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Yao Li,Haiqing Si,Yitong Zong,Xiaojun Wu,Peihong Zhang,Hongyin Jia,Shuqing Xu,Dayong Tang 한국항공우주학회 2021 International Journal of Aeronautical and Space Sc Vol.22 No.6
The process of obtaining flight data from flight test is complex and costly, which makes it difficult to identify aerodynamic parameters. Therefore, Cessna172 flight simulator was used for flight data extraction, which ensures the convenience, efficiency and economy of the test. To obtain aerodynamic model, based on the idea of machine learning, a recurrent neural network was used to process multi-dimensional nonlinear flight test data, and a real-time recursive learning algorithm was proved to be suitable for dynamic training. Due to the large amount of state parameter data generated by aircraft, which will cause the real-time recursive learning algorithm to train slowly. So, Kalman filter algorithm was introduced for system identification. Considering validity analysis, the comparative verification method was used to verify system identification model. Results show that the aircraft aerodynamic and aerodynamic moment models have good applicability and can be popularized and applied.
Optimization design research of air flow distribution in vertical radial flow adsorbers
Yao Li,Haiqing Si,Bing Wang,Lu Xue,Xiaojun Wu 한국화학공학회 2018 Korean Journal of Chemical Engineering Vol.35 No.4
Non-uniform flow distribution usually exists in a vertical radial flow adsorber, which significantly decreases the utilization of adsorbents. We adopted numerical simulation methods based on the ANSYS Fluent 15.0 software to study the flow pattern in vertical radial flow adsorber, where programs of user-defined functions (UDF) were set up to interpret component equation, momentum equation and energy equation. To solve the problem of non-uniform air distribution, the relationship between the radial pressure drop across the bed and the ratio of cross-sectional area of the central pipe to that of the annular channel was studied, and optimization design of the distributor inserted in the central channel was given by parametric method at the same time. Through comparative analysis in the given experimental condition, the uniformity reached about 99.1% and the breakthrough time extended from 564 s to 1,175 s under the present optimized design method.
Development of signal analysis method for the motional Stark effect diagnostic on EAST
FU, Jia,LYU, Bo,LIU, Haiqing,LI, Yingying,LIU, Dongmei,WEI, Yongqing,FAN, Chao,SHI, Yuejiang,WU, Zhenwei,WAN, Baonian IOP Publishing 2017 Plasma science & technology Vol.19 No.10
<P>A pilot single-channel Motional Stark Effect (MSE) diagnostic has been developed on EAST since 2015. The dual photo-elastic modulators (PEM) were employed to encode the polarization angle into a time-varying signal. The pitch angle was related to the ratio of modulation amplitude at the second harmonic frequency. A digital harmonic analyzer (DHA) technique was developed for extracting the second harmonic amplitude. The results were validated with a hardware phase lock-in amplifier, and is also consistent with the software dual phase-locking algorithm.</P>
Liang Tiebiao,Liang Anshan,Zhang Xianbo,Wang Qi,Wu Haiqing,He Jun,Jin Tianbo 한국유전학회 2022 Genes & Genomics Vol.44 No.9
Background: Coronary heart disease (CHD) is a disease that seriously harms human health. Genetic factors seriously affect the CHD susceptibility. The CYP20A1, CYP4F2 and CYP2D6 are important drug metabolism enzymes in the human body. Objective: We aimed to explore the association between CYP20A1, CYP4F2, CYP2D6 single nucleotide polymorphisms (SNPs) and CHD risk in the Chinese Southern Han population. Methods: Based on the 'case-control' experimental design (505 cases and 508 controls), we conducted an association study between 5 candidate SNPs selected from CYP20A1 (rs2043449), CYP4F2 (rs2108622, rs3093106, rs309310), CYP2D6 (rs1065852) and CHD risk. Logistic regression was used to analyze the CHD susceptibility under different genetic models. Multi-factor dimensionality reduction (MDR) was used to analyze the interaction of 'SNP-SNP' in CHD risk. Results: Our results showed that under multiple genetic models, CYP2D6 rs1065852 significantly increased the CHD risk in these participants who are ≤ 60 years old (OR 1.40, CI 1.07-1.82, p = 0.013), smokers (OR 1.40, CI 1.02-1.93, p = 0.039), or have family history (OR 1.24, CI 1.02-1.51, p = 0.035). CYP4F2 SNPs rs2108622 (OR 0.63, CI 0.43-0.93, p = 0.020), rs3093106 (OR 0.52, CI 0.29-0.92, p = 0.023), and rs309310 (OR 0.55, CI 0.31-0.96, p = 0.033) were potentially associated with the course of CHD patients. Conclusion: Our study found that CY2D6 rs1065852 has an outstanding and significant association with increased CHD risk. Our study provided data supplements for CHD genetic susceptibility loci, and also provided a new and valuable reference for CHD drug treatment.