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Xiaojiao Gu, Yang Tian, Chi Li, Yonghe Wei and Dashuai Li
The fault diagnosis method proposed in this paper can be applied to the diagnosis of bearings in machine tool spindle systems.
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Jinxiu Ma, An Li, Fangjun Qin, Wenbin Gong and Hao Che
The marine atomic interferometric gravimeter is a vital precision instrument for measuring marine geophysical information, which is widely used in mineral resources exploration, military applications, and missile launches. In practical measurements, vibr...
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Qingyong Zhang, Changhuan Song and Yiqing Yuan
Vehicle gearboxes are subject to strong noise interference during operation, and the noise in the signal affects the accuracy of fault identification. Signal denoising and fault diagnosis processes are often conducted independently, overlooking their syn...
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Zeqin Tian, Dengfeng Chen and Liang Zhao
Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large ...
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Hongfeng Gao, Tiexin Xu, Renlong Li and Chaozhi Cai
Because the gearbox in transmission systems is prone to failure and the fault signal is not obvious, the fault end cannot be located. In this paper, a gearbox fault diagnosis method grounded on improved complete ensemble empirical mode decomposition with...
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Sriniketan Sridhar, Anibal Romney and Vidya Manian
Mild Cognitive Impairment (MCI) and Alzheimer?s Disease (AD) are frequently associated with working memory (WM) dysfunction, which is also observed in various neural psychiatric disorders, including depression, schizophrenia, and ADHD. Early detection of...
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Shanshan Chen, Sheng Guan, Hui Wang, Ningqi Ye and Zexun Wei
Ship type identification is an important basis for ship management and monitoring. The paper proposed a new method of ship type identification by combining characteristic parameters from the energy difference between high and low frequencies and the sens...
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Yen-Chang Chen, Hui-Chung Yeh, Su-Pai Kao, Chiang Wei and Pei-Yi Su
In this study, a novel model that performs ensemble empirical mode decomposition (EEMD) and stepwise regression was developed to forecast the water level of a tidal river. Unlike more complex hydrological models, the main advantage of the proposed model ...
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Lan Luo, Yanjun Zhang, Wenxun Dong, Jinglin Zhang and Liping Zhang
Water quality prediction is an important part of water pollution prevention and control. Using a long short-term memory (LSTM) neural network to predict water quality can solve the problem that comprehensive water quality models are too complex and diffi...
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Lingxiao Zhao, Zhiyang Li, Junsheng Zhang and Bin Teng
In recent years, wave energy has gained attention for its sustainability and cleanliness. As one of the most important parameters of wave energy, significant wave height (SWH) is difficult to accurately predict due to complex ocean conditions and the ubi...
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