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This development would certainly contribute to significant reduction of fossil fuel consumption and cut of carbon emission.
The combined efficiencies of the system (thermal + electrical) are increased with increase of the air mass flow rate due to reduction in the system losses, and decreases with increase of module's length and the photovoltaic cell temperature.
In addition, a numerical model was developed and validated the experimental data.
They concluded that there is a close agreement between the measured and predicted data.

The peripheral set includes: three configurable video ports; a management data input/output (MDIO) module; a VCXO interpolated control port (VIC); one multichannel buffered audio serial port (McASP0); an inter-integrated circuit (I2C) Bus module; two multichannel buffered serial ports (McBSPs); three 32-bit general-purpose timers; a user-configurable 16-bit or 32-bit host-port interface (HPI16/HPI32); a peripheral component interconnect (PCI); a general-purpose input/output port (GP0) with 16 GPIO pins; and a 64-bit glueless external memory interface (EMIFA).
Video median filtering algorithm [5][6] In signal processing, it is often desirable to be able to perform some kind of noise reduction on an image or signal before processing.
Journal of Data Acquisition & Processing, 1997, Vol. 12, No. 2:155-158 [4] Zhan Yanmou, Wang Zhuozhu, Sun Jihong, et al.
Combination of space and time adaptive mean filters for noise reduction in antenna measurement[C].

All 3D-CAD data were constructed by a 3D-CAD software, Rhinoceros 6.
Tetrahedron solid elements were automatically generated from the 3D-CAD data.
This is due to the macroscopic shear band formation, which causes the reduction of the flow stress.
FE analysis revealed that the high energy absorption in the disordered cell structure was due to the reduction of stress concentrations.

To get rid of the influence of beginning wave on the test result,the corresponding data are set to be zeros.
Through the adaptive filtering period[3][4], noise reduction of the detection signals can be achieved.
Figure 4 Time-domain Waveform After Noise Reduction Multi-resolution analysis of wavelet transform By processing the signals through FFT, the Spectrum can be achieved, and one can note the existence of noise, which will influence our determination of the domain frequency for the signals.
Put the data of the four signals in figure 6 into formula 1 to calculate,and we can obtain the corresponding result as is shown in table 2.

Reduction bleaching.
The color strength (K/S) of the dyed samples was measured using Computer Color Measurement Data-color SF 600-PLUS (US).
Although the two-time oxidation bleached fibers show highest whiteness, their bursting strength decreased obviously (data not shown), therefore, in this paper, one-time oxidation bleaching process is selected.
Whiteness of soybean protein fiber under various bleaching process Process Unbleached Reduction bleaching One-time oxidation bleaching Two-time oxidation bleaching Wh (%) 60.2 71.1 74.5 76.7 Dyeing with Megafix or Unisol dyes.

In this study, we oxidized ND particles and characterized them after the heat treatment to confirm the reduction of amorphous sp2 carbon.
X-Ray diffraction analysis (data not shown) reveals very similar patterns of oxidized and unprocessed ND powders.
These diffractograms are also consistent with the data of JCPDS card #6-0675 in terms of peak intensity and diffraction angle.
The reduction of resistivity in ND hybrids suggests the unavoidable existence of amorphous carbon in the LANDs.

Ongel et al. (2007) [7] reported that the reduction in noise levels by using open graded mixes is directly related to the air void content and surface texture.
In order to analyse the noise data captured by the SLM, SVAN PC suite was used to transfer the noise data to a personal computer.
The increase in noise levels in the case of AC pavements with a reduction in temperature is essentially due to increased stiffness of asphalt concrete.
A TPIN linear regression model of the form shown in Eq. 1 is considered to fit the noise data collected on all the roads using the independent variables discussed previously.
The data pertaining to all the 8 asphalt pavements and 4 cement concrete pavements has been used to develop the comprehensive TPIN models for asphalt pavements (Eq. 2 to 7) and cement concrete pavements (Eq. 8 to 11), respectively for each mode.

Stress reduction effect of the CTE layer calculated by FEM.
Figure 6 presents the trend data of the maximum junction temperature (Tjmax) and thermal resistance (Rth) obtained from each test condition.
Fig. 6 Trend data of the maximum junction temperature (Tjmax) and thermal resistance (Rth) relative to the initial state.
Fig. 9 Trend data of the maximum junction temperature (Tjmax) and thermal resistance (Rth) relative to the initial state obtained at a condition of 115-250 ℃.
Fig. 10 Transient thermal resistance data of Structures J, K, and L measured before the test and after the EOL.

Its essence is to observe data from a different angle, and find the optimal projection method which can greatest extent to reflect data characteristics and fully exploit data information.
The projection pursuit method is an effective technique for dimensionality reduction.
A P-dimensional data are become to one-dimensional projection value which are the projection direction.
It reveals certain characteristic structure of high dimensional data.
Projection Pursuit Algorithm for Exploratory Data Analysis[J].

However, what can make researchers really encouraged is to know how to process those data to make timely, objective and scientific assessments to the bridge safety.
In the operation, we can get the real-time load effect from bridges’ health monitoring data, and then get a real-time internal force Figure by the real-time finite element analysis.
Acquirement of Bridge Structural Real-time Internal Force Figure Technology of Acquirement of the Real-time Response Data of the non-monitoring Cross-section.
We can draw the real-time internal force Figure via doing the real-time finite element analysis on data of the Bridge Monitoring Database by the way of the above mentioned bridge structure finite element model.
Conclusions The key of the health monitoring system is to offer a guide to maintenance by assessing the safety state of large bridges on basis of the monitored data.