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Online since: March 2007
Authors: Jason S.C. Jang, K.J. Lee, S.W. Lee
Experimental results indicate that open porosity decreased and bulk density increased with the numbers of infiltration cycle.
Morphological observations also show that the number of open pores at surface site for these materials decreased and they exhibited a denser and smoother morphology as the number of infiltration cycle increased.
Introduction: Boehmite (�-AlOOH), an aluminium hydroxide, is one of the candidate precursor materials for fine grained low temperature alumina ceramics.
As expected, the open porosity decreased and bulk density increased as the number of infiltration cycles increased.
Conclusion The open porosity decreased and bulk density increased with the numbers of infiltration cycle.
Online since: November 2011
Authors: Xin Gong Tang, Liang Jun Yan, Xing Bing Xie
The recorded second field potential difference is a complex number and so the result is called complex resistivity.
Polarization, time constant, frequency-dependent factor and magnetic relaxation number are the four commonly used parameters to characterize the polarization properties of mineral resources [3].
Good uniformity correspond to the small frequency-dependent coefficient, and the electromagnetic relaxation number (relaxation time with relevant reservoir) are quite different for different materials.
ranges from 0.1~0.6 and indicates the shape of mineral grain and variation range of the size [6].
Online since: February 2012
Authors: Xian Min Wei
Distance-independent Underwater Localization ALS Algorithm ALS algorithm[5] can estimate region position of the node, based on the attenuation degree of the signal transmission, the algorithm divided network into a number of fan-shaped area, one of node receiving signal energy level corresponds to a fan-shaped area, shown in Fig.2 .
Underwater localization problem has attracted the attention of a growing number of researchers, the positioning resarch of the current UWSN have shown the following trends: (1) It is a hot spot to improve the ranging technology and the accuracy of ranging[11].
(2)Greater emphasis on cost control[7], in the context of ensuring efficiency and accuracy, as far as possible to reduce the number of GPS buoys, AUV and underwater beacon nodes, and to reduce the constraints and preconditions of positioning algorithm.
IEEE OCEANS Asia Pacific Conf., pp.1-8,(2006) [12] Sav vides A, Han C C, Srivastav a M b: Dynamic Fine-Grained Localization in Ad-hoc Networks of Sensors.
Online since: July 2008
Authors: Kazuhiro Morino, Eiji Kondo, Norio Kawagoishi, T. Iwamoto, Nu Yan
The mean grain size was about 21µm.
Original aged (673K) 糫 Stress amplitude, ıb (MPa) Number of cycles to failure, Nf cycle Re-aged steel (R.T.)
Re-aged steel (673K) 0.1 1 10 100 1000 10000100000 450 500 550 600 650 700 750 800 850 900 0.1 1 10 100 630 650 700 750 800 850 900 Hardness Hv Aging time, t (ks) 糫473K 糫673K 753K Hardness Hv Aging time, t (ks) Fig. 2 S - Nf curves Fig. 3 〉〉〉〉a /〉〉〉〉B - Nf curves Fig.4 Crack morphology 30╋m (b) R.T. 〉〉〉〉a=950MPa Nf=8.59©©©©104 (a) 673K 〉〉〉〉a=950MPa Nf=3.33©©©©104 (a) R.T. (〉〉〉〉a=950MPa, Nf=8.59©©©©10 4) (b) 673K (〉〉〉〉a=950MPa, Nf=3.33©©©©104) Fig.5 Fracture surfaces Fig.6 Aging curves Fig.7 S - Nf curves of re-aged steel 10 4 10 5 10 6 10 7 10 8 0 200 400 600 800 1000 1200 糫 Stress amplitude, ıb (MPa) Number of cycles to failure, Nf (cycle) 糫R.T.糫糫 糫473K 糫673K 10 4 10 5 10 6 10 7 10 8 0.2 0.3 0.4 0.5 0.6 糫 糫 ıb!
0ıC Number of cycles to failure, Nf (cycle) 糫R.T.
Online since: November 2011
Authors: Bao Hui Wang, Chong Zhuo Wang
Opening the system implementation of the file under the windows operating system and choosing iron spectrum firstly, we use iron spectrum option button, and then deal with abrasive grinding grain readings and readings.
While the corresponding data is input, and select the corresponding button , and number of car I or car II is automatically input the space.
Especially in the experiment they accessed to a large number of relevant data and did a large number of experiments , obtained the scientific judgment conclusion finally.
Online since: December 2010
Authors: Jian Min Hao, Chao Ping Jiang, Ya Zhe Xing, Hong Chen
Recently, our experiments found that yttria-stabilized zirconia coatings deposited by plasma spraying on a preheated substrate surface up to about 800oC led to continuous growth of columnar grains across splat interfaces [6].
With increasing the mean substrate temperature to 180oC (C2), a significant decrease in the number of the horizontal cracks was presented (Fig. 2b).
Consequently, the number of the horizontal cracks decreased with the increase of the substrate temperature from 50oC to 240oC.
Therefore, the decrease in the number of the annular cracks represents the improvement in the quality of the interlamellar bonding.
Online since: December 2014
Authors: Peng Jie Qi, Zhao Zhun Zhong, Miao Guan, Bin Na Song
According to these characteristics, collecting the number n of each pixel that gray value is 1 in the neighborhood.
When the number n is less than the setting value N, the pixel point is regarded as noise and is removed.
The mathematical formula of statistical a pixel (x, y) with its gray value of the number 1 in the 8 connected domain is: (1) where g(x, y) is the gray value of (x, y).
Precisely Located Difference Image of the Stored-grain Insects Image Based on Directional Projection.
Online since: July 2020
Authors: Bo Chen, Kun Feng, Xin Jin
This was because the alloy of 7.5%Cr had the largest number of dendrites and the least eutectic structure, and the resistance to the cutting motion of the abrasive grains into the substrate was relatively weak, so the wear resistance was the lowest.
As the Cr/C ratio was increased, the hardenability of the alloy could be improved, the hardness of the matrix could be improved, and the number of eutectic hard phases that play the wear skeleton could be increased [6], therefore, the wear resistance was greatly improved.
The alloy of 7.5%Cr had a small number of eutectic structures with a wear-resistant skeleton, and the most amount of softer matrix, which made it easy for abrasive particles to cut in [7].
Online since: November 2023
Authors: Daniele Mirabile Gattia, Luciano Pilloni, Giuseppe Corallo
The potential of these technologies is considerable: - rapid prototyping; - the ability to produce components with complex geometries and lightweight; - potential reduction of energy demand for production; - reduction of the amount of waste materials and optimization of raw material utilization; - reduction of the number of components to be assembled.
In fact, depending on the type of technology used, there are a limited number of materials available for the additive processes, thus limiting the number of possible applications [2-4].
Figure 3 shows the microstructure in the parallel (a) and in the perpendicular (b) direction, respect to the sample growing direction, revealing the presence of oblongs and fine grains respectively in the first and in the second case.
Online since: June 2013
Authors: Xiao Guo Wang, Ru Jia
Then we encapsulate these theme-organized resources in the forms of service so that upper layer can call the fine-grained sub service through open service interfaces.
Through summarizing three data scales from general to detail (discipline class, major category and major), we can obtain three corresponding service (discipline class number statistical service, major category number statistical service and major number statistical service).
Table 1 Major subject service scale Service ID Service subject Data Table Data Attributes Service Scale Service Description FW_ZY_1 Major ZY_HZ_1 XKMLDM Discipline class Discipline class number statistical service FW_ZY_2 Major ZY_HZ_2 ZYDLDM Major category Major category number statistical service FW_ZY_3 Major ZY_HZ_3 ZYDM Major Major number statistical service Platform functional framework.
Fig. 6 Customized service entity and relation diagram Service implementation examples For essential service, Fig.7 shows the result of querying the philosophy major category and the result contains infomation: major count, undergraduate student number, enrollment student number, graduation number and predicted graduation number.
Teacher-student ratio R is the teacher number S divide into student number X in designated year, and the equation is R=S/X [5].
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