Search results

Mechanical properties such as ultimate tensile strength, percentage of elongation, yield strength, Poisson’s ratio and percentage reduction in area were found out.
The data is often used to specify a material, to design parts to withstand application force and as a quality control check of materials.ASTM D 638 specimens are placed in the grips of the universal testing machine at a specified grip separation and pulled until failed at test speed of 50mm/min for measuring strength and elongation.

[9] grew SiC nanowires by carbothermal reduction of silica xerogels embedded with Fe nanoparticles, etc.
Based on the Chemical Thermodynamics data [13], under 1300 o C/0.1Mpa, the whole process in the system could be described by the follow reactions: ( ) ( ) ( )vCOvOsC 2 2 2 →+ ············································································· molJ G /500982−=∆ (1) ( ) ( ) ( )vSiOvOlSi 2 2 2 →+ ············································································· molJ G /473796−=∆ (2) ( ) ( ) ( ) ( )vSiOsSiCvCOlSi +→+2 ··································································· molJ G /50520−=∆ (3) ( ) ( ) ( ) ( )vCOsSiCvSiOsC +→+2 ··································································· molJ G /77709−=∆ (4) ( ) ( ) ( )sSiCsSisC →+ ···················································································· molJ G /63368−=∆ (5) According to the description above, the growth mechanism of SiC nanowire under microwave heating is similar to the VLS mechanism, but there is a basic difference between them, in the present

The collected data contains noise or incomplete, BP neural network has been powerless.
Output set B is a data, it could be a set of data, it is expressed by a collection of A's input is expected to want to get the results.
Rough Set is for contradictions property input, incomplete and uncertainty with fair data analysis and reasoning ability, this paper based on Rough Set Theory and design combination of rough sets and BP neural network advantages of the R-BP neural network.
Conclusion Using neural network calculation, enter the number of features and quality of the sample, directly determine the neural network computing and fitting the output is good or bad input data.
Data for simple, clear classification boundaries, the traditional neural network can have better result, but enter the attribute collection, classification boundaries are not clear, low convergence efficiency and classification accuracy, there may even be not convergence condition, using the Rough Set theory, the input data kept the samples tested, the input feature non-stop screening, in order to achieve the purpose of deletion of the number of input feature, thereby improving the fit of the input data, after use of the EEG signal collected, verified, and reached the deletion of the number of features and improve the accuracy of the purpose.

For all experimental conditions, HD-WPI solution viscosities and gels Young modulus data fall, respectively, on two single curves when plotted against the computed aggregates concentration.
In the food industry, this can be achieved through the reduction of the pH, by enzyme addition or by salt addition.
Data of force, distance and time were recorded, at a sampling rate of 50 points per second.
In Fig. 2(b), the viscosity data of Fig. 2(a) have been re-plotted as a function of the aggregates concentration, which has been computed for each solution according to equation (1).
The main output of this concentration re-scaling is that all viscosity data fall on a single curve, whatever the initial protein concentration or denaturation conditions.

Before GA operation can be carried out, the accuracy of the mathematical model has to be checked using Crocker's experimental data.
The outline of a one-chamber perforated muffler selected as the noise-reduction device is shown in Figure 2.
As revealed in Figure 4, the accuracy comparison between the theoretical and the experiment data for the single-chamber perforated muffler model is in agreement.
Table 2 The optimal design data and STL with respect to different targeted pure tones -150 Hz, 550 Hz, 950 Hz- [popuSize=80; gen_no=200; bit_n=20; pc=0.7;pm=0.06;elt=1].
Since there was no initial design data to start with, the use of GA was easier than that of the classical optimization approach.

The 3D data are acquired as a large number of consecutive tomograms by moving the ultrasonic probe.
Thus, the data provide the information on 1024 points × 239 lines × 60 frames.
Classification of acquired data.
Configuration of data acquisition.
(a) original data, (b) result of log-compression and mean value reduction processing , (c) result of LOG/CFAR processing , (d) extracted information.

The mathematical model described as follows: First, set a data queue length N (N is a positive integer), find the maximum and minimum values in the queue.
Finally, each time a data placed into the queue at the end of the first data is automatically removed, ensuring always queue length N, followed by recycling to achieve the purpose of moving average.
The coefficient determined according to the actual signal characteristics, is usually taken 3 times The fourth step, From the X in the long line for the interception of the data queue K, find the maximum and minimum values min max and calculated the number of remaining K-2 on average, get the first output data.
Pressure Monitoring Study Signal De-noising Self-modification experiments on deep-hole drilling, speed 450r/min, feed 0.0308 mm/r, work piece length 370mm, data acquisition devices use self-developed multi-sensor Fusion MSM data acquisition instrument, the sampling frequency 1K , sample length is 8000 points, the pressure sensor is YCB-30FD strain gauge pressure sensors.
First, it is specially designed for image and video data.

Out of various adhesion tests, indentation cracking or scratch test yields somewhat better data than the others for the fracture-mechanical analysis of hard porous coatings of micro scale on soft polymer substrates, such as Pt/Ru on Nafion in DMFCs [3].
Figure 1(a), a schematic drawing of the DMFC during operation, shows the interfacial failure site and mechanism between the Pt/Ru and Nafion layers: the catalyst layer contracts due to the coalescence of Pt/Ru grains by the continuous electrochemical reactions, and the polymer layer swells by absorption of moisture conducted with protons or generated in the reduction process.
Scratch data : d-Fn , d-Ft , d-f Default : R, tf, ts, … ( tf + ts )/ dCR = tanθ Æ Line 1.2 Æ DCR f Æ dCR Optical data : CV in ( d-Fn ); DCR Æ Fn, CR Co = tf tanθ in ( d-Fn ); CV － Co = CCR Plot : ∂Fn /∂C Par t Ⅱ Par t Ⅰ (b) X d t X tf ts tf + ts dCR Cv Co X R θ θ Line 1 R cos θ Line 2 DCR CCR (a) O Scratch data : d-Fn , d-Ft , d-f Default : R, tf, ts, … ( tf + ts )/ dCR = tanθ Æ Line 1.2 Æ DCR f Æ dCR Optical data : CV in ( d-Fn ); DCR Æ Fn, CR Co = tf tanθ in ( d-Fn ); CV － Co = CCR Plot : ∂Fn /∂C Par t Ⅱ Par t Ⅰ Scratch data : d-Fn , d-Ft , d-f Default : R, tf, ts, … ( tf + ts )/ dCR = tanθ Æ Line 1.2 Æ DCR f Æ dCR Optical data : CV in ( d-Fn ); DCR Æ Fn, CR Co = tf tanθ in ( d-Fn ); CV － Co = CCR Plot : ∂Fn /∂C Par t Ⅱ Par t Ⅰ (b) X d t X tf ts tf + ts dCR Cv Co X R θ θ Line 1 R cos θ Line 2 DCR CCR (a) O X d t X tf ts tf + ts dCR Cv Co X R θ θ Line 1 R cos θ Line
From the above slope data, we thus get the critical distance DCR as: ()fsCR f CR t t / d ) cos R t R ( D +⋅ += θ－ .
We can obtain several different data sets of normal force and crack length at the coating-substrate interface because of the different behavior of the tip at the different scratching speeds.

Table 2 and Table 3 list the streams and their input data as well as the unit operation models and their input data, respectively.
The input data was assumed based on the technology available for 500 ton per day incineration power plant.
Streams and their input data Stream Function Input data MSW Fresh MSW - 20,833 kg/h, 25°C and 1 bar - Average value of proximate and ultimate analysis AIR Combustion air - 180°C and 1 bar - Mass flow rate is calculated by defining the combustion temperature to be 850°C (T-SPEC) FW Feedwater to HRSG - 85°C and 1 bar Table 3.
Base on general data, the generator efficiency for converting mechanical work to electrical power is ranged from 0.90 to 0.99 [10].
It should be noted that the NOx is lower than emission standard, therefore NOx reduction strategy such as SCR or SNCR is not required.

Introduction Hemorrhagic shock (HS), defined as a reduction in the perfusion of vital organs, remains the dominant cause of death after trauma injury [1].
The sensor part collects the heart rate (HR), systolic BP (SBP) and buccal PCO2 of the patients in real time and transmits data to the control part.
The control part receives the data and calculates the appropriate rate of fluid resuscitation.
Flow chart diagram of the system The detailed procedure is as follows: (1) signal processing: the data form the sensor part is first pre-processed which includes removing/deleting missing values, sampling data, checking/removing outliers, calculating descriptive statistics and smoothing/filtering data to provide the cleaned data for the diagnosis process; (2) input saturate processing: the input data is further set as ‘Normal’, ‘Mild’, ‘Moderate’ and ‘Severe’ based on a fuzzy model [9]; (3) fuzzy control decision: a set of rules, which are derived from all possible levels of hemorrhage and clinical experience of treatment, are designed to analyze the data mentioned above and provide treatment advice in real time according to the patient’s status; (4) output saturate processing: the treatment advice is finally defuzzied to ‘No’, ‘Low’, ‘Middle’ and ‘Fast’ which helps the on-scene rescuers adjust the rate of the pump for optimal fluid resuscitation.
Arefianc, Estimating the depth of anesthesia using fuzzy soft computation applied to EEG features, Intelligent Data Analysis, (2008) 393-407 [10] William T.