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The data are acquired spirally at the sampling interval of 1 mm.
Result and discussion LS WT by filtering frequency, amplitude and both (combined method) are applied on the sample data to study their performance of noise reduction.
Fig. 12 shows the distributions of GBIR of (a) raw profile data and three profile data filtered at (b) frequency domain, (c) amplitude domain and (d) combined method.
Average values of all noise data are nearly zero.
Standard deviations of filtered noise are equivalent that of raw data.

Mechanochemical Reduction of MoO3 Powder by Silicone to Synthesize Nanocrystalline MoSi2 H.
Further milling resulted in a gradual decrease in MoO2 peak intensities because of its continuous reduction.
Milling time [h] d(MoO2) [nm] d(Si) [nm] d(βMoSi2) [nm] d(αMoSi2) [nm] Strain η [%] 6 16.6 49.2 --- ---- 0.275 12 --- 204 --- ---- 0.687 17 --- 15.2 32 48.2 ------ 22 --- --- 17.6 26 0.36 28 --- --- 17.6 25.7 0.52 33 --- --- 12.5 19.2 0.64 50 --- --- 11 9 0.93 Table 2. shows the thermodynamic data for the starting materials and the reactions occur during the mechanical alloying [10].
Right after, due to release of the reactions heat, temperature rises up to which the reaction between molybdenum and silicone starts concurrent with MoO2 reduction reaction.
Thermodynamic data for starting materials and reactions occur during the mechanical alloying at room temperature [10].

Anjos et al. [5] proposed a reduction in weight of the pick-up loading bed by replacing the metal pan-els with polyamide 66 composites with 40% glass fiber, with a weight reduction of 56% in comparison to the traditional structure.
According to Klein et al. [2], a promising technology to improve product performance by weight reduction are carbon fiber reinforced plastics (CFRP).
Table 3 shows the listing of some materials that met the requirements in place of the components evaluated, together with the materials to be replaced, listing the properties and costs according to data from the CES EduPack™ 2018 program.
The materials were compared using the Pugh matrix according to the data from the previous version (Table 3).
Weight reduction of the pickup cargo bed by replacing metal panels with polymer composite materials.

This analytical model can explain almost all experimental data: oxidation-temperature dependence, oxidation-time dependence, and initial-Z1/2-concentration dependence of the defect reduction.
Based on these data, an analytical model of defect reduction is proposed.
Each symbol indicates the experimental data and each line indicates the calculated nI curve obtained from Eq. (1)-(3).
Each symbol indicates the experimental data and each line indicates the calculated nV curve.
Each symbol indicates the experimental data and each line indicates the calculated nV curve.

They are specified in a veryhigh-dimensional tensor space and recognition methods operatingdirectly on this space suffer from the curse of dimensionality[3].Dimensionality reduction is commonly used to transform a high-dimensional data set to a low-dimensional subspace while retaining most of the underlying structure in the data [4].
In this paper, we use Kernel Principal Component Analysis (KPCA) for dimensionality reduction and feature extraction in railway tunnel deformation data analysis.
KPCA reduce dimensionality of original data.
The projected data could basically represent the original railway tunnel deformation data information, but its distribution in Eigen space is not compact.
Jain, "Incremental nonlinear dimensionality reduction by manifold learning," IEEE Trans.

The energy consumption data were derived from China Energy Statistic Yearbook 2002-2011 [6].
Shanghai’s population and annual GDP data were derived from Shanghai Statistics Yearbook 2002-2011[9].
Data of ESn and β were derived from Shanghai energy-saving report [10].
The historic data on carbon intensity reduction showed that the measures taken by Shanghai worked effectively in the past few years.
The data in Fig. 2 shows that industry sector accounts for over 90% of the total carbon emissions over the period 2002-2010.

Finally, the width reduction method was proposed to calculate the stiffness of beam after fire.
Multi function experimental furnace would be used in fire test, while the MIP board in data acquisition system.
This paper derived a method of stiffness calculation according to the width reduction method.
Section width reduction coefficient.
The section width reduction coefficient: (1) : The material strength reduction coefficient of unit (I, J)

In these methods worker intervention may be required, but it is minimized and the actions of the employee are supported with hardware and software solutions, that enable the reduction of the error rate of data acquisition and increase its speed.
Manual data acquisition Automation, robotics and mechanization allows a significant reduction in human participation in production process, but there are still processes that are not automated, mainly due to insufficient profitability.
Primary data sources.
Secondary data sources.
Secondary data sources can also create new data based on the raw data, that is necessary for control purposes.

This paper describes an investigation into the reversing process of H-beam using MARC software and compares the results with rolling data from the production line.
The influence of rolling reduction, the reduction rate between the web and flange are also discussed.
The rolling forces of all-passes obtained from the FEM analysis were compared with the forces recorded in the process data acquisition system (shown in Fig. 2).
The reduction rate between web and flange was adjusted by keeping the web reduction and changing the flange reduction.
For example, the theoretical rolling reduction is 7mm but the actual rolling reduction is 23.2mm.

When reduced, boundary region of neighborhood-based rough set narrows, on the contrary, open out, indicated that the neighborhood-based rough set has a certain degree of tolerance to the data noise, which can enhance the robustness of the produced rules.
The results show that the model can not only deal directly with the continuous attributes, but also has a certain degree of noise tolerance, it can enhance the robustness of data analysis and processing and is better adapted to practical problems in engineering.
References [1] Berka P, Bruha I., “Discretization and grouping: preprocessing steps for data mining”.
The 6th European Conference on Principles of Data Mining and Knowledge Discovery, Helsinki, Finland, 1998, pp.239-245
[4] Jensen R., Shen Q., “Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches”, IEEE Transactions on Knowledge and Data Engineering 16(12), 2004, pp. 1457-1471