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Using principal component analysis method to extract characteristic parameters, and proposed classification to identify discrimination mean control chart time series is an ordered set of random data according to time,space or otherwise arranged.
AIC criterion： （4） The formula: indicates Length of the data samples, indicates the number of the statistical model, .
In Chapter IV ,Finite Element Analysis of the two conditions as the basic model, get sample measurement information numbers as shown in Figure 2.Assuming that damage is located three measuring points, the bending stiffness reduction of 30%.
Calculate the collection frequency is 204.8Hz, output amplitude frequency response data under normal state and the state to be detected .
Establish recognition model For normal state and the state of the data to be detected are selected sampling frequency 204.8Hz, collected 5min, 61440 data points of access.

Data center production line is mainly server-oriented for enterprises, producing VMware vSphere, VMware GSX Server and VMware vCloud.
Power consumption in data centers mainly lies in the IT devices such as refrigeration and ventilation, but power consumption of computers only accounts for the minority.
Data centers are in need of constant fund to update and maintain IT infrastructures, including purchasing cost of softwares as well as operation and maintenance cost.
Property data calculation in computer teaching rooms. 4.1 Use ratio of CPU.
References [1] Wu Ronglong, Design and Implementation of Data Collection and Control System based on VMware Visualization, 2008

The measurement from the test model verified the predicted data and a yearly potential from nocturnal cooling in this region was established.
The calculated value of Two inherit certain amount of uncertainty due to the uncertainty of input data into the mathematical model such as the actual wetted area of the roof, climatic data such as cloud cover, wind speed and ambient air temperature, relative humidity and dew point.
Measured and calculated temperatures shown together with climatic data for night cooling on 28th May 2015 in Kuching, Sarawak.
The data was input to the model to simulate the 7pm-7am night sky cooling for each month.
Monthly prediction of final Two at 7am in the morning for a year was established from the 5 years climatic data of the this region.

The management STD data are called spatial data.
The data included basic road data, road map data, statistical information, etc.
Of course data recovery, data backup, and data management function are included.
If the data shows information on transformation or changing events it will result in reduction of the multi-granularity set.
On the other hand the reduction can be carried out through system inquiries or operation such as indirect correction of the frequency threshold and dividing the data density.

To obtain the minimum sphericity error from the form data, an approximations technique is devised.
First the method is modeled, then the process of approximations is constructed, and finally software for data processing is programmed.
The sphericity data must be obtained using appropriate inspection devices such as coordinate measurement machines (CMM).
The data must also be processed using appropriate techniques to obtain reliable results during the evaluation of sphericity error.
After taking into account all possible results and branches in the process of the geometric approach, we have a data processing software programmed.

Synthetically, short term experiment data and long term simulated results were considered to decide the optimum air injection position. 2.
In accumulated data, ambient temperature can present the temperature of the lysimeter inside.
This can also be demonstrated by comparing the TOC reduction data in the three lysimeters.
Results shows that the data of simulation meet the experiment data very well, which prove the model is qualified to predict longer period data for reviewing optimum air injection condition.
The initial conditions were given according to previous experiment sampling data.

Further, computer enabled machining centre made it possible to enhance the process capability through utilization of digital setup and real time data acquisition system.
Table 2 Experimental data Factor Units Low Level Medium Level High Level Output Responses Cutting Speed (v) m/s 1.25 (V1) 2 (V2) 3 (V3) Cutting force, Chip reduction coefficient Feed rate (f) mm/rev 0.112 (f1) 0.168 (f2) 0.246 (f3) Insert type - SNMG120408 (T1) CNMG120408 (T2) CNMG120412 (T3) Machining conditions - Dry (E1) and Minimum Quantity Lubrication (MQL) E2 Experimentation on cutting forces has been conducted with three different inserts like SNMG 120408, CNMG 120408 and CNMG 120412 using PSDNN 2525M12 and PCBNR 2525M12 tool holders.
The details of all input data has been shown in Table 4 enlisting 54 experiments with values of cutting forces and chip reduction coefficient.
Data have been collected as per the experimental observation and most appropriate value has been taken for the comparison purpose in all cases.
APPENDIX-1 Table 5 Data for Analytical Measurement of Cutting forces for SNMG120408 insert Sr No.

RFID is a non-contact automatic identification technology without touching the object, which automatically identifies the object and captures relevant data through the radio frequency signals.
Information decoded by the reader will be sent to central information system to carry out relevant data handling.
Data capacity is big.
Tag data can be dynamically changed.
Portable data terminal are the basic equipment of RFID.

The reduction of manganese and CODMn met the China Drinking Water Regulations.
Coagulation and ozone pretreatment aimed at the reduction of negative effect of membrane fouling.
The results of total iron and manganese of the permeate water shown in Fig.3.As can be seen from data presented in iron of permeate water was below 0.3mg/l, which met Chinese drinking water regulations.
A 36% reduction in DOC was obtained in the experiment, and 49% of UV254 was removed.

However, the size in height does not monotonically reduce as the reduction of the ink, which is affected by the interaction among the relative humidity, AFM tip, substrate material, surface roughness, etc.
However, the size in height does not monotonically reduce as the reduction of the ink.
The data of nanodots in diameter and height dimensions No. 1 2 3 4 5 Diameter[nm] 218 179 153 129 75 Height[nm] 10.642 16.784 10.381 2.958 4.448 Figure 2.
However, the size in height does not monotonically reduce as the reduction of the ink, which is affected by the interaction among the relative humidity, AFM tip, substrate material, surface roughness, etc.