Search results

A broad investigation, data analysis and comparative analysis are conducted in this paper to get insightful conclusions regarding to the feasibility of technology, the effect of emission reduction, the economic feasibility and bunkering pathway.
Quantitative analysis Research methods Research study Data analysis Experiment Qualitative analysis Field survey Summarize opinion Data analysis Literature analysis Exploratory research Case comparison Summarize experience Chart analysis Research cooperation Fig. 6 Research methodological frame Feasibility Study Technical Feasibility and Safety.
Reduction Feasibility.
Ø NOX emission reduction.
Ø SOX emission reduction.

All catalysts are prepared by impregnation reduction method and characterized by XRD and TEM.
Catalyst Ag/C has higher catalytic activity to H2O2 electro-reduction [7].
Preparation of electro-catalysts 10% Ag/C catalysts was prepared by impregnation-reduction method.
The data tell us that elevating temperature and increasing the proportion of Ag can contribute to improving the catalytic activity of Pt-Ag/C catalysts.
Conclusions Six catalysts with different atomic Pt/Ag ratio were prepared by impregnation-reduction method.

EDS spectrum of PSC and NZVI/PSC: A, PSC; B, NZVI/PSC 3.2 Nitrate reduction For the reduction performance of NZVI/PSC and the adsorption performance of PSC, the performanc was change with time in Fig. 4.
Huang et al. [30] suggested that the stoichiometry of nitrate reduction in their experiments could be described by Eq.(1).
The reason why the content iron of PSC has practically no nitrate reducing ability can be explained by the fact that the iron in PSC is mainly iron oxide which was less, and it is consistent with the results of XRD, SEM-EDS data for PSC as described in the section of characterization of PSC.
As expected, the more reduction takes place, the higher the pH of the solution rises.
During the nitrate reduction, the solution pH of the nitrate reduction increased rapidly to 9-10 within a few minutes after the beginning of the reaction and remained between 9-10 throughout the reaction for the NZVI/PSC. while the reaction with PSC resulted in the least pH change.

There are infinitely many choices of data that meets the requirements presented to.
Introduced data in Tab. 1 and Tab. 2 have been selected just for the sake of values related to size.
Figs. 5, 6, 7, 8 present the comparative analysis of the system with and without vibration reduction.
Białas, Mechanical and electrical elements in reduction of vibrations, Journal of Vibroengineering, 14, 1 (2012) pp.123-128
Białas, Electrical Elements in Reduction of Mechanical Vibrations, Applied Mechanics and Materials, 371 (2013) pp. 657-661

Spending a lot of time to account the logistic cost data can only play a role in the financial budget, and cost accounting is just to know how much the logistic is, which does not apply the logistic cost data.
Then the data base will be used [6].
It is assumed that there are appraisable schemes and indexes, and there are primitive data.
The primitive data is shown in Table 1.
The data are listed in tables 2 and 3 respectively.

However, a primary problem of these systems is that each data stream for one special user will encounter interference from the other data streams.
Thus, we introduce an adaptive data streams reduction method to joint design with the greedy ordering algorithm called ADSR-GUO to satisfy different QoS requirements.
After performing the proposed greedy ordering to, we use the data streams reduction method to reduce the number of the last some encoding users’ data streams to achieve better BER performance.
Meanwhile, the sum-rate for the whole system decreases with the increase of owing to the reduction of the number of data streams to be transmitted.
Besides, assisted with the adaptive data steam reduction method, greedy user ordering BD-GMD-THP can achieve lower BER with small loss on the sum-rate.

Based on a two-week benchmark data log, the result shows a total energy reduction from 210 kWh to 71 kWh, representing a saving of 65%.
Table 1 lists the technical data of all the lamps in the system.
Power is computed from voltage and current data logs.
Figure 1 Typical half hour power consumption plot This part of the research involves capturing live data from the system by the aid of a data logger to get a clear understanding of the usage pattern.
As the demand was not uniform, it was inevitable to go for long term data capturing and monitoring.

Therefore, it preserves the variation of data and characterizes the global Euclidean geometric structure of data, which is suitable for linearly separable data analysis [2-4].
Fig. 1 Distribution of nonlinear data points Motivated by manifold learning approaches, a novel method, called local variation projection (LVP), is proposed for dimensionality reduction.
LVP Learning the variation Given training data matrix , denotes the th data; the number of training data.
Feature Extraction Criterion Our aim is to seek the projection which makes the projected data preserve the variation among nearby data.
Although these manifold learning approaches preserves the local geometric structure of data and have been shown to be useful for dimensionality reduction of image, they ignore the variation of data, which is important to avoid the over-fitting in LPP and NPE.

Furthermore, According to the historical data of civil aviation industry development, the feasibility and prospect of three solutions are analyzed with the method of AHP.
With the Copenhagen conference held in Dec 2009 and EUETS started on January 1 2010, aviation emission reduction draws intensive attentions around the world.
The prediction of future air transportation demand indicates: the goal of aviation emission reduction cannot be reached with only one of above solutions.
At least according to current research, it is the only effective way to validly mitigate jet-fuel crisis and aviation emission reduction.
[2] Yiping Lin: Emission Reduction and Alternative Jet-fuel.

Big data-driven hybrid model for operation optimization of thermal power units 3.1.
How to analyze these data effectively is clearly of importance.
With the exergy analysis in Section 2, big data analytics emphasis the huge volume of data and imply that the collected data set covers almost the whole population as well.
In this paper fuzzy rough set (FRS) is selected to perform big data analytics.
Table 1 Data sets description Data set Load range （MW） Ambient temp. （℃） Coal quality* （MJ/kg） Samples Original attributes Num.