Search results

The influence of both strain amplitude and strain mode on the development of the deformation and recrystallization textures is discussed based on experimental data and results of crystal plasticity calculations. 1.
The results are discussed based on experimental data and results of simulations with crystal plasticity models. 2.
The orientation data were post-processed with the MTM-FHM software [3].
To the purpose of obtaining a reliable comparison between deformation and recrystallization textures, all orientation data were post-processed with the MTM-FHM software.
Both experimental data and results of crystal plasticity calculations show that recrystallization is controlled by low stored energy nucleation, i.e. nucleation is favored on microstructural sites with less than average stored energy.

Cone data indicate that the incorporation of 1% GNP leads to a 28% reduction in the peak heat release rate (PHRR) compared to pure PS.
Measurements The X-ray diffraction (XRD) data were obtained at room temperature using a Rigaku Miniflex II Desktop X-ray diffractometer equipped with a Cu-Ka source (λ=1.5404Å).
Increasing the GNP loading does not result in a further reduction in PHRR; the reductions are 20.3% and 19.5% at 2% and 3% loading, respectively.
As can be seen from Fig. 3, the addition of 20% intumescent flame retardants (IFR) into PS results in a 54.8% reduction in PHRR in comparison with pure PS; the reason for the reduction is probably attributed to the formation of excellent char.
The addition of 20% intumescent flame retardants (IFR) into PS results in a 54.8% reduction in PHRR, and a further reduction in PHRR (64.5%) is observed if 3% IFR is substituted by GNP (i.e. 17% IFR + 3% GNP).

A sample of C-150 noise data reconstruction taken at t = 0.0 until t = 1.65 s Fig. shows the reconstruction of C-150 noise.
The data is taken at t=0.00 until t=1.65 second.
The data shown in Fig. 4 and those reconstructed and shown at fig. 10 are taken from a distance of 20 m at an elevation of 1.2 m exactly at the left of the C-150 aircraft propeller.
The data is then analyzed using FFT (Fast Fourier Transform) analyzer to obtain the frequency response.
The data is then inverted to obtain the anti noise source.

A large amount of industrial data are accumulated, these industrial data implies the relationship between the process parameters and production rules.
When operational pattern database has a large amount of data, attribute reduction method provides the necessary conditions for fast matching of operational pattern.
In this paper, a PCA method based on data for the attribute reduction of operational pattern is adopted, which improves the matching speed and accuracy of the operational pattern.
Consequently, it needs data reduction, and then it is the pattern matching. 3 Fast Matching Algorithm of Operational Pattern Considering the nonferrous metallurgical process with the characteristics of many process parameters, coupling, and containing noise of the data, a PCA method is introduced in this paper[7].
Secondly, choose production data of December, 2010 for simulation.

In order to correctly evaluate these diagrams, an advanced own developed software tool was used for the data filtering and appropriate modifications.
The initial purification/transformation technique applied on the data was the elimination of any duplicate points and the reduction of the number of points by forming the arithmetic mean of the coordinates of a given number of consecutive points in time.
Similarly, the next data modification is motivated by the nature of the experimental system.
Then, the resulting gaps in the data sequences were filled with newly created points by using polynomial approximation gap filling method (Fig. 3c).
a) The measured Pv–CMOD diagram b) The diagram after modifying its ascending part and elimination of any duplicate points and the reduction of the number of points (1. step) c) The diagram after “snap-down” and creating a new points by using polynomial approximation gap filling method (2. step) d) The recalculated Psp–CMOD diagram e) The final diagram after obtaining the points at the end of the diagram f) The indication of deduction the input data for the Double-K fracture model Fig. 3 Particular steps of the processing of the diagram for specimen V_1 and indication of deduction the input data for the Double-K fracture model Double-K fracture model.

Data from different repositories (eq.
Data processing chain from the physical values to the stored data in the data repository.
Recorded building data can be split into two main groups in general, periodic data and event -triggered (or event-related) data.
Example of generated periodic data set (red) based on typical data from an occupancy sensor (blue).
This can be useful if the data is subjected to analyzing processes using binary input data.

The Preparation and Characterization of LiFe0.98Mn0.02PO4/C by Spray-drying and Low Temperature Reduction Route Jia-feng, ZHANG1, a, Bao ZHANG1,b , Xue-yi GUO1,c, He-zhang CHEN1,d, Jian-long WANG1, Chao SHEN1and Jun-chao ZHENG1 1School of Metallurgical Science and Engineering, Central South University, Changsha 410083, Hunan, China acsuzjf@163.com,bcsuzb@vip.163.com,cxyguo@mail.csu.edu.cn,dcsuzcl@163.com Keywords: LiFePO4/C, Mn doping, Spray-drying , Low temperature reduction Abstract: The LiFe0.98Mn0.02PO4/C was synthesized by spray-drying and low temperature reduction route using FePO4·2H2O as precursor, which was prepared by a simple co-precipitation method.
Except to enhance the conductivity and the mean operation voltage, a high capacity was also expected for the LiFePO4-based Li ion batteries. 2 Experimental The LiFe0.98Mn0.02PO4/C was synthesized by means of spray-drying and low temperature reduction route using FePO4·2H2O as precursor.
The data were collected in the range between 10 and 90° in steps of 8°/min.
Conclusions The LiFe0.98Mn0.02PO4/C composite was synthesized via spray-drying and low temperature reduction route using FePO4·2H2O as precursor.

Therefore the isolation technology for high-rise structure also has better reduction vibration effect.
Test Data Analysis Acceleration time history analysis.
Due to sensors placed the top failed in test, the data collected were abnormal amplification, so they are not in the table.
Layer displacements of model structure concentrated in the isolation laye mostly, it is consistent with principles of isolation and reduction vibration.
Therefore, the isolation technology for high-rise structure also has better reduction vibration effect.

With the data of Jiangmen as objective, this study investigates driving factors of Jiangmen’s CO2 emission using STIRPAT model that is effective in environment pressure studies.
Since carbon emission statistical data is deficient, the current carbon emission state of Jiangmen is mainly analyzed by the energy data in 2011 statistical yearbook.
Original data is in J unit.
To be consistent with statistical data, energy unit is converted into standard coal.
The STIRPAT model based on ridge regression The original data used in the calculation of each factor of Jiangmen are mostly derived from Jiangmen statistical yearbook over years.

Considering that data availability is a major concern and that data available is qualitative in nature, this paper proposes a set of indicators and a scoring system that can be used with a broad qualitative understanding of the sustainability attributes of the mini-grids.
The paper first presents the framework and the scoring system and applies this to the data gathered from the fieldwork in Kenya.
The framework has shortcomings especially when it comes to data collection as getting reliable data at installation level can be painstaking especially in remote areas.
The framework however relies on survey data from one country and this limits its generalizability.
Data for 15 mini-grids were used in this study to present an evaluation of their sustainability.