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Yunhai Geng

dblp:164/1902 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
0000-0002-1515-3240ORCID · corroborated

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

Artificial intelligence and machine learning · 1Applied, interdisciplinary, general and emerging computing · 1

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.

Artificial intelligence
1 paper
Image recognition and object detection · 77% Legged, aerial and field robots · 23%

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

TopicWeightPapersLastEvidence papers
Computer vision › Image recognition and object detection › object detection
multi-scale object detection
0.312018
Multi-scale rock detection on Mars · Sci. China Inf. Sci. 2018
Robotics › Legged, aerial and field robots › field robotics
planetary exploration robot
0.112018
Multi-scale rock detection on Mars · Sci. China Inf. Sci. 2018

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

multi-scale image analysis · 0.3
YearPublicationVenuePosition
2020 Neural-Network-Based Adaptive Event-triggered Control for Spacecraft Attitude Tracking
abstract
The problem of attitude tracking control for spacecraft with limited communication rate is addressed in this article. To reduce the communication burden, an adaptive event-triggered control scheme is proposed. In the control scheme, only the sampling states at the event-triggering instants are sent to the control module, which can considerably decrease the data transmission rate. To address the inertia uncertainties and external disturbances, a radial basis function neural network (NN) is introduced. The bound of the uncertainties and disturbances is estimated for the proposed control scheme, which can simplify the NN and reduce the computation. Since the event-triggered error signal is discontinuous due to the event-triggered mechanism, the closed-loop system is formulated as an impulsive dynamical system to obtain the stability properties of the system. Finally, simulation results are given to demonstrate the effectiveness of the proposed control scheme.
Yunhai Geng, Baolin Wu, Danwei Wang
IEEE Trans. Neural Networks Learn. Syst.2
2018 Multi-scale rock detection on Mars
Yunhai Geng, Xueming Xiao
Sci. China Inf. Sci.2