Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Naotake NATORI"'
Publikováno v:
Sensors, Vol 22, Iss 8, p 2876 (2022)
Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption. However, deep SNNs are not easy to train due to the excessive firing of spiking neurons in the hidden layers. To tackle this problem, w
Externí odkaz:
https://doaj.org/article/21548fe9a6374ca3998226c9a4d4ffed
Autor:
Thanh, Phan Thi Huyen, Bui, Minh Quan Viet, Nguyen, Duc Dung, Pham, Tran Vu, Duy, Truong Vinh Truong, Naotake, Natori
Publikováno v:
In Image and Vision Computing January 2024 141
Autor:
Hironori Washizaki, Foutse Khomh, Yann-Gael Gueheneuc, Hironori Takeuchi, Naotake Natori, Takuo Doi, Satoshi Okuda
Publikováno v:
Computer. 55:30-39
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d858b2ce102314f6419835664c825f59
https://doi.org/10.1109/mc.2021.3137227
https://doi.org/10.1109/mc.2021.3137227
Autor:
Manogna Sreenivas, Sawa Takamuku, Soma Biswas, Aditya Chepuri, Balasubramanian Vengatesan, Naotake Natori
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250712
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c394921a1fa3a4071b606f32c934f21a
https://doi.org/10.1007/978-3-031-25072-9_17
https://doi.org/10.1007/978-3-031-25072-9_17
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2506272c3d8e2f0e2e15333fb1de74c9
https://doi.org/10.1109/iros47612.2022.9981322
https://doi.org/10.1109/iros47612.2022.9981322
Publikováno v:
2022 International Workshop on Acoustic Signal Enhancement (IWAENC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfeee690f403e3ed856c01b13be3b077
https://doi.org/10.1109/iwaenc53105.2022.9914738
https://doi.org/10.1109/iwaenc53105.2022.9914738
Autor:
Megh Bhalerao, Anurag Singh, Balasubramanian Vengatesan, Sawa Takamuku, Aditya Chepuri, Soma Biswas, Naren Doraiswamy, Naotake Natori, Titir Dutta
Publikováno v:
CVPR Workshops
Recently, semi-supervised domain adaptation (SSDA) approaches have shown impressive performance for the domain adaptation task. They effectively utilize few labeled target samples along with the unlabeled data to account for the distribution shift ac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0cdb5c59ae2a9a3aef845b3f8fca5e3e
https://doi.org/10.1109/cvprw53098.2021.00305
https://doi.org/10.1109/cvprw53098.2021.00305
Autor:
Naotake Natori, Truong Vinh Truong Duy
Publikováno v:
ICMLA
This paper deals with the problem of detecting surface cavities on metal castings that must be distinguished from their lookalike non-defective substances embedding on the surface. As general detection methods working on the basis of single 2D appear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34446cd8655742f86cf943e4233afa62
https://doi.org/10.1109/icmla51294.2020.00080
https://doi.org/10.1109/icmla51294.2020.00080
Autor:
Satoshi Okuda, Hironori Takeuchi, Foutse Khomh, Hironori Washizaki, Naotake Natori, Takuo Doi
Publikováno v:
ICSME
Machine-learning (ML) software engineering design patterns encapsulate reusable solutions to commonly occurring problems within the given contexts of ML systems and software design. These ML patterns should help develop and maintain ML systems and so
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bfe8e0bf1e5a10bc454cc49b973a39f2
https://doi.org/10.1109/icsme46990.2020.00095
https://doi.org/10.1109/icsme46990.2020.00095
Publikováno v:
Cryogenics. 80:199-203
This paper deals with the cooling system for high-Tc superconducting (HTS) generators for large capacity wind turbines. We have proposed a cooling system with a heat exchanger and circulation pumps to cool HTS field windings designed for 10 MW-class
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b0598f5f910df10d306686d0f70364f
https://doi.org/10.1016/j.cryogenics.2016.05.009
https://doi.org/10.1016/j.cryogenics.2016.05.009