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Research contents Firstly, through consulting data and sampling methods, we determine the amount of fresh water of offshore drilling platform on one day, and the minimum water production capacity should be determined; and determine the structural frame work platform sea area near the stable system and weather related natural factors (example: wind speed, wave height, wave cycle).The related natural factors in each season the working platform nearby the offshore drilling platform should be determined.
Secondly, according to the analysis of survey results and data, we calculate the dilution tank capacity and the minimum pressure to bear; designing shape of floater , the determination of its size, the reasonable selection of materials, rational design of the wave shape, determine the size, screening materials; reasonable arrangements for the design of negative pressure fresh water conversion device installation.
Fourthly, we calculate the minimum driving force, the average working distance of float and power of the pump by the data Fifthly, we considering the various data, then testing weather design of the device is very reasonable through the reasonable calculation; And according to the minimum driving force and float force, we calculate parameters of intermediate transmission mechanism, the design efficient transmission device; by considering the proper placement of solar energy device.
Through the water thermodynamic relation, under low pressure, then saturation temperature is low , and corrosion rate is slow, In its background of advocating energy-saving emission reduction policies , the device have a profound effect.

This can be used as an EMI reduction technique [3]-[10].
Afterward, we present applications of the proposed DTC to SSCG for EMI reduction.
Data conversion in theory is simply the process of working with signals in different domains.
Temes, Understanding Delta-Sigma Data Converters, IEEE Press (2005)
Iijima, "Spread-Spectrum Clocking in Switching Regulators for EMI Reduction", IEICE Trans.

The carbothermal reduction of the xerogels produced porous SiC with a surface area of 112 m2 /g.
Parmentier et al. prepared mesoporous SiC by infiltration of carbon into MCM-48 and then carbothermal reduction.
The carbothermal reduction of the xerogel.
The FTIR datas were collected using a Nicolet 470 FTIR spectrometer operating at a resolution of 4 cm-1.
In other hand, cobalt component in the xerogels also acts as catalyst during the carbothermal reduction.

As a result of the presence of noise in the measured vibration signal has a great influence on the results of calculation of fractal dimension, Therefore the empirical mode decomposition method for noise reduction of gear vibration signal is used, calculation fractal dimension, extraction fault feature of Gear in different conditions.
The decomposition step of signal as follows: 1) Determine the local extreme point of all the signal, then use the three spline curve method to connect all local maxima and form upper envelope; 2) then use the three spline curve method to connect all local maxima and form lower envelope, the upper envelope and lower envelope should envelope all data points; 3)the upper envelope and lower envelope is signed m1(t), The original signal x(t) minus m1(t) is the first component h1(t): (1) 4)If h1(t) don’t fulfill these necessities, Then take h1(t) as the original data, Repeat steps 1)~3), Get the average m11(t), Judge whether the component h11(t)=h1(t)－m11(t) meet the conditions of IMF.
If it doesn’t fulfill, then cycle K times. 5)Let separated from (2) Take as the original data, repeat steps 1)~4),get the second component to satisfy the IMF condition, repeat n times, obtained some satisfying IMF condition component of the signal.

This paper categorizes and analyzes some classic durability of concrete parameters calculation models for the deterioration of concrete and the steel corrosion. 1 Relevant Models of Concrete Deterioration 1.1 Concrete Carbonation Scholars put forward many concrete carbonation depth prediction models, these models can be divided into two types: one is based on experimental data or the actual structure of the carbonation depth measuring, using mathematical methods such as statistical or neural network model fitting the actual data; another is the theoretical model which is based on quantitative analysis of carbonation reaction process.
In the application, choose one to calculate according to the measured data.
The model of average strength of concrete by the time: （9） The model of the standard deviation strength of concrete: （10） Concrete strength under the above two prediction models are based upon a large number of long-term exposure tests and measured data on the basis of the building, due to less internal information, statistical data based mainly in Japan, therefore, in the model application, yet Should be combined with the actual adjustment. 2 Related to Corrosion of Steel Bars in Concrete Model 2.1 Start time for reinforcement corrosion (1) Atmospheric Environment Studies have shown that[8], the reinforcement of concrete, there are two critical PH value, one is PH = 9.88, when the steel surface passive film began production, that is to say, less than this critical value, there cannot be passive film on the reinforced surface, that is completely activated; the other is PH = 11.5, when the steel surface passive film to form a complete, or less than this critical value
Steel corrosion loss rate as: （25） 2.3 Strength factor reduction model of corroded reinforced Hui Yun-ling model [14] (26) (27) Where: ky (t) is the reduction factor of yield strength of corroded reinforced; ku(t) is the ultimate strength reduction factor of corroded steel; ηs (t) for the loss rate of corrosion steel.
Summary ⑴{TTP}9332 Although the application of empirical models are convenient, but the data used is based on the specific environmental and materials obtained, the model coefficient is not very clear about the physical concept, the model applies to a limited extent. ⑵{TTP}9333 Theoretical model has clear concept while modeling, all parameters have clear definitions and dimensions, but the selection and calculation of parameters in the model will be difficult, so it lacks practicability. ⑶{TTP}9334 Based on the test data or the actual structure of the measured value, through the use of mathematical statistics or neural networks, support vector machines and other modern optimization model to fit the experience gained from the existing model results, we can get a relatively high accuracy.

The first term is the empirical count of feature k in the training data, and the second term is the expected count of this feature under the current trained model.
Suppose that in the training data a feature appears times, while under the current model, the expected count of is .
The training data consists of 8,000 printed characters from document images with various fonts and sizes of 16, 18, 20 and 22, and different styles of regular, italic and boldface.
The test data consists of 1,227 unknown characters gathered from 30 websites and license plates.
The training data are collected from a number of documents with various font sizes and styles.

On the surface of the more positive alloy, these excess electrons are consumed by a cathodic reaction that, in seawater, is usually oxygen reduction.
The individual behavior of coupled alloys Fig.1 shows the self-corrosion potential data plots of TA2, B10 and 921A.
Potential data plot at 25°C of TA2, B10 and 921A Fig.2.
Fig.3 shows, as an example, the galvanic potential and the galvanic current density data of 921A, B10 and TA2 members immersed in natural seawater at 25℃ during 240h.
According to this result, anodic dissolution of iron takes place on the surface of 921A as anode, oxygen reduction reaction occurs on the surface of the B10 and TA2.

As experimental data are unavailable, the model is validated by comparing the predicted entropy generation history with that from the detailed analytical model of Dash et al. [2].
The predicted non-dimensional entropy generation per unit surface area (Ṡ¢/d¢2) shows a slight drop in the initial stages due to the reduction in entropy generation rate and the low rate of change of droplet surface area (see Fig. 2a).
A similar dip in Ṡ¢ is seen in the data provided by Dash et al. [2] for a vaporizing water droplet.
Although the entropy generation rate falls (see Fig. 2a) after about 40% of the liquid has vaporized due to the drop in heat and mass transfer rates, the consistent reduction in surface area again causes Ṡ¢/d¢2 to rise in the later stages.
Data Monogr. 9 (1998) 1-1951

According to the data, this variant is less energy intensive than smelting reduction processes of the entire volume of the slag without prior restorative procedures.
These data in general correspond to culculated parameters.
The chemical composition metallic part of the melt after liquid-phase recovery (composition excluding carbon) The type of slag in the initial mixture The content of component in received metal, [wt. %] Fe Cr Ni Mn Cu The initial nonmagnetic 87.68 7.56 4.45 0.31 - The initial magnetic 89.40 5.22 4.92 0.46 - Non-magnetic, prereduced at 1200 °C 88.95 4.84 6.21 - - Magnetic, prereduced at 1200 °C 89.09 5.91 3.54 0.52 0.94 The obtained data confirm previously conducted in vitro studies on liquid-phase recovery of slag melt [3, 4] and demonstrate the effectiveness of recovery components of slag coke carbon (graphite) and gaseous environment that includes carbon monoxide.
Eremyashev, Solid-phase reduction of waste products in steelmaking, Metallurgist. 56 (2012) 91-96

Samples are taken for 3 times to analyze the vapor-liquid equilibrium data by using titration with 1M HCl.
An increase in cyclic capacity will lead to decreased absorbent circulation rate thus leading to the reduction of the overall capital and operating costs and resulting in the decrease in carbon dioxide capture cost.
Summary In this study, the vapor-liquid equilibrium data of CO2 in aqueous solution of 2-MAE and 3-AP are investigated in term of CO2 loading capacity in comparison with MEA solution.
This means that an increase in cyclic capacity can lead to the decrease in energy requirement for solution regeneration and liquid-circulation rate, leading to the reduction of the overall capital and operating costs and resulting in the decrease in cost of carbon capture.
Data Vol. 37 (1994), p. 96 [8] H.