Zobrazeno 1 - 10
of 619
pro vyhledávání: '"Yang, Jidong"'
Autor:
Jiang, Jing, Zhao, Sicheng, Zhu, Jiankun, Tang, Wenbo, Xu, Zhaopan, Yang, Jidong, Xu, Pengfei, Yao, Hongxun
Panoramic semantic segmentation has received widespread attention recently due to its comprehensive 360\degree field of view. However, labeling such images demands greater resources compared to pinhole images. As a result, many unsupervised domain ad
Externí odkaz:
http://arxiv.org/abs/2408.16469
Harnessing the power of Large Language Models (LLMs), this study explores the use of three state-of-the-art LLMs, specifically GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash severity inference, framing it as a classification task. We generate te
Externí odkaz:
http://arxiv.org/abs/2408.04652
Autor:
Ma, Shihan, Yang, Jidong J.
Publikováno v:
Eng. 2023; 4(1):444-456
This paper introduces a novel approach to leverage features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehicle classification. Two state-of-the-art self-supervised learning meth
Externí odkaz:
http://arxiv.org/abs/2302.00648
Autor:
Huang, Yongcan, Yang, Jidong J.
Publikováno v:
Applied Computing and Intelligence, 2022, Volume 2, Issue 2: 99-114
Inspired by the recent success of deep learning in multiscale information encoding, we introduce a variational autoencoder (VAE) based semi-supervised method for detection of faulty traffic data, which is cast as a classification problem. Continuous
Externí odkaz:
http://arxiv.org/abs/2212.13596
Publikováno v:
Applied Computing and Intelligence, 2023, Volume 3, Issue 1: 13-26
Classification using supervised learning requires annotating a large amount of classes-balanced data for model training and testing. This has practically limited the scope of applications with supervised learning, in particular deep learning. To addr
Externí odkaz:
http://arxiv.org/abs/2212.13589
Given the increasing popularity and demand for connected and autonomous vehicles (CAVs), Eco-driving and platooning in highways and urban areas to increase the efficiency of the traffic system is becoming a possibility. This paper presents Eco-drivin
Externí odkaz:
http://arxiv.org/abs/2205.09618
Publikováno v:
In Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 15 January 2024 305
Cluster-guided denoising graph auto-encoder for enhanced traffic data imputation and fault detection
Publikováno v:
In Expert Systems With Applications 1 February 2025 261
Autor:
Qin, Shanyuan1 (AUTHOR) b23010004@s.upc.edu.cn, Yang, Jidong1 (AUTHOR) jidong.yang@upc.edu.cn, Qin, Ning2 (AUTHOR) tiankunwudi@163.com, Huang, Jianping1 (AUTHOR), Tian, Kun2 (AUTHOR)
Publikováno v:
Fractal & Fractional. Mar2024, Vol. 8 Issue 3, p174. 17p.
Autor:
Su, Gang1 (AUTHOR) gang.su@uga.edu, Yang, Jidong J.1 (AUTHOR) jidong.yang@uga.edu
Publikováno v:
Eng. Mar2024, Vol. 5 Issue 1, p104-115. 12p.