IEICE TRANSACTIONS on Communications
Electromagnetic Wave Pattern Detection with Multiple Sensors in the Manufacturing Field
Summary :
Multiple wireless communication systems are often operated together in the same area in such manufacturing sites as factories where wideband noise may be emitted from industrial equipment over channels for wireless communication systems. To perform highly reliable wireless communication in such environments, radio wave environments must be monitored that are specific to each manufacturing site to find channels and timing that enable stable communication. The authors studied technologies using machine learning to efficiently analyze a large amount of monitoring data, including signals whose spectrum shape is undefined, such as electromagnetic noise over a wideband. In this paper, we generated common supervised data for multiple sensors by conjointly clustering features after normalizing those calculated in each sensor to recognize the signal reception timing from identical sources and eliminate the complexity of supervised data management. We confirmed our method's effectiveness through signal models and actual data sampled by sensors that we developed.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E106-B No.2 pp.109-116
- Publication Date
- 2023/02/01
- Publicized
- 2022/08/23
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2022CEP0005
- Type of Manuscript
- Special Section PAPER (Special Section on Emerging Communication Technologies in Conjunction with Main Topics of ICETC 2021)
- Category
Authors
Ayano OHNISHI
Advanced Telecommunications Research Institute International (ATR)
Michio MIYAMOTO
Advanced Telecommunications Research Institute International (ATR)
Yoshio TAKEUCHI
Advanced Telecommunications Research Institute International (ATR)
Toshiyuki MAEYAMA
Advanced Telecommunications Research Institute International (ATR),Takushoku University
Akio HASEGAWA
Advanced Telecommunications Research Institute International (ATR)
Hiroyuki YOKOYAMA
Advanced Telecommunications Research Institute International (ATR)
Keyword
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Ayano OHNISHI, Michio MIYAMOTO, Yoshio TAKEUCHI, Toshiyuki MAEYAMA, Akio HASEGAWA, Hiroyuki YOKOYAMA, "Electromagnetic Wave Pattern Detection with Multiple Sensors in the Manufacturing Field" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 2, pp. 109-116, February 2023, doi: 10.1587/transcom.2022CEP0005.
Abstract: Multiple wireless communication systems are often operated together in the same area in such manufacturing sites as factories where wideband noise may be emitted from industrial equipment over channels for wireless communication systems. To perform highly reliable wireless communication in such environments, radio wave environments must be monitored that are specific to each manufacturing site to find channels and timing that enable stable communication. The authors studied technologies using machine learning to efficiently analyze a large amount of monitoring data, including signals whose spectrum shape is undefined, such as electromagnetic noise over a wideband. In this paper, we generated common supervised data for multiple sensors by conjointly clustering features after normalizing those calculated in each sensor to recognize the signal reception timing from identical sources and eliminate the complexity of supervised data management. We confirmed our method's effectiveness through signal models and actual data sampled by sensors that we developed.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022CEP0005/_p
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@ARTICLE{e106-b_2_109,
author={Ayano OHNISHI, Michio MIYAMOTO, Yoshio TAKEUCHI, Toshiyuki MAEYAMA, Akio HASEGAWA, Hiroyuki YOKOYAMA, },
journal={IEICE TRANSACTIONS on Communications},
title={Electromagnetic Wave Pattern Detection with Multiple Sensors in the Manufacturing Field},
year={2023},
volume={E106-B},
number={2},
pages={109-116},
abstract={Multiple wireless communication systems are often operated together in the same area in such manufacturing sites as factories where wideband noise may be emitted from industrial equipment over channels for wireless communication systems. To perform highly reliable wireless communication in such environments, radio wave environments must be monitored that are specific to each manufacturing site to find channels and timing that enable stable communication. The authors studied technologies using machine learning to efficiently analyze a large amount of monitoring data, including signals whose spectrum shape is undefined, such as electromagnetic noise over a wideband. In this paper, we generated common supervised data for multiple sensors by conjointly clustering features after normalizing those calculated in each sensor to recognize the signal reception timing from identical sources and eliminate the complexity of supervised data management. We confirmed our method's effectiveness through signal models and actual data sampled by sensors that we developed.},
keywords={},
doi={10.1587/transcom.2022CEP0005},
ISSN={1745-1345},
month={February},}
Copy
TY - JOUR
TI - Electromagnetic Wave Pattern Detection with Multiple Sensors in the Manufacturing Field
T2 - IEICE TRANSACTIONS on Communications
SP - 109
EP - 116
AU - Ayano OHNISHI
AU - Michio MIYAMOTO
AU - Yoshio TAKEUCHI
AU - Toshiyuki MAEYAMA
AU - Akio HASEGAWA
AU - Hiroyuki YOKOYAMA
PY - 2023
DO - 10.1587/transcom.2022CEP0005
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2023
AB - Multiple wireless communication systems are often operated together in the same area in such manufacturing sites as factories where wideband noise may be emitted from industrial equipment over channels for wireless communication systems. To perform highly reliable wireless communication in such environments, radio wave environments must be monitored that are specific to each manufacturing site to find channels and timing that enable stable communication. The authors studied technologies using machine learning to efficiently analyze a large amount of monitoring data, including signals whose spectrum shape is undefined, such as electromagnetic noise over a wideband. In this paper, we generated common supervised data for multiple sensors by conjointly clustering features after normalizing those calculated in each sensor to recognize the signal reception timing from identical sources and eliminate the complexity of supervised data management. We confirmed our method's effectiveness through signal models and actual data sampled by sensors that we developed.
ER -
English
