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- 저자 : Huixiang Liu (검색결과 4 건)
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Fixed‑time non‑singular terminal sliding mode control for PMSM drive systems
Huixiang Liu,Keqi Mei,Lu Liu,Yafei Chang,Shihong Ding,Hanzhang Zhang,Jun Wang 전력전자학회 2024 JOURNAL OF POWER ELECTRONICS Vol.24 No.2
To further improve the response speed and anti-interference capability of permanent magnet synchronous motors (PMSMs), many advanced control algorithms have been developed. Among the various advanced controls, the fixed-time terminal sliding mode control is one of the effective methods. However, there are still some problems, e.g., too many parameters imposed on the control design in the existing results. In this paper, a fixed-time non-singular terminal sliding mode control (FTNTSMC) method is proposed for the speed loop of a PMSM drive system. First, to improve the closed-loop performance of the PMSM drive system, the relationship between the reference q-axis current and the speed of the PMSM is described in a second-order model. Next, a sliding mode surface with variable exponential coefficients is selected, and the expression of the controller is given. Then, the stability of the PMSM drive system is demonstrated by using Lyapunov functions. Using this method, the fixed-time convergence of the PMSM drive system can be realized and the occurrence of singularity phenomena can be avoided in a simpler and easier method. Finally, the effectiveness of the proposed method is verified by simulation and experimental results.
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A Ratiometric Fluorescent Assay for Fluazinam Based on FRET Between CdTe Quantum Dots and Porphyrin
Yue Wang,DANQUN HUO,Huixiang Wu,Hui Liu,Junjie Li 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2017 NANO Vol.12 No.10
A fluorescent detection system for fluazinam was reported using fluorescence resonance energy transfer (FRET) method based on CdTe quantum dots (CdTe QDs) and 5, 10, 15, 20-tetrakis(4-methacryloyloxy) phenyl porphyrin (TMaPP). TMaPP and water-soluble CdTe QDs were synthesized successfully and characterized using FT-IR, 1H NMR, XPS and TEM, respectively. FRET mechanism between CdTe QDs and TMaPP was confirmed by detailed studies on their fluorescent spectra. After a co-culture of TMaPP and CdTe QDs, fluorescent intensity of CdTe QDs decreased significantly while that of TMaPP increased concomitantly due to altered FRET. Addition of fluazinam led to impaired energy transfer from CdTe QDs to TMaPP and therefore fluorescence recovery of CdTe QDs with fluorescence quenching of TMaPP. The correlation of fluazinam concentration with the fluorescence intensity ratio FQDs / FTMaPP provided the basis for quantitative analysis, and a broad linear range varying from 0.01 μM to 5 μM with a low detection limit of 2.3 nM was obtained. As-reported sensor system demonstrated excellent reproducibility, selectivity and sensitivity in real sample detection.
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Desert classification based on a multi-scale residual network with an attention mechanism
Liguo Weng,Lexuan Wang,Min Xia,Huixiang Shen,Jia Liu,Yiqing Xu 한국지질과학협의회 2021 Geosciences Journal Vol.25 No.3
Desert classification is the fundamental for preventing and/or controlling desertification. Topographical features of desert remote sensing images change constantly due to the uncertainty of desert terrain, illumination, and other properties. Therefore, it is a very challenging task to accurately classify desert areas. In order to quickly and accurately classify desert from remote sensing images, this paper proposed a multi-scale residual network based on an attention mechanism. The network used conventional convolutions to perform preliminary feature extraction on images, and subsequently adopted a multi-scale residual module to further process the feature maps. Based on the idea of fusing multi-scale features, the multi-scale residual module effectively reduced information loss and possible gradient disappearance because of using skip connections. By introducing the attention mechanism, dependencies between feature channels were established, as a result, the network could recalibrate channel characteristic responses adaptively. Experimental results showed that the proposed network had better generalization ability and a higher accuracy on classification of multispectral desert remote sensing images compared with other methods.
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