Yong-Hyuk Kim

Computer Networks and Communications · 2013–2018

A researcher in the Science corpus: 3 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 13 citations into it
• Electrical and Electronic Engineering — 9 citations into it

Whom Yong-Hyuk Kim trusts overall

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

1. David E. Goldberg
2. Bang Wang
3. Yi Zou
4. Andrew Howard
5. Krishnendu Chakrabarty
6. Yaochu Jin
7. Maja J. Mataric

Who trusts Yong-Hyuk Kim overall

1. Chaoli Sun

Papers from Yong-Hyuk Kim's perspective

Ordered by how strongly the researchers Yong-Hyuk Kim trusts endorse each paper. Same corpus, a different order for every researcher.

1. Mobile Sensor Network Deployment using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem (International Symposium on Distributed Autonomous Robotic Systems)
2. Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks (IEEE Trans. Computers)
3. Sensor deployment strategy for target detection (ACM International Conference on Wireless Sensor Networks and Applications)
4. An Incremental Self-Deployment Algorithm for Mobile Sensor Networks (Auton. Robots)
5. Sensor placement for grid coverage under imprecise detections (Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997))
6. Practical Bayesian Optimization of Machine Learning Algorithms
7. Maintaining Sensing Coverage and Connectivity in Large Sensor Networks (Ad Hoc Sens. Wirel. Networks)
8. The NP-Completeness Column: An Ongoing Guide
9. Genetic Algorithms in Search Optimization and Machine Learning
10. The Hungarian method for the assignment problem

Their papers

• An Efficient Genetic Algorithm for Maximum Coverage Deployment in Wireless Sensor Networks (2013) — 312 citations
• Is it worth to approximate fitness by machine learning?: investigation on the extensibility according to problem size (2018) — 4 citations
• On the hardness of parameter optimization of convolution neural networks using genetic algorithm and machine learning (2018)