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Online since: July 2011
Authors: Qiao Hua Shen, Zi Min Jin, Ming Shan Fan, Jian Wei Tao
Samples’ numbering and basic specification are shown in Table 1.
Samples Fiber type Knitting weave GSM (g/m2) Thickness (mm) Stitch density (number of stitch/cm) Horizontal density Vertical desity 1 Triangle-hollow nylon Weft plain 236 0.75 19.9 33.3 2 Triangle-hollow nylon One-and-one mock rib 289 1.00 23.5 41.7 3 Triangle-hollow nylon One-and-three mock rib 263 0.91 21.6 35.7 4 Circle-hollow nylon Weft plain 232 0.80 19.8 32.5 5 Circle-hollow nylon One-and-one mock rib 292 1.06 23.5 41.0 6 Circle-hollow nylon One-and-three mock rib 262 0.98 21.7 34.1 7 Ordinary nylon Weft plain 258 0.73 20.1 35.5 8 Ordinary nylon One-and-one mock rib 302 0.94 23.0 44.9 9 Ordinary nylon One-and-three mock rib 278 0.89 21.8 37.5 Table 1 Samples’ numbering and basic specification Thermal and Moisture Comfort Testing Air Permeability Testing.
After absorbing moisture, fiber swells, which makes its fabric close-grained.
Samples Fiber type Knitting weave GSM (g/m2) Thickness (mm) Stitch density (number of stitch/cm) Horizontal density Vertical desity 1 Triangle-hollow nylon Weft plain 236 0.75 19.9 33.3 2 Triangle-hollow nylon One-and-one mock rib 289 1.00 23.5 41.7 3 Triangle-hollow nylon One-and-three mock rib 263 0.91 21.6 35.7 4 Circle-hollow nylon Weft plain 232 0.80 19.8 32.5 5 Circle-hollow nylon One-and-one mock rib 292 1.06 23.5 41.0 6 Circle-hollow nylon One-and-three mock rib 262 0.98 21.7 34.1 7 Ordinary nylon Weft plain 258 0.73 20.1 35.5 8 Ordinary nylon One-and-one mock rib 302 0.94 23.0 44.9 9 Ordinary nylon One-and-three mock rib 278 0.89 21.8 37.5 Table 1 Samples’ numbering and basic specification Thermal and Moisture Comfort Testing Air Permeability Testing.
After absorbing moisture, fiber swells, which makes its fabric close-grained.
Online since: October 2013
Authors: Zhao Feng Zhang, Jie Zhu, Shu Fang Wu
The this convex problem can be solved by LARS-Lasso algorithm [8], In this problem, the number of training samples n is usually larger than the relatively small sample dimension m.
Here we construct n dictionaries, the size of n is dependent on the number of bioinformatics, in this paper, we only consider shape, color and texture.
The weight is based on the importance of each atom patch in dictonray, a large weight means the patch is important for the image representation, and a small value means that the atom is less important.Finally, each patch can be represented by Equation 6 (6) Here, n is the number of bioinformatic, in this way, a patch is the product of weighted coefficient.
Combining randomization and discrimination for fine-grained image categorization[C]//Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on.
Here we construct n dictionaries, the size of n is dependent on the number of bioinformatics, in this paper, we only consider shape, color and texture.
The weight is based on the importance of each atom patch in dictonray, a large weight means the patch is important for the image representation, and a small value means that the atom is less important.Finally, each patch can be represented by Equation 6 (6) Here, n is the number of bioinformatic, in this way, a patch is the product of weighted coefficient.
Combining randomization and discrimination for fine-grained image categorization[C]//Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on.
Online since: May 2012
Authors: Tie Yi Zhang, Yun Zhang, Xiao Rong Zhou
The important attribute of the MSComm control are as follows: CommPort attribute is used to set or return the communications port number Settings attribute is used to set the initialization parameters including baud rate, parity, data bits and stop bits.
H and L stand for high bit and low bit of station address. “#” stands for station number.
Send text is made of command code, device names and numbers.
Grain Circulation Technology.
H and L stand for high bit and low bit of station address. “#” stands for station number.
Send text is made of command code, device names and numbers.
Grain Circulation Technology.
Online since: October 2014
Authors: Jian Yong Guo, Tao Sheng Zhou, Ji Hong Liao
The sample numbers have been given in Table 1.
Sample numbers x/y 0.06 0.10 0.15 0.20 0.20 a1 b1 c1 d1 The starting materials were ball-milled for 2 h in ethanol.
The piezoelectric and dielectric properties of the BNK-BT samples Sample number d33(10-12 pC/N) tand/10-2 kp ε33T/ε0 a1 108 5.15 0.23 990 b1 121 5.70 0.23 1078 c1 149 3.62 0.26 1087 d1 90 5.80 0.16 862 The addition of BT can promote the polarization characteristics, which may improve the piezoelectric and dielectric properties.
Both the samples had the same average grain size of 1-2 μm.
Sample numbers x/y 0.06 0.10 0.15 0.20 0.20 a1 b1 c1 d1 The starting materials were ball-milled for 2 h in ethanol.
The piezoelectric and dielectric properties of the BNK-BT samples Sample number d33(10-12 pC/N) tand/10-2 kp ε33T/ε0 a1 108 5.15 0.23 990 b1 121 5.70 0.23 1078 c1 149 3.62 0.26 1087 d1 90 5.80 0.16 862 The addition of BT can promote the polarization characteristics, which may improve the piezoelectric and dielectric properties.
Both the samples had the same average grain size of 1-2 μm.
Online since: November 2016
Authors: U. Achutha Kini, S.R. Harisha, Sathyashankara Sharma
Microstructural analysis is done using high resolution optical microscope Fig. 1 shows the microstructure of normalized specimen showing small grains of proeutectoid phases.
Continuous spheroidization above the optimum duration may coarsen the pro eutectoid phases by dissolving some of its phases so that the number of pro eutectoid phases decrease.
The unit is VHN (Vicker’s hardness number) or VPN (Vicker’s pyramid number).
Continuous spheroidization above the optimum duration may coarsen the pro eutectoid phases by dissolving some of its phases so that the number of pro eutectoid phases decrease.
The unit is VHN (Vicker’s hardness number) or VPN (Vicker’s pyramid number).
Online since: February 2011
Authors: Mei Yang, Su Qing Cao, Xiao Fei Xin
Table 1 Compression strength and slump of carbon fiber-reinforced concretes
The number of concrete
Compression strength /MPa
Slump (cm)
Mean value
standard deviation
A
48.7
0.23
2
B
49.1
2.66
3
C
49.2
3.25
2
D
52.2
1.01
1.5
E
50.1
2.61
1
Analysis of breaking strength
In fiber reinforced concrete, because the fiber will absorb the energy released when concrete crack and increase the energy needed to fracture, the adding of fiber would enhance the breaking performance of reinforced concrete.
Table 2 Bending strength and tensile strength of carbon fiber-reinforced concretes The number of concrete Bending strength /MPa tensile strength /MPa A 4.4 2.2 B 5.1 2.6 C 5.5 2.9 Can be seen from table 2 that the bending strength increased 15.9% and 25% respectively and the tensile strength increased 18.2% and 31.9% respectively with the increasing of carbon fiber dosage.
When adding fiber into the concrete, fiber evenly distributed in concrete and improved the microcosmic structure, which make the concrete more close-grained.
Know from figure 4, the cracking-resistance property of carbon fiber reinforced concrete is greatly enhanced compare to ordinary concrete and the average cracking area and number of crack and total cracking area reduces 38%, 55% and 72% respectively.
Table 2 Bending strength and tensile strength of carbon fiber-reinforced concretes The number of concrete Bending strength /MPa tensile strength /MPa A 4.4 2.2 B 5.1 2.6 C 5.5 2.9 Can be seen from table 2 that the bending strength increased 15.9% and 25% respectively and the tensile strength increased 18.2% and 31.9% respectively with the increasing of carbon fiber dosage.
When adding fiber into the concrete, fiber evenly distributed in concrete and improved the microcosmic structure, which make the concrete more close-grained.
Know from figure 4, the cracking-resistance property of carbon fiber reinforced concrete is greatly enhanced compare to ordinary concrete and the average cracking area and number of crack and total cracking area reduces 38%, 55% and 72% respectively.
Online since: January 2004
Authors: Z. Kajcsos, K. Havancsák, V.A. Skuratov, Paulo M. Gordo, Adriano P. de Lima, L. Liszkay
The number of atomic displacements created can be estimated by using the TRIM code (Table 1),
assuming a displacement energy of 51 and 31 eV per O and Al atoms, respectively [5].
Due to recombination and possible local annealing the number of the remaining defects (C) must be in the low 10 16 cm-3 range or even below that.
Another possible origin of the long-lived component can be the formation of grain boundaries or cracks due to thermoelastic stresses.
Fluence (ions cm-2) Mean lifetime (ps) Al displ. by TRIM (cm-3) as received 145 0 5x1010 180 6x1016 1013 186 1.2x1019 1014 212 1.2x1020 Table 1 Mean positron lifetime and number of displacements in the Al sublattice calculated by TRIM in sapphire after Kr irradiation at 245 MeV.
Due to recombination and possible local annealing the number of the remaining defects (C) must be in the low 10 16 cm-3 range or even below that.
Another possible origin of the long-lived component can be the formation of grain boundaries or cracks due to thermoelastic stresses.
Fluence (ions cm-2) Mean lifetime (ps) Al displ. by TRIM (cm-3) as received 145 0 5x1010 180 6x1016 1013 186 1.2x1019 1014 212 1.2x1020 Table 1 Mean positron lifetime and number of displacements in the Al sublattice calculated by TRIM in sapphire after Kr irradiation at 245 MeV.
Online since: February 2014
Authors: Kevin Spencer, Michael Saleh, Vladimir Luzin
Around the deposited particle’s periphery (in the jetting region) the temperatures are significant, this lends itself to the presence of grain refinement at the periphery of sprayed particles and the propagation of dynamic recrystallization which is closely coupled with the thermal softening of the particle.
A number of studies [1-6] sought to understand the deformation sequences and the bonding mechanism associated with the CS technique.
Microstructure of the Al coating material (gray areas are single phase Al with limited porosity appearing as black areas) A cold spray sample presented herein was produced alongside a number of other specimens [24] by a Kinetic Metallization (KM).
The particle drag coefficient were calculated according to the local Reynolds number and Henderson’s correlation by evaluating the gas nozzle exit velocity (1025 m/s) based on the gas stagnation temperature (140°C), pressure (620 kPa), and the He gas thermodynamic properties using a 1-dimensional isentropic model.
Excessive temperatures, however, can lead to localized stress relaxation, grain refinement at the periphery of sprayed particles and the prorogation of dynamic recrystallization in the narrow band along the periphery which is closely coupled with the thermal softening of the particle.
A number of studies [1-6] sought to understand the deformation sequences and the bonding mechanism associated with the CS technique.
Microstructure of the Al coating material (gray areas are single phase Al with limited porosity appearing as black areas) A cold spray sample presented herein was produced alongside a number of other specimens [24] by a Kinetic Metallization (KM).
The particle drag coefficient were calculated according to the local Reynolds number and Henderson’s correlation by evaluating the gas nozzle exit velocity (1025 m/s) based on the gas stagnation temperature (140°C), pressure (620 kPa), and the He gas thermodynamic properties using a 1-dimensional isentropic model.
Excessive temperatures, however, can lead to localized stress relaxation, grain refinement at the periphery of sprayed particles and the prorogation of dynamic recrystallization in the narrow band along the periphery which is closely coupled with the thermal softening of the particle.
Online since: July 2011
Authors: Jia Zhi Ren, Guo Xin Jia, Qing Guo Feng
Table 1 Number of repeated combing
The feed amount per cycle[mm]
Number of repeated combing
Forward feed
Back feed
4.2
4.9
5.5
4.7
4.4
5.0
5.2
4.0
4.6
From table 1 it can be known that when feed mode is the same, the shorter the length in feed amount per cycle is, the bigger the number of the repeated combing will be, so the combing efficiency can be improved; when the length of feed amount per cycle decreases from 5.2mm to 4.7mm, the number of repeated combing can be increased by 10%.
When the length of feed mount per cycle is identical, the number of repeated combing of back feed is larger than that of forward feed.
The main process parameters of the comber and combing preparation are shown in table 4 Table 3 Fiber parameters Test term Numerical value Classer's staple [mm] 28.5 1%fiber span length [mm] 36.1 Average length(n) [mm] 21.0 Content of fiber below 12.5mm [%] 6.8 Neps number[grain·g-1] 46 Mic value 4.4 Fiber strength [cN·tex-1] 3.1 Table 4 The processing parameter of combing preparation and comber Experiment project Project 1 Project 2 Project 3 UNllapE32 Super lap machine Bat weight [ktex] 72 72 77.7 Tex of feeding sliver [tex] 4500 4500 4500 Blending number 24 24 26 Drawing times 1.453 1.453 1.453 Total drawing multiple of combing preparation 7.265 7.265 7.265 E65 comber The feed amount per cycle [mm] 5.2 4.7 4.7 Speed [pliers sequence·min-1] 450 450 450 Ekartement gauge [mm] 9.6 9.6 9.6 Feed mode Back feed Back feed Back feed Scale of detaching dial 0.5 0.3 0.5 Nip gauge of main drawing area [mm] 45 45 45 Nip gauge of pre drawing area [mm] 54 54 54 Pre drawing multiple 1.37 1.37 1.37
This is because that the decreasing length of feed amount per cycle can increase the number of repeated combing of cylinder to tuft (as table 1), and can increase the fiber straightness of combed sliver, the structure of combed sliver is therefore improved.
Table 5 Comparisons of yarn qualities Experiment project project 1 project 2 project 3 Percentage of noil 18.99 18.87 19.18 Output [kg·h-1] 65 59 64 Yarn unevenness CV [%] 13.05 12.94 12.87 -40%thin place [entris·km-1] 119 118 111 -50%thin place number [entris·km-1] 2 4 3 +35%thick place number [entris·km-1] 376 363 338 +50%thick place umber [entris·km-1] 34 28 26 +140% neps number [entris·km-1] 286 257 208 +200%neps number [entris·km-1] 58 58 42 IPI-stat value [entris·km-1] 781 738 657 IPI stat value [entris·km-1] 94 84 71 Notes: IPI- is the sum of statistical value of -40% thin place, +35% thick place and +140% nep: IPI is the sum of statistical value of +50% thick place, -50% thin place, and +140% nep: The effect of bat weight on yarn quality Fix the length of feed amount per cycle, and increase the bat weight from 72g/m to 77.7g/m, carry on the spinning experiment according to process parameters of comber and combing preparation in table 4, the test result of yarn quality parameters
When the length of feed mount per cycle is identical, the number of repeated combing of back feed is larger than that of forward feed.
The main process parameters of the comber and combing preparation are shown in table 4 Table 3 Fiber parameters Test term Numerical value Classer's staple [mm] 28.5 1%fiber span length [mm] 36.1 Average length(n) [mm] 21.0 Content of fiber below 12.5mm [%] 6.8 Neps number[grain·g-1] 46 Mic value 4.4 Fiber strength [cN·tex-1] 3.1 Table 4 The processing parameter of combing preparation and comber Experiment project Project 1 Project 2 Project 3 UNllapE32 Super lap machine Bat weight [ktex] 72 72 77.7 Tex of feeding sliver [tex] 4500 4500 4500 Blending number 24 24 26 Drawing times 1.453 1.453 1.453 Total drawing multiple of combing preparation 7.265 7.265 7.265 E65 comber The feed amount per cycle [mm] 5.2 4.7 4.7 Speed [pliers sequence·min-1] 450 450 450 Ekartement gauge [mm] 9.6 9.6 9.6 Feed mode Back feed Back feed Back feed Scale of detaching dial 0.5 0.3 0.5 Nip gauge of main drawing area [mm] 45 45 45 Nip gauge of pre drawing area [mm] 54 54 54 Pre drawing multiple 1.37 1.37 1.37
This is because that the decreasing length of feed amount per cycle can increase the number of repeated combing of cylinder to tuft (as table 1), and can increase the fiber straightness of combed sliver, the structure of combed sliver is therefore improved.
Table 5 Comparisons of yarn qualities Experiment project project 1 project 2 project 3 Percentage of noil 18.99 18.87 19.18 Output [kg·h-1] 65 59 64 Yarn unevenness CV [%] 13.05 12.94 12.87 -40%thin place [entris·km-1] 119 118 111 -50%thin place number [entris·km-1] 2 4 3 +35%thick place number [entris·km-1] 376 363 338 +50%thick place umber [entris·km-1] 34 28 26 +140% neps number [entris·km-1] 286 257 208 +200%neps number [entris·km-1] 58 58 42 IPI-stat value [entris·km-1] 781 738 657 IPI stat value [entris·km-1] 94 84 71 Notes: IPI- is the sum of statistical value of -40% thin place, +35% thick place and +140% nep: IPI is the sum of statistical value of +50% thick place, -50% thin place, and +140% nep: The effect of bat weight on yarn quality Fix the length of feed amount per cycle, and increase the bat weight from 72g/m to 77.7g/m, carry on the spinning experiment according to process parameters of comber and combing preparation in table 4, the test result of yarn quality parameters
Online since: September 2013
Authors: Tao Sun, Guo Sheng Zhao, Jian Wang, Yan Su, Hai Long Liu
Firstly, failure models of survivable system were formally described based on stochastic Petri nets, in which a number of measurable index parameters for recoverment were also given out.
(4) MTBF-(Mean Time between Faults) Mean time between faults, MTBF is approximately equal to the ratio between the normal service time and the number of failures, that is MTBF= (7) Recoverment Strategy for Survivable System Emergency recoverment is a technology of preventive and proactive survivability enhancement.
The survivability evolvement process Conclusions In order to make a reasonable fine-grained emergency recovery strategy of the survivable system, this paper presented a autonomous recoverment strategy of the survivable system, which made use of SPNs to formally describe the implementation process of the recursive reboot recovery.
(4) MTBF-(Mean Time between Faults) Mean time between faults, MTBF is approximately equal to the ratio between the normal service time and the number of failures, that is MTBF= (7) Recoverment Strategy for Survivable System Emergency recoverment is a technology of preventive and proactive survivability enhancement.
The survivability evolvement process Conclusions In order to make a reasonable fine-grained emergency recovery strategy of the survivable system, this paper presented a autonomous recoverment strategy of the survivable system, which made use of SPNs to formally describe the implementation process of the recursive reboot recovery.