Automated Author ProfileYung-An Hsieh
Yung-An Hsieh
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.4 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
"We introduce 3DCrack, a new 3D pavement image dataset to support deep learning-based crack detection. Data was collected using the Georgia Tech Sensing Vehicle (GTSV), equipped with dual 3D line laser sensors. These sensors capture a 4-meter-wide pavement profile at speeds up to 60 mph. Each frame produces a dense 1,000 \u00d7 2,080 point cloud, with 1 mm \u00d7 1 mm resolution after interpolation, and 0.5 mm height precision. The dataset covers diverse cracking conditions, including varying geometries such as transverse, longitudinal, alligator, and compound cracks, as well as challenges like pavement joints and other visual distractions, ensuring robustness in trained models across real-world scenarios."
Authors
- Xinan Zhang ;
- Haolin Wang ;
- Yung-An Hsieh ;
- Zhongyu Yang ;
- Anthony Yezzi ;
- Yi-Chang Tsai