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        Prediction of bolted joint looseness based on feature weighted-multiple kernel support vector machine

        Chuanbo Liu,Yanzhao Zhu,Kui Zhan,Shenghui Guo,Kang Liu 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.6

        Given that traditional theoretical diagnosis is difficult to use for the accurate prediction of the looseness of bolted joints, this paper will use the support vector machine (SVM) method to solve this problem. The paper establishes a database by studying the loosening mechanism of bolted joints and by collecting the connection information of the fastening points of the vehicle chassis. Key influence features are determined by the information gain value. The average prediction accuracy of the standard SVM model is 76.94 %, which is better than the theoretical diagnosis method. To achieve optimization, the feature weighted support vector machine and the multiple kernel function support vector machine (MKL SVM) are established, and the average prediction accuracy is 82.18 % and 84.19 %, respectively. The fusion weighted support vector machine (FW-MKL SVM) is built by combining the advantages of two weighted optimization SVMs. The average prediction accuracy is 94.5 %, which meets practical engineering requirements.

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        Identification of acidic species extracted from heavy crude oil by fourier transform ion cyclotron resonance mass spectrometry

        Bencheng Wu,Jianhua Zhu,Xiaohui Li,Yanzhao Huang,Hui Cheng 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.6

        Acidic species were extracted from heavy crude oil by alcohol-alkali solution and ion exchange resin. Theacidic species in the obtained extracts and extracted crude oil were characterized by negative-ion ESI FT-ICR MS. Theanalytical results indicated that some class species, O2, O1, N1, N1O1, N1O2, N1S1 and O2S1 etc., were identified in thenegative-ion spectrum, in which O2, O1 and N1 class showed much higher abundance than others. Compared to O1 andN1 class, O2 class could be extracted from heavy crude oil by using the two methods more efficiently, which meansthat a great majority of O2 class can be extracted; meanwhile, almost half of O1 and N1 class still remained in residualphase. Detailed analysis demonstrated that alcohol-alkali method was effective for extracting O2, O1 and N1 class withlower molecular weight; ion exchange method, however, was helpful to extract higher molecular weight O1 and N1class and showed almost equal extraction selectivity to all kind of O2 class.

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        Aromatic Diamino-bridged Bis(β-cyclodextrin) as Fluorescent Sensor for the Molecular Recognition of Bile Salts

        Yan Zhao,Juan Gu,Shao Ming,Shao Ming Chi,Yong Cun Yang,Yu Fei Wang,Hong You Zhu,Jian Hong Liu,Rong Huang 대한화학회 2008 Bulletin of the Korean Chemical Society Vol.29 No.11

        representative bile salts, i.e., cholate (CA), deoxycholate (DCA), glycocholate (GCA) and taurocholate (TCA), has been investigated at 25 °C in phosphate buffer (pH 7.20) by fluorescence, circular dichroism and 2D NMR spectroscopy. The result indicated that the bis(β-cyclodextrin) 2 acts as fluorescent sensor and displays remarkable fluorescence enhancement upon addition of optically inert bile salts. Form the induced circular dichroism (ICD) and ROESY spectra, it is deduced that the phenyl moiety in the linker of bis(β-cyclodextrin) 2 is partially self-included in the CD cavity, and is not expelled out of the CD cavity upon complexation with bile guests. Owing to the cooperative host-tether-guest binding mode in which the linker and guest are coincluded in the two CD cavities, bis(β-cyclodextrin) 2 significantly enhanced binding ability and molecular selectivity as compared with the native β-cyclodextrin 1 through the simultaneous contributions of hydrophobic, hydrogen bond, and electrostatic interactions. The complex stability constants are discussed comparatively and globally from the viewpoints of multiple recognition between host and guest.

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