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Introduction The vast reduction in size and power consumption of CMOS circuitry has led to a large research effort based around the vision of ubiquitous networks of wireless sensor and communication nodes [1].
Next low-power temperature sensor and wireless module are selected and the real-time data receiving and plotting software is designed.
For the wireless sensor circuit, power supply for supplying power to the 5V circuit power consumption as its power, were tested in parallel SSHI control method of opening and closing, RF at different rates when data is transmitted power.
The piezoelectric cymbal harvesting can be used the parallel SSHI control method, the output power can be measured when control methods work and do not work. the result shows that the harvester can supply necessary energy to power wireless sensor to transmit data at a certain frequency.
Number of different array device contains cymbal can continue to work to maintain a wireless temperature sensor wireless data transmission rate of 12Hz.

The fuzzy theory not only provides natural tool for describing quantitative data but also generally produces good performance in many applications.
In addition, fuzzy rules allow to effectively classify data having non-axis-parallel decision boundaries, which is difficult for the conventional attribute-based methods [6, 7].
Besides, the resultant design takes into account the needs that can be prescribed by the physician which again can result in a need of pressure reduction in some areas.
Chan: First IEEE International Conference on Data Mining, USA, Vol. 1 (2001), pp. 35-42
Ryu: IEEE International Workshop on Integrating AI and Data Mining Australia, Vol. 1(2006), pp. 50-57.

Introduction Because of their compact size and high reduction ratios, harmonic gear drives are often favored for robot system, communications satellite and automatic weapon as well as other high accuracy positioning system.[1,2] Harmonic gear drives inevitably have vibration and noise, with the effect of nonlinear factors, which affect their performance.[3,4] Therefore, the analysis of the nonlinear influence in dynamic behavior of harmonic gear drives has been widely concerned.
The function of nonlinearity torsional stiffness is obtained by fitting the torsional experimental data.
Figure 1 Torsional stiffness experimental setup Figure 2 Experimental data and fitted curve The data of angular displacement obtained from ten tests with varying torque are shown in Fig. 2.

Fig.2 The mechanical model of honeycomb paperboard Test results and analysis. 20mm thick honeycomb double layer of data is shown in table 1.
By Eq.2 we can get the damping ratio of honeycomb . 40mm thick honeycomb double layer of data is shown in table 2.
With single-layer honeycomb vibration were observed and compared the experimental data shows that, with the honeycomb thickness increases, the transmission rate of vibration reduction and damping ratio increases.

Experimental parameter of the coke specific consumption In case of natural gas injection coke is saved due to increase of indirect and decrease of direct reduction, replacement of a part of coke carbon by natural gas carbon and decrease of sulphur incoming into the furnace and slag outcoming due to decrease of the coke consumption.
(11) The values of Δai ratios of the linear dependency (2) as to the set of statistical data may be defined, for example, by the least square method.
Thus we can build extreme parameters of the blast-furnace process by a set of experimental data of iron making, air input and natural gas consumption.
Statistical modeling of charcoal consumption of blast furnaces based on historical data.

Study on the Resistance Loss Model of Slurry Pipeline for Iron ore Concentrate with High Concentration Tingting Leng1,a, Hua Wang2,3,b and Jiande Wu4,c 1 Faculty of Civil and Architectural Engineering, Kunming University of Science and Technology, Kunming 650000, China 2 Faculty of Metallurgy and Energy Engineering Kunming University of Science and Technology, Kunming 650224, China 3 Kunming University of Science and Technology, Engineering Research Center of Metallurgical Energy Conservation and Emission Reduction, Ministry of Education, Kunming 650500, China 4 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China alengtingting@126.com, bwanghuaheat@hotmail.com, cwjiande@gmail.com Key words: slurry pipeline; pressure loss; Dahongshan Abstract.
We made comparison analysis of the results of model calculations on the measured data of Yunnan Dahongshan iron concentrates pipeline, finding resistance loss calculation model for high concentrations iron concentrates slurry pipeline, to provide reliable reference for the design, operation of similar slurry pipeline engineering.
The verification and analysis of mathematical model We compare the data of resistant loss which is measured by Dahongshan iron concentrate ore slurry pipeline with other result calculated by Durand’s model and Fei JunXiang’s model.
Fig.1 The comparison between the measured data and calculated The Fig.1 shows that the curve of resistant loss which is measured and calculated by two mathematical models is similar with the clear water.

Application of Wavelet Transform in Image Preprocessing Wavelet coefficients can be used to describe the image data after wavelet transform.
Wavelet coefficients reflect the nature of the original data, local characteristic of image data can be changed by handling the wavelet coefficient[4].
SIFT feature matching method itself has a strong matching rate and noise reduction effect, the article make a further improvement on this basis.

Those include fixture error, machine tool error and datum surface error [1].
All these results are based on an assumption that there is a fixture error corresponding to the machine tool error and datum error.
And withis the fixture error, and are the equivalent fixture error transformed from machine tool error and datum error, separately: and , (2) wherewith is the coordinate of the locator of fixture, is the machine tool error and is the datum surface error.
It is noted that similar results can be easily obtained on the datum variation.
Huang, Reuven Katz, Multi-operational machining processes modeling for sequential root cause identification and measurement reduction, Transactions of the ASME. 127 (2005), 512-521

At the same time, CAM technology has been asked for higher requirement due to the application of new material and new tools, the continuous development of NC machining and cutting theory, and the increasingly pursuit of high product quality, low cost and manufacturing cycle reduction.
The bottom half content of Fig. 7 dialog box represents all the data base of parameter setting buttons in UG programming.
You can move the content from data base on the current panel or move the content back to the data base by the upward or downward arrow in the middle.

The establish of the grey prediction model Deformation observation points are n interconnected on a deformable body,the deformation observation data of M Period, the deformation observation sequence corresponding to:{xi(0)(k)}（k=1,2,Λ,n）, the primary accumulating generating sequence is , in the formula, k=1,2,Λ,m；i=1,2,Λ,n.
For the sake of model parameters A and B, the type 1 discrete valuation, and obtained by the least square method: (3) in the formula: ，， inside: () It can be gotten the identification values of A and B from type 3 matrix: (4) The formula (2) in discrete model: (5) in the formula: ,the formula (5) as these reduction, there is, (=1，2，3，) (6) When km, is predicted value.
Conclusion This paper takes MATLAB as the working environment, to realize the modeling of grey model, and taking Shenyang subway station in Xinle Site ground subsidence monitoring data as an example, verify the multi-point grey prediction model to meet the requirement of accuracy. 
Make sure the latest data in modeling, try to make correction parameters in each prediction step, the accuracy of model will be more accurate.