Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Jidong J. Yang"'
Autor:
Gang Su, Jidong J. Yang
Publikováno v:
Eng, Vol 5, Iss 1, Pp 104-115 (2024)
The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area th
Externí odkaz:
https://doaj.org/article/d8bb115e70c44ba38c3d58d8cad48d8d
Publikováno v:
Information, Vol 15, Iss 10, p 654 (2024)
Traffic sensors are vital to the development and operation of Intelligent Transportation Systems, providing essential data for traffic monitoring, management, and transportation infrastructure planning. However, optimizing the placement of these sens
Externí odkaz:
https://doaj.org/article/7da66a498e0943b6bd701d3962e6660e
Publikováno v:
Computers, Vol 13, Iss 9, p 232 (2024)
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 analysis and inference, framing it as a classification task. W
Externí odkaz:
https://doaj.org/article/66eb844c5f9644ca87b7bce21f0d513a
Publikováno v:
Infrastructures, Vol 9, Iss 9, p 140 (2024)
Flooding and consequential scouring are the primary causes of bridge failures, making the detection of such events crucial for structural safety. This study investigates the characteristics of accelerometer data from bridge pier vibrations and propos
Externí odkaz:
https://doaj.org/article/d810355afa754107af3744a77af40a00
Autor:
Shihan Ma, Jidong J. Yang
Publikováno v:
Eng, Vol 4, Iss 1, Pp 444-456 (2023)
This paper introduces a novel approach to leveraging 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 me
Externí odkaz:
https://doaj.org/article/508d5c138a444feb9bfbca3a6abb421a
Autor:
Yunxiang Yang, Jidong J. Yang
Publikováno v:
Systems, Vol 11, Iss 12, p 577 (2023)
Traffic sensors play a pivotal role in monitoring and assessing network-wide traffic conditions. However, the substantial costs associated with deploying an extensive sensor network across real-world highway systems can often prove prohibitive. Thus,
Externí odkaz:
https://doaj.org/article/0079b8cd8d504329892907776632e69f
Publikováno v:
Machine Learning with Applications, Vol 6, Iss , Pp 100178- (2021)
The pavement Mechanistic-Empirical (ME) design requires high-dimensional traffic feature inputs by categories, including Vehicle Class Distributions (VCD), Monthly Distribution Factors (MDF), Hourly Distribution Factors (HDF), and Normalized Axles Lo
Externí odkaz:
https://doaj.org/article/cf7ccdfcb9d74f459ca23c029794591e
Publikováno v:
Infrastructures, Vol 7, Iss 11, p 150 (2022)
Weigh-In-Motion (WIM) data have been collected by state departments of transportation (DOT) in the U.S. and are anticipated to grow as state DOTs expand the number of WIM sites in order to better manage transportation infrastructure and enhance mobil
Externí odkaz:
https://doaj.org/article/eec0caa1d9084cba9d5dbd6084d212e8
Publikováno v:
Applied Sciences, Vol 12, Iss 20, p 10359 (2022)
Data collected using sensors plays an essential role in active bridge health monitoring. When analyzing a large number of bridges in the U.S., the National Bridge Inventory data as been widely used. Yet, the database does not provide information abou
Externí odkaz:
https://doaj.org/article/5edc3d687c584a579ed205eae4f3ebc6
Autor:
Clint Morris, Jidong J. Yang
Publikováno v:
Machine Learning with Applications, Vol 5, Iss , Pp 100070- (2021)
Highway safety is largely influenced by weather conditions that have become increasingly volatile due to the climate change. It well known that wet pavement significantly reduces surface friction, leading to inflated collision risk. Thus, timely know
Externí odkaz:
https://doaj.org/article/28207c732e314a8da53b5d30ea6fee22