Search results

The knowledge of the mechanism of SFs reduction is crucial for the development of a high quality material for devices fabrication.
Despite the great amount of work on the hetero-epitaxy of 3C-SiC, the mechanism of SFs reduction is not completely clear.
The data were represented through Reciprocal Space Maps [12] in the qx-qz scale as shown in Fig. 1.
By cross-section TEM it was consistently found that SFs density is reduced far from the original interface, in full agreement with the HR-XRD data.
So homo-epitaxy is an alternative solution to the growth of 3C-SiC on compliant substrates for the reduction of defects in the material.

The hatched areas highlight the drag-increase cases and the zero-R lines (dashed) are found by linear data interpolation.
of velocity components and Reynolds stresses, where u, v, w are indicated in the legend. 0 161 0 1610 287 574 861 1148 0 161 −0.50 −0.13 0.25 0.63 1.00 1.38 1.75 2.13 2.50 x+ z+ Fig. 8: Contour plot of ⟨udvd⟩ + in the y−z plane at x+=0, x+=143.5, and x+=287 respectively.Outlook on the future Empirical correlations from Pope [14] were used to estimate data for low-speed laboratory conditions and for three flows of technological interest for disc-flow parameters leading to R=20%.
Turbulent drag reduction through rotating discs.
Compliant coatings for drag reduction.
Aircraft viscous drag reduction using riblets.

Additionally, the results are consistent with the data of electrochemical noise (EN) that is generated of type 40Cr steel exposed to the acidic chloride solution and seawater.
Time to failure (Tf), reduction in cross-sectional area (RoA) and specific fractographic features of SSRT test specimens are chosen to characterize SCC susceptibility.
In this study Electrochemical noise (EN) analyses by direct observation were carried out on the potential and current data obtained on type 40Cr steel exposed to the different media.
The electrochemical noise data were collected using a sampling rate of 2 Hz.
The results of reduction of area (ROA) at different strain rates are shown in Figure 1.

The author presents the mathematical basis used for an identification of internal structures of mechatronic machines and reduction it to the series-connected blocks.
Most problems are connected with gathering of process data (especially an operational history of considered devices).
Presented representation of system includes the possibility of: universal application in case of technical devices with the same or similar structural and functional characteristics, an identification of the current state and the volatility of the key numerical values (states classification used in a predictive model), reproduction of the error propagation path (based on the identified key parameters and their contents), registration of key parameters (the possibility of using database systems) in order to a modification or the development of predictive models of monitored devices based on historical data [4], [5].
However, a larger number of variables requires processing of data by numerical methods.
Sławik, Application of a nonlinear data-driven model to rapid design of disc-spring valve systems, Management Manufacturing and Materials Engineering 452–453 (2012) 1365–1369

Firstly, all fault data are discretized via the MEPA-based discretation method proposed by Ref.[1], and then according to the steps of Section 4.1, the total 753 reductions are obtained.
The fault data of the 4135 diesel engine No.
(2) The reduction based on RST can lower the workload of data acquisition and reduce the size of FPN structure, so then avoid the dimensional disaster problem of the FPN diagnosis method
(4) The application in 4135 diesel engine fault diagnosis data verifies the feasibility and validity of the proposed method.
With enough sample data, it can be applied to solve other classifier problem.

This emphasis on waste reduction drove practices such as inventory reduction, process simplification and the identification and elimination of non-value-adding tasks.
Fig. 2: Process Flow Chart Data Collection and Analysis Lean Waste Taken into Consideration: In this research the impact of major waste that occurs in the valve assembly area were focused with space optimization, improve flexibility and the reduction of floor space and material movement by the execution of appropriate lean tools.
Time Study: The time study was carried out using stop watch and the data was collected for different operations from the industry for the assembly of valve and tabulated in Table 8.
This data is used for mapping the current state using the value stream mapping.
Before implementing Kaizen, October 2019 month data was collected whereas, data were collected for the month of November and December 2019 after implementing the kaizen.

The data of total clinker productions tems from the China Cement Almanac and relevant literature survey[4], asshownin Table 2.
Year SECfor clinker (GJ/t clinker) Overall SEC (GJ/t cement) NSP kilns Shaft kilns OR kilns 1990 3.66 4.72 5.86 5.74 1995 3.60 4.54 5.65 5.13 2000 3.57 4.40 5.42 4.75 2005 3.55 4.22 5.22 3.72 2008 3.34 4.07 5.07 3.05 2009 3.31 4.04 4.95 2.78 2010 3.28 4.01 4.89 2.70 Source: [4] Asfar as some production data and SEC data are concerned,the different sources are not always completely consistent.
Given this situation, we first took data from the literature review from the China Cement Association, and when other wise not available,we complete the time-series data with the data from other literatures.
Some SEC data for single years are estimated according to other literatures.
Despite this uncertainty, the resulting time-series data set is judged sufficiently robust for the analysis and shows noun explainable out liersorpeaks.

When ，，, is the reduction of . 1.3 Calculation of reduction is a set of attribute which satisfies attribute of ,so, if ，then . is a expression of Boolean, if,, then ，else, is the extract of the attribute which is contained in .
The expression is as follows: （3） （4） 1.4 Decision rules Any decision-making table can be seen as a form of generalized decision-making rules:, in the formula, ，is called conditional parts of the rule, is called decision-making parts of the rule. 3 Knowledge discovery from oil analytical ferrography of hydraulic system The decision-making table was shown in Table 1 by the use of analytical ferrography data from oil sample which was sampled from military launch vehicle hydraulic system.
The problem to find the optimal decision rule was limited to the problem of attribute reduction.
The attributes of {a3, a4} were the reduction of decision-making table after calculation.
The simple and intuitive decision-making rules were obtained from the decision-making table after reduction

Ni catalysts were obtained by the impregnation/reduction method and by electroless deposition.
Ni catalysts supported on carbon were synthesized by two different methods: impregnation/reduction [[] Z.
The constitution of the catalyst obtained by the impregnation/reduction method was analyzed by RBS.
Cyclic voltammetries were registered after 10 cycles in all experiments, to obtain identical and reproducible data without any appreciable changes.
Results and discussion Ni/C electrocatalysts obtained by the impregnation/reduction method were analyzed by RBS.

Then noise reduction is performed on the operating noise signal by spectral analysis.
Final drive out of production line End Y N Repair Reverse loading operation Fig.1 Detection processing of abnormal sound for final drive (a) before noise reduction processing (b) after noise reduction processin Fig.2 Waveform of qualified final drive (a) before noise reduction processing (b) after noise reduction processin Fig.3 Waveform of tooth surface roughness final drive (a) before noise reduction processing (b) after noise reduction processin Fig.4 Waveform of tooth scraping sound final drive (1).
The waveform of these final drive noise signals after noise reduction processing is shown in Fig.3 (b).
The waveform of these final drive noise signals after noise reduction processing is shown in Fig. 5 (b).
Discrete-time Signal Processing (Xi'an Jiaotong University Press, China 2001) [6] Qingjie Liu, Guiming Chen, et al: Data Acquisition and Processing 2009,(24):58-60