Search results

Estimation of reduction depreciation charges in power network when DG source is connecting Chechushkov Dmitrii1,a, Bunkova Elena1, b 1Ural Federal University, Yekaterinburg, Mira st. 19, Russia a chechushkov@gmail.com, b ea.bunkova@gmail.com, Keywords: distributed generation, depreciation charges, optimization, power supply systems, power transformer.
Introduction It is well known that the connection DG sources affect electricity system through three main components: reduction of the current load of the network elements, reducing the losses of active energy and reducing voltage loss on the network elements [1].
Based on the data calculated by (4), determined the annual reduction of depreciation charges.
Summary In this paper we proposed to allocate cost reductions in depreciation charges when connecting DG source for parallel operation with the network.
Also, reduction of temperature in the hottest point equal the 2%, even at the minimum power DG source from the possible range is used.

The space averaged method is employed to analyze the scale of fluctuation of soil in the vertical direction of Tianjin combining geology data, and regional representative values are obtained, which is of high application value on risk assessment and reliability analysis in geotechnical engineering.
Introduction Test data of soil properties indexes just represents characteristics of points, not space average properties.
In this paper, the two main methods are introduced, and geology data of Tianjin Port is analyzed and the correlation distance of this area is obtained, which can be applied to practical project.
Compared with other sampling tests, sampling distance of cone penetration test is smaller, for which more data can be obtained in the same soil layer; and more cones in the lateral direction can reflect more truthfully of the soil profile.
(3) Bayes theory provides an approach to employ existing similar engineering experience, which is of highly importance to summarize regional data of correlation distance.

Data collected by the wireless monitoring system is compared to data from a traditional wired monitoring system.
The result is low power consumption as well as mass and volume reduction.
The microcontroller is also responsible for compressing the processed data.
Such a solution guarantees additional reduction of power consumption.
The procedure of data processing is staged.

Get in neural network after the test data repository and simulation.
What’s more only with the experience of site technical personnel or simple fault data base it is very hard to find where the fault is and remove the failure.
Select the first result set, then the reduction rate[9] will be: e=25%.
Carry out the attribute reduction of the Table 1 with the Rosetta, a data and spreadsheet tool based on the rough set theory, and then we can achieve the simplest fault logic rule table, the results as shown in Table 2 below.
, DF1, DF2, DF3, DF4, DF5, DF6, DF7 as the output data.

Application of Improved Back-propagation Neural Network and Genetic Algorithm to the Preparation Processing of the Mg,Al-hydrotalcite/polymer Nanocomposite Qiang Luo 1 and Qingli Ren2,b 1 The Second Artillery Engineering College, Xi'an 710025, P.R.China 2 School of Technical Physics, Xidian University, Xi'an 710071, P.R.China b qlren@mail.xidian.edu.cn Keywords: Neural network; Genetic algorithm; Dimensionality reduction and pre-whitening Abstract.
And in order to accelerate the converging rate and avoid the local minimum, dimensionality reduction and pre-whitening methods were used.
Training 5000 times and the results are shown in Table 1. 1x 2x M nx 1y 2y my M V Fig.1 dimensionality reduction and pre-whitening of data Table 1 Test data and predicted results Sample No. 1 2 3 4 5 6 7 N (%) X1 5 5 5 5 5 1 2 pH value X2 10 10 10 10 10 10 10 A (%) X3 80 70 60 50 30 80 80 s (%) Y 193 408 421 633 390 190 225 s * (%) Y 198 412 424 629 287 189 226 Sample No. 8 9 10 11 12 13 14 15 N (%) X1 3 4 6 5 5 5 5 5 pH value X2 10 10 10 11 10 9 9.5 12 A (%) X3 70 70 70 70 70 70 70 90 s (%) Y 273 310 325 293 422 420 450 165 s * (%) Y 269 314 327 295 425 423 452 167 Notes: N: the amount of oleic acid sodium; A: the amount of Mg,Al-hydrotalcite; s : the break percentage elongation of Mg,Al-hydrotalcite/PE composites; * represents the predicted results.

., LTD, Shenzhen, China ahjlfoxes@163.com, bhugodunne@yahoo.com.cn, chugodunne24@gmail.com, wujuebo@gmail.com Keywords: Generalized Green Computing; Data Storage; Application Virtualization; Cloud Computing Abstract.
The energy distribution of Desktop 3.2 Data Center Improvement.
Now the most pivotal problem of data center is the high consumption and high calorific value [5], as shown in Fig. 2.
Rate of Server Management, Power and Cooling Cost Increase According to a 2007 EPA report, U.S. data centers alone consumed 61 billion kWh in 2006- enough energy to power 5.8 million average households.
Reducing data center energy consumption is a pressing issue for the entire IT industry now and into the future.

But, [8] verifies the non-existence of the energy the environmental Kuznets curve by using the world's total energy consumption and GDP data of 113 countries.
Due to the missing of total energy consumption data in  Hunan, therefore, Hunan province relevant data is not listed  in Central region.
Furthermore, in view of the division of  Chongqing from Sichuan in1996, Sichuan and Tibet do not have an associated energy consumption data, therefore, the above three provinces are not included too.
[9] Phu Nguyen-Van.Energy consumption and income: A semiparametric panel data analysis.
An Empirical Study of the Environmental Kuznets Curve for China’s Carbon Emissions——Based on Provincial Panel Data,Vol.9(2011),p.66-76.

Fig. 5(b) shows the process to generate the function from the test data.
BOP shearing test data.
Illustrations of using shearing test data to develop the damage parameter function, f.
Illustrations of using shearing test data to develop the damage parameter function, f.
The predicted shearing pressure agrees with the test data, and max error is 11%.

Josefstr. 27, A-9500 Villach, Austria a*harald.okorn-schmidt@llrc.at, bphilipp.engesser@llrc.at, cmlinder@4tex.at, djhofermoser@4tex.at Keywords: cleaning, BEOL, titanium nitride hard mask, fluoropolymer, point of use temperature control, hydrogen peroxide, single wafer processing, cost of consumable reduction, formulated chemistry, chemical stability, etch uniformity Abstract.
The novel process control mechanism enables a 50% reduction in chemical consumption while achieving an at least equivalent TiN etch uniformity.
The TiN etch rates used are based on published temperature dependence data for an industry typical TiN material [6].
The achieved result for the chemical consumption reduction in this model was >50% while the etch uniformity stayed approximately the same (see summary of key data in Table I).
Alignment of experimental data (grey) with the modeled temperature profile (black) theoretically required for a 50% chemical cost saving on a TiN etch process (st.dev. <0.5°C).

The strength reduction rate after dry process is not dependent on fiber content.
So the strength reduction of the composite is due to the effect of matrix.
The experimental data of HGC were also compared with those of hemp fiber and matrix resin.
When the strength reduction rate of this HGC is compared with the untreated HGC, the reduction rate of Wet specimen is 59% at 28 days and 70 % at 182 days.
So, it is understood that when the fiber content is high, strength reduction rate is also high, the strength reduction after long duration of immersion is almost equal.