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Xuefeng Yan,Qingchao Jiang,Zhaomin Lv,Meijin Guo 한국화학공학회 2013 Korean Journal of Chemical Engineering Vol.30 No.6
Considering that kernel entropy component analysis (KECA) is a promising new method of nonlinear data transformation and dimensionality reduction, a KECA based method is proposed for nonlinear chemical process monitoring. In this method, an angle-based statistic is designed because KECA reveals structure related to the Renyi entropy of input space data set, and the transformed data sets are produced with a distinct angle-based structure. Based on the angle difference between normal status and current sample data, the current status can be monitored effectively. And,the confidence limit of the angle-based statistics is determined by kernel density estimation based on sample data of the normal status. The effectiveness of the proposed method is demonstrated by case studies on both a numerical process and a simulated continuous stirred tank reactor (CSTR) process. The KECA based method can be an effective method for nonlinear chemical process monitoring.
Recycling of Sintered Nd-Fe-B Magnets Doped with PrNd Nanoparticles
Xuefeng Zhang,Fei Liu,Yanli Liu,Qiang Ma,Yongfeng Li,Qian Zhao,Gaofeng Wang,Zhubai Li 한국자기학회 2015 Journal of Magnetics Vol.20 No.2
The waste of sintered Nd-Fe-B magnets was recycled using the method of dopingPrNd nanoparticles. The effect of PrNd nanoparticle doping on the magnetic properties of the regenerated magnets has been studied. As the content of the PrNd nanoparticles increases, the coercivity increases monotonically, whereas both the remanence and the maximum energy products reach the maximum values for 4 wt% PrNd doping. Microstructural observation reveals that the appropriate addition of PrNd nanoparticles improves the magnetic properties and refines the grain. Domain investigation shows that the self-pinning effect of the rare earth (Re)-rich phase is enhanced by PrNd nano-particle doping. Compared to the magnet with 4 wt% PrNd alloy prepared using the dual-alloy method, the regenerated magnet doped with the same number of PrNd nanoparticles exhibits better magnetic properties and a more homogeneous microstructure. Therefore, it is concluded that PrNd nanoparticle doping is an efficient method for recycling the leftover scraps of Nd-Fe-B magnets.
Yufeng Dong,Xuefeng Yan 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.6
Quality prediction is a challenging task due to the nonlinearity and complexity of batch processes. In real batch processes, the presence of different sampling frequencies complicates data processing and information digging, making it difficult to fully investigate process information. To address this dilemma, this work developed a multi-flow multi-scale convolutional neural network (MFMSCNN) for the quality prediction of batch processes. MFMSCNN adopts a multi-branch structure to cope with data with different sampling frequencies. A multi-scale feature branch will be adopted to extract the multi-hierarchy features of data containing rich information. Meanwhile, a 1D convolution branch will be applied to the mining process characteristics of data containing less information. Finally, all features in each branch are fed into the fully connected layers to make a quality prediction. In this manner, the process data are fully exploited, and the multi-level features are extracted to better interpret the batch processes. MFMSCNN was evaluated on an industrial ethanol fermentation process and an injection molding process. It obtained remarkable performance on both batch processes. The prediction results of the proposed method are superior to many other methods.
Bei Wang,Xuefeng Yan,Qingchao Jiang 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.6
Considering the huge number of variables in plant-wide process monitoring and complex relationships(linear, nonlinear, partial correlation, or independence) among these variables, multivariate statistical process monitoring(MSPM) performance may be deteriorated especially by the independent variables. Meanwhile, whether related variableskeep high concordance during the variation process is still a question. Under this circumstance, a multi-block technologybased on mathematical statistics method, Kullback-Leibler Divergence, is proposed to put the variables having similarstatistical characteristics into the same block, and then build principal component analysis (PCA) models in each lowdimensionalsubspace. Bayesian inference is also employed to combine the monitoring results from each sub-blockinto the final monitoring statistics. Additionally, a novel fault diagnosis approach is developed for fault identification. The superiority of the proposed method is demonstrated by applications on a simple simulated multivariate processand the Tennessee Eastman benchmark process.
Bei Wang,Xuefeng Yan,Yongfei Jin 한국화학공학회 2017 Korean Journal of Chemical Engineering Vol.34 No.2
Principal component analysis (PCA) has been widely used in monitoring industrial processes, but it is still necessary to make improvements in having a timely and effective access to variation information. It is known that the transformation matrix generated from real-time PCA model indicates inner relations between original variables and new produced components, so this matrix would be different when modeling data deviate due to the change of the operating condition. Based on this theory, this paper proposes a novel real-time monitoring approach which utilizes polygon area method to measure the variation degree of the transformation matrices and then constructs a statistic for monitoring purpose. The on-line data are collected through a combined moving window (CMW), containing both normal and monitored data. To evaluate the performance of the proposed method, a simple numerical simulation, the CSTR process and the classic Tennessee Eastman process are employed for illustration, with some PCA-based methods used for comparison.
Yu Gao,Xuefeng Xu,Yan Liang,Dianhua Jiang,Huaping Dai 연세대학교의과대학 2013 Yonsei medical journal Vol.54 No.2
Purpose: The present study was designed to determine whether rapamycin could inhibit transforming growth factor β1 (TGF-β1)-induced fibrogenesis in primary lung fibroblasts, and whether the effect of inhibition would occur through the mammalian target of rapamycin (mTOR) and its downstream p70S6K pathway. Materials and Methods: Primary normal human lung fibroblasts were obtained from histological normal lung tissue of 3 patients with primary spontaneous pneumothorax. Growth arrested, synchronized fibroblasts were treated with TGF-β1 (10 ng/mL) and different concentrations of rapamycin (0.01, 0.1, 1, 10 ng/mL) for 24 h. We assessed m-TOR, p-mTOR, S6K1, p-S6K1 by Western blot analysis, detected type III collagen and fibronectin secreting by ELISA assay, and determined type III collagen and fibronectin mRNA levels by real-time PCR assay. Results: Rapamycin significantly reduced TGF-β1-induced type III collagen and fibronectin levels, as well as type III collagen and fibronectin mRNA levels. Furthermore, we also found that TGF-β1-induced mTOR and p70S6K phosphorylation were significantly down-regulated by rapamycin. The mTOR/p70S6K pathway was activated through the TGF-β1-mediated fibrogenic response in primary human lung fibroblasts. Conclusion: These results indicate that rapamycin effectively suppresses TGF-β1-induced type III collagen and fibronectin levels in primary human lung fibroblasts partly through the mTOR/p70S6K pathway. Rapamycin has a potential value in the treatment of pulmonary fibrosis.
AR model-based crosstalk cancellation method for operational transfer path analysis
Wei Cheng,Yan Zhu,Xuefeng Chen,Chao Song,Le Zhang,Lin Gao,Yilong Liu,Zelin Nie,Hongrui Cao,Yang Yang 대한기계학회 2022 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.36 No.3
To reduce the crosstalk effects between the signals at reference points in operational transfer path analysis (OTPA) and improve the reliability of solutions, a novel OTPA method based on auto-regressive (AR) model is proposed in this paper. The key to this method is to obtain the power spectrum by AR model for estimating the prior transmissibility function matrix. Firstly, in view of the lack of noise reduction process in conventional crosstalk cancellation method, singular value decomposition (SVD) is applied to reduce the noise of original signals. Secondly, Burg algorithm is used to calculate AR model parameters. Thirdly, auto-power spectrum and cross-power spectrum are obtained by AR model and the periodogram method respectively. Fourthly, the transmissibility function matrix is obtained by Landweber iterative method based on corrected signals at reference points. Finally, the proposed method can significantly reduce the crosstalk effects, resulting in more accurate evaluation of transfer path contributions. Generally, this study can provide accurate and reliable evidences for vibration & noise monitoring and reduction for mechanical systems, and thus benefit for the active control of vibration & noise.
Inter - grain Exchange Interactions for Nanocrystalline Nd2.33Fe₁₄B1.06Si0.21 Magnets
Jin Hanmin,Yan Yu,Wang Xuefeng,Su Feng 한국자기학회 2003 Journal of Magnetics Vol.8 No.4
The strengths of the inter-grain exchange interaction were evaluated for nanocrystalline Nd_(2.33)Fe₁₄B_(1.06)Si_(0.21) magnets of different grain size by comparing the iHc calculated by micromagnetics with the experiments. With increase of the grain boundary thickness to that of the magnet of grain diameter 12.4, 24.8, 37.2 and 49.6 ㎚, the strengh of the interaction in reference to that without the grain boundary phase decreases to 83%, 69%, 54% and 42%.