Ekin Ozer

Civil and Structural Engineering · 2015–2021

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Civil and Structural Engineering — 36 citations into it; #5,133 of 6,160 by trust here, #4,455 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #18,988 of 21,575 by trust here, #18,266 by citations
• Mechanics of Materials — 11 citations into it
• Artificial Intelligence — 10 citations into it
• Mechanical Engineering — 8 citations into it
• Control and Systems Engineering — 7 citations into it
• Media Technology — 5 citations into it

Whom Ekin Ozer trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Charles R. Farrar
2. Keith Worden
3. Radford M. Neal
4. Sandeep Sony
5. Vin de Silva
6. Zhengyou Zhang
7. Dongming Feng
8. T.G. Ritto
9. M. Mitra
10. Saúl Solorio-Fernández

Who trusts Ekin Ozer overall

1. Chuan-Zhi Dong

Papers from Ekin Ozer's perspective

Ordered by how strongly the researchers Ekin Ozer trusts endorse each paper. Same corpus, a different order for every researcher.

1. Vibration–based structural damage identification (Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences)
2. Pattern Recognition and Machine Learning
3. The application of machine learning to structural health monitoring (Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)
4. A review of structural health monitoring literature 1996-2001
5. Effects of environmental and operational variability on structural health monitoring (Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)
6. An introduction to structural health monitoring (Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)
7. Emerging artificial intelligence methods in structural engineering (Engineering structures)
8. Damage identification using inverse methods (Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)
9. Structural Health Monitoring: A Machine Learning Perspective
10. Global Versus Local Methods in Nonlinear Dimensionality Reduction

Their papers

• Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021) — 524 citations
• A Vision-Based Sensor for Noncontact Structural Displacement Measurement (2015) — 440 citations