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From the measured data can be analyzed, the total displacement sprint stage an oar in still and catch stage and recovery stage time is inversely proportional to the ratio of increase, in the catch and return time paddle water phase ratio was accompanied by a reduced total displacement of a propeller; a total displacement and dynamic stage time is proportional to the ratio of example, increase power stage time ratio increases with the growth of the total displacement of a propeller.
From the measured data can be analyzed, sprint stage or a total displacement (i.e. stroke distance) is inversely correlated with pitch frequency; the dynamic displacement and pitch frequency is proportional to the inserted; catch and recovery phase displacement inverse relationship with the stroke frequency.
Sprint stage, data show that domestic and foreign team entry angle and inserted the catch ratio inversely related; the entry angle is proportional to the power stage ratio.
Data show that increasing water entry angle increases with the inserted the catch ratio decreases obviously, dynamic phase ratio; inverse relationship between outlet angle and recovery phase proportion, increase water angle along with the decrease of recovery phase ratio.
Sprint stage: from the measured data can be seen, in the catch time and the proportion of the ship's speed is inversely proportional to the power stage; time scale with the ship's speed is directly proportional to the average speed to catch the water; in time and the proportion of the ship to low proportional relationship; average velocity ratio and recovery stage of ship inverse relation.

Beyond point c there is a marked reduction of load-carrying capacity until rupture, c-d.
We use the on-site data to build the numerical model in the 9th tunnel in the Water Supply Project of Transferring Water from Tao River in Gansu. 17in constant cross section disc cutter is used in the TBM tunnelling.
Fig.10 The installation of eddy current sensor Fig.11 Data acquisition flow Experimental results.
In accordance with Fig.8, the rotating speed of the disc cutter changes during rock cutting process, which assure the rationality of the data acquisition method and the simulation results.
And the experimental data are in accordance with the simulation results.

EM231 module shall be used to acquire data related to temperatures. 4-20mA standard current signals delivered by the flow sensor may be converted to engineering data through PLC programs and be displayed on the monitoring interface of the touch screen.
The data shall be compared with exhaust gas temperature, furnace temperature, exhaust-gas flow rate alarm values pre-set in the PLC in real time.
Since the cooling-air and exhaust gases are transferred through the same pipe, increases in volume of cooling-air may lead to reduction in volume of exhaust gases, and vice versa.
Remote control system With consideration to the distance between incinerator operation area and the central control room, remote-control solutions can be adopted for uploading and remote control of data related to the incinerator system.
Cluster communication programs conforming to Modbus[2] protocol can be generated in S7-200PLC for real-time transmission of incinerator system data to the upper computer through RS485 bus line.

Find Fk2-Fk+Jkdk2 is Non-negative, butFk2-Fk+Jk(dk+dk)2 is not necessarily non-negative.Actual reduction in value of the defined function is: Aredk=Fk2-F(xk+dk+dk)2.
Simulation results and analysis Simulation comparison standard BP algorithm, LMBP algorithm, and this paper improved the LM algorithm's advantages: Take six groups of temperature values obtained by the simulated fire test, the value of the original temperature of the neural network sample acquisition, as shown in table 1: Table 1 Neural network to obtain the original temperature of the sample Time(s) 0 50 100 150 200 250 300 350 400 450 500 Temperature Group 1 20.2 40.2 54.7 66.4 75.4 81.2 87.6 91.4 97.8 101.5 102.3 Group 2 21.8 41.8 59.3 68.7 76.5 82.1 88.6 90.9 97.4 99.7 103.2 Group 3 20.4 38.4 49.0 61.3 71.8 79.3 85.3 90.1 96.5 97.4 102.4 Group 4 19.6 36.3 48.6 58.8 69.5 77.4 82.9 88.4 93.5 96.7 100.2 Group 5 17.6 32.2 44.6 55.3 68.2 76.9 83.6 86.3 91.8 95.3 98.1 Group 6 17.4 28.2 41.4 52.6 63.4 74.2 82.1 84.2 91.4 92.8 94.6 Simulation process, in order to better training effect, followed by data processing is more convenient and usually requires the original sample data to normalization
processing, in which we have adopted the following normalized formula: RX=0.1+xi-xminxmax-xmin+0.8 After normalization of the data in the range [0.1,0.9].
Simulation results show that: this article by temperature experimental data of a large number of specific fire three convergence improved LMBP algorithm convergence speed and accuracy are superior to standard LMBP algorithm and three convergence algorithm in the calculation of the computer's memory and CPU consumption smaller, more convenient, especially suitable for the online calculation of the amount of data in engineering practice.

Experimental procedure Commercially available AlN powder (produced by carbothermal reduction method, Sinocera Advanced Materials Company, Fujian, China) was used as the starting material, which has an average particle size of 1.0 µm.
According to the data supplied by the manufacturer, the powder's oxygen impurity content was 1.2% and the nitride content was 32.5%.
All Raman data of the AlN ceramics were taken using a ×25 objective lens and 120 s collection time for consistency.
The results of hexagonal lattice parameters (ah, ch and unit cell volume) based on X-ray data are calculated and presented in Table 2.
The calculated lattice parameters based on X-ray data of AlN ceramics sintered at high pressure are little smaller than those calculated from the X-ray data of AlN powder.

For each actuation mode, the SMAHC beam was brought to a desired temperature set point, in which dynamic data were captured.
One run consisted of capturing dynamic data at all the chosen set point through a thermal cycle from room temperature to about 90 ºC.
Power current supplier for SMA wires resistive heating SMAHC Shaker Thermocouples Accelerometer Control system Data acquisition Microcomputer with control software Figure 2 Different modes for electrical activation of the active composite beam.
Resistive heating of the Ni-Ti SMA wires is done by a programmable DC power supply current (Agilent, E3633A model) (6) controlled by a specific software (7), while temperatures of the beam and electrical resistance of the Ni-Ti SMA wires are stored in a data acquisition system (Agilent, 34970A model) (8).
Time histories with a total length of 700 seconds were captured with a sampling rate of 1024 Hz to allow 50-frame averages with a bandwidth of 0 - 512 Hz and a frequency resolution of 0.25 Hz by Cattman of HBM data acquisition Spider 8 (9).

Direct polarization (DP) data were collected using a 30 s recycle delay, and cross polarization (CP) experiments were performed using a mixing time of 3ms, recycle delay of 4s, with proton decoupling.
Based on the TEM data the CdS NPs synthesized in PSS by ion-exchange and precipitation have diameters in the range of 3-7 nm and are well dispersed in the polymer matrix.
A HRTEM micrograph, Fig. 1, shows several particles near the film edge, and the selected area electron diffraction data reveals that the NP have adopted the cubic lattice type.
If the frequency/magnitude of the motion of TG has increased upon composite formation then one expects to see a narrowing of the peaks in the NMR data, especially in the 1H spectrum.
Using crystal structures located in the Cambridge Crystallagraphic Data Base, a model for the interface structure has been developed and is shown in Fig. 5.

However, even if some data which indicate that Al has an influence on the stress levels and the dynamic softening kinetics have been reported [[] E.I.
Fig. 2 shows the fractional softening experimental data obtained for the three steels studied at different deformation temperatures and pass-strains.
In all cases the experimental data could be fitted to an Avrami type equation of the form: (1) where X is the fractional softening corresponding to a time t, t0.5 is the time to reach a 50% fractional softening and n is the Avrami exponent.
According to the results shown above, the mechanical softening data determined for the C2Mn2Al2 steel at temperatures of 1000ºC and 950ºC cannot be considered representative of static recrystallization occurrence.
The significance of the retardation exerted by Al addition in this range of compositions is better appreciated taking into consideration that according to the data obtained in this work the retardation exerted by 1%Al would be equivalent to that exerted by 0.026%Nb addition, and that exerted by an addition of 2%Al equivalent to 0.05%Nb addition.

The exponential fitting performed on the measured intensity data gives the spot lifetime of 260 days for 30W, a reasonable lifetime for various applications.
Since the measured beam count at a given blade position is proportional to the integration over the exposed portion of the source, the source profile can be reconstructed mathematically from the scanned data.
The spot position change causes the incident X-rays to deviate from Bragg’s angle, resulting in a flux reduction.
The ray-tracing simulations are performed to compare with the measured data.
The simulated data and the measured data are in good agreement.

The data obtained from the PSQI scores were analyzed using the Wilcoxon Signed-Rank Test (significance level < 0.05) due to the non-random nature of the sampling method, which precluded the use of parametric statistical techniques.
The data was processed with the help of SPSS software.
This is evidenced by a reduction in the severity levels of the subjects, as indicated by a decrease in pre-test to post-test scores measured using the ISI and PSQI scales.
The detailed comparison of the score data is presented as follows.
Data of Measurement Comparison Values Description PreTest Post Test Mean Standar Deviasi Min-Max Mean Standar Deviasi Min-Max ISI 18,17 4,26 13-23 15,33 4,13 10-20 PSQI 15,33 4,37 10-21 10,50 5,01 5-17 Table 1 presents the comparative data of average values from pre-test and post-test measurements of ISI, PSQI, and sleep efficiency.