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Different methods have been used to compensate the effect of humidity, which usually require utilization of other parallel environmental sensors and costly data fusion methodology.
It is shown that by utilizing the “thermal shock-induction” method for the temperature modulation of the sensor, the drift levels are low, and with a single set of training data collected at RH=50%, responses obtained in the whole humidity range can be discriminated from each other.
The influence of drift-like terms such as temperature and humidity caused by environmental fluctuations can severely affect the responses of a metal oxide gas sensor [11,12] and this can induce significant variations in the calibration data.
The method introduced in the reference [10] was utilized for feature vector formation and dimension reduction.

Investigating the property of metal dry friction damping can give many usable data for response analysis of damping blade which is important in damped blade designs.
The variations of friction coefficient of stick-slip area, equivalent stiffness and equivalent damping were calculated based on experimental data.
The effects of the material damping and the aerodynamic damping are not significantly on the turbine blades vibration reduction.
Fig. 3 Stationary friction specimen Fig. 4 Moveable friction specimen Data Acquisition and Analysis During the tests, the relative displacement u and the friction force f were measured under different exciting force amplitudes, exciting force frequencies and normal contact forces.

Data collection is composed of non-contact laser scanner Comet300 scan.
Point cloud data preprocessing in the integration of point clouds, noise, filter, grid and other functions of the software platform in.
Fig 1 The heights of tractor reverse design Fig 2 Tractor wheel cover of cloud data Point cloud data acquisition and preprocessing Experiment data acquisition is the first step of reverse engineering modeling, it is to use certain experimental device for physical measurements to obtain the real surface data.
This experiment is to use laser non-contact 3D measurement part surface data, is based on the structure light COMAT300 non contact coordinate scanning system with high resolution high speed scanning object surface [2]. 2.1 Point cloud data merging Scanning high of wheeled tractor wheel cover, due to wheel cover original size is bigger, need several scanning, so with the use of software automatic registration system, with the help of shot from multi-view cloud reference point coordinate position, and then use these reference points to establish the corresponding relationship between the point cloud, the system comes with software in the automatic registration of point cloud. 2.2 Point cloud noise reduction filter Because of our incoming data to be brought into noise, especially near the borders and corners are relatively obvious, the laser scanning the obtained mass data, it will seriously affect the surface reconstruction speed and precision, so must the point cloud data preprocessing
We should first remove while scanning scanning parts except the extra point, add missing the point, eliminate the error of measurement ( digital filter, mean filter ), removing data stitch overlapping redundant data, in ensure the existence of retention characteristics under the premise of point cloud, filtering and so on, after preprocessing the rear wheel cover of cloud data, as shown in figure 2.

Hitherto, no attempts have been published that predict nitrogen concentration profiles in low temperature nitrided stainless steel, which most probably owes to a lack of reliable thermodynamic and kinetic data.
The model relies on recent data obtained by gaseous thermochemical methods, under well-defined thermodynamic conditions [3,4].
This interval corresponds to the range where nitrogen can be removed from solid solution by a reduction in pure hydrogen, contrary to the region yN < 0.177 where nitrogen cannot be retracted by reduction in hydrogen gas [3], i.e. trapped by chromium.
For yN > 0.177 the composition dependence in Fig. 1A was obtained by fitting of a second order polynomial through the experimental data from ref. [4].
The surface concentrations for the calculations of the profiles in Fig. 1B were kept constant thus reflecting the occurrence of local (para)equilibrium at the gas/solid interface, i.e. the equilibrium data for nitrogen solubility obtained in ref.[3] were applied.

Methods and Data Sources Functional Unit and System Boundary.
Data Sources.
In addition, the supplement and improvement of the data was based on Chinese steel industry clean production level evaluation standards and steel waste residue recycling technology. [15] Results and Discussion Water Consumption Inventory Analysis.
The data includes amount of fresh water input and evaporation water (WRC1), solidification water (WRC2) and wastewater (WRC3) consumption.
Conclusion Data collection and quantifying the water consumption intensity of steel production have been carried out by using the innovative process-based water-accounting model for BF-BOF technology in China.

Palm oil showed about 50% reduction in engine torque for the blends of 3 % and 5 %, while naphthalene showed about 11% reduction for the engine torque when the engine speed is more than 4000 rpm.
The engine was left to be idle operation to warm up for 5 minutes before starting the data collection.

Linear Regression Analysis Data Regression analysis is a statistical technique for investigating and modeling the relationship between variables.
The Ppk value is used by the FMEA team as guidance to assist in determining an occurrence ranking when valid statistical data is available [12].
This is because i-FMEA data should be sharing during new model discussion between design and process group especially during hand over job for new model.
Potential Failure Mode Critical Dimensional Data (22 samples data) Key Process Factor (22 samples data) Regression Analysis Results (p) Significant Ppk Results Old Occurrence New Occurrence Gap between matching parts (D1) 11 Rivet Diameter Riveting Force P = 0.000 YES 0.61 8 9 Rivet pin diameter out of spec.
[9] Chin, K.S, Wang, Y.M , Poon, G.K.K and Yang, J.B, Failure Mode and Effect Analysis by Data Envelopment Analysis.

In the model, the historical data of inner profile was saved and analyzed, If L1 ≥ Lmax, the value of Lmax will be updated by the value of L1.
An excellent online system is largely dependent on a reliable data acquisition.
Fig. 4 shows the structure of the data network which was designed to ensure the real-time reliability and security of the data in the system.
The needed data was saved to the table (TempData) of the real-time database (XG9GL) on the SQL SERVER platform and therefore the models could call them at any time.
IFIX network data server IFIX local real-time database LAN SQL SERVER model database(XG9GL) Online system VBA program Read online data Save calculation result Fig.4 The structure of the data acquisition system Fig.5 An interface of erosion and skull monitor The software system had been running with the XISCO No.9 blast furnace since June 2010.

The parameters are determined from the construction data, and a few of them from the field test data.
The model is validated against the unique plant data and the results are presented.
Introduction System or process identification is a mathematical modeling of systems from test or experimental data.
Fig.1 Integrated Boiler model Results and Discussions The data needed for the dynamic simulation of integrated boiler model are collected from the Neyveli Thermal power plant and the input data are listed in Table 1.
Steady State results are compared with the actual plant data.

In 1994 and 2000 there was no data available about permanent private households.
The period selected for analysis was from 1970 to 2009, with some exceptions, for which no data was available.
Data of the 1991 PC were compared with the BH of 1992, 2000 PC with 1999 BH and 2010 PC with 1999 BH.
Data of 1995, and before, was dismissed for using a different methodology.
These data are linked with the characterization of the public research participant profile.