado, US. To improve our understanding of wildfire evacuation response, we surveyed individuals affected by the Marshall fire to analyze their evacuation decisions and resulting behavior. We used linear and logistic regression models to determine the factors influencing individuals’ risk perceptions, their decisions to evacuate or stay, and the associated evacuation delay times. We found higher levels of risk perception at the time of the evacuation decision were associated with higher levels of pre-fire perceived risk, having mid-level household income, the receipt of fire cues and having a medical condition. Increased pre-event risk perception increased the likelihood of evacuating, along with gender (female-identified), being aged between 55 and 64 years, and having a higher household income. On the other hand, having a prior awareness of wildfires had a negative effect on evacuation likelihood. Additionally, having previous experience with fire damage, owning their home, having a larger household size and being alerted later in the fire event reduced the delay time; whereas engaging in preparation activities and having children in the home led to longer delay times. These research findings can be used by emergency managers to better prepare WUI communities for future wildfire events.
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    Effect of iron-manganese oxide on the degradation of deoxynivalenol in feed and enhancement of growth performance and intestinal health in weaned piglets.
    (Elsevier B.V., 2024-10-28) Wu C; Song J; Liu X; Zhang Y; Zhou Z; Thomas DG; Wu B; Yan X; Li J; Zhang R; Wu F; Cheng C; Pu X; Wang X
    Deoxynivalenol (DON), a prevalent and highly toxic mycotoxin in animal feed, poses significant risks to livestock health and productivity. This study evaluates the effectiveness of iron-manganese oxide (Fe/Mn oxides) in degrading DON. The DON degradation rate of Fe/Mn oxide reached 98.46 % in a controlled solution under specific conditions (0.2 % concentration, 37-85 °C, pH 6-7, 1-minute reaction time). When applied to actual feed, it reduced DON levels by approximately 49.3 % and remained stable in simulated gastrointestinal environments of weaned piglets. A 28-day trial involving 48 weaned piglets assessed the impacts of Fe/Mn oxides on health and growth. Results indicated that piglets consuming contaminated feed without the treatment exhibited reduced growth and compromised gut integrity, which were significantly mitigated by the addition of Fe/Mn oxides. Therefore, Fe/Mn oxides effectively reduce DON in feed and alleviate adverse health effects in piglets, making them a viable option to enhance safety and performance in mycotoxin-prone environments.
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    Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices.
    (MDPI (Basel, Switzerland), 2025-01-22) Yan X; Zhang L; Liu B; Qu G; Amerini I; Russo P; Di Ciaccio F
    Traditional limb kinematic analysis relies on manual goniometer measurements. With computer vision advancements, integrating RGB cameras can minimize manual labor. Although deep learning-based cameras aim to offer the same ease as manual goniometers, previous approaches have prioritized accuracy over efficiency and cost on PC-based devices. Nevertheless, healthcare providers require a high-performance, low-cost, camera-based tool for assessing upper and lower limb range of motion (ROM). To address this, we propose a lightweight, fast, deep learning model to estimate a human pose and utilize predicted joints for limb ROM measurement. Furthermore, the proposed model is optimized for deployment on resource-constrained edge devices, balancing accuracy and the benefits of edge computing like cost-effectiveness and localized data processing. Our model uses a compact neural network architecture with 8-bit quantized parameters for enhanced memory efficiency and reduced latency. Evaluated on various upper and lower limb tasks, it runs 4.1 times faster and is 15.5 times smaller than a state-of-the-art model, achieving satisfactory ROM measurement accuracy and agreement with a goniometer. We also conduct an experiment on a Raspberry Pi, illustrating that the method can maintain accuracy while reducing equipment and energy costs. This result indicates the potential for deployment on other edge devices and provides the flexibility to adapt to various hardware environments, depending on diverse needs and resources.
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    Efficient Monocular Human Pose Estimation Based on Deep Learning Methods: A Survey
    (IEEE, 2024-05-09) Yan X; Liu B; Qu G
    Human pose estimation (HPE) is a crucial computer vision task with a wide range of applications in sports medicine, healthcare, virtual reality, and human-computer interaction. The demand for real-time HPE solutions necessitates the development of efficient deep-learning models that can be deployed on resource-constrained devices. While a few surveys exist in this area, none delve deeply into the critical intersection of efficiency and performance. This survey reviews the state-of-the-art efficient deep learning approaches for real-time HPE, focusing on strategies for improving efficiency without compromising accuracy. We discuss popular backbone networks for HPE, model compression techniques, network pruning and quantization, knowledge distillation, and neural architecture search methods. Furthermore, we critically analyze the existing works, highlighting their strengths, weaknesses, and applicability to different scenarios. We also present an overview of the evaluation datasets, metrics, and design for efficient HPE. Finally, we identify research gaps and challenges in the field, providing insights and recommendations for future research directions in developing efficient and scalable HPE solutions.