Search results

Therefore, this paper presents an approach to interpret in-process measurement data and to derive electric energy savings potentials.
Based on this, the gathered data are used to derive suitable measures to realise energy savings.
Figure 2: Modular methodology allocated to the data levels of a factory The developed methodology consists of the five modules.
To be applied by machine operating companies, the developed module supports a holistic analysis by considering both quantitative and qualitative input data.
During the load curve interpretation, the energy performance indicators ϰ are calculated to elaborate the gathered measurement data.

The study was conducted at discrete intervals ∆t = 8 h using data recorded by the furnace computer measuring system and methods of statistical data processing.
In spite of furnaces are equipped with modern computer systems for recording and visualization of measurement data, metallurgical parameters of the process are not directly measured.
Research was carried out at discrete intervals ∆t = 8 h using data recorded by computer measuring system of the furnace.
Appropriate selection of data and statistical tests showed that the electrodes slipping was one of the most significant factors impact on performance of the examined ferrosilicon furnace.
This range was determined by appropriate data selection and statistical data processing.

Drug data preprocessing of data mining with constraints Data preprocessing.
In order to solve the problem of redundancy, use groups in the process of attribute reduction from the number of all the methods for elimination
Use some drugs attribute data tables for data analysis, the summary of the data processing includes the following steps: using drug data table to generate more data summary, only keep part of data information.
The drug after data preprocessing the data summary table, the table in the cluster analysis as a mining pharmaceutical data source, the target output data is clustering data summary table.
Experiments using (2) in the type k medoids clustering analysis method, using this method for drug data reduction and the actual noise processing, simulation of the effect is very obvious.

The solid line represents a linear fit to the data.
Data for the isothermal sintering of alumina as reported by Nettleship [6]; 170 nm alumina at 1325°C for various times with a regression line fit to the data.
The regression straight line fits the data with a correlation of 0.9887.
As a demonstration of this simple approach to sintering data take the copper sintering data of Coble and Gupta [10].
The alumina data in Fig. 20 shows this behavior [37].

The orientation distribution functions (ODFs) of the carbon steel A and the Si steel B were calculated and analyzed by orthorhombic symmetry with the Bunge method [6] based on the measured pole figure data.
Based on the ODF data in Fig. 2 at s = 0.9 for carbon steel sheet (A), the f(g) variations of main texture components are shown in Fig. 3.
Based on the ODFs data in Fig. 5 at s=0.9 for Si steel sheet (B), the f(g) variations of main texture components are shown in Fig. 6.
Main texture component f(g) variation from ODFs data at S = 0.9 for carbon steel sheet (A) samples a through d Fig. 4.
Main texture component f(g) variation based on ODFs data at S= 0.9 Si steel sheet (B) samples a through d The good formation of the Goss texture in the carbon and Si steel sheets after asymmetric rolling is related to shear deformation during the asymmetric rolling.

The apparent activation energy of the reduction process of the carbon deposit was determined.
After carburisation and reduction the samples were examined by XRD and HRTEM.
Introduction Magnetic metal nanoparticles have many applications, including magnetic data storage, magnetic toners in xerography, magnetic inks and ferrofluids.
The thermogravimetric data were used to plot a dependence of the conversion degree as a function of hydrogenation time (Fig. 3a).
Diffraction pattern No1 corresponds to the sample after reduction (before the carburisation process).

In the present work, the orientation-dependent stored energy inside of the deformed IF steels was evaluated by dislocation structure reconstructed from EBSD data.
After reconstructing the subgrain structure from EBSD data, the subgrain size and misorientation can be determined.
In order to make a clear of subgrain structure, smoothing technique is necessary in the processing of EBSD data.
Subgrain identification angles : (a) 1° (b) 2° Recrystallization Simulation The orientation data measured using EBSD analysis and stored energy data evaluated by Eq. 1 were used as input data for a Monte Carlo simulation by mapping on square lattice.
Therefore, EBSD measurements for more wide area are required for obtaining more statistically representative data.

Using experimental sample data, the relation of environmental factor and shelter factor and distance measurement error is studied quantitatively.
The unknown node S moves continuously, 200 groups of experimental data is collected finally.
Using the experimental data, “distance-loss” model adapting to the environmental scene is obtained through the data fitting method, and the fitting result is shown in figure 2.
Fig. 1 Experimental scene diagram Fig. 2 “Distance-Loss” model based on experiment data Experimental Factor n Affecting Analysis.
Using variable incremental method within this range, “distance-loss” model expression is constantly adjusted, and measurement distance between the unknown node S and anchor nodes Si is acquired. 20 group data is extracted from the experimental results as the final distance measurement sample data.

Further, based on the obtained inspection data, we discuss the quality issues of the present SiC wafers from the standpoint of device manufacturing.
The surface data are collected by spiral scan of the laser beam on the spinning wafer.
From the analysis of the long-term trend of the incoming inspection data, the number of micropipes on a 2-inch SiC wafer is almost zero.
These data imply that the cleaning process before the shipping of SiC wafers is insufficient.
Based on the inspection data, the quality issues of the present SiC wafers are discussed.

Regarding this study, a head dummy was used for the 3D scanning process for the data acquisition.
From the point cloud data, horizontal plane was used to obtain sections for the head area.
The cloud data then transfer to CATIA for 3D modeling for the padding as shown in figure 1.
A section curve from the cloud data is use as an example for this study.
It was constructed from tangent curves with a low deviation from the original data.