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Tianpeng Wei

dblp:243/3728 · DBLP profile ↗
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3ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1Computer networks · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Wireless sensing and localization · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization
smartphone sensing
0.412020
VLD: Smartphone-assisted Vertical Location Detection for Vehicles in Urban Environments · IPSN 2020
Smart cities and intelligent transportation › navigation
vehicle navigation
0.112020
VLD: Smartphone-assisted Vertical Location Detection for Vehicles in Urban Environments · IPSN 2020

Methods — techniques the papers use, named apart from their topics

sensor fusion · 0.9pressure-temperature model · 0.9pressure-height model · 0.9
YearPublicationVenuePosition
2023 A Dynamic Graph Convolutional Network for Anti-money Laundering
Tianpeng Wei, Biyang Zeng, Wenqi Guo, Shikui Tu, Lei Xu 0001
ICIC (5)1
2020 VLD: Smartphone-assisted Vertical Location Detection for Vehicles in Urban Environments
abstract
As the most widely used outdoor navigation system, GPS can provide accurate localization on the horizontal plane. However, the vertical localization accuracy exhibits a poor performance. Vehicles on the elevated road usually receive wrong navigation instructions since GPS fails to detect vehicles' vertical location. In this paper, we present a vertical location system named VLD for vehicles in metropolises mainly leveraging smartphones' barometers, a low-power sensor found in an increasing number of smart devices. When initially on the ground, VLD combines the height and angle detection algorithms to confirm the vertical location with low complexity. Once vehicles have exited the ground and start to travel on the elevated road, a pressure-height model trained by a novel proposed sensor fusion algorithm is activated to measure vehicles' relative height. Then, it tracks the relative height in real time according to a pressure-temperature model which is calibrated by current weather. Comparing the relative height with the single-level elevated road's height, VLD can determine which level vehicles travel on in many highway interchanges and when they exit the elevated road. Experiments covering one month demonstrate that the detection accuracy of VLD exceeds 99% under different weather conditions, and it shows a more accurate relative height measurement compared to available literature, including Baidu Maps and one barometer based application-Altitude. Finally, VLD demonstrates a high efficiency with respect to both the detection delay and power consumption.
Xiong Wang 0006, Linghe Kong, Tianpeng Wei, Liang He 0002, Guihai Chen, Jiangtao Wang 0001, Chenren Xu
IPSN3
2019 ECASS: Edge computing based auxiliary sensing system for self-driving vehicles
Xiong Wang 0006, Tianpeng Wei, Linghe Kong, Liang He 0002, Fan Wu 0006, Guihai Chen
J. Syst. Archit.2