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Commonly used data mining technique include: Classification trees, association discovery, clustering, artificial natural networks, optimized set reduction, Bayesian network and visualization data mining etc.
Compared with other approaches used for data mining, Bayesian networks can combine prior knowledge with observed data, which is very important when data is scarce or very expensive.
Fig. 1 Software Engineer based on Data mining From above, the model of software engineer based on data mining have two mainly process: data preparation and data mining.
Data preparation is key work; abundance data are precondition to make efficiency data mining.
Data preparation This stage is divided into two sub-steps: data selection, data preprocessing.

With the increment of temperature difference and reduction ratio the slab head bends more significant.
Furthermore, the reduction ratio of slab in one pass rolling is 10％, 20％ and 30％ in the research works.
The distribution of equivalent strain, stress and strain rate with 20% reduction ratio is shown in the Fig. 2.
(a) Equivalent strain (b) Equivalent stress (c) Equivalent strain rate Fig.4 Distribution of equivalent strain, stress and strain rate with double phase (a) (b) Fig.5 Y displacement of slab Summary The influence of temperature difference, reduction ratio and phase transformation on the front end bending have been discussed in this article, based on the 3D FEM and HyperMesh method with the production data of slab steel from a plant in China.
The temperature difference, less reduction and single phase rolling is beneficial to control front end bending.

In this model,gives a knowledge acquisition method that based on rough set theory,the Rough-Fuzzy RBF neural network are constructed according to the results of the knowledge acquisition,the PSO are used to optimize the network parameters.This paper take number plate for example to conduct a simulation experiment.The results shows that the model can simplify the network training sample,optimize the network structure and enhance the systems study efficiency and the precision. 1, Introduction Rough set theories[1]（Rough Sets,RS）is raised by the scientist Z.Pawlak in Poland in 1982,it uses decision table to expression knowledge base,based on the thought of the equivalence class and the indistinguishability,describe the importance of different attribute in knowledge representation,through the algorithm of knowledge reduction,to reduce the dimension of the space of knowledge representation,and access data from the rules for logical reasoning.Particle swarm optimization algorithm (PSO)[2]
The knowledge in the knowledge base are not equally important,even some knowledge is redundant.Knowledge reduction is on the condition of to keep the classification ability of knowledge is same,delete the knowledge which not relevant or not important.
（2）Discretization of decision table:When treatment decision table using the rough set theory,required values of attributes in decision table by using the discrete data expression.This paper in order to achieve discretization,using good clustering characteristics of SOFM[4] network in order for each condition attribute clustering sample attribute value.The input nodes of SOFM network is 1,the number of output nodes is various properties of discrete interval number,the trained network weights on behalf of the cluster center for each discrete interval
（3）Reduction of decision table:Attribute reduction requirement on the premise of to keep the dependencies between decision table decision attributes and attribute do not change ,delete irrelevant or unimportant attributes.This paper uses Johnson algorithm [5]to reduction
The first layer: Input layer,its value is the exact value of the feature vector,expressed as ,the number of neuron in this layer is the attributes number be reductioned.

Micro-orientation data of a high purity Al rolled up to total thickness reduction of 80% at room temperature were determined using SEM-EBSD technique, conceptions of describing substructure information, such as subgrain misorientation (θcry), and average misorientation (θenv) of circumjacent subgrains for a special subgrain, etc., were suggested and corresponding GCDP-OI soft package was developed.
There are a lot of methods for analyzing micro-orientation data determined by SEM-EBSD technique.
An Analysis of micro-orientation data from OIM for a scan of the investigated specimen is shown in Fig.1.
In order to obtain more details on deformed substructures, it is necessary to establish a quantitative method for analyzing micro-orientation data.
Fig.1 Analysis of the micro-orientation data determined by SEM-EBSD for the high purity Al rolled to total thickness reduction of 80% at room temperature.

Those data would be used in the farming, salinization management and production prediction in the Northwest Tarim Basin.
Results Test Data.Test data were collected from seven groups, and listed in Table 2.
Data Processing.
In order to eliminate the other influence in each test area, the data have to be processed with standardization[15,18,19].
Application Form 2 data, using nonlinear method of the minimum squares, to deduce the cotton tolerance of salinity equation, as Table 4, Figure 4 shown.

But this is the use of attributes reduction properties of the rough sets, to get the mechanism model.
Knowledge reduction is one of the core contents of rough set theory.In the knowledge system of S = ( U, A ), there are a lot of redundant attributes in A, while maintaining the same classification ability of condition, we can delete redundant attributes, this is the reduction of knowledge.
In the combustion rate under perturbation, the dynamic characteristics of bed temperature of pure delay can be approximated by two order inertial link a non minimum phase, according to Figure 2 income data, using the system identification method, get the combustion rate disturbance transfer function out of bed temperature response is as follows
Input error of traditional PI control simulation system E, input and output deviation change rate of EC u for data cleaning, data generated as the fuzzy control rules, first of all to initialize network subtractive clustering is used to generate the original fuzzy controller, controller, membership function is shown in Figure 4.
Summary Through data mining on the actual plant data, using rough set attribute reduction in the theory of the core properties, are all properties have great influence on the boiler bed temperature on the data processing of evolutionary, and get them to its influence degree, system identification model to generate the bed temperature.

Data cycles are the life blood of a development effort.
Figure 2: Effective Cycle Time Targeting Take for example, the data in figure 2.
HOW TO LOOK AT CYCLE TIME Yield department: Everyone is engaged in defect reduction: Inline defect data can also be backward looking.
Defect data is immediately fed back to those who can impact defect performance.
In-line data is fed back to the tool owner as quickly as it is collected.

Quantitative and qualitative comparisons of the results obtained by the new method with the results achieved from the other speckle noise reduction techniques demonstrate its higher performance for speckle reduction in SAR images.
Recently, there has been considerably interest in using the wavelet transforms as a powerful tool for recovering SAR images from noisy data [2].
For a range of shifts, one shifts the data, De-noises the shifted data, and then un-shifts the de-noised data.
In order to change the multiplicative nature of the noise to additive one, we apply a logarithmic transformation to the image data.
Taking logarithm of the both sides of Eq. (5), we will have (5) where and represent logarithms of the noisy data, signal and noise, respectively.

The proposed target is 50% reduction in the number of defects per month until the end of the year.
With the implementation, step-by-step, of the method DMAIC, it had a significant reduction in the number of defects observed in plant.
Nowadays, the process data vary around a fixed average of a stable and predictable manner, which is a process "under control".
Moreover, obtain a reduction of $ 89.000,00 in monthly production cost.
For the company to maintain the excellent outcome requires that the process is controlled with monitoring data related to product quality and operator training.

There is 37.68% reduction in junction temperature of FPGA when we increase airflow from 250LFM to 500LFM.
Whereas there is 32.08% reduction in Logic Power on 25 GHz, 21.78% reduction in Signal Power on 25 GHz, 15.04% reduction in DSPs Power on 25GHz and 15.75% reduction in IOs Power on 25GHz with change in Airflow as shown in Table 4.
There is 23.88% reduction in junction temperature of FPGA when we increase airflow from 250LFM to 500LFM.
There is 37.68% reduction in junction temperature of FPGA when we increase airflow from 250LFM to 500LFM.
Kittichaikarn, "A Study of Air Flow through Perforated Tile for Air Conditioning System in Data Center", Applied Mechanics and Materials (Volumes 249 - 250), pp. 126-131, December, 2012 [2] H Deng, J P Gao, X F An, X S Yi, "Tailoring of Thermal Transition Temperature and Toughening of Shape Memory Epoxy Polymer", Applied Mechanics and Materials (Volumes 182 - 183), pp. 93-98, June 2012 [3] T.