Demo Data from SIGIR 2026 / reviewers in the wild / expert
Seung Hwan Park
dblp:42/5600
· DBLP profile ↗
4ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0003-3864-0639ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
environment mapping |
0.0 | 1 | 2004 | Practical Environment Modeling Based on a Heuristic Sensor Fusion Method · ICRA 2004 |
Methods — techniques the papers use, named apart from their topics
heuristic sensor fusion · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | SSDTW: Shape segment dynamic time warping
Jae Yeol Hong, Seung Hwan Park, Jun-Geol Baek |
Expert Syst. Appl. | 2 |
| 2020 | Random-forest-based real-time contrasts control chart using adaptive breakpoints with symbolic aggregate approximation
In-Seok Lee, Seung Hwan Park, Jun-Geol Baek |
Expert Syst. Appl. | 2 |
| 2017 | Real-time contrasts control chart using random forests with weighted voting
Seongwon Jang, Seung Hwan Park, Jun-Geol Baek |
Expert Syst. Appl. | 2 |
| 2004 | Practical Environment Modeling Based on a Heuristic Sensor Fusion MethodabstractThis work presents a new practical environment modeling for mobile robots in an unknown circumstance. To perform environment modeling, obstacles located in the circumstance should be detected using sensor data. Then some sensor fusion methods are used to model environment better. Environment modeling shown In this work is based on the heuristic sensor fusion method. In this method, we define a segment as one ultrasonic sensor and two adjacent infrared sensors. Especially, infrared sensors are used to reduce defects of ultrasonic sensor such as poor directionality and frequent misreading. Whether infrared sensors of a segment detect obstacles or not, ultrasonic sensor data are modified. To evaluate the usefulness of the sensor fusion method, we used semicircular sensor module composed of 6 ultrasonic sensors and 7 infrared sensors. With this module, several experiments in indoor environment are performed. Seung Hwan Park, Beom Hee Lee 0001 |
ICRA | 1 |