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Qingyu Wu,Qiushneg Li,Xiangyun Hu,Zhanwu Lu,Wenhui Li,Xiaoran Wang 한국지질과학협의회 2023 Geosciences Journal Vol.27 No.2
Internal structure imaging of the Earth, along with determining basin structure, can aid in evaluating potential seismic hazards. However, the high operating cost limits the current geophysical exploration methods; moreover, it is difficult to apply these techniques over a large area, which limits our understanding of the Quaternary structure and the development of earthquake prevention science. A combination of dense array observation technology and ambient noise surface wave tomography is being rapidly developed as a high-resolution urban detection method. Here, we report the ambient noise imaging results of a high-density array experiment. In the ambient noise surface wave tomography method (e.g., surface wave tomography; Eikonal tomography), the signal is assumed to be a single mode. However, several multimode signals were detected in this dataset. With the use of traditional methods to measure the dispersion, mode confusion occurs and the extracted dispersion curve jumps. To solve this problem, by combining the advantages of phase-matched filtering and dispersion compensation, we realized the automatic pickup of fundamental group velocity using reference phase velocity. From this, a Rayleigh wave group velocity map was obtained. The regional average phase velocity information was included in the inversion steps to reduce the uncertainty in the inversion of shear wave velocity. Finally, an S-wave velocity structure model was obtained within a depth of 500 m. The velocity structure was roughly layered and grew with depth. In the depth range of 240–320 m, obvious decreases in the S-wave velocity were observed. Compared with geothermal drilling data, this was speculated to be the reflection of a water-rich (confined water) sand layer. This study provides a technical approach for and a processing example of a high-density array, and its velocity model can be used as a reference for urban subsurface structure, underground space utilization, and earthquake disaster prevention and control.
Inverse decoupling sliding mode control for multilevel buck converters in low‑power applications
Jiarong Wu,Liping Luo,Chunming Wen,Qingyu Wang 전력전자학회 2023 JOURNAL OF POWER ELECTRONICS Vol.23 No.8
Multilevel buck converters are gradually gaining attention in low-power applications. To realize the decoupling of the flying capacitor voltage and the output voltage, this paper proposes an inverse decoupling sliding mode control approach. A nonlinear mathematical model of the multilevel buck converters is built. The reversibility of the model is analyzed based on the inverse system theory, and linearization and decoupling are achieved. In addition, multiple pseudo-linear subsystems are obtained. Then sliding mode controllers are designed to control the linear subsystems. Furthermore, the global asymptotic stability of the control system is verified using the Lyapunov theory, and the robustness of the closed-loop system is demonstrated. Simulation and experimental results show that the proposed approach provides a better dynamic response when compared with existing methods.
Few-shot transfer learning with attention for intelligent fault diagnosis of bearing
Yao Hu,Qingyu Xiong,Qiwu Zhu,Zhengyi Yang,Zhiyuan Zhang,Dan Wu,Zihui Wu 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.12
The bearing is one of the key components in modern industrial equipment. In the past few years, many studies have been carried out on bearing diagnosis through datadriven methods. However, there are two practical problems. First, under actual working conditions, the lack of fault samples is a major factor that hinders the application of these methods in industrial environments. Second, there is a lack of full utilization of a priori knowledge in the current stage of methods using relational networks for fault diagnosis. It is manifested by the incompleteness of the relational network structure. To address these problems, we present a new diagnosis method based on few-shot learning, which is suitable for the environment where the data is scarce. In this method, we train the model with the data generated by the artificial damaged bearings instead of the data from the real bearing. We experimentally validate the performance improvement of the complete relational network structure. It is able to perform the few-shot learning task better. In addition, we also reduce the global feature discrepancy by introducing an attention mechanism to improve the performance of the model. And the impact of the number of layers of the attention mechanism on the model is also discussed in detail. In this paper, our model performs better under the same experimental conditions compared with other transfer learning models.
Fault Detection for Interconnected Systems Subject to Packet Dropouts via Switching Scheme
Jian Li,Jingjing Wang,Qingyu Su,Chun-Yu Wu,Xiao-Qi Zhao 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.12
The design of fault detection filter for interconnected systems with packet dropouts occurring in measurement channels is proposed in this paper. Taking into account arbitrary disconnected interconnection among subsystems, switching scheme is introduced to simplify the analysis of the problem. By modeling these situations as independent subsystems, the novel systems based on arbitrary switched systems are remodeled. The design of the filter makes the augmented system exponentially stable and satisfies the disturbance attenuation performance. A feasible linear matrix inequality for the existence of filter is derived by switched Lyapunov method based on stochastic function. Finally, two examples are shown to illustrate the method and its effectiveness.