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In order to meet data acquisition mobility and convenient requirements of the portable ECG monitor, the data acquisition system of ECG with dual CPU is designed in this paper.
Experimental data shows that the system has such advantages such as good repeatability, high accuracy, quick response and excellent anti-interference.
The measurement data can be recorded to the SD memory card with SPI mode.
Using this algorithm, 56 groups of ECG measuring experiments were selected as major test data.
Aiming at incorrectness data appeared in experimental results, the majority of failure data comes from random error.

Fig.1 The framework for EEG signals classification 1.2 EEG Data Acquisitions and Preprocessing The experiment data collected from the subjects aged 21~30 are elicited by three different simple schematic faces (happy, neutral, sad) using neuroscan with 66 electrodes.
Fig. 2 illustrates the EEG data acquisition processing using visual stimulus [7].
Fig.2 The EEG data acquisition The purpose of preprocessing the selected EEG signals is getting clean EEG signals without other noises.
Thank EEG data provided by Dr.
[5] Koelstra S, Muhl CandPatras I,in: EEG analysis for implicit tagging of video data, in: Affective Computing and Intelligent Interaction and Workshops, 2009

Data collection was done by using stop watch time study and work study was done by video analysis.
TAKT time was calculated based on demand of entire cell and TAKT time against cycle time, based on these data the current state value stream mapping has been drawn by using i-Grafix 2009 software.
Data of cycle time of the operations under scope are video captured, which are then carefully analyzed to eliminate NVA and reduce NNVA.
Based on work standardization methodology there are three cycle time study were performed during data collection.
Line balancing concepts were applied for the existing layout, based on data collected for the standardization operations.

In this report, we try to apply the SCEM to the growth rate data at various oxygen partial pressures and discuss the oxidation mechanism of SiC more in details by making a comparison between calculated growth rate curves and the observed ones.
The figures indicate that: (1) the observed data show an abrupt reduction in the thickness region less than 10 nm especially in the case of lower partial pressure, (2) after the abrupt reduction they gently reduce, (3) the calculated curves successfully reproduce the observed growth rates though restricted in the mild deceleration region, (4) the calculated curves show the rapid reduction more remarkable as higher partial pressure, which is opposite tendency to that of observed data.
The figures and point (1) indicate that the growth rate in the abrupt reduction region slightly decreases with decreasing partial pressure [5].
Although the calculated growth rate curve is still deviated from the measured data, we consider that further refined parameters will make a better fit to the observed data.
The calculation results exhibit that a rapid growth rate reduction in the initial oxidation stage eased as lower pressure, which is opposite tendency to the observed growth rates.

According to the reports[5], reduction of U(VI) to U(IV) by SRB involves at least three processes: (1) U(VI) binding to the cell surface and to extracellular biopolymers (biosorption); (2) chemical reduction of U(VI) by microbially generated hydrogen sulfide (H2S);and (3) bioreduction of U(VI), which is enzymatic dissimilatory metal reduction with U(VI) acting as a terminal electronacceptor.
As shown in Fig.3, the rate of reducing U (VI) by the reduction of electron transfer was fast, esp. in the first 6 hours.
It could be seen by comparing and analyzing the data that the U (VI) removed through the reduction mechanism of electron transfer accounts for nearly 80% of the total amount of U (VI) removed by original bacteria fluid.
SRB may detoxicate Cu2+ in some other way, which will also affect the rate of removing U (VI) by SRB. 2.4.1 Inhibition effect of Cu2+ on U (VI) reduction and removal by H2S It could be seen from Fig.6 that Cu2+ could inhibit U (VI) reduction and removal by H2S to some extent.
The reduction of U (VI) by H2S is realized by the H2S generated during the process of electron transfer.

PCA is a method of a dimension reduction where it discards the noise by projection of the data onto the main principal components.
Methodology Our denoising step consisted of using the dimension reduction technique and find an adaptive basis in the projection data set.
This because the hidden or "latent" data structure is masked by noisy dimensions and becomes evident after the dimension reduction.
It does this by transforming the data to a coordinate system so that the greatest variance of the data by a projection of the data into the principal components.
PCA can be done via SVD of the data matrix.

This article proposes a method for incrementally changing the cutting data in order to minimize the manufacturing cost.
However, companies with a large part machining time will have the most to gain in terms of reduction in manufacturing cost.
The data obtained when using the Incremental Production Improvement method could be used to model the tool life.
During this case the cutting data was varied according to Table 2.
The index 0 denotes for the original cutting data and index i denotes the cutting data for the current machining case.

Methodology The methodology we used in this study draws on data envelopment analysis (DEA).
The reason for selecting only ten firms is data availability during the review period 2014–2018.
Table 1 illustrates the data set we used in our study (the full data set is present in Table A1 in the Appendix).
However, it should be noted that VW emissions data were allegedly manipulated during the review period [33] Hence, the validity of these data is questionable.
In Handbook on data envelopment analysis, Springer, Boston, MA, 2011, pp. 273-295

Based on the site survey and observational data, this paper aims to determine the deformation mechanisms and development stages of the landslide, apply the strength reduction method to calculate the slope stability and put forward the corresponding control measures .
According to the drilling data, the lower part of the sliding body obviously extruded and disturbed, it can be seen a smooth sliding surface at the contact surface with the underlying bedrock, and acicular scratches and sliding bands step.
Figure 6 Horizontal displacement relationship between strength reduction factor Ft and slope soil The maximum horizontal displacement of the soil body and the strength reduction factor Ft is not a linear relationship, with Ft increasing, horizontal displacement also increases slowly, but when ft = 1.3, the displacement mutation, indicating that the slope beginning sliding, when Ft ≥ 1.5, slope calculation model does not converge, indicating that the slope is completely destroyed. based on failure criterion of the strength reduction, determining the safety factor is Fs = 1.3.
The finite element strength reduction factor of slope stability [J].
Finite element strength reduction for slope stability analysis [J].

Traditional studies often use statistical analysis to process monitoring data.
These analysis methods can provide more intuitive information support for air pollutant data management, dispersion modeling and spatial distribution analysis.
Study area and data processing methods Yantai city, locating on the east tip of Shandong Peninsular (36°16′-38°23N, 119°34′-121°67′E) , bordering on the Yellow Sea and the Bohai Bay, lies to Japan and Korea across the sea.
The monitoring data is collected by Model 43 pulsed-fluorescent SO2 analyzer that includes daily data of SO2 from 2008 to 2010.
And the geospatial analysis methods provided intuitive information support for air pollutant data management, dispersion modeling and spatial distribution analysis[[] Pulugurtha S S, James D.