Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Yunji Zhao"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15971-15988 (2024)
Multimodal remote sensing data can portray land-cover characteristics more comprehensively. Deep learning has powerful feature extraction capability. Therefore, deep learning-based methods have been widely used for collaborative classification of hyp
Externí odkaz:
https://doaj.org/article/5bd83829d0284c1cb713ad18a819e91f
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 8, Iss 3, Pp 1014-1028 (2023)
Abstract In view of the influence of aliasing noise on the effectiveness and accuracy of bearing fault diagnosis, a bearing fault diagnosis algorithm based on the spatial decoupling method of modified kernel principal component analysis (MKPCA) and t
Externí odkaz:
https://doaj.org/article/1ddee25663f744ec9a5ac675f8f7ea3b
Publikováno v:
IEEE Access, Vol 11, Pp 34407-34420 (2023)
The main bearing is the core component of gas-fired generator, and its reliability directly affects the stability of the whole system. Therefore, it is of great significance to study the fault diagnosis of the main bearing of gas-fired generator. In
Externí odkaz:
https://doaj.org/article/319b7b1a682b4b76bbb750866e10b775
Publikováno v:
IET Electric Power Applications, Vol 17, Iss 1, Pp 47-57 (2023)
Abstract Permanent magnet linear synchronous motor (PMLSM) is one of the ideal driving sources of ropeless elevators. However, such motors, whether moving magnetic or moving coil, will produce the problem of excessive cost under long travel. The perm
Externí odkaz:
https://doaj.org/article/c4bd33205f39447bab6fb2b45a188634
Publikováno v:
IET Electric Power Applications, Vol 17, Iss 1, Pp 80-91 (2023)
Abstract In this article, a suspension‐guided permanent magnet synchronous linear motor (SG‐PMSLM) for ropeless elevator is proposed, which can meet the requirements of high thrust and high thrust density for ropeless elevator, and reduce vibrati
Externí odkaz:
https://doaj.org/article/7242796f174c42f09cc8baf8024e1be8
Publikováno v:
Frontiers in Energy Research, Vol 10 (2022)
To reduce the impact of series battery pack inconsistency on energy utilization, an active state of charge (SOC) balancing method based on an inductor and capacitor is proposed. Only one inductor and one capacitor can achieve a direct transfer of bal
Externí odkaz:
https://doaj.org/article/9036c4fbfead43b196c318c436a6a00f
Publikováno v:
Systems Science & Control Engineering, Vol 9, Iss S1, Pp 142-149 (2021)
The cascaded deep-learning network of YOLOv3 emphasizes on the layer-wise feature extraction. It neglects the sequential influence among the layers that contributes to the subtle features for the objects detection. An improved YOLOv3 model with skipp
Externí odkaz:
https://doaj.org/article/1be6caeb810e41989613904283d47c5a
Publikováno v:
Systems Science & Control Engineering, Vol 9, Iss S1, Pp 96-102 (2021)
The fault detection and diagnosis of a gas turbine is of great significance for guaranteeing the complicated dynamic systems working normally and safely. Most of the existing fault diagnosis methods, based on convolutional neural networks (CNN), have
Externí odkaz:
https://doaj.org/article/c479ff2579764f008caeb681ef6c1488
Publikováno v:
Systems Science & Control Engineering, Vol 9, Iss S1, Pp 161-167 (2021)
Voxel grid is widely used in point cloud segmentation due to its regularity. However, the memory consumption caused by high resolution restricts the performance of voxel grid. This paper proposes an improved voxel grid deep network (IVDN) model to re
Externí odkaz:
https://doaj.org/article/ed2f29042d6f4bf2816dfa1aa8a82869
Publikováno v:
IEEE Access, Vol 8, Pp 212599-212607 (2020)
The data-driven method based on deep learning is one of the popular issues in the field of fault diagnosis. The completeness and representativeness of the feature matrix from massive and high-dimensional fault data have a great impact on fault diagno
Externí odkaz:
https://doaj.org/article/9374db40c67f455e85c8e221106def04