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According to electrochemical polarization data, some investigators reported that the synergistic effect of Sc and Zr increased the corrosion resistance of 7xxx series Al alloys [3,4], while some authors reported that the combined action did not reveal any improvement in the corrosion resistance of 7xxx series Al alloys [5].
Table 2 Electrochemical data from analyses of polarization curves.
The reduction of maximum corrosion depth indicates that the addition of Sc addition can enhance IGC resistance of medium strength Al-Zn-Mg alloy.
Discussion Wloka et al. [6] reported that the addition of Sc get the alloy less stable against corrosive attack on a macroscopic scale, which was attributed to the reduction in the protective property of oxide layer across the coarse primary Al3(Sc, Zr) particles/matrix interface.

Therefore, magnesium products has been come to the fore since the industry has a demand for lightweight construction materials regarding legal requirements for CO2 reduction.
The bars with an initial diameter of 40 mm were rolled to an end diameter of 8 mm within 14 passes without using lubricant or reheating during the process, which corresponds to an area reduction of 96 %.
Technical data of the three-stand rolling mill at the Institute of Metal Forming Calibre sequence Pass Elongation λ Pass Elongation λ 1 1,09 8 1,22 2 1,25 9 1,38 3 1,48 10 1,21 4 1,21 11 1,33 5 1,50 12 1,16 6 1,19 13 1,19 7 1,43 14 1,05 Microstructural investigations.
Mordike (Eds.), Magnesium technology: Metallurgy, design data, applications, Springer, Berlin, Heidelberg, 2006

Data of Public Transport System in Czestochowa [8] Specification unit 2005 2006 2007 Number of passengers (total) mln 69,416 65,817 63,104 Lines length: Buses City Suburbs Trams km 603 593 398,1 194,9 10,2 603 593 398,1 194,9 10,2 552,8 544,3 405 139,3 8,5 Number of lines: Buses City Suburbs Trams Pcs. 36 26 10 1 36 26 10 1 33 26 7 1 No of buses No of trams pcs 154 48 164 48 148 48 No of km Buses Trams 13811 11437 2374 13848 11505 2343 Technical availability factor Buses Trams % 83 87,5 85,8 85,4 Fleet utilization rate Buses Trams % 78,7 64,6 80 62,5 The above table shows that urban transport in Czestochowa loses its passengers, year by year.
The adopted transport policy for the city of Czestochowa, leads to the following specific objectives: - Increasing the efficiency of the city with the increasing levels of motorization, - Improving the quality of services offered by the municipal transport company, - Improving parking conditions, - Reducing emissions generated by transport, - Noise reduction, - Improving road safety, - Improving the economic efficiency of transport.
The data for the year 2015 are only estimates, however, given the nature of the investments and sustainable transport policy objectives, it can be assumed that the use of public transport and cycling will increase, together with the reduction in the number of accidents and pollution emissions into the atmosphere.

Introduction Process data are collected and stored in process industries with the rapid development of computer and communication technologies.
However, PCA can only extract the Gaussian information and the process data often contain non-Gaussian information.
However, due to a measuring delay of 15 min existing in the analyzer, the number of data in each run is just 192.
Strandt: Data-driven soft sensors in the process industry.
Gu: A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine.

The data set X = {x1，x2，…，xj，…，xn} is divided into C type.
Firstly, the collected data is processed by principal component analysis.
According to the formula (3), the main element of the training sample data is ti（ti∈Rp，i=1, 2，…, m）.
The data is converted 12 to 4 dimension, so the data of feature extraction for fuzzy C means clustering algorithm can greatly reduce the computational complexity and the amount of calculation, so as to improve efficiency of the fault diagnosis.
Because of the principal component analysis for feature extraction, data dimension reduction can reduce the amount of calculation.

They differ in ways of forming data flows and effective data rates within the communication channels.
The version of the recording systems is selected mainly depending on the performance data of the monitoring items.
Network topology generally defines efficient allocation of data flows among computer-aided tools of the recording system.
This unit receives data flows from industrial computers, organizes a data base and forms data flows of oscillograph visualization for the client computers on their requests [6].
Secondly, the use of the data base as a link between industrial and client computers significantly decreases the main effective rate of the data flows received by clients.

PLC control pneumatic solenoid valve action, fixed both ends of the square steel tube, so that testing data have a baseline; then PLC control servo motor drag inspection car drive.
The maximum deflection or near of square steel as the pressure point pressure on the two support points that symmetrical to the pressure point distribution as far as possible; input total reduction (straightening stroke and direction) through the touch screen, send to the PLC, start the correcting button.
On the display there are administrator rights, square steel tube bending curve and scale, each measuring curve comparison, mobile caliper, sending the correct amount to PLC, test data, and so on.
When finish it, the PLC send signal to test car to test, and the test car start testing and show the test data on the IPC touch screen.
Table 1 PLC communication parameter setting The PLC receives data VD230 Instructions To send data Vb101 PLC Instructions 10 PLC and PC communication is normal; Laser displacement sensor initialization is completed 10 Check whether the communication is normal; Initializes the laser displacement sensor 18 PC laser displacement sensor data acquisition and data processing >100 Specific correction data volume 19 Correct amount no incoming PLC, alarm Table2 Industrial PC communication parameter setting PC receives the data Instructions PC data sending data Instructions 10 Testing the car starting position error;Initializes the laser displacement sensor 10 PLC and PC communication is normal;Laser displacement sensor initialization is completed 18 Laser displacement sensor data acquisition and data processing 19 Correct amount no incoming PLC, alarm >100 Correct amount of data transformation: transmitting data = 5000 + - (actual amount corrective/(10/398), rounded integer), amount is

The real-time monitoring data produced by a large number of vibration sensors are vast quantities, and compared with these data, the CPU, local storage, data communication device installed in the mobile equipment are very limited.
Thus, all data uploading or local long-term saving is almost impossible.
Therefore, it is necessary to carry out online anomaly detection in sensor data streams, in order to retain the real-time data of the anomaly condition for further diagnoses and processing.
Data Driven Anomaly detection via Symbolic Identification of Complex Dynamical Systems.
Symbolic time series analysis of ultrasonic data for early detection of fatigue damage.

Especially with the continuous increase of production quantity and production time, some oil wells in our country are in a state of reduction or stop production and therefore have conducted a large number of hydraulic fracturing tests and accumulated a large number of accurate production data.
If we don't preprocess the data of fracture pressure, the data might lose authenticity.
Examples and analysis of results Use the data of 25 producing wells in a certain oil field as the input data to train the BP artificial neutral network.
A part of the input data is listed in the following table.
A PART OF DATA OF PRODUCING WELLS.

Although the different images have different three-dimensional data types, the data connection and the spatial distribution, the process of extracting three-dimensional data from the image reconstruction is basically the same.
The second step is the refining and processing of data.
Generally, for the raw data, proper selection and deletion is needed to reduce the amount of data in the reconstruction process.
This method considers the voxel in data field as a translucent material, and gives it some color and opacity to simulate the value of each sampling point and the gradient of sampling point in the volume data, and so on.
Then we assorted volume data to ascertain the transform function of each type voxel color and opacity.