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Saboori et al., 2006 have determined the extrusion energy for the two optimal conical and curved dies, for aluminum and lead billets, in forward and backward extrusion by using FEM with verification of experimental data.
All the input data generated in the pre-processor can be saved (i) in a text form which enables the user to access the input data through any text editor; and/or (ii) in a binary form which is used by the simulation engine (Kim et al., 1994) explained below.
Punch travel for For 40% reduction 70% reduction Fig.7.
The computed results for different area reductions are shown in Table 1.
The extrusion pressure has been predicted for different reductions.

On the contrary, as for CO2 emissions, the recycled rebar showed a 88.3% reduction possibility of greenhouse gas (GHG) emissions comparing to statistics of input-output analysis based on competition inducement coefficient, but it was found to be more disadvantageous from the perspective of GHG reduction than the statistics of input-output analysis based on non-competition inducement coefficient. 1.
System boundary of CO2 emission in manufacture of recycled rebar using wasted steel 2.2 Data sources of energy consumption and CO2 emission in this study In the system boundary defined in Fig.1, the major data basis used for calculating energy consumption and GHG emissions for this study is as in the following Table 1.
In addition, for the data basis for energy consumption and CO2 emissions using natural iron ore, this study used the basic units of energy consumption and CO2 emissions [2] consequent on production of 1kg of rebar by the input-output analysis.
Data sources in system boundary of this study Main steps Detail steps Sources of data Generation process of wasted steel from demolition Demolition of buildings ü Quantities of destruction waste from specific measured data Separation and sorting of wasted steel ü Equipment used for separation from specific measured data Load of wasted steel ü Equipment used for load steel scrap from specific measured data Transportation process of steel scrap Transportation of wasted steel ü Vehicle used for transportation of steel scrap from specific measured data Production process of rebar using steel scrap Production of recycling rebar using wasted steel ü Korea Environmental Industry & Technology Institute, Korea LCI Database Information Network (www.dep.or.kr/lcidb) [3] ü H steel company in Korea, the Investor Relations Report (2006) [4] Virgin material ü Korea Institute of Construction Technology (KICT), The Environmental Load Unit Composition and Program Development for LCA of Building (2004)
In addition, for the energy consumption and CO2 emissions consequent on rebar production using natural iron core, this study used the Environmental Load Unit Composition and Program Development for LCA of Building (2004) [2] by Korea Institute of Construction Technology (KICT) based on the input−output analysis as the data for this research (see Table 3).

Data Acquisition and Processing of Weak Low-Frequency Magnetic Signals Shouqiang Men1,2,a and Christian Resagk2,b 1Department of Mathematics and Physics, Xi’an Technological University, 710032 Xi’an, P.
Box 100565, D-98684 Ilmenau, Germany ashqmen@yahoo.com, bchristian.resagk@tu-ilmenau.de Key words: data acquisition and processing, fluxgate sensor, magnetic field tomography Abstract.
On the contrary, trapezoidal numerical integrals were exploited to process the fluctuated-data, which were in good agreement with the predictions.
Fig. 4 Signal of sensor 5 by lowpass filter Fig. 5 FFT result of raw signal by sensor 5 Alternatively, fast Fourier transforms were performed as traditional data processing.
Since it was inconvenient to process the 40s data if it was separated to 10 parts, and it was not certain that the exact amplitude could be extracted for each part successfully.

Rough point cloud data Point Cloud Data Processing A. noise reduction and smoothness.
Effective noise reduction and smoothness algorithm should remove the noise points and retain the sharp feature of the point cloud to prevent that the point cloud data was over-smoothing.
In the past years, Most noise reduction and smoothness algorithm were about the 3D mesh, very few were about the 3D point cloud data.
The point cloud data was separated into the detail data and the foundation data.
The foundation data was taken noise reducing and smoothing, and the detail data was retained.

Data reduction is a consequence of the fact that the number of end-members presents in the scene is usually much smaller than the number of bands.
Dimensional reduction is categorized in two ways: Feature Extraction is done by mapping the correlation of high dimensional data onto uncorrelated low dimensional data.
Unmixing of hyperspectral data is usually carried out the two steps.
Initializing the data by eliminating noise [2].
Ihsan Ul Haq and Qaisar Mushtaq, “Hyperspectral Data Dimensional Reduction and Source Separation Using MAD and Maximum Log-Likelihood”, not published but submitted to journal, June 2011

Stress and temperature dependent constants and in Eq. (1) are obtained from the uniaxial creep rupture test data.
A fit to the data gives a value of .The failure mechanism control parameter value is 0.44.
It is clear in Fig. 6 that when the representative stress is used, the rupture data is in good agreement with uniaxial data.
A code of practice for notched bar creep rupture testing: Procedures and interpretation of data for design [S].
A code of practice for conducting notched bar creep rupture tests and for interpreting the data [S].

Field Data Collection Design of the Interface Based on Data Collected.
Disposed Data processing Do/Data processing Wait Do/Check event queue Receive Entry/ Receive data Receive events Stop Fig. 4 Bus data processing state diagram Data Processing Processing time for data update.
Data filtering.
State updating Waiting processing State checking …… Data_control_disable(); Check_data_state(); Data_control_enable(); …… Fig. 5 Control updates and sample status updates Reliability of Data Processing Data overflow.
T P FI Thermometer Pressure gauge Flow meter Moisture tester Pipeline pumps Fig. 6 Pipeline diagram Thus the final data reduction is the original model, that is, users need the data in units of a pipeline data organization unit [9].

Data set The data set taken from Chou and Cai [5] are used to test the current method.
If the high-dimensional data lies on or near a curved manifold, such as in the Swissroll dataset, MDS might consider two data points as near points, whereas their distance over the manifold is much larger than the typical interpoint distance.
However, Isomap is a technique that resolves this problem by attempting to preserve pairwise geodesic (or curvilinear) distances between data points.
These neighborhood relations are represented as a neighborhood graphover the data points, with edges of weight between neighboring points. 2.
A global geometric framework for nonlinear dimensionality reduction.

By measuring the recording data, we got the speed range corresponding to different water depth.
Data Collection Measuring Vehicle flow characteristics commonly uses traffic counters.
It is an effective method for obtaining large amounts of data.
Therefore, it was used camera data for obtaining vehicle and water status data simultaneously for parsing.
The data on car length was obtained from relevant data on car model length from the internet.

The data obtained from the properties test as Table 1.
The erosion experiment process under the hypothesis of erosion behavior testing, artificial rainfall was used in the simulation erosion experiment, artificial rainfall event based on the data observed from the Lamtakong Dam area.
The runoff reduction ratio (RRR) is a ratio showing the reduction of the surface runoff due to the erosion prevention system.
The sediment rate (SR) of the erosion experiment test with the geocell erosion-preventing system, the SR values were shown in Figure 4, which has a similar data arrangement as Figure 3, which shows the data of RR values.
Runoff rate and the runoff reduction ratio Fig. 4.