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820 results on '"Cheng, Yuhua"'

153. Temperature Compensated Wide-Range Micro Pressure Sensor with Polyimide Anticorrosive Coating for Harsh Environment Applications

Author

Gaofeng Wang, Cheng Yuhua, Ye Fan, Bowen Ji, Wang Minghao, Mengru Jiao, and Bangbang Guo

Subjects
Technology, Materials science, QH301-705.5, QC1-999, Silicon on insulator, engineering.material, temperature compensation, Coating, General Materials Science, pressure sensor, Composite material, Biology (General), polyimide anticorrosive coating, Instrumentation, QD1-999, Fluid Flow and Transfer Processes, Microelectromechanical systems, Process Chemistry and Technology, Physics, harsh environment, General Engineering, Atmospheric temperature range, Engineering (General). Civil engineering (General), Pressure sensor, Piezoresistive effect, Computer Science Applications, Surface micromachining, Chemistry, Anodic bonding, wide-range, engineering, TA1-2040
Abstract

In this work, a MEMS piezoresistive micro pressure sensor (1.5 × 1.5 × 0.82 mm) is designed and fabricated with SOI-based micromachining technology and assembled using anodic bonding technology. In order to optimize the linearity and sensitivity over a wide effective pressure range (0–5 MPa) and temperature range (25–125 °C), the diaphragm thickness and the insulation of piezoresistors are precisely controlled by an optimized micromachining process. The consistency of the four piezoresistors is greatly improved by optimizing the structure of the ohmic contact pads. Furthermore, the probability of piezoresistive breakdown during anodic bonding is greatly reduced by conducting the top and bottom silicon of the SOI. At room temperature, the pressure sensor with 40 µm diaphragm demonstrates reliable linearity (0.48% F.S.) and sensitivity (33.04 mV/MPa) over a wide pressure range of 0–5.0 MPa. In addition, a polyimide protection layer is fabricated on the top surface of the sensor to prevent it from corrosion by a moist marine environment. To overcome the linearity drift due to temperature variation in practice, a digital temperature compensation system is developed for the pressure sensor, which shows a maximum error of 0.43% F.S. in a temperature range of 25–125 °C.

Published
2021
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175. Detecting Hierarchical and Overlapping Network Communities Based on Opinion Dynamics.

Author

Ren, Ren, Shao, Jinliang, Cheng, Yuhua, and Wang, Xiaofan

Subjects
COMMUNITIES, EIGENVECTORS, HEURISTIC algorithms, ALGORITHMS
Abstract

It is common for communities in real-world networks to possess hierarchical and overlapping structures, which make community detection even more challenging. In this paper, by investigating consensus process of the classical DeGroot model in opinion dynamics, we propose a novel method based on the cumulative opinion distance (COD) to discover hierarchical and overlapping communities. It is shown that this method is different from those classical algorithms relying on static fitness metrics that depict the inhomogeneous connectivity across the network. The proposed method is validated from two aspects. First, by estimating the eigenvectors of adjacency matrices, we investigate the detectability limit of our algorithms on random networks, which together with the results concerning the convergence speed of consensus guarantees the performance of our method theoretically. Second, experiments on both large scale real-world networks and artificial benchmarks show that our method is very effective and competitive on hierarchical modular graphs. In particular, it outperforms the state-of-the-art algorithms on overlapping community detection. [ABSTRACT FROM AUTHOR]

Published
2022
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200. Intelligent fault diagnosis of rotating machinery based on a novel lightweight convolutional neural network

Author

Lu, Yuqi, Mi, Jinhua, Liang, He, Cheng, Yuhua, and Bai, Libing

Abstract

For most existing fault diagnosis methods, feature extraction is always based on a complex artificial design and the complete feature extraction from an original signal. With the gradual complication of modern industrial machinery and equipment, it has become more difficult for traditional feature extractors to achieve the desired results. Deep convolutional neural networks (DCNNs) have been developed as effective techniques for fault classification but require large-scale high-intensity computing and prohibitive hardware resource requirements. This paper proposes a lightweight CNN that can be easily used for the fault diagnosis of rotating machinery by adjusting the network structure and optimizing the network. First, the raw vibration acceleration signal is transformed into a two-dimensional gray image. Second, two mature and commonly used modules named LeNet and NIN are combined to form a new model with a simple structure. Then, through parameter adjustment and optimization, an improved and optimized CNN with a lightweight structure and fewer parameters is constructed. The experimental verification has shown that this method has high accuracy and stability in fault diagnosis. Finally, the application of this new network for the fault diagnosis of rolling bearings with different damage levels but similar fault types shows high diagnostic accuracy and good generalization ability. In addition, we attempt to explain how the feature filters of a CNN work by visualizing the convolutional layer of the network.

Published
2022
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