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(4) Read and process corrosion monitoring data after continuously detecting for about 8 hours
Corrosion rate value which is obtained after ​​corrosion rate curve stabilized can be read out by Microcor tool data processing software.
The corrosive condition curve drawled according to records of monitoring data is shown in Figure 4.
(2) Compared with corrosion data obtained by traditional coupon method, Microcor corrosion monitor has better stability.
In the corrosion monitoring system, combination of two or more monitoring systems is often used to provide a broader basis for collecting the corrosion rate data.

The study demonstrates a significant reduction, 90 percent, in the usage of wood as a fuel source for drying the mold.
Methodology The following procedures were taken to create an efficient heat recovery system: a) Data gathering b) Analytical calculations c) Development of CFD model d) Validation of CFD result Data Gathering The data given in Table 1 was acquired from the shop's management.
Table 1: Data gathered from the shop.
Items Quantity weight of consumed metal scrap (each batch's) 150 [kg] Fuel consumption for furnace 55 [liters] / batch Wood consumed for drying of mold 50 [kg] Hours of operation (per day) 4 Mass of moist sand mold (to be dried) 12.5 [kg/h] The data in Tables 2 and 3 were collected using k-type thermocouples, an infrared thermometer, and an anemometer.
Temperature of flue gas at the hood[0C] Temperature of flue gas at Middle of chimney [0C] Temperature of flue gas at the outlet of the chimney[0C] 1 302.00 210.00 131.00 2 334.00 207.00 122.00 3 208.00 182.00 139.00 4 238.00 201.00 146.00 5 205.00 153.00 138.00 6 192.00 163.00 143.00 7 205.00 169.00 140.00 Table 3: Average values of measured data.

I-V data for a sample annealed at 1000°C and 1100°C.
The RBS data showing out-diffusion of Si and C provide a possible explanation for the increase in both Si and C and a reduction in the P and Ni content in the film as seen in the EDX data.
After further annealing at 1100°C for 5 minutes at a pressure of approximately 2 ´ 10-7 Torr, the I-V data showed ohmic characteristics.
Table 2 shows the resistance values calculated from TLM data according to the equation: where RT is the total resistance between the contact pads, ρc is the specific contact resistivity, RS is the sheet resistance, L is the spacing between the contacts, and W is the width of each contact.
Preliminary data on a lightly doped 4H-SiC epilayer show that reasonably low contact resistivity of about 4.8 ´ 10 -6 Ωcm2 can be obtained using electroless nickel plating.

For a piece of equipment in service, this can be done by a periodical review of the performances based on field failure data (e.g., see [2] and [3]).
The time data have been rescaled and both the user and manufacturer are not disclosed here for the confidentiality reason.
We have carried out a “data cleaning” to reduce the inconsistency in failure descriptions.
This requires operational and maintenance-related data (both failed and normal) and needs data analysis tools (e.g., root cause analysis).
l To periodically analyze the collected data so as to generate and implement effective corrective actions.

The main theoretical basis of threshold denoising is as follow:wavelet transform,especially orthogonal wavelet,has strong ability in removing data correlation and can make wavelet coefficients reflect the energy of signal in wavelet domain[8];the energy of the noise is distributed in the whole wavelet domain,by wavelet decomposition ,wavelet coefficient amplitude of signal can compare with that of the noise .So the method of threshold denoising can keep the signal components and inhibit most noise components.
An important application of wavelet analysis is the noise reduction of one-dimensional signal,and its principle is as follows: The model of one-dimensional signal containing noise can be expressed as the following form: s(i) =f(i)+e(i) i=0，…，n-1 f(i) is the useful signal,e(i) is the noise and s(i) is the signal containing noise.
The data in the test are collected by the data acquisition card,and sampling frequency of the data is 3500HZ.
We decompose the wavelet packet into 4 layers and extract wavelet packet coeffients of every node after many experiments.And we select wavelet function sym8 at last by analysing experimental results.Data points are reduced by half along with the increase of decomposition in the process of wavelet packet decomposition;if the oringinal data length is 2N and decomposes L times，the length of each frequency band result becomes 2N-L .

Therefore, in order to accomplish ITS ECO energy saving program, the prediction of reduction in electrical energy consumption in which CFC retrofitted with Musicool-22 has been done.
This data is then analyzed to get a picture of how the facility usages and possibility of energy waste and to provide nominated area for the auditor to examine.
Occupancy Rate Occupancy rate is taken based on secondary data and primary data from the Department of facilities and infrastructure ITS Surabaya.
Table 2 shows the temperatures, humidity, and light intensities data in the building.
The electrical energy cost is obtained from the electrical energy consumption data of Civil Engineering Department of ITS Surabaya within a period of four years (the period January 2009 – December 2012).

Therefore, the data recorded in the development process can be reserved for later research and simulation efforts.
Based on requirements of simulation demands, whether all the processes, entities, data and relationships can reflect the users’ demands is tested.
The true and false of each expression in the program are executed if sufficient testing data are provided.
The logic of each conditional expression is executed if sufficient testing data are provided.
The true and false and logic of each conditional expression is executed if sufficient testing data are provided.

Under the creep-fatigue interaction, the material degradation is accelerated by micro cavities and brings out the life reduction [2,3].
As shown in Fig. 1, a new parameter of backscattered Rayleigh surface wave, SDA (Slope of Decaying Amplitude), γ, was introduced in order to correlate backscattering data to the materials degradation.
For the experimental data, the following linear regression relation was used given by Eq. 1 .CN f =⋅ αγ (1) where γ is the SDA and Nf is the corresponding cycle to failure. α and C are the two regression parameters to be determined by the regression analysis.
As is presented in Fig. 4, experimental data fit the form of Eq.1 very well.
The presentation of experimental data, Fca vs.

Engers found that the spillover effects of international tax incentives for domestic R&Dinputs have a negative effect, which will result in the reduction of domestic R&Dinvestment.
Like Zhu Pingfang and Xu Weimin, they divided random effects models use panel data analysis from a dynamic perspective, empirical study of the impact of the science and technology of the Shanghai Municipal Government incentives for large and medium-sized industrial enterprises self-R&Dinput and its patent output.
Study the issue of this paper, we two aspects of the statistical tests; respectively inspection of the degree of fit of the regression equation for the sample data, the regression equation was significant.
Analysis of the regression equation fitting degree of sample data for more scientific research model of this paper, the coefficient of determination, through empirical investigation, inspection and explain the linear relationship between the variables in a multiple regression model, and we deduce whether to set up a whole, and whether significant.
Impact of corporate R&D investment factors - from the experience of China's private manufacturing data [J].

Inserting zero instead of missing values in parts of the data area could help PARAFAC quickly converge, leading to solutions having physical and chemical significance.
Analysis was performed for a PARAFAC model of the data to determine the correct number of factors.
After inputting data of EEFM into PARAFAC, the comparisons of sum-of-squares residuals, factors and explained variation for PM 2.5~10 µm and PM 2.5 µm were totally listed in Table 1.
Møller, Stabilizing the PARAFAC decomposition of fluorescence spectra by insertion of zeros outside the data area, Chemometrics and Intelligent Laboratory Systems, 71 (2004) 97-106
Vidal, EEMizer: Automated modeling of fluorescence EEM data, Chemometrics and Intelligent Laboratory Systems, 106 (2011) 86-92.