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A company look back on historical data for components produced in the same process line within a specific period from Jan’20 until May’20, each type of defects can be classified as presented in Fig. 2.
Thus, these measured data can be used for estimation the process variability well.
Regarding the residual plot, the assumption checking is done by observation of plotted data.
Process capability analysis is calculated by using per cent graphite nodularity data from 36 and 30 production lots compared before and after improvement respectively.
Contactless Chip Module Defect Reduction.

Based on a two-week benchmark data log, the result shows a total energy reduction from 210 kWh to 71 kWh, representing a saving of 65%.
Table 1 lists the technical data of all the lamps in the system.
Power is computed from voltage and current data logs.
Figure 1 Typical half hour power consumption plot This part of the research involves capturing live data from the system by the aid of a data logger to get a clear understanding of the usage pattern.
As the demand was not uniform, it was inevitable to go for long term data capturing and monitoring.

The monitoring system of water environment is composed of data collection, data transmission, data storage and data reasoning components.
Introduction Cluster analysis is an important component of data mining, mainly used for finding the valuable data distribution and data models in the potential data.
The algorithm is not sensitive to dirty data and abnormal data.
Summary The water environment monitoring system composed of data collection, data transmission, data storage and data reasoning components.
Outliers in Statistical Data[M] .

A newly developed response prediction technique has been successfully used for the identification of more detailed information from limited sets of data.
They are written as follows Simple Vibration Test Upgrade and Optimization Response Prediction Structure Limited Data Full Field Response Data { }{ } { }{ } { }{ } { }{ } { }{ } { }{ } 2 T n n 2 2 T T n n n n U v RU v MAC= U v U v RU v RU v                  
Conclusion With the response prediction technique, the full field response data can be deduced from the limited data obtained by simple vibration test instead of conducting full-size test, which can present reference for the succeeding upgrade and optimization in dynamic design.
References [1] Chipman,C, Expansion of Real Time Operating Data [D],Master's Thesis,University of Massachusetts Lowell,May 2009 [2] P.Pingle, C.Niezrecki, P.Avitabile.
Real Time Operating Data Expansion for Dynamic Stress and Dynamic Strain Fatigue Accumulation [A].

All partial data affected by the presence of other vehicles was removed to maintain the most consistent experimental conditions.
Data Manipulation Data that affected the driver's physiological and psychological and driving behavior characteristics in non-tunnel lighting conditions was excluded in the 50 groups of sample data obtained in the experiments and from the 15 groups of samples in the test of car drivers in free-flow traffic conditions.
Based upon the calculation principle of the representative value, we calculated the 15 data groups shown in Table3.
(1)Relationship between CRP and LRC The relationship between the CRP and the reciprocal of the LRC in the 15 groups of sample data from the tunnel threshold zone lighting can be drawn on a scatter diagram in Fig.2.
Fig.2 Relationship between CRP and the reciprocal of LRC (2)Relationship between CRP and V The relationship between the CRP and V of the 15 sample groups of data at the tunnel threshold zone lighting is shown in the scatter diagram in Fig.3.

Data in Cleveland database comes from the Cleveland clinic foundation, is provided by Robert Detrano.
Thus, each data sample contains 20 attributes, all data samples are divided into two classes.
Second, run the attribute reduction algorithm based on the rough set theory, take 13 items of properties with better distinguish ability as reduction set, and then construct the corresponding reduction data sets.
References [1] Derchiang Li, Yaohwei Fang, An algorithm to cluster data for efficient classification of support vector machines [J].
Rough sets theoretical aspects of reasoning about data [J].Kluwer Academic Publishers, Dordrecht,1991, 39(1):110-113

There are infinitely many choices of data that meets the requirements presented to.
Introduced data in Tab. 1 and Tab. 2 have been selected just for the sake of values related to size.
Figs. 5, 6, 7, 8 present the comparative analysis of the system with and without vibration reduction.
Białas, Mechanical and electrical elements in reduction of vibrations, Journal of Vibroengineering, 14, 1 (2012) pp.123-128
Białas, Electrical Elements in Reduction of Mechanical Vibrations, Applied Mechanics and Materials, 371 (2013) pp. 657-661

It can be effective for large-scale incomplete ocean data reduction and it also provides a strong basis for decision making for the marine environment processing and follow-up processing.
The prevalence of incomplete data in marine monitoring and other areas of the internet of things bring tremendous difficulties to data fusion, data mining.
In order to mining knowledge from incomplete data, Literature [6] constructed a new similar relationship.
These studies are for static data, but in reality in many databases are dynamic.
Conclusions The traditional approach to deal with incomplete data is make it completed.

For this paper, we use the Co loading values measured by EDS analysis in the data analysis and discussion.
Fig. 3 Koutecky-Levich plots for the ORR on CoNC coated glassy carbon electrodes Fig. 3 shows the Koutecky-Levich plots for the ORR data taken at the potential from -0.1 to 0.2 V.
The data comes from the current-potential curves (Fig. 2) obtained using CoNC2.
Its approach contrasts with the theoretical plots for 2- and 4-electron reduction reaction in Fig. 3.
It could be suggested that the CoNC2 can alter the ORR reaction mechanism via both the 2-electron reduction reaction and the 4-electron reduction reaction. 2.3 Scanning electron microscopy measurement Fig. 4 shows SEM images of a sample of CoNC2 catalyst magnified 5000 and 10000 times.

“Advances in knowledge discovery and data mining”[M],AAAI/MIT press, 1996. pp. 83-115
“Data mining: An overview form database perspective[J], IEEE Trans.
Knowledge and Data Engineering,1996,8, pp. 833-866
Data-drive discovery of quantitative rules in relational databases[J] IEEE Trans.
Knowledge and Data Engineering,1993,5, pp. 29-40 [8] R.GOLAN.