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Point Cloud Data Acquisition and �oise Reduction.
Data acquisition is the first step in RE.
The measured data from CMM (model ATOSⅡ400) are saved as a general format and then imported into a data preprocessing program.
In the processing, noise points in point cloud data will be eliminated by noise reduction processes.
Reconstruction of Point Cloud Data.

According to the traffic survey data and meteorological data of a certain section between Jinshan and Wuhan on Beijing-HK-Macao Expressway， the change of the weighted average speed and the speed of different vehicle types under different visibility grades was studied, and the linear speed-density model of Green Shields was improved using the method of curve estimation and regression analysis. 
Sources of Data Traffic survey data were obtained by the microwave detector on a certain section between Jinshan and Wuhan on Beijing-HK-Macao Expressway, the detecting time is from April 22, 2012 to July 18, 2013, a total of about 400000 pieces of original data.
First of all, the relationships between speed (V) and visibility (D) and vehicle types (S) are estimated by the method of curve estimation using data set.
In order to test the rationality of the improved model, according to the actual testing data，regression analysis is applied to calibrate the linear speed density model and the modified model.
The vehicle speed for 4 data sets in mist, fog, strong fog and dense fog on this section is calculated and compared with the actual speed.

This is the foundation for any data analysis concerning position sensitive process data and any machine learning model utilizing data from multiple manufacturing facilities and work steps.
Data Acquisition Each manufacturing facility contains different sensors for process data acquisition.
This paper presents a similarity-based approach to join information from different data sources with production data.
A short-term benefit is the time reduction for several manual tasks.
Schreck, Visual Data Analysis of Production Quality Data for Aluminum Casting: HICSS (2021), 1530-1605 [2] P.

These data were interpreted to find the potential region for adsorption or desorption of the fluorosurfactant and its influence on metal deposition.
An EcoChemie µ-Autolab II potentiostat with NOVA 1.7 software was used to carry out potential sweeps and record data.
Figure 2 shows that the addition of surfactant has cause a shift in reduction potential.
The CV trace shows that Cu reduction now occurs at -0.43 V.
These data show that the surfactant adsorption blocks the Cu discharge.

A complete DRX microstructure occurred at the reduction of 72%.
EBSD was employed by using a Hitachi SU3500 scanning electron microscopy (SEM) and the data was analyzed by the OIM software.
By comparing with the specimen subjected to the reduction of 30%, the rate of static recrystallization of the 49% reduction specimen was higher.
Nevertheless, the texture of the specimen after 49% reduction was higher than that after 30% reduction.
At the full recrystallization, the average grains size of the 30% reduction specimen was more than twice larger than that of the 49% reduction specimen. 3.

The process is repeated until all input data points are assigned to an output neuron.
Through using the successful constructed data and design experience within the database, it will exactly acquire the initial data without additional design time and cost.
In step2, the FCM is utilized to cluster the data with the given number of clusters.
Fig. 5 shows the clustering result distribution status of the data in step 2.
Clustering result distribution status of the heat exchanger data in stage 2.

The Laser Polishing tests results have been used as the input data for a design of experiments (DoE), therefore the optimum operation parameters for the process as well as its degree of influence in the melted surface have been defined.
Maximum roughness reduction parameters with CO2 laser.
Maximum experimental roughness reduction with HPDL laser.
Therefore, for the same material and similar roughness reduction rates, the influence of the laser type is relevant.
Conclusions The laser polishing process, in tool steel DIN 1.2379, allows roughness reductions over the 80 percent.

Iron pollutions were no more than 0.1% (XPES data).
The data obtained by SEM and the laser analyzer showed that the characteristic powder particles size was about 40-50 nm, and the particles themselves were incorporated in large agglomerates around 200-500 nm in diameter.
Changes of chemical composition of the sample upon grinding the manganese oxide (III) a - Set D; b - Set А+С; c - Set B (selective data) It is shown, during grinding in a closed volume the oxide phases formed (Fig.1a) can stay long enough in a state close to a "dynamic equilibrium".
Changes of crystal lattice parameters of the phases forming upon mechanical treatment of a manganese oxide (III). a - Seria D; b - Seria А+С; c - Seria B (selective data) Apparently, a speed of phase formation for this or that phase can be adjusted both by changing the intensity of grinding (by varying M/m) and by managing the gas atmosphere over the reaction zone.
Evidently, the reduction of MnO proceeded at an advanced pace, and the gas atmosphere generated over the mixture suppressed a reduction of the Mn2O3 oxide.

Up to 48.26 percent CO2 reduction may be achieved.
Estimation of amounts of WPT availability and potential products conversion Data on the amount of waste palm trees due for felling were generated based on Malaysian Palm Oil Board (MPOB) published data [1].
The data on the hectarage of palm trees that will mature and due for felling is presented in Fig. 1.
Data collected were the type of oil palm biomass used as raw material, product produced and amount utilized.
Ea(WPT) = Ea(Trunk) + Ea(Fronds) + Ea(Fuel used during felling) (2) Calculation of Ea for various parts of WPT uses the data shown in Table 2 where the carbon contents of the respective parts of WPT are shown.

When the diameter is small, narrow band noise reduction is gotten.
The larger the diameter increases, the broader noise reduction would be.
When it got to 80mm, a wide band of the noise reduction is reached.
However, wave propagation is due to the combined effect of inertia and elasticity of the medium in the presence of flow in the ducts (Zhenlin Ji, Engineering Acoustics and Noise Control, Harbin, China, unpublished data), and therefore a wave moves relative to the particles of the medium.
Using non-flow simulation data, the simulations of the acoustic performance of the angle between the main duct and the bypass duct and the changes of the diameters with mean flow were taken.