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RISS 인기검색어
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- 저자 : Xunyu Ye (검색결과 2 건)
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Study on micro-grinding mechanism and surface and subsurface quality of 20 vol% SiCp/Al composites
Xunyu Yin,Qi Gao,Quanzhao Wang,Ye Chen,Tianyang Cui,Ke Zhang 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.1
A finite element model of single abrasive grinding was established to analyze the material removal during the grinding process and analyze the surface and subsurface defects of the material after grinding. Using the orthogonal test method to machine the surface of the workpiece with a grinding rod of 2 mm grinding head diameter, and to investigate the influence degree of the machining parameters on the roughness. It was concluded that the grinding depth has the most significant effect on surface roughness, followed by the spindle speed and the feed speed, and the minimum roughness of the ground surface obtained was 0.066 μm. The rough surface was simulated by the digital filtering method based on fast Fourier transform (FFT), and the morphological characteristics of the rough surface at Ra = 0.116 μm were described, and the simulated surface was consistent with the detected surface. The laser scanning confocal microscope (LSCM) and scanning electron microscope (SEM) were used for inspection, and the analysis showed that the main removal forms of the enhanced particles were broken, fractured, and pulled out. The surface defects were cracks, pits, and burrs, and the subsurface damages were voids, cracks, and pits.
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A Dynamic, Volume-Weighted Average Price Approach Based on the Fast Fourier Transform Algorithm
Handong Li,Xunyu Ye 한국증권학회 2013 Asia-Pacific Journal of Financial Studies Vol.42 No.6
We propose a model for decomposing a volume series based on the Fast Fourier Transform (FFT) algorithm. By setting a threshold for the power spectrum, the model extracts the periodic and nonperiodic components from the original volume series and then predicts them. By analyzing samples from four major stock indices, we find that a too small threshold and a too large threshold cause negative effects on the performance of the FFT model. Appropriate thresholds are found at approximately the 93rd to 95th percentile for the four indices studied. The out-of-sample experiment for the 50 stocks of the Shanghai 50 Composite Index shows that the FFT model is superior to the classic moving average model in terms of both volume prediction and Volume-weighted Average Price (VWAP) tracking accuracy. Meanwhile, for almost all of the 50 stocks, the FFT model outperforms the Bialkowski et al. (2008) model in terms of volume-prediction accuracy. The two models perform comparably in terms of the VWAP tracking error.
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