摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-在一份新的报告中讨论了人工智能的研究结果。根据NewsRx编辑在马亚圭斯波多黎各大学的新闻报道，研究表明，"过境发光事件(TLEs)是短命的高层大气光学现象，与雷暴有关。"这项研究的资助者包括Nasa。我们的新闻记者引用了位于Mayaguez的Puert O Rico大学的一项研究：“它们的快速和随机发生使得人工分类费时费力。本研究提出了一种利用最先进的卷积神经网络(CNNs)和视觉转换器(ViT)来计算TLEs分类的有效方法。ViT架构和四种不同的CNN架构，即ResNet50、ResNet18、GoogLeNet和SqueezeNet。”通过旋转、平移和翻转技术对模型进行增强，以增加模型的尺寸和多样性。此外，通过双边滤波对图像进行预处理，以提高图像的红外质量。结果表明，所有模型的分类精度都很高，其中H ResNet50的分类精度最高。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting out of the Uni versity of Puerto Rico at Mayaguez by NewsRx editors, research stated, “Transien t Luminous Events (TLEs) are short-lived, upper-atmospheric optical phenomena as sociated with thunderstorms.” Funders for this research include Nasa. Our news journalists obtained a quote from the research from University of Puert o Rico at Mayaguez: “Their rapid and random occurrence makes manual classificati on laborious and time-consuming. This study presents an effective approach to au tomating the classification of TLEs using state-of-the-art Convolutional Neural Networks (CNNs) and a Vision Transformer (ViT). The ViT architecture and four di fferent CNN architectures, namely, ResNet50, ResNet18, GoogLeNet, and SqueezeNet , are employed and their performance is evaluated based on their accuracy and ex ecution time. The models are trained on a dataset that was augmented using rotat ion, translation, and flipping techniques to increase its size and diversity. Ad ditionally, the images are preprocessed using bilateral filtering to enhance the ir quality. The results show high classification accuracy across all models, wit h ResNet50 achieving the highest accuracy.”
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