Demo Data from VLDB 2026 / reviewers in the wild / expert
Mingao Lv
dblp:269/2399
· DBLP profile ↗
2ranked-venue papers
2as first author
1since 2021 · last 2025
0000-0002-4820-6994ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 2 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.
| Artificial intelligence
1 paper |
Legged, aerial and field robots · 50% Planning, search and constraint satisfaction · 50% | |
| Computer networks
1 paper |
Wireless networking · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Legged, aerial and field robots › field robotics › maritime robotics
autonomous surface vehicle |
0.4 | 1 | 2020 | Event-triggered neural network control of autonomous surface vehicles over wireless network · Sci. China Inf. Sci. 2020 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › intelligent control
neural network control |
0.4 | 1 | 2020 | Event-triggered neural network control of autonomous surface vehicles over wireless network · Sci. China Inf. Sci. 2020 |
Wireless networking
wireless networked control system |
0.1 | 1 | 2020 | Event-triggered neural network control of autonomous surface vehicles over wireless network · Sci. China Inf. Sci. 2020 |
Methods — techniques the papers use, named apart from their topics
neural network control · 0.9event-triggered control · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Human-in-the-Loop Coordinated Path Following of Marine Vehicles Based on Continuous Twisting ControlabstractThis article addresses the coordinated path-following (CPF) control under human supervision for marine vehicles (MVs) with unknown disturbances. A human-in-the-loop coordinated path-following (HCPF) control architecture is proposed based on the robust exact differentiator (RED) observer and the output feedback continuous twisting control (OFCTC) method. Specifically, a human manipulation is introduced into a virtual leader to regulate its path update speed in response to sudden circumstances for the follower MVs. All follower MVs synchronize indirectly with the human-in-the-loop virtual leader over a communication graph. Then, the path-following control problem is transformed into the controls of a second-order along-track error dynamic and a third-order cross-track error dynamic. By using the path-following errors only, the RED observers are developed to recover the unknown states of the error dynamics. Finally, the OFCTC laws are designed to achieve the individual path following in finite time regardless of the lumped disturbances. The stability of the closed-loop system is analyzed by employing the cascade theory. A salient feature of the proposed HCPF control architecture is that the CPF for MVs can be implemented under the supervision and intervention of human. Simulation results are given to illustrate the effectiveness of the proposed HCPF control method. Mingao Lv, Nan Gu, Dan Wang 0001, Bing Han 0009, Zhouhua Peng |
IEEE Trans. Ind. Informatics | 1 |
| 2020 | Event-triggered neural network control of autonomous surface vehicles over wireless network
Mingao Lv, Dan Wang 0001, Zhouhua Peng, Lu Liu 0003 |
Sci. China Inf. Sci. | 1 |