Long-Range Wireless Communication for In-Line Inspection Robot: 2.4 km On-Site Test
Author:
Affiliation:
1. Mechatronics Engineering, Mechanical Engineering, Yildiz Technical University, Istanbul 34349, Türkiye
2. TUBITAK Rail Transport Technologies Institute, Kocaeli 41470, Türkiye
3. TUBITAK Marmara Research Center, Kocaeli 41470, Türkiye
Abstract
Publisher
MDPI AG
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Link
https://www.mdpi.com/2071-1050/15/10/8134/pdf
Reference39 articles.
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4. Geng, L., Dong, S., Qian, W., and Peng, D. (2022). Image Classification Method Based on Improved Deep Convolutional Neural Networks for the Magnetic Flux Leakage (MFL) Signal of Girth Welds in Long-Distance Pipelines. Sustainability, 14.
5. Aldosari, H., Elfouly, R., and Ammar, R. (2020, January 16–18). Evaluation of Machine Learning-Based Regression Techniques for Prediction of Oil and Gas Pipelines Defect. Proceedings of the 2020 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.
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