Search results

See Table 2 for detailed data.
According to the test data, anti-skid performance degradation model is set up to evaluate pavement performance degradation.
Table 1 Friction coefficient (BPN) data of the experimental section within 15 months 0.5 mouths 3 mouths 6 mouths 9 mouths 12 mouths 15 mouths 0+005 78 74 73 71 70 69 0+010 79 75 74 73 71 69 0+015 80 77 71 72 70 64 0+020 79 74 73 75 67 65 0+025 78 74 72 71 68 65 average value 78.8 74.8 72.6 72.4 69.2 66.4 Establishment of Anti-skid pavement performance degradation model To select the best regression model to express related data relation, Curve Estimation process provided by IBM SPSS Statistics19.0 software (Chinese version) is applied for linear fitting.
In view of anti-skid performance standards of asphalt road (BPN of expressway and first-class road≥45) and BPN (27) of blank comparative test piece (see the friction coefficient data measured through pendulum instrument method in this study), it is believed that under the traffic volume of the experimental pavement section, anti-skid performance index of coating-type color anti-skid pavement after 5 years will not satisfy requirements of expressways and first-class roads.
Cost reduction (especially colloid cost) and development of functional fillers are two development directions.

The first step to optimize production process is to find out the relationship between and, according to the analyses of data.
Moreover, the historical data, the production plan are also needed in model building, as shown in Figure 5.
Historical data plays a main role in the model which can be obtained by measuring the secondary equipment of DMS when all the equipments are operating normally.
It is forecasted according to the historical data, because the production parameters do not changed significantly.
It is forecasted according to the historical data, production parameters adjustment and production progress.

· All data can be saved and stored in electronic form without of the need of paper.
Data can be transported via the storage medium and sent by means of mail or other electronic ways
The coordinate systems for CL data definition Coordinate systems are one of the elements of operation and NC sequence in CAM system.
They define the orientation of the workpiece on the machine and act as the origin 0(0,0,0) for CL data generation.
Generally can be find two types of coordinate systems in CAM programs: [6,7] · machine – acts as the default origin for all CL data.

The analysis of the experimental phenomena, experimental law, and statistical results of the experimental data are as follows.
The detailed data can be seen in the following statistical figures.
Fig. 4 shows the bending angle after the test piece failed: test data indicates that the bent degree of the test piece is small and the bent degree is up as angle between weld line and drawing force becomes bigger.
Test data of the change of weld line’s skewing trend indicates that the whole curve tends to horizontal and every data doesn’t deviate far.
On average, these data have markedly decrease compared with base metal.

PV module cooling system performance test The prototype data was measured in the following conditions for its performance test.
Performance measurement] Measurement date & time Oct. 28,29,30,31, and Nov. 1, 2013 (5 days) - 9 hours a day (9 am~ 6 pm) Measured elements - Internal/external temperatures of PV collector - Insolation - Power use & power output - Power generation efficiency Measurement device - Insolation meter, temperature sensor, wattmeter, inverter Measurement method - Auto temperature measurement by a control system - Power measurement and generating efficiency calculation - Real-time PC recording of power, current and voltaic data from PV generator through inverter and PLC Cooling system operation - Generating efficiency, internal temperature, etc. are measured at the same time both before and after the cooling system operation.
[Figure 8] and [Figure 9] are data on daily power generation amount of the prototype module herein.
Daily power generation of PV cooling system (Comp, Fan operated)] As a result of comparing those data, under Comp and Fan non operation, its daily power generation was 2,505W and under their operation, the amount was 2,638W, [Figure 10] and [Figure 11] are the graphs of comparing the measurement data.
If non-cooling system and cooling system generation amounts are compared to analyze their system efficiency, temperature, economic data, the necessity and economic favorability of the cooling system are expected to emerge more clearly. 2) Power generation and temperature reduction data according to the kinds of refrigerant gas inside a cooling system need to be analyzed in experiments to select an optimal refrigerant material for the system developed herein. 3) In addition, by applying a control system that manages refrigerant gas injection according to temperature rise, a more efficient and economic system can be established. 4) If a cooling system is used, PV module cell temperature could be lowered by 3~5℃ and it could be even cooler depending upon chilling time. 5) The daily power generation amounts were analyzed based on the developed PV module herein and power production was found to increase by about 130W/h with the cooling system in place.

In the following chapters I will deal with a proposal for a methodology and the benefits brought by suppliers' evaluation data to a building company.
The proposed algorithms will in the future be entered into the software application, supported by a knowledge base containing all the relevant data on subcontractors with the option of permanently updating data after the completion of each construction contract.
This along with other data is transferred to the subcontractors' database.
The company's expert team will enter the basic data (knowledge) in the database – in particular, it will adjust the weights of individual criteria and the assignment of indexes.
The meaningfulness of the evaluation and administration of the database is given by the measure of quality of the data input and maintenance.

Model performance is assessed by comparing the prediction of crack initiation against experimental data.
The model makes use of the microscopic energy dissipation criterion to predict crack initiation, and of diffraction postprocessing in order to extract orientation-specific elastic lattice strains for comparison with experimental measurements carried out using in situ neutron or high energy X-ray diffraction [2]. 0 100 200 300 400 500 600 700 0 0.0005 0.001 0.0015 0.002 0.0025 0.003 0.0035 0.004 0.0045 0.005 Elastic lattice strain Applied stress (MPa) 111, FEDPP 311, FEDPP 200, FEDPP 111, diffraction experimental data 200, diffraction experimental data 311, diffraction experimental data 10 15 20 25 30 35 400 1 10 4 2 10 4 3 10 4 y = 559.45 * x^(-0.37844) R= 0.99558 Microscopic EDC N cycles Fig.1.
(b) Illustration of the correlation between the prediction of microscopic energy dissipation criterion based on FE simulation and experimental data for C263.
The former validation step is more straightforward, both due to the availability of experimental data, and since a good match can be achieved using various combinations of model parameters.
Fig. 1(a) illustrates the comparison between the prediction of finite element diffraction postprocessor and the experimental data obtained from TOF neutron diffraction measurements carried out on the ENGIN-X instrument at ISIS, Rutherford Appleton Laboratory, UK.

Ningbo City in 2010 are 58 desalination plants sea sand. 1.1 The amount of sea sand in the mud testing 1.1.1 Test Methods 1, take a sand heap desalination plants have been diluted with sand up and down from each of not less than 3000g, the heap is not diluted with sand up and down from each of not less than 3000g, 4 specimens were assigned to each quartation reduction 1100g, on the oven dried at 105 ± 5 ℃ to constant cooling to room temperature. 2, accurately weighed 400g sample container to release the wash, into the water.
The surface of the water above the sample 150mm, stir after soaking for two hours, then sand by hand washing in water, about a minute, so that dust, silt, clay and gravel to separate suspended or dissolved in water make it gently drive the turbid liquid into a set of 1.25mm and 0.080mm sieve (l.25mm screen on the 0.080mm sieve above), filter out the stars is less than 0.080mm place in the whole process should be careful to prevent sample loss . 3, again adding to the container with water and repeat the operation to the visual clean water container up. 1.1.2 Analysis of test results Table 1 Determination of sea sand in the clay content data Clay content /% a b c d Average Dilute the sand (on) 0.046 0.053 0.42 0.59 0.05 Dilute the sand (below) 0.185 0.172 0.164 0.179 0.175 Not dilute the sand (on) 0.74 0.78 0.62 0.86 0.75 Not dilute sand (below) 0.95 0.92 0.96 0.89 0.93 Data from the table1 and change can be seen that two points, first is a pile of sand with either dilute or not dilute
Washed and placed temperature (105 ± 5ºC) in the oven and remove to cool to room temperature, weighing. 1.2.2 Analysis of test results Table2 Determination of sea sand in the shell of data Shell content /% a b c d Average Dilute the sand (on) 6.82 6.86 7.01 6.60 6.83 Dilute the sand (below) 7.53 7.55 6.84 6.96 7.22 Not dilute the sand (on) 7.21 5.93 6.72 7.22 6.77 Not dilute the sand (below) 7.68 7.01 6.24 6.95 6.97 From Table 2 in the data, regardless of the sand, no matter what bit position in the sand, sea sand content in the shells difference is not large, relatively uniform.
Analysis, although some fine sand in the shell powder desalination process would be washed away with fresh water, but the sand grains in the shell film is generally more dominant in terms of quality, so the sand content in the data on the performance of shell for the differences small.
Record consumption ml silver nitrate standard solution. 2), the blank test: 50mL pipette imbibe in distilled water to the flask in 5% chromic indicator 1mL, used and 0.01mol / L solution of silver nitrate solution titration to show brick red date, record the point of consumption with silver nitrate ml standard solution. 1.3.2 Analysis of test data and results Chloride ion content /% 1 2 3 4 5 Dilute the sand 0.0062 0.0019 0.0055 0.0114 0.0107 Not dilute the sand 0.0293 0.0279 0.0302 0.0575 0.0539 Table 3Determination of chloride ion in sea sand Data from Table 3 can be two points, one desalination plant in the sand with different sand content of chloride ion there are some differences, the second is the dilution of all chloride ion content in sea sand has reached the national specifications.

Water resources management requires making full use of water resource, but involved in various data of domestic, industrial, agricultural aspects and so on.
In order to eliminate the influence of the evaluation indexes dimension, firstly the sample data set {x (i, j)} needs to be made standardized treatment [8].
To keep consistent with change information of the evaluation indexes, standardized formula can be taken for: (4) The bigger the x(i, j) is, the more optimal type index is : (5) The smaller the x(i, j) is, the more optimal type index is : Where, r(i, j) is normalization data of the first i sample of the first j index, i=1~n，j=1~m; x(i, j) is the first j index first i of the samples the original data of the first i sample of the first j index; is maximum value of the first j index; is minimum value of the first j index.
To guarantee the WPI calculated value between 0~100, during the process of the specific calculation normalization data needs to be multiplied by 100.
Data Processing Methods and Application in Agriculture .Beijing: Science Press.2006,156 [9] Harbin Economic Statistical Yearboo;China Statistical Press. 2001,2004,2007,2010Year

(3) where r is the reduction and 101rhh =− , µ is the friction coefficient.
The neural network has the capability of establishing models according to input and output data directly.
The data of 1000 coils from a cold strip mill were introduced as optimization and training data.
In addition, the values of 254 coils were selected as testing data that is different with the analysis data.
Fig. 3 shows the comparison of material deformation resistance in cold strip mill between optimization method and empirical data.