• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
Junseok Boo, Dongkyoung Chwa, 2020, Image-based Visual Servoing of an Omnidirectional Mobile Robot without Velocity Sensor Using Multi-layer Artificial Neural Network Dynamics, The Transaction of the Korean Institute of Electrical Engineers, Vol. 69, No. 4, pp. 594-601Google Search
2 
Ran Zhao, Hong-Kyu Lee, 2017, GA-Fuzzy based Navigation of Multiple Mobile Robots in Unknown Dynamic Environments, The Transaction of the Korean Institute of Electrical Engineers, Vol. 66, No. 1, pp. 114-120DOI
3 
Jong-Hun Park, Uk-Youl Huh, 2015, Local Path Planning for Mobile Robot Using Artificial Neural Network - Potential Field Algorithm, The Transaction of the Korean Institute of Electrical Engineers, Vol. 64, No. 10, pp. 1479-1485DOI
4 
Soo-Young Lee, Jae-Young Kim, Se-Hyoung Cho, Chang-yong Shin, 2019, Educational Indoor Autonomous Mobile Robot System Using a LiDAR and a RGB-D Camera, Journal of IKEEE, Vol. 23, No. 1DOI
5 
S. H. Kim, K. H. Yu, P. U. Seo, K. H. Sim, G. H. Lee, 2018, Program development for driving autonomous mobile robot using deep learning, Proceedings of The Korean Society of Manufacturing Technology Engineers Conference, pp. 102-102Google Search
6 
Hyeong-il Lee, Jin-myeong Kim, Jai-weun Lee, 2019, Implementation of Autonomous Mobile Wheeled Robot for Path Correction through Deep Learning Object Recognition, Journal of The Korea Contents Association, Vol. 19, No. 2, pp. 164-172DOI
7 
Y. Bengio, A. Courville, P. Vincent, 2013, Representation Learning: A Review and New Perspectives, IEEE Trans. PAMI, special issue Learning Deep Architectures, Vol. 35, No. 8Google Search
8 
Tao Zhang, Qing Li, Chang-shui Zhang, Hua-wei Liang, Ping Li, Tian-miao Wang, 2017, Current trends in the development of intelligent unmanned autonomous systems, Frontiers of Information Technology & Electronic Engineering, Vol. 18, pp. 68-85DOI
9 
Jung-Ju Kim, Dong-Jin Kim, Kyung-Wan Koo, 2021, Position recognition and driving control for an autonomous mobile robot that tracks tile grid pattern, The Transaction of the Korean Institute of Electrical Engineers, Vol. 70, No. 6, pp. 945-952Google Search
10 
R. Smith, 2007, An Overview of the Tesseract OCR Engine, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), pp. 629-633Google Search
11 
R. R. Palekar, S. U. Parab, D. P. Parikh, V. N. Kamble, 2017, Real time license plate detection using openCV and tesseract, 2017 International Conference on Communication and Signal Processing (ICCSP), pp. 2111-2115Google Search
12 
S. Thakare, A. Kamble, V. Thengne, U. R. Kamble, 2018, Document Segmentation and Language Translation Using Tesseract-OCR, 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS), pp. 148-151DOI
13 
Alex Sherstinsky, 2020, Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network, Physica D: Nonlinear Phenomena, Vol. 404DOI
14 
Yong Yu, Xiaosheng Si, Changhua Hu, Jianxun Zhang, 2019, A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures, Neural Comput, Vol. 31, No. 7, pp. 1235-1270DOI
15 
Ray Smith, 2016, 7.Buildiung a multilingual OCR Engine Training LSTM network on 100 language and test results, http://github.com/tesseract-docsGoogle Search
16 
M. Filipenko, I. Afanasyev, 2018, Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment, 2018 International Conference on Intelligent Systems (IS), pp. 400-407Google Search