Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yong-Qian Zhang
dblp:42/1545
· DBLP profile ↗
4ranked-venue papers
0as first author
0since 2021 · last 2007
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3Systems, architecture and hardware · 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 |
Robot navigation and mapping · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › sensor fusion
feature-level fusion |
0.1 | 1 | 2007 | Sonar Feature Map Building for a Mobile Robot · ICRA 2007 |
Robotics › Robot navigation and mapping › robot mapping
feature map building |
0.1 | 1 | 2007 | Sonar Feature Map Building for a Mobile Robot · ICRA 2007 |
Methods — techniques the papers use, named apart from their topics
three measurements association model · 0.1iterated least squares estimation · 0.1data-level fusion · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Sonar Feature Map Building for a Mobile RobotabstractThis paper presents an approach for sonar feature map building. The approach is composed of extracting features at the data-level fusion stage and fusing the extracted features with the registered features in the map at the feature-level fusion stage. A data-level fusion model, termed three measurements association model (TMAM), has been developed for associating three measurements with a line or a point feature. By use of TMAM, different sets of measurements obtained from a single sonar sensor at consecutive steps are associated with the line and point features. Subsequently, the parameters of the identified features are estimated by use of the iterated least square estimation method. Finally, when a feature is extracted, a simple feature-level fusion strategy is used to update the map. The proposed approach has been tested both in simulation and on real data. Hong-Ming Wang, Zeng-Guang Hou, Jia Ma, Yun-Chu Zhang, Yong-Qian Zhang, Min Tan 0001 |
ICRA | 5 |
| 2007 | A Genetic Algorithm-Based Artificial Neural Network Approach for Parameter Selection in the Production of Tailor-Welded Blanks
De Xu, Xiuqing Wang, Min Tan 0001, Yong-Qian Zhang |
ISNN (3) | 5 |
| 2006 | Visual Navigation for a Power Transmission Line Inspection Robot
Yun-Chu Zhang, Si-Yao Fu, Xiaoguang Zhao, Zi-ze Liang, Min Tan 0001, Yong-Qian Zhang |
ICIC (2) | 6 |
| 2006 | Adaptive Segmentation of Color Image for Vision Navigation of Mobile Robots
Zeng-Guang Hou, Min Tan 0001, Yong-Qian Zhang |
ISNN (2) | 4 |