Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Peihua XU"'
Publikováno v:
International Journal of Electrical Power & Energy Systems, Vol 159, Iss , Pp 110002- (2024)
Non invasive load monitoring (NILM) is beneficial for enhancing the monitoring capability of the distribution network and is crucial for improving the safety of smart grid operation. However, household appliances involve a variety of devices and a la
Externí odkaz:
https://doaj.org/article/b80b4b4ddcdb46de8da566311d63bdd9
Publikováno v:
南方能源建设, Vol 11, Iss 1, Pp 73-84 (2024)
[Introduction] To test the reliability of the CMA-WSP wind speed product in short-term wind resource forecasting, the CMA-WSP 3 d wind speed forecasting product with a wind speed of 100 m is tested and analyzed. [Method] This research was based on th
Externí odkaz:
https://doaj.org/article/255a7d5e72dd429e91fdd72d1be90239
Publikováno v:
南方能源建设, Vol 11, Iss 1, Pp 85-95 (2024)
[Introduction] With the extensive construction of wind farms, the combination of researches on different machine learning algorithms and meteorological forecasting modes has received widespread attention. [Method] This paper was based on the spatial
Externí odkaz:
https://doaj.org/article/7b5b95ec52a14c94894de6fb905556dc
Publikováno v:
南方能源建设, Vol 11, Iss 1, Pp 112-121 (2024)
[Introduction] The volatility and intermittency of wind energy pose significant challenges for large-scale wind power integration. An effective approach to address this issue is to provide accurate wind power forecasting. [Method] In response to this
Externí odkaz:
https://doaj.org/article/f5e54fa95dd740aba55736bd136004c5
Autor:
Hao Lu, Lida Zhu, Shuhao Wang, Boling Yan, Pengsheng Xue, Yanpeng Hao, Jinsheng Ning, Peihua Xu, Shaoqing Qin
Publikováno v:
Journal of Materials Research and Technology, Vol 26, Iss , Pp 1238-1259 (2023)
Holes are an essential component in achieving the desired function of implants. As the prevalent application of additive manufacturing in the field of implants, the hole-forming quality of additively manufactured parts using conventional machining ha
Externí odkaz:
https://doaj.org/article/a592da2cebb0430e9fb547c7ed193644
Autor:
Zhichao Yang, Lida Zhu, Yichao Dun, Jinsheng Ning, Shuhao Wang, Pengsheng Xue, Peihua Xu, Miao Yu, Boling Yan, Bo Xin
Publikováno v:
Virtual and Physical Prototyping, Vol 18, Iss 1 (2023)
Ultrasound-assisted directed energy deposition (UADED) is a promising technology for improving the properties of printed parts. However, process monitoring during UADED remains a challenge as ultrasound obscures the physical characteristics of DED. H
Externí odkaz:
https://doaj.org/article/98b8d8591a89485dbff6331870e0499f
Publikováno v:
Geocarto International, Vol 38, Iss 1 (2023)
Landslides in high-order position areas pose a serious threat to residents located below such areas. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based on landslides t
Externí odkaz:
https://doaj.org/article/818ffcc354754c1da119763b6f10906d
Autor:
Jinsheng Ning, Lida Zhu, Shuhao Wang, Zhichao Yang, Peihua Xu, Pengsheng Xue, Hao Lu, Miao Yu, Yunhang Zhao, Jiachen Li, Susmita Bose, Amit Bandyopadhyay
Publikováno v:
International Journal of Extreme Manufacturing, Vol 6, Iss 2, p 025001 (2024)
Additive manufacturing provides achievability for the fabrication of bimetallic and multi-material structures; however, the material compatibility and bondability directly affect the parts’ formability and final quality. It is essential to understa
Externí odkaz:
https://doaj.org/article/a080709c1e364f7892f039f480431a98
Autor:
Lida Zhu, Shuhao Wang, Hao Lu, Dongxing Qi, Dan Wang, Zhichao Yang, Jinsheng Ning, Yichao Dun, Pengsheng Xue, Peihua Xu, Bo Xin
Publikováno v:
Virtual and Physical Prototyping, Vol 17, Iss 2, Pp 220-238 (2022)
Direct energy deposition (DED), as a flexible and economic manufacturing method, has drawn extensive attentions, whereas low surface quality and dimensional accuracy hinder its development. Hybrid manufacturing perfectly solves these problems without
Externí odkaz:
https://doaj.org/article/8dd83f0fa38e4ec3a717cb1f3bb892e1
Publikováno v:
Energies, Vol 16, Iss 21, p 7250 (2023)
This study introduces a data augmentation technique based on generative adversarial networks (GANs) to improve the accuracy of day-ahead wind power predictions. To address the peculiarities of abrupt weather data, we propose a novel method for detect
Externí odkaz:
https://doaj.org/article/6b3bc10eada54b02ac18dab4e95d5dce