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Online since: January 2026
Authors: Masaharu Shiratani, Kazuki Nagamine, Kizuku Ikeda, Daichi Wakita, Kunihiro Kamataki
The observed stress reduction is attributed to the milder ion-induced structural damage by lighter noble gas ions.
Two possible mechanisms are proposed to explain this stress reduction.
Raman spectroscopy showed a correlated decrease in the FWHM of the G peak with stress reduction.
The observed reduction in stress without a change in film density deviates from previously reported trends, and this is considered to be partly due to the higher precursor gas concentration used in this study.
Song et al., “Cross Sections for Electron Collisions with Methane,” Journal of Physical and Chemical Reference Data, vol. 44, no. 2, p. 023101, 2015, doi: 10.1063/1.4918630
Two possible mechanisms are proposed to explain this stress reduction.
Raman spectroscopy showed a correlated decrease in the FWHM of the G peak with stress reduction.
The observed reduction in stress without a change in film density deviates from previously reported trends, and this is considered to be partly due to the higher precursor gas concentration used in this study.
Song et al., “Cross Sections for Electron Collisions with Methane,” Journal of Physical and Chemical Reference Data, vol. 44, no. 2, p. 023101, 2015, doi: 10.1063/1.4918630
Online since: September 2013
Authors: Wei Rong Qin
Study on the Extraction of the Water Bodies from Remote Sensing Image Using ENVI Software—Applied to the River Environmental Protection in Qinzhou
Weirong QIN
Qinzhou University, Qinzhou, Guangxi 535000, China
Keywords: Maximum; Likelihood; Data Mining; Classification; Water
Abstract.
Extracting the main water body of remote sensing image with the maximum likelihood algorithm of ENVI software based on Qinzhou's remote sensing images 2008 and 2011, and also analyzing the water body overview of Qinzhou Fig.1.Remote sensing image of Qinbeifang Fig.2.The combination of 543 wave bands of Qinzhou's remote sensing image in 2008: Purple-urban area of Qinzhou; Blue-drainage Fig.3.The latest remote sensing image by Google earth in 2003 Fig.4.A remote sensing image in 2008, which is a drainage remote sensing image classified with the maximum likelihood classification of ENVI software in recent years: blue-water body; red-urban area; green-others Fig.5.A remote sensing image of Qinzhou in 2011 Fig.6.A Qinzhou's drainage remote sensing image classified with the maximum likelihood classification of ENVI software in 2011: blue-water body; red-urban area; green-forest; yellow-others Fig.7.The remote sensing data of extracted water body in 2008 and 2011
Figure 5 and figure 6 are included in the processed data statistics after classified by ENVI software, and then the numerical values of blue water are compared (P5water is the pixels of the water body in figure 5, and p6water is the pixels of the water body in figure 6).
Reasons for the reduction of water bodies in Qinzhou Too fast increasing planting area of fast-growing eucalyptus The advantages of fast-growing eucalyptus include fast growth speed, and high economic benefit.
Extracting the main water body of remote sensing image with the maximum likelihood algorithm of ENVI software based on Qinzhou's remote sensing images 2008 and 2011, and also analyzing the water body overview of Qinzhou Fig.1.Remote sensing image of Qinbeifang Fig.2.The combination of 543 wave bands of Qinzhou's remote sensing image in 2008: Purple-urban area of Qinzhou; Blue-drainage Fig.3.The latest remote sensing image by Google earth in 2003 Fig.4.A remote sensing image in 2008, which is a drainage remote sensing image classified with the maximum likelihood classification of ENVI software in recent years: blue-water body; red-urban area; green-others Fig.5.A remote sensing image of Qinzhou in 2011 Fig.6.A Qinzhou's drainage remote sensing image classified with the maximum likelihood classification of ENVI software in 2011: blue-water body; red-urban area; green-forest; yellow-others Fig.7.The remote sensing data of extracted water body in 2008 and 2011
Figure 5 and figure 6 are included in the processed data statistics after classified by ENVI software, and then the numerical values of blue water are compared (P5water is the pixels of the water body in figure 5, and p6water is the pixels of the water body in figure 6).
Reasons for the reduction of water bodies in Qinzhou Too fast increasing planting area of fast-growing eucalyptus The advantages of fast-growing eucalyptus include fast growth speed, and high economic benefit.
Online since: December 2012
Authors: Costin Ene
Integral Sliding-Mode Control With Applications to Aircraft Dynamics
Costin ENE1, a
1Faculty of Aerospace Romania, University “Politehnica” of Bucharest, Romania, street Polizu no.1-7, RO-011061, Bucharest, Romania
ararik_cos@yahoo.com
Keywords: Sliding-mode control, integral control, chattering reduction, longitudinal dynamics, conditional integrator
Abstract.
The asymptotic regulation achieved by integral action [3] is followed by the reduction of the transient performance.
From [12], the nominal system data are: A=-1.019010.8223-1.0774, B=00.1756, C=01; Considering the approximation to be ideal, we can choose δe=-k2satk0σ+k1eμ.
The asymptotic regulation achieved by integral action [3] is followed by the reduction of the transient performance.
From [12], the nominal system data are: A=-1.019010.8223-1.0774, B=00.1756, C=01; Considering the approximation to be ideal, we can choose δe=-k2satk0σ+k1eμ.
Online since: March 2015
Authors: Godfrey Omonefe Ariavie, Joseph Oyetola Oyekale
Finally, field data was used in this study to validate the model which can be applied to any natural gas pipeline risk assessment scenario.
A pipeline risk assessment model is a set of algorithms or rules that use available information and data relationship to measure levels of risks along a pipeline.
Such a rare case would be where we have exactly the same situation as that from which the past observations were made and we are making estimates for a population exactly like the one from which the past data arose-.
It was sectioned into 12 segments at 1km interval and data were collected based on the following assumptions: I.
Applying the data above to the 12km pipeline length, we get: 0.1428/12 = 0.0119 events/km-yr.
A pipeline risk assessment model is a set of algorithms or rules that use available information and data relationship to measure levels of risks along a pipeline.
Such a rare case would be where we have exactly the same situation as that from which the past observations were made and we are making estimates for a population exactly like the one from which the past data arose-.
It was sectioned into 12 segments at 1km interval and data were collected based on the following assumptions: I.
Applying the data above to the 12km pipeline length, we get: 0.1428/12 = 0.0119 events/km-yr.
Online since: February 2013
Authors: Ji Ping Jiang, Yu Liu, Li Na Zhang, Yi Wang, Yuan Hua Chen
The system adopts traditional three-level architecture: data layer, support layer and functional layer (Fig.1).
Fig.1 System architecture Remote dynamic data collection platform.
Microsoft SQL Server is used for data management, supporting the subsequent analysis and processing in other platforms.
Fig.2 On-line data acquisition Fig.3 Real-time and historical monitoring data Risk source information management platform.
Normal data management operation like adding, modification deletion and inquiry are realized.
Fig.1 System architecture Remote dynamic data collection platform.
Microsoft SQL Server is used for data management, supporting the subsequent analysis and processing in other platforms.
Fig.2 On-line data acquisition Fig.3 Real-time and historical monitoring data Risk source information management platform.
Normal data management operation like adding, modification deletion and inquiry are realized.
Online since: January 2011
Authors: Azman Jalar, Shahrum Abdullah, Mohd Faridz Mod Yunoh, F. Harun
However, driven by the relentless pursuit of dimensional reduction in wire bonding technology which calls for reduction in wire diameter, these conventional methods may not be sufficient.
It was clearly observed that one more time , pull test above the ball ( hook location A) did not adequately test the 2nd bond quality and that location B is found to be more appropriate to test the bond quality Basing to the earlier rule of thumb which stated that the 2nd bond strength is sufficient to maintain a consistent manufacturing process as long as it can achieve either half or more of its breaking load [3], the earlier data which was considered failed or rejected per the current MIL Standard 883E can still be considered as pass as most of the bond strength values are still either half or more than its breaking load .
It was clearly observed that one more time , pull test above the ball ( hook location A) did not adequately test the 2nd bond quality and that location B is found to be more appropriate to test the bond quality Basing to the earlier rule of thumb which stated that the 2nd bond strength is sufficient to maintain a consistent manufacturing process as long as it can achieve either half or more of its breaking load [3], the earlier data which was considered failed or rejected per the current MIL Standard 883E can still be considered as pass as most of the bond strength values are still either half or more than its breaking load .
Online since: January 2014
Authors: An Jie Wang, Chen Guang Liu, Bin Liu
Visibly, the linear fitting of the experimental data points indicated that the first order assumption in this temperature range was reasonable.
Fig. 5 shows the Py-IR spectra of the CoMoS/γ-Al2O3 catalyst at different reduction temperatures in the region of 1400 - 1700 cm-1.
They may be firstly transformed into new SH groups, however some SH species may also be released as H2S under hydrogen reduction conditions.
Fig. 5 FT-IR spectra of pyridine adsorbed on CoMoS/γ-Al2O3 catalyst pretreated at different reduction temperatures.
The linear fitting of the experimental data points indicated that the first order assumption was reasonable.
Fig. 5 shows the Py-IR spectra of the CoMoS/γ-Al2O3 catalyst at different reduction temperatures in the region of 1400 - 1700 cm-1.
They may be firstly transformed into new SH groups, however some SH species may also be released as H2S under hydrogen reduction conditions.
Fig. 5 FT-IR spectra of pyridine adsorbed on CoMoS/γ-Al2O3 catalyst pretreated at different reduction temperatures.
The linear fitting of the experimental data points indicated that the first order assumption was reasonable.
Online since: March 2007
Authors: I. Khmelevskaya, Sergey Prokoshkin, E. Bastarash, K.E. Inaekyan, Vladimir Brailovski, Vincent Demers
Materials and Experimental Techniques
An ∅0.9 mm Ti-50.0 and 50.7at%Ni wire supplied by the Central Research Institute for Materials
(St-Petersburg, Russia) is cold-rolled using a FENN four-height laboratory rolling mill to obtain
e=0.88 to 1.9 logarithmic thickness reductions (Ti-50.0at%Ni) and e=0.77 to 1.55 reductions (Ti50.7at%Ni).
A logarithmic thickness reduction of e=0.88 gives rise to the formation, along with the welldeveloped dislocation substructure, of the nanocrystalline and partially amorphous structures.
The same trend is observed for the Ti-50.7at%Ni alloy after cold rolling up to e=1.55 and 0.77 logarithmic thickness reductions.
When comparing data on the thermal stability of amorphous alloys, their processing method must be taken into account.
DSC testing From the DSC thermograms for Ti-50.0at%Ni (Fig.6) and Ti-50.7at%Ni alloys, two types of data are extracted: latent heat and temperatures for direct and reverse transformations (Fig.7).
A logarithmic thickness reduction of e=0.88 gives rise to the formation, along with the welldeveloped dislocation substructure, of the nanocrystalline and partially amorphous structures.
The same trend is observed for the Ti-50.7at%Ni alloy after cold rolling up to e=1.55 and 0.77 logarithmic thickness reductions.
When comparing data on the thermal stability of amorphous alloys, their processing method must be taken into account.
DSC testing From the DSC thermograms for Ti-50.0at%Ni (Fig.6) and Ti-50.7at%Ni alloys, two types of data are extracted: latent heat and temperatures for direct and reverse transformations (Fig.7).
Online since: January 2021
Authors: Roberto Spotorno
Figure 1 reports the weight gain data over time.
(Full markers) experimental data; (open markers) data corrected with Cr-evaporation; (solid line) calculated values from kinetic model.
The parabolic rate constant obtained from the weight gain data is 9.42 ×10-14 g2cm-4s-1 and agrees with those calculated from literature data [6].
Calculated data.
Corrected data.
(Full markers) experimental data; (open markers) data corrected with Cr-evaporation; (solid line) calculated values from kinetic model.
The parabolic rate constant obtained from the weight gain data is 9.42 ×10-14 g2cm-4s-1 and agrees with those calculated from literature data [6].
Calculated data.
Corrected data.
Online since: June 2013
Authors: Felicia Aurora Cristea
This could lead to the reduction of occupational illnesses that are frequently found in workplaces like this.
Sometimes, the construction material is the one that facilitates the vibration reduction (such as rubber) [1, 2].
In this case, the selection of the x, y, z coordinates in the data window will be: x = 0; y = 0; z = 1.
To these graphics minimization (reduction) of the displacements, it vary from 0.001m till 0.0025m (n = 250 rpm) (Fig. 6c) and from 0.01m till 0.00025m (n = 1000rpm) presented in the figure 7c.
Also, it could not solve the equations in the lack of the anthropometric input data (mass, length, etc.) and the rigidity and dampness coefficients (k, c).
Sometimes, the construction material is the one that facilitates the vibration reduction (such as rubber) [1, 2].
In this case, the selection of the x, y, z coordinates in the data window will be: x = 0; y = 0; z = 1.
To these graphics minimization (reduction) of the displacements, it vary from 0.001m till 0.0025m (n = 250 rpm) (Fig. 6c) and from 0.01m till 0.00025m (n = 1000rpm) presented in the figure 7c.
Also, it could not solve the equations in the lack of the anthropometric input data (mass, length, etc.) and the rigidity and dampness coefficients (k, c).