Zobrazeno 1 - 10
of 1 132
pro vyhledávání: '"LI HongRu"'
Publikováno v:
Frontiers in Earth Science, Vol 11 (2023)
During construction of concrete faced rockfill dam (CFRD), the extrusion-sidewall will be protruding or deficiency under normal. Before panel construction, local slope cutting should be taken for the squeezed side wall or supplementary filling measur
Externí odkaz:
https://doaj.org/article/5734122dc9a44ca1a8393aefc80cc7d0
Publikováno v:
Measurement Science Review, Vol 21, Iss 5, Pp 123-135 (2021)
The performance of feature is essential to the degradation state identification for hydraulic pumps. The initial feature set extracted from the vibration signal of the hydraulic pump is often high-dimensional and contains redundant information, which
Externí odkaz:
https://doaj.org/article/42824fc3780d4f79a1896fcdd0343fdb
Publikováno v:
Measurement Science Review, Vol 21, Iss 3, Pp 82-92 (2021)
Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely
Externí odkaz:
https://doaj.org/article/85f814dc2e4c4353bb1fb1aa03f8d2ab
Publikováno v:
Jixie qiangdu, Vol 43, Pp 1280-1288 (2021)
Extracting degradation features is an important part of Monitoring the health status of machinery. The performance of degradation features fluctuates or even declines with the continuous operation of the rotating machinery for a long time, which make
Externí odkaz:
https://doaj.org/article/8075b8c4216b4c438f2943c6da27f112
Publikováno v:
Measurement Science Review, Vol 19, Iss 5, Pp 195-203 (2019)
Fault prognosis plays a key role in the framework of Condition-Based Maintenance (CBM). Limited by the inherent disadvantages, most traditional intelligent algorithms perform not very well in fault prognosis of hydraulic pumps. In order to improve th
Externí odkaz:
https://doaj.org/article/6a334cba4bdf409a942da305bf439b9a
Task-oriented communication aims to extract and transmit task-relevant information to significantly reduce the communication overhead and transmission latency. However, the unpredictable distribution shifts between training and test data, including d
Externí odkaz:
http://arxiv.org/abs/2405.09514
Autor:
Xie, Songjie, He, Hengtao, Li, Hongru, Song, Shenghui, Zhang, Jun, Zhang, Ying-Jun Angela, Letaief, Khaled B.
Deep learning-based joint source-channel coding (DJSCC) is expected to be a key technique for {the} next-generation wireless networks. However, the existing DJSCC schemes still face the challenge of channel adaptability as they are typically trained
Externí odkaz:
http://arxiv.org/abs/2401.11155
Publikováno v:
Jixie chuandong, Vol 40, Pp 32-37 (2016)
Aiming at the problem that the fault feature of rolling bearing can be easily overwhelmed by random noise,a novel denoising method on the basis of local characteristic- scale decomposition and Fast Kurtogram（ LFK） is presented. Firstly,the signal
Externí odkaz:
https://doaj.org/article/fd73479239f74756b09083ba9219e6e3
Publikováno v:
Jixie chuandong, Vol 39, Pp 121-125 (2015)
Aiming at the problems of multi- scale morphological difference filter,a new fault feature extraction method of rolling bearing based on multi- scale morphological difference filter and singular difference spectrum is proposed,which could effectively
Externí odkaz:
https://doaj.org/article/2bd71cab561c45d4ade8211515512132
Publikováno v:
Jixie chuandong, Vol 39, Pp 97-100 (2015)
Aiming at the problem of rolling bearing fault severity recognition,a fault severity recognition method of rolling bearing based on compensation distance evaluation technique（ CDET） and grey relational analysis（ GRA） is proposed. By utilizing
Externí odkaz:
https://doaj.org/article/efd1c7c41a1343e3b499669516755169