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Data based process monitoring methods can be divided into qualitative and quantitative methods.
Qualitative methods aim to analyze the dynamic tendency of process data.
Chen [5] puts forward an improved PCA to solve the dimensionality reduction problem of process data.
On the other hand, support vector data description (SVDD) method can deal with non-Gaussian information existing in process data perfectly without any restriction in data distribution.
All the researches will promote the development of data-driven process monitoring methods.

Liu Cobalt Imidazolate Framework as Precursor for Oxygen Reduction Reaction Electrocatalysts.
Unraveling the Nature of Sites Active toward Hydrogen Peroxide Reduction in Fe-N-C Catalysts.
Data Br. 19 (2018) 281–287
Single Cobalt Atoms with Precise N-Coordination as Superior Oxygen Reduction Reaction Catalysts.
Cu,N-codoped Hierarchical Porous Carbons as Electrocatalysts for Oxygen Reduction Reaction.

The data obtained were used to examine the effectiveness of the current treatment process in removing NOM.
The samples were collected at approximately the same time (0900 until 1100) to avoid variations of the data obtained.
The results are consistent with the data reported by [6], where 70% and 80% of UV254 were reduced at two different plants using the conventional treatment process.
Consequently, a lower reduction is expected.
Thus, a higher reduction in UV254 was observed in LBWTP.

The variable parameters of the process were: rotational speed asymmetry factor, av ; strip shape factor, h0/D; and cross-sectional area reduction ε.
The paper presents the results of rolling 10 mm stock with a rolling reduction of ε=0.15.
Strip curvature for a constant strip shape factor of ho/D=0.0091, rolling reduction of ε=0.15 and changing asymmetry factors: a) av=1.01, b) av=1.03, c) av=1.05, d) av=1.08, e) av=1.10, f) av=1.15, g) av=1.20 As indicated by the data in Figure 1, a straight strip was only obtained for the asymmetry factor values of av=1.01 and av=1.08.
av ld [mm] Upper roll Lower roll Strip curvature ρ [1/m] Lde [mm] Lde/ld [mm] Lad [mm] Lde [mm] Lde/ld [mm] Lad [mm] 1.01 28.72 13.64 0.48 15.08 16.49 0.57 12.23 -0.04773 1.03 28.72 15.13 0.53 13.59 17.75 0.62 10.97 -0.47981 1.05 28.72 14.09 0.49 14.63 19.03 0.66 9.69 -0.53971 1.08 28.72 11.49 0.40 17.23 23.24 0.81 5.48 -0.11136 1.10 28.72 9.45 0.33 19.27 26.31 0.92 2.41 0.527612 1.15 28.72 3.30 0.12 25.42 28.72 1 0 1.392111 1.20 28.72 3.56 0.12 25.16 28.72 1 0 1.453841 Based on the data shown in Figs. 2 and 3 and provided in Table 2 it can be stated that introducing a roll peripheral speed asymmetry has an effect of differentiating the lengths of the advance and the delay zones in the rolled strip on the upper and lower roll sides.
Distribution of rolling moments on the lower and the upper rolls during rolling 10 mm stock with a rolling reduction of ε=0.15 for the varying magnitude of speed asymmetry factor av The data shown in Figs. 4 and 5 indicate that as the speed asymmetry factor av increases, both the roll separating force magnitude and the rolling moment magnitude decreases.

For the case of China, as for the GHG reduction pressure, China is planning national GHG reduction actions besides preparing for the restoring international cooperation mechanisms.
GHG Emission Reduction baseline is set according to investigation of building industry.
Data is mainly collected from the published international and Chinese documents, and local contacts at architecture design practices [9].
Microeconomic efficiency and GHG emission reduction is calculated using interpolation algorithm.
Emission reduction of low carbon scenario is 0.68×106t, and emission reduction of ideal scenario is 1.36×106t.

CBR solves a problem very quickly since it only reads the training data without further processing[1].
In this study, a set of association rules with greater predictive capability is derived from the training data.
Let D be a training data set where each transaction has n distinct attributes A1, A2,…, An and a class label in G = {G1,G2,…,Cm}.
Any data with missing values are eliminated.
Whenever a rule does not classify any rows of the data, it will be removed because a rule preceding it has correctly classified its instances.

Introduction Environment and disaster reduction small satellites (A and B satellites) were launched by rocket on September 6 2008.
B.Hyperspectral data preprocessing 1) Data format conversion: First converting the .HDF format to ENVI standard format file and the projection transformation was performed, which projection parameters were: Projection - UTM Zone 47, Datum is WGS 84. 2) Radiometric calibration: The Level2 HSI data stored radiated intensity, so the true radiance was calculated as (1) Lλ = L/10 (1) Where: Lλ is radiance on the satellite, its unit is W/ (m2.sr.um), L is radiation intensity.
Atmospheric Correction of Hyper- spectral Imagery: Evaluation of the FLAASH Algorithm with AVRIS Data.
[8] GHULAMAbduWasit, Qin Qiming,Zhu Lijiang. 6S Model Based Atmospheric Correction of Visibleand Near-Infrared Data and Sensitivity Analysis.
[10] Xu Meng, Yu Fan, Li Yachun, et al., The Method of Atmospheric Correction on the EOS/ MODIS Data with 6S Model.

Based on improved linear regression filter method presented by Iwatani and wind tunnel test data, the program simulated 3-D time history of fluctuating wind pressure of Fuzhou Strait International Conference and Exhibition Center considering spatial correlation of wind loads, which is consistent with the object power spectrum .The simulation result is used in nodes of the structure to analyze response of wind-induced vibration.
Analytical results show it has the maximum displacement response in 0° wind direction .Due to the installation of TMD, node displacement response of roof truss is reduced from 44.14mm to 33.16mm, with best reduction 26.67%.
Fig. 1 Effect Diagram of FSCEC 3-D Fluctuating Wind Load Simulation Method Considering Spatial Correlation In the paper, improved linear regression filter method presented by Iwatani[6-8] is applied With the Davenport spectrum as object power spectrum and wind tunnel test data supplied by Hunan University, it is to simulate six cases of time history of fluctuating wind pressure of FSCEC under 0°, 45°, 90°, 135°, 270°and315°wind direction such as W00, W45, W90,W135,W270,W315.
Based on improved linear regression filter method presented by Iwatani and wind tunnel test data, the program simulated 3-D time history of fluctuating wind pressure of FSCEC considering spatial correlation of wind loads, which is consistent with the object power spectrum. 2.
Due to the installation of TMD, node displacement response of roof truss is reduced from 44.14mm to 33.16mm, with best reduction 26.67%.

The rough set theory is widely applied in data fusion, fuzzy recognition, artificial intelligence[6].
Software system for intelligent data processing and discovering based on the fuzzy-rough sets theory.
Rough sets-theoretical aspects of reasoning about data.
Rough sets intelligent data analysis [J].
Decision table reduction based on conditional information entropy.

In this case, the power reduction vs. travel distance curve, designated by the blue dashed line, is identical to the black dashed line, but the reduction in power is initiated before the melt pool size begins to increase.
Because those melt pool size increases now do not occur, the power reduction magnitudes are larger than they need to be.
This is clearly seen in the melt pool size data as the laser source moves away from the free edge.
The melt pool depth does not return to its steady-state value because the power (which is based on the red solid line data) has not been increased to its steady-state value.
Instead, power reductions must be initiated in advance of observed increases in melt pool size.