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Asadi, Mozaffar,Ghatee, Mohammad Hadi,Torabi, Susan,Mohammadi, Khosro,Moosavi, Fatemeh Korean Chemical Society 2013 대한화학회지 Vol.57 No.1
Some vanadyl complexes were synthesized by treating a methanolic solution of the appropriate Schiff base ligand and one equivalent of $VO(SO_4)_2$ to yield [($VOL_2^{1-14}$)](L=Salicylaldehyde's derivatives, Schemes 1, 2). These oxovanadium (IV) complexes were characterized based on their FT-IR, UV-Vis spectroscopy and elemental analysis. The IR spectra suggest that coordination takes place through azomethine nitrogen and phenolate oxygen. In addition, the formation constants of the oxovanadium (IV) binary complexes were determined in methanolic medium. The ab initio calculations were also carried out to determine the structural and the geometrical properties of one of the complexes and its calculated vibrational frequencies were investigated.
Induction Machine Fault Detection Using Generalized Feed Forward Neural Network
Ghate, V.N.,Dudul, S.V. The Korean Institute of Electrical Engineers 2009 Journal of Electrical Engineering & Technology Vol.4 No.3
Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 Hz induction motor.
Induction Machine Fault Detection Using Generalized Feed Forward Neural Network
V. N. Ghate,S. V. Dudul 대한전기학회 2009 Journal of Electrical Engineering & Technology Vol.4 No.3
Industrial motors are subject to incipient faults which, if undetected, can lead to motor failure. The necessity of incipient fault detection can be justified by safety and economical reasons. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. This paper develops inexpensive, reliable, and noninvasive NN based incipient fault detection scheme for small and medium sized induction motors. Detailed design procedure for achieving the optimal NN model and Principal Component Analysis for dimensionality reduction is proposed. Overall thirteen statistical parameters are used as feature space to achieve the desired classification. GFFD NN model is designed and verified for optimal performance in fault identification on experimental data set of custom designed 2 HP, three phase 50 ㎐ induction motor.
Practicable Synthesis of 1-(1-Phenylethyl)-1H-pyrido[2,3-b][1,4]oxazine
Gim, Gyeong-Hyeon,Lijuan, Meng,Hua, Zuo,Ghate, Manjunath,Ahn, Chul-Jin,Won, Tae-Jin,Kim, Tae-Hyun,Reddy, Ch. Raji,Chandrasekhar, S.,Shin, Dong-Soo Korean Chemical Society 2007 Bulletin of the Korean Chemical Society Vol.28 No.12