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They lacked substantial procedures, leading to incessant modifications of the data analysis methods and major steelmaking variables.
To improve the current manufacturing process, this study implements six sigma-based systematic approaches called DMAIC (define, measure, analyze, improve, and control) to optimize steelmaking variables of the sulfur free-cutting steels for process improvement and cost reduction.
We first proceed to define and measure phases based on the underlying production data of the sulfur free-cutting steels, then we find the optimal process parameters as the result of the analyzed phase, and finally, we carry out improved and controlled phases to ensure the process improvement and cost reduction.
Based on the data collected from January - June 2012 with 20 heats/month, we find the linear regression between EP[Mn] (dependent variable) and EP[C], FeO, and EPT (independent variables) shown in Eq. (1).

This task can be easily done with the data, employers give to the system.
In addition, the default data such as daily and seasonal changes, design conditions and equipment selection helps to choose the best choice.
By using various interior and exterior sensors and also computer network, getting permanent and immediate data of temperature, pressure, humidity, air flow rate, oxygen and carbon dioxide are possible.
Figure 3 - Electrical demand in building [5] Fig. 3 shows a 50 percent reduction in electrical demand before 2002 and after 2003 retrofits.
Result of retrofits Energy consumption reduction 50% Annual energy demand decline 1,220,000 [KW] Annual saving USD 172,000 Payback period 1.7 years References [1] Figueiredo J. and Costa J.

The final cold rolling reduction was 75%.
After hot rolling, steel B was given two cold rolling treatments with intermediate anneals to refine the grain structure prior to the final 75% cold rolling reduction.
The latter data were based on grain reconstruction with a minimum disorientation of 5º used to define a grain boundary.
These give both the correlated misorientations (for distributions based on boundary length) and the uncorrelated data which show the contribution that can be assigned purely to texture.
A better representation is achieved by taking the ratio of these two data sets which has the effect of normalizing the grain boundary misorientations to eliminate the texture effect.

. *** 679 q q q q a q v q T m m L L q v Li x x L R L K L K J KR J KR J KR J K i x x P                   +                 '                       −− − − =                 ' 1 0 0 0 0 000 0 0 01000 00 0 00010 2 ω θ ω θ (2) where: is motor torque constant, is motor electromotance constant, v is the speed of the machine table, R is the reduction rate of the mechanical chain, K T Kv θ is motor rotor displacement, ω is motor rotor speed, i is torque control current.
L is torque loop inductance, R is torque loop resistance, is the inertia of motor rotor, K and are equivalent rigid coeficecent and inertia of the drive chain at the shaft of motor rotor, and can be calculated with the reduction rate of the mechanical chain and the coeficents of and x of Eq. 1.
The database layer is a common data exchange district where the server exchange design data with client by the ADO technology.
It exchanges data with IIS by the CGI programer and outputs dynamical curves in JPG format to the client.

The XRD data was refined by using Jade 6.5 software.
It can be inferred from the data in Table 1 that the decrease of Al2O3 phase in the sample is mainly related to the crystallization of ZnAl2O4 phase in the sample.
Each of the obtained samples was subjected to a bending strength test, the bending strength data are plotted in Figure 2.
The initial increase is due to the reduction of the porosity, and the latter decrease is due to the increase in the glass phase.
The initial increase is related to the reduction of the porosity and the increase of the crystal phase in the sample, As shown in Figure 1, the crystalline phase BaAl2Si2O8 is the main phase and the peak intensities of ZnAl2O4 are gradually enhance with the content of Al2O3 increasing.

We represent 2D image as {x(n,m)n = 0,1,…{TTP}8230 ,N-1,m = 0,1,…{TTP}8230 M-1} (1) It can also be expressed as the matrix form: (2) where x is input data matrix(N × M), X is DCT coefficient matrix(N × M),C(N) is DCT transform matrix and CT(N) is transpose.
(7) Items of ∑{TTP}8721 operation in (6) are formally the products of input data multiply cosine function and are similar to 1D-DCT which could be transformed into 1D-DCT style by taking appropriate subscript mapping.
(3) Transform binary image watermark into one-dimensional sequence using dimension reduction.
A Fast DCT Algorithm and Filter Structure and Image Noise Reduction of Wavelet Transform Domain Research.
“Fast Algorithm for Computing Prime-Length DCT Using Cyclic Convolutions”, Journal of Data Acquisition & Processing, vol. 34, Aug. 2001, pp. 42-46 [6] Q.

However, with the influence of petroleum reserve reduction and the demand of work efficiency, the development of new pumping unit is developed rapidly.
Therefore, in ensuring the yield, increasing length of stroke and reducing times of stroke is a basic requirement to realize energy saving and consumption reduction.
Then, through the real-time acquisition of data by EQEP and GPIO, the collected data as the initial data of vector control algorithm or there is no sensor algorithm, directly participate in the program operation.

Fig.1 Data upgrading process Fig.2 Division according to system implementation process 1) Global base data processing: Personnel database, financial foundation data dictionary, fixed assets data dictionary, spatial data. 2) Professional application system implementation: Human resource management, financial management, construction project management, electric power production operation management. 3) System operation, maintenance and upgrade: Unification of operation and maintenance scheme and the standard.
Overall Planning Based on The Data Process.
Information sharing can’t be achieved ,without standardization of data.
The system really play a proper role, only good management of data, making full use of data, uniqueness of the data, completeness, accuracy, betimes be guaranteed.
Also, before complete planning, company should give full consideration to the data standard.

In this article, the typical phenomenon of bed armoring was firstly summarized based on both the field and experiment data, and then followed by the analysis on its internal influence on the reformation process to the balance status for the gravel-sand rivers.
In this article, the typical phenomenon of bed armoring was firstly summarized based on both the field and experiment data, and then followed by the analysis on its internal mechanism and effect in the reformation process to the balance. 2 Phenomenon of bed armoring The phenomenon of the bed armoring in gravel-sand rivers is generally caused by the chosen of the flow, whose consequence is that the finer sediment particles will be washed away and the coarser ones will be gathered on the river bed surface.
By judging the field and experiment data, as shown in fig.º2, it can be seen that the starting velocity will be proportional to the particle diameter when it grows to some extent.
It can be seen that the growth of the diameter will produce a bigger resistance, thus followed by a reduction of the flow velocity.
Another empirical formula of the average section flow velocity derived based on plenty of field and experiment data by Changjiang River Scientific Research Institute can be written as[6]: (2) In Eq.º2, D50 is the median particle diameter of the particles above the river bed surface, whose weight percentage of the particles, smaller than this diameter, is 50% and k is the empirical parameter.

The results are compared with experimental data and previous research works.
Fig. 3 provides the comparison of chloride diffusivity of this analytical formulation and experimental data of Van Dalen [9].
The analytical formulation as proposed in this paper shows good agreement with experimental data approximately.
In this figure, it is clear that the densification of the microstructure of concrete due to ongoing hydration of cement should lead to a significant reduction of the chloride diffusivity of concrete.
It is clear that carbonation can influence on diffusivity of concrete greatly. 0 5 10 15 20 25 30 35 40 1 10 100 1000 Time (days) Chloride diffusivity w/c 0.45 w/c 0.50 w/c 0.55 [1E-8 cm 2 /s] Fig. 2 Chloride diffusion coefficient of concrete 0 5 10 15 20 25 0 50 100 150 200 Time (days) Chloride diffusivity Exerimental data This study w/c 0.50, OPC [1E-8 cm 2/s] Fig. 3 Comparison of calculated and measured D (in concrete) 0.58 0.60 0.62 0.64 0.66 0.68 0.70 0.45 0.50 0.55 w/c ratio Dc / Dnc 5 months Fig. 4 Dc / Dnc with w/c ratio Summary The chloride diffusivity is of particular important material parameter for calculating the service life of marine concrete.