Sort by:
Publication Type:
Open access:
Publication Date:
Periodicals:
Search results
Online since: June 2010
Authors: A.K. Tyagi, Rakesh Shukla
The phase analysis using the XRD data are given in Table 3.
Table 4: Phase analysis and flame temperature data as a function of oxidant-to-fuel ratios No.
The structure of combustion-synthesized nano-crystalline HoCrO4 was refined from the XRD data.
The black dot represents the observed data, black solid line indicates the calculated pattern.
The data followed a Curie-Weiss behavior between 50-150 K.
Table 4: Phase analysis and flame temperature data as a function of oxidant-to-fuel ratios No.
The structure of combustion-synthesized nano-crystalline HoCrO4 was refined from the XRD data.
The black dot represents the observed data, black solid line indicates the calculated pattern.
The data followed a Curie-Weiss behavior between 50-150 K.
Online since: September 2018
Authors: Seenaa I. Hussein
The observed reduction in the hardness could be attributed to the samples brittleness.
By applying equation (1) the flexural strength (σf) was determined [16]: σf = 2Fπd h (1) Thermal conductivity coefficient was calculated to the data that measurement by using the lee's disk {manufacture by Griffin and George/England}, thermal conductivity coefficient was calculated by using the following equations [17]: K [TB-TA/ds]=e[TA+2/r[dA+dS/4]TA+1/2r(dSTB) (2) H=IV=πr²e(TA+TB)+2πre[dATA+(1/2)dS(TA+TB)+dBTB+dCTC] (3) Where K is the thermal conductivity coefficient, e represents the amount of thermal energy passing through a unit area per second disk material, H represents the thermal energy passing through the heating coil unit of time, d is the thickness of the disks (mm), r is the radius of the disk (mm), dS is the thickness of the sample (mm), and T is the
By applying equation (1) the flexural strength (σf) was determined [16]: σf = 2Fπd h (1) Thermal conductivity coefficient was calculated to the data that measurement by using the lee's disk {manufacture by Griffin and George/England}, thermal conductivity coefficient was calculated by using the following equations [17]: K [TB-TA/ds]=e[TA+2/r[dA+dS/4]TA+1/2r(dSTB) (2) H=IV=πr²e(TA+TB)+2πre[dATA+(1/2)dS(TA+TB)+dBTB+dCTC] (3) Where K is the thermal conductivity coefficient, e represents the amount of thermal energy passing through a unit area per second disk material, H represents the thermal energy passing through the heating coil unit of time, d is the thickness of the disks (mm), r is the radius of the disk (mm), dS is the thickness of the sample (mm), and T is the
Online since: September 2008
Authors: Michael C. Edmondson, L. Tang, A. Kern
The FLASH memory can be reprogrammed,
providing non-volatile data storage.
The reduction in baud rate did produce an improvement in the errors list.
Serial communication interrupt routines can simply clock received data into the buffer.
The solution to the transmission of data from the microcontroller is again using a buffer.
The effect of the buffer approach is that data are sent in the background of the control algorithm rather than stopping the control algorithm to transmit the data.
The reduction in baud rate did produce an improvement in the errors list.
Serial communication interrupt routines can simply clock received data into the buffer.
The solution to the transmission of data from the microcontroller is again using a buffer.
The effect of the buffer approach is that data are sent in the background of the control algorithm rather than stopping the control algorithm to transmit the data.
Online since: December 2012
Authors: Fang Ma, Xiao Xin Zhang, Li Wang, Ping Li
Data analysis refers to the specific reaction mechanism of unclear or uninvolved pollutant in water environment.
Only input and output data of the goal system is mastered, by a lot of testing data support, the response relationship of the input and output can be completed.
It is a kind of experience mode with high demands of data quality and quantity, requesting rich data samples.
Taking Neural Network model as a typical example, Data analysis has already been toolized and programmed.
Integrated with GIS, model data and geographic graphics and images are combined.
Only input and output data of the goal system is mastered, by a lot of testing data support, the response relationship of the input and output can be completed.
It is a kind of experience mode with high demands of data quality and quantity, requesting rich data samples.
Taking Neural Network model as a typical example, Data analysis has already been toolized and programmed.
Integrated with GIS, model data and geographic graphics and images are combined.
Online since: September 2017
Authors: Olga V. Samoilova, Larisa A. Makrovets, Gennady G. Mikhailov
According to experimental data [8], limiting oxygen solubility in the liquid copper at 1200 °С is equal to 2.092 % wt, and at 1300 °С – to 3.805 % wt.
The data used in calculation are given in the Table 1.
The temperatures and heats of melting data of oxides of Cu2O–La2O3 system.
Experimental data confirm the modeling results.
Gontarz, Reduction and oxidation of simple oxocuprates, Journal of Thermal Analysis and Calorimetry. 60 (2000) 219-227
The data used in calculation are given in the Table 1.
The temperatures and heats of melting data of oxides of Cu2O–La2O3 system.
Experimental data confirm the modeling results.
Gontarz, Reduction and oxidation of simple oxocuprates, Journal of Thermal Analysis and Calorimetry. 60 (2000) 219-227
Online since: June 2008
Authors: G. Ahmed, N.L. Heda, A. Rathor, B.K. Sharma, M. Itou, Y. Sakurai, Vinit Sharma, B.L. Ahuja, Soma Banik
In such experiments, it is very difficult to measure
the background data.
Therefore, the experimental Compton profile at pz = 6.0 a.u. after the above mentioned data reduction was subtracted from the entire range of Compton data.
Due to reasonable statistics of raw Compton data from a single detector, the data of only one detector was used to derive the charge Compton profiles.
To confirm the theoretical anisotropy in the momentum densities, the experimental data on single crystalline Ni2MnGa are required.
Mann: Atomic Data and Nuclear Data Tables Vol. 16 (1975), p. 201 [13] V.R.
Therefore, the experimental Compton profile at pz = 6.0 a.u. after the above mentioned data reduction was subtracted from the entire range of Compton data.
Due to reasonable statistics of raw Compton data from a single detector, the data of only one detector was used to derive the charge Compton profiles.
To confirm the theoretical anisotropy in the momentum densities, the experimental data on single crystalline Ni2MnGa are required.
Mann: Atomic Data and Nuclear Data Tables Vol. 16 (1975), p. 201 [13] V.R.
Online since: May 2010
Authors: Lin Niu, Zhen Yu Zhou, Ke Jun Li, Jian Guo Zhao
It is based on the
principle of structural risk minimization, which aims at minimizing the bound on the generalization
error (i.e., error made by the learning machine on data unseen during training) rather than the
minimizing the mean square error over the data set.
Training samples for the RVM framework are extracted form the simulation data.
The TSA data set has a large percentage of stable cases, with the ratio of stable to unstable cases of approximately 10:1.
The resulting classifier is then tested using the testing data sets for performance.
[6] N.Kaplowicz, "Learning from imbalanced data sets: a comparison of various strategies," Proceeding of learning from Imbalanced Data Sets.AAAI Press.Menlo Park.CA.
Training samples for the RVM framework are extracted form the simulation data.
The TSA data set has a large percentage of stable cases, with the ratio of stable to unstable cases of approximately 10:1.
The resulting classifier is then tested using the testing data sets for performance.
[6] N.Kaplowicz, "Learning from imbalanced data sets: a comparison of various strategies," Proceeding of learning from Imbalanced Data Sets.AAAI Press.Menlo Park.CA.
Online since: June 2013
Authors: Yue Wu Wang, Zhen Tong Gao, Jia Ling Yang, Shuang Wu, Da Fang Wu
To obtain the real temperature value, a simple linear interpolation is conducted on the data in cells and on either side of temperature .
As the calibrated temperature value is finally obtained via linear interpolation between the two temperature data values, the error is very small in global scope.
This increase in significant data does not affect the conversion time of the real-time control process using the above conversion principle.
For the K-type thermocouple, ‘E-T’ conversion of data in the range 0-1372 oC was carried out using both the proposed method and the piecewise linearization method.
(In Chinese) [7] A.A Puntambekar: Advanced Data Structures and Algorithms (Technical Publications, 2008)
As the calibrated temperature value is finally obtained via linear interpolation between the two temperature data values, the error is very small in global scope.
This increase in significant data does not affect the conversion time of the real-time control process using the above conversion principle.
For the K-type thermocouple, ‘E-T’ conversion of data in the range 0-1372 oC was carried out using both the proposed method and the piecewise linearization method.
(In Chinese) [7] A.A Puntambekar: Advanced Data Structures and Algorithms (Technical Publications, 2008)
Online since: June 2018
Authors: Mihai Chisamera, Iulian Riposan, Stelian Constantin Stan
The paper reviews original data obtained by the authors, from recent separate publications, specifically concerning graphite formation in the solidification pattern of industrial cast irons, focussing on grey cast iron versus ductile cast iron.
Additional unpublished data and selected data from literature are represented in the paper.
The main objective of the present paper was to review original data obtained by the present authors, from recent separate publications, specifically concerning graphite formation in the solidification pattern of industrial cast irons, focussing on grey cast iron versus ductile cast iron.
Additional unpublished data and selected data from literature are represented in the paper.
Experimental data included the characterization of compounds as possible graphite nucleation sites [presence of simple or complex compounds, chemistry and elements distribution on the section, size, shape factors, count etc].
Additional unpublished data and selected data from literature are represented in the paper.
The main objective of the present paper was to review original data obtained by the present authors, from recent separate publications, specifically concerning graphite formation in the solidification pattern of industrial cast irons, focussing on grey cast iron versus ductile cast iron.
Additional unpublished data and selected data from literature are represented in the paper.
Experimental data included the characterization of compounds as possible graphite nucleation sites [presence of simple or complex compounds, chemistry and elements distribution on the section, size, shape factors, count etc].
Online since: December 2019
Authors: Pavel Hutař, Tomáš Kruml, Hector A. Tinoco, Benoit Merle, Mathias Göken
Taking as reference ( obtained with the first part of experimental data) and comparing it with all experimental data and ; a deviation model between and can be determined through an error function as follows .
Therefore, applying to experimental data, the load-deflection curve was reduced until 2.75 kPa.
To determine which pair approximates better the experimental data of load-deflection, finite element simulations were running with the newly extracted data set shown in Figure 3a.
In the figure, there are observed some differences between the simulations and the experimental data.
The load-deflection curve showed a good agreement with the experimental data in the elastic regime.
Therefore, applying to experimental data, the load-deflection curve was reduced until 2.75 kPa.
To determine which pair approximates better the experimental data of load-deflection, finite element simulations were running with the newly extracted data set shown in Figure 3a.
In the figure, there are observed some differences between the simulations and the experimental data.
The load-deflection curve showed a good agreement with the experimental data in the elastic regime.