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Online since: October 2014
Authors: Chun He Xia, Feng Bin Zhang, Tian Bo Wang
Licheng Jiao et al. presented NSA based on depth training strategy in the next year[11], which generated detector sets covered sufficiently with self space through depth training and reduced computational cost in the testing stage.
EL-Ola Hanafi, Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system, Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on, IEEE2012, pp. 597-602
EL-Ola Hanafi, Detectors generation using genetic algorithm for a negative selection inspired anomaly network intrusion detection system, Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on, IEEE2012, pp. 597-602
Online since: October 2011
Authors: Arun Kumar Pandey, Avanish Kumar Dubey
Sivarao et al. have used ANFIS modeling for laser cutting in order to analyze the effect of input process parameters such as stand off distance, focal distance, gas pressure, laser power, cutting speed, frequency and duty cycle on the output parameters kerf width and surface roughness and they found that the ANFIS model developed is suitable for predicting the surface roughness and kerf width [11].
El-Tayeb and V.C.
El-Tayeb and V.C.
Online since: October 2006
Authors: Wilson Acchar, Ana M. Segadães, Sonia Regina Homem de Mello-Castanho, Antônio Carlos da Silva
The Q
n abundance is also affected by the presence of Boron or Aluminum, given that Al
3+
and B3+
show a strong tendency to replace Si
4+
in the Q4 species.
Navarro: El Vidrio, 2nd ed.
Navarro: El Vidrio, 2nd ed.
Online since: April 2005
Authors: Franz Dieter Fischer, Jaroslav Ženíšek, Jiří Svoboda
Application of Darken's or Manning's theory requires to know the tracer diffusion coefficients Di
as functions of chemical composition, which were determined by Hartmann et al [2] in a Monte
Carlo simulations by means of Einstein relation.
Allnat and E.L.
Allnat and E.L.
Online since: September 2011
Authors: Yong En Guo, Shao Peng Wen, Ya Mei Zhang, Guo Zhen Fang, Jing Cao, Cai Ju Zhou
Elemental analyses were performed on Elementar Vario EL, or Carlo-Erbo 1106 Elemental analysis apparatus.
Pancreas 2009, 38(1), 71-77 [12] Jun Guo, Kai Sun, Chuanshe Wang, Suping Fang, Yoshinori Horie, JiyingYang, et al.
Pancreas 2009, 38(1), 71-77 [12] Jun Guo, Kai Sun, Chuanshe Wang, Suping Fang, Yoshinori Horie, JiyingYang, et al.
Online since: July 2015
Authors: Thomas Vincent, Togay Ozbakkloglu
Naguib and Mirmiran [26] monitored the shrinkage strain development of a single CFFT specimen, whereas Karimi et al. [27] studied the compressive behaviour of CFFTs with internal steel I-beams manufactured with and without a shrinkage reducing agent.
Karimi, K., Tait, M., and El-Dakhakhni, W. (2011) "Testing and modeling of a novel FRP-encased steel-concrete composite column" Compos.
Karimi, K., Tait, M., and El-Dakhakhni, W. (2011) "Testing and modeling of a novel FRP-encased steel-concrete composite column" Compos.
Online since: December 2009
Authors: Majid Abbasi, S. Kheirandish, Y. Kharrazi, J. Hejazi
. %)
Type of Steel
C Mn Si Ni Cr Others
Hadfield (Casting) 1.4 14 0.3 - - Al=0.2
316L (Bar) 0.03 2 0.75 12 18 Mo=2
Mild (Bar) 0.2 0.6 0.5 - - -
Table 2.
Strength (MPa) Type of Pin Yield UTS el. (%) Hardness (HB) ρ ( / 3kg m ) Microstructure Hadfield Steel (Casting*) 395 875 48 195 7700 Austenitic Matrix free from any carbides 316L Stainless Steel (Bar) 170 485 40 217 8000 Austenitic Matrix free from any carbides Mild Steel (Bar) 330 448 36 150 7850 Ferritic-Pearlitic Disc Selection.
Strength (MPa) Type of Pin Yield UTS el. (%) Hardness (HB) ρ ( / 3kg m ) Microstructure Hadfield Steel (Casting*) 395 875 48 195 7700 Austenitic Matrix free from any carbides 316L Stainless Steel (Bar) 170 485 40 217 8000 Austenitic Matrix free from any carbides Mild Steel (Bar) 330 448 36 150 7850 Ferritic-Pearlitic Disc Selection.