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Based on field data of sediment and water volume in the Lower Yellow River from 1950 to 2000, the sediment-transport water volume and unit sediment-transport water volume in LYR are calculated.
C.,2000); (3) data analyzing method, i.e. analyzing the field data and then calculating sediment-transport water volume by using experimental formula.
According to flow and sediment field data of lower Yellow River, the annual incoming sand volume was 4.8~6.5 108 t after construction of Xiaolangdi Reservoir (Chen J.G., 1997; Liu J.X, 1996).
L., etc. 1997, Water-sand Variability and Silting reduction in Yellow River, Yellow River publishing company, p. 104 (in Chinese)
the efficient sediment-carrying water-sand combinations in the lower Yellow River after the construction of Xiaolangdi reservoir The water-sand data of Xiaolangdi station in large discharge The water-sand data of Xiaolangdi station in food period T(day) Q(m3/s) S(kg/ m3) W(108m3) S (kg/ m3) Ws(108t) 30 2600 69.89 115.60 40.74 4.71 25 3500 67.13 126.40 40.15 5.07 20 6000 63.15 157.08 41.68 6.55 25 5000 62.96 158.80 42.82 6.80 TABLE III.

Environment data collecting system mainly collects pressure, pressure and data of CNG gas storage pot.
The text message is sent to the data center of Mobile Corporation, the communication module is connected to the data center via computer serial interface.
If the pressure data is normal, the working status of the pipes can be monitored.
When the system starts to work, it usually sets an internal (can be set at will, the system choose 10 minutes) to collect environment data, ARM works at low-power mode, when the timer starts up, the ARM microcontroller collects pressure and read environment temperature data and gas consistence data, then the collected data are compared with set threshold, if the data exceed the threshold, the monitoring system immediately send alarming signal, if the data are normal, the environment data will be sent into the monitoring center in the set time, and the communication module is charge of receiving normal data.
Taylor, “Design and validation of a power supplynoise reduction technique,” IEEE Trans.

Data is acquired by the X-Rite monitor optimizer and its supporting software Color Shop via data cable from the computer COM2 interface, measurement data is saved as a text file.
Monitor color within the color samples, which is acted by monitor experimental data directly, as the gamut mapping model of BP network input sample data.
Experimental monitor displayed color data, 1000 sets. 2.
Experimental printer printed color data after gamut mapping, 1000 sets.
Data 1 and data 2 are shown via the same image pixel values.

Primary Content Analysis (PCA) is one of a relatively mature data compression and indices dimension reduction technology.[3] However, traditional PCA treat every ingredient equally, so the customer requirements’ specialties are often not considered which also leads to over control or under control of the production process.
We randomly extract 60 records from the inline quality inspection data as shown in Table 1.
After the customer requirement-weighted process the standardized data of elements is turned to be ZC, ZP and ZS etc, as shown in Table. 3.
Table 3 Customer requirement-weighted data of elements (%) n ZC … ZP ZS … ZTi 1 0.0084 … 0.0168 -0.2888 … -0.0325 2 -0.1923 … -0.2355 -0.1925 … -0.0519 3 0.0084 … -0.2355 -0.1925 … -0.0325 … 60 -0.0920 … 0.0799 0.1925 … 0.0966 Primary Content Analysis method for CRW data After weighted processing, the characteristics mostly exist positive or negative correlation, For this example, the correlation between vectors of elements are shown in Table.4, so we have to compress the data by PCA method.
Put these data into SPSS or matlab software, we can obtain the multivariate control charts of average and range R as shown in Fig.3 and Fig.4.

Model feature parameters involve geometry, topology, process reference, and assembly reference data of cost feature model, which is the data interface of cost feature model and other feature domain by means of feature mapping and parameters transmission.
Levels: Levels of the model is used to distinguish product data carrier embedded in the cost model[4].
Input data starts from input layer, then hidden layer and output layer in the end.
Feature description data: refers to basic data of cost feature, including feature name and ID number.
Other related data: refers to non-common data of model, including data set of enterprise resources.

A statistical work is presented, illustrating the referred information and focusing on the data related with the damage assessment of the considered monuments.
This work presents an intuitive overview of the seismic effects over the surveyed churches, allowing not only to better understand the response of these structures to this particular action, but also correlating the earthquake data with its effects on the churches.
The data gathered by the UNIPD, during the 1 st level technical surveys performed to the churches in the region of Abruzzo, in result of the 6 th April earthquake, was later inserted in a database, in order to have the information organized and to allow an easy access to these results.
After the preparation of the database, a statistical analysis focusing on the churches was carried out, in order to understand and correlate the earthquake data with its effects on this type of structures, (Costa 2009).
Considering the data collected, it was of interest to analyze the relation between the damage index of the surveyed buildings and its viability in respect to the distance of these same buildings to the epicenter of the 6 th of April 2010 seismic event, Figure 5.

Peak load management as one action from the DSR does not include the energy price on the market and has the following characteristics [27]: · A minimizing of the power gradients by: o Reduction of peak loads.
Frequently DSM actions can be taken by the transmission system user, energy flexibility focuses on the energy market and uses the fluctuating price for electricity cost reduction [24,28,29].
Insulation also enables the reduction of peak loads if the storages can be activated quickly.
data[article_id]=87726, 2017 (accessed 16.10.2017)
[39] Deutscher Bundestag, Vor- und Nachteile verschiedener Energiespeichersysteme, https://www.bundestag.de/blob/412904/ca2dd030254284687a1763059f1f4c0c/wd-8-032-14-pdf-data.pdf, 2014 (accessed 8.03.2018).

The software package was provided by the Institute of Environmental Physics, Heidelberg University, and except a dewow filter for removing the low frequencies from the measured spectrum, no averaging or filtering was performed on the acquired data.
From the statistical analysis of this two types of data, the TDR measured soil moistures of surface 10 cm deep vary greater than those within 20 cm deep and which GPR measured above the reflective layer at 0.460 ~ 0.558 m deep.
The latter two data is some closer and soil moisture of surface 20 cm deep measured by TDR is 0.027 higher in mean than the soil moisture above reflective layer measured by GPR
(4) In this study, GPR has provided continuous and fine data which cannot be obtained by TDR, remote sensing or other traditional point measurements in the field scale, and given more details to help grasp the distribution of soil moisture.
Kaatze: submitted to Journal of Chemical and Engeneering Data (1989)

Additionally, the test results are compared with the recommended data of international general standards, such as BS 7608 and DNV C203, by which the fatigue safety of pipes could be evaluated.
Therefore, this study is not only beneficial to the accumulation of full-scale fatigue data of pipes, but also can provide quantitative basis for evaluating the fatigue life and ensuring the safety operation cycles of pipes.
The control unit is a computerized data acquisition and closed-loop control system and required to storage, read and write the testing data in real time.
The crack-like defects could grow in the subsequent service process, which results in the reduction of loading capacity and the rigidity of the structure as well as the change of the resonance frequency of pipes.
Therefore, this study is not only beneficial to the accumulation of full-scale fatigue data of pipes, but also can provide quantitative basis for evaluating the fatigue life and ensuring the safety operation cycles of pipes.

The hardness of three surfaces data were collected experimentally from various process times, i.e. 140 minutes, 280 minutes and 560 minutes.
Lagrange polynomial interpolation was then used to generate quadratic mathematical formula of the surface hardness based on experimental data.
Therefore, this current research tries to formulate the mathematical equation prediction based on data results obtained from the ion implantation experiment.
Basically Lagrange give a set of k + 1 data points, (x0,y0), … , (xj,yj), … , (xk,yk) where no two variables xj are the same.
For the input data are vector t=140,280,560T and vector H=88.97,125.51,130.2T, execution of this code yields c1 = -0.000581548, c2 = 0.505250000, and c3 = 29.633333333.