Search results

Figure 1.Failure rate data taken form Tavner[4] averaged from 1994-2004.
The statistical data shows that there are a variety of failures associated with the turbine control system.
Prepare Your Paper Before Styling Wiggelinkhuizen et al. [7] also reported that in the CONMOW project comparison of the trends of the normal historic record against the SCADA(Supervisory Control and Data Acquisition System) monitoring data can detect the potential failure.
However, the large quantity of data involved can overwhelm the data acquisition and processing system but also the engineers involved.
In addition, a low data rate torque signal could be achievable from the turbine SCADA system.

This limitation is due to not only the difficulty of getting data from various kinds of limitations, but also the lack of research on the matter.
Results of field measurements are carried out the model by scaling the size of the field data into a laboratory.
From field measurement data that has been carried out for three times the measurement, it will be analyzed annual growth rate of casuarina.
The tsunami wave is then measured by using a sensor that is connected to the data logger to be read and connected to the computer.
Data generated in the form of digital surge tsunami wave height.

With the aid of OST, the interference to channel estimation form data symbols can be eliminated completely.
Moreover, data symbols can serve as pseudo-pilots to enhance the estimation performance.
A well-designed fully pilot based method, i.e., a pilot inserting-scheme assists us to trace the channel changing timely with little bandwidth reduction (rate loss) and few data distortion.
Define as input vector, as data vector, as training vector.
With the initial estimation, we can detect the data vector using Kalman Detector.

The principal component analysis[1,2] is a classical data dimension reduction algorithm, using less as possible of the independent data representation of the original data main information, so PCA algorithm not only can reduce the dimension data, but also remove the correlation of the data, the major message of the original data has also been retained.
First use SVM-RFE algorithm to get the best feature subset of data set, and then get the dimension reduction processing of the best subset through the PCA, dimension reduction of the data set as the SVM classifier experimental training set and test set, the experimental results show that the method in improving the SVM classifier experimental classification and recognition rate was significantly shorten the training and test of time.
Fig. 1 PCA geometric explanation To avoid data dimension and the effects of different amplitude, the first step to a set of data is normalized.
The experimental results and analysis Because many category data set can turn to several two category data set one to one, in this paper we take two category data set as subjects to test the actual resultsof the new characteristic choosing method.
Use VS2010 programming to realise characteristic chosing, take the 5 data set in the UCI data bank[8] as subjects, as Table 1 shows.

Due to its location, most of the site data used for analyses will be based on available site data in Benin City or similar location and/or in-built parameters in the RETScreen software database.
Benin City is located at latitude 6.3176° N and longitude 5.6145°E and its annual weather data is based on ground measurement and NASA supplied data recorded in RETScreen software database.
NCEE gets its weather data from on-site installation of an anemometer and a pyranometer, which are used to measure the wind speed and the solar radiation data respectively.
The acquired data is recorded at an average of 5 minutes in the memory of the data logger.
[20] US Department of Commerce, ‘‘Biomass Energy Data Book.

The validation process has been based on the calibration of m parameter that was multiplied for a specific pre-factor (that is different for each steel grade and depends on the chemical composition of the steel), in order to gain the congruence between the results of the model, in terms of grain size at different recrystallization times, with the corresponding experimental data reported in literature [13-14].
Comparison between the model results, in terms of grain size, and the experimental data found in literature.
Reduction rate effect.
Mean radius over time for three different reduction rates.
Recrystallized volume fraction over time for three values of cold reduction rate.

The cotton sound signals are exposed to interference and influence of some kinds of noise signal in the process of the data measured.
It is well known to any scientist and engineer who work with a real world data that signals do not exist without noise, which may be negligible (i.e.
However, there are many cases in which the noise corrupts the signals in a significant manner, and it must be removed from the data in order to proceed with further data analysis.
It must be removed from the data in order to proceed with further data analysis.
So in practical applications, before analysis of the data measured, it is the need for de-noising processing.

Study on metal oxide catalyst for selective catalytic reduction of NOx at low temperature Xiang Goua, Kai Zhang, Liansheng Liu, Wenyong Liu, Zifang Wang, Guang Yang, Jinxiang Wu, Enyu Wang School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China aCorresponding author email: gouxiang@sina.com Keywords: SCR; NOx; Low temperature; Metal oxide catalyst Abstract.
The method of selective catalytic reduction (SCR) is now one of the mature applications of NOx removal technology, which can meet the practical requirements due to the more stringent laws and regulations on air pollutions.
The method of selective catalytic reduction (SCR) has become the main denitration technology due to its high efficiency.
Supported metal oxide catalyst Al2O3 has high thermal stability, and its surface acidic site is helpful to adsorption and reduction of nitrogen oxides, so it is widely used as metal oxide catalyst support.
Experiment data showed that the existence of H2O inhibited the catalytic reaction.

Some of the annealed CR56 sheets were subsequently cold rolled to 10% or 20% reductions for strengthening them.
A higher reduction and lower Tp resulted in finer grain size.
The CR reduction seemed to have a slight effect on the anisotropy.
The Com-301LN and 690-60-56 structures were cold rolled to 10% and 20% thickness reductions, Table 3.
In Fig. 5, the stress amplitude vs. number of cycles to failure for annealed and cold-rolled Com-301LN and 690-60-56 structures was calculated from the strain-stress-lifetime data.

The eight artificial defects with a different thickness reduction in the range between 3 % and 20 % to the thickness are machined for those reduction ratios such as 3%, 5%, 7%, 10%, 12%, 15%, 17%, 20%.
Even at a fixed frequency, a thickness reduction represents the decrease in thickness value, d and the mode existing in the area without wall thinning disappears when it encounters a corroded zone where thickness reduction takes place.
Table 1 represents the reduction ratio of group velocity caused by thickness reduction and thickness reduction is determined by the measured group velocity change.
The data of Table 1 is plotted on dispersion curves in Fig. 6.
While the results for 7 and 10 % reductions show a satisfactory agreement with the real data, a more experimental deviation takes place for the 3 and 5 % ones.