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It may not only bring reductions in CO2 emission, but also may lead to savings in expenditure on energy.
The reduction of costs via energy saving could be the first purpose instead.
These data can teach all those involved how much energy is consumed uselessly and how they can reduce it.
Among the group members, from data analysis, essential problems for reducing energy consumption will be discussed, and their countermeasures are to be proposed and implemented selectively.
In this case power reduction rate was 30 percent and return on investment (ROI) was 2.7 years[6].

Water removal during the SS composting is considered to be an effective sludge reduction process that benefits the storage and transportation of SS composting material [1].
Table 1 Design of the turning strategy in each treatment Treatment Turning time (day) temperature-increasing phase Thermophilic phase Cooling phase T1 - - 12, 16, 20 T2 - 8 12, 16, 20 T3 2 8 12, 16, 20 T4 2 8 12, 14, 16, 18, 20 Data acquisition and sample analysis.
Formulae for data computation.
Fig.1 Moisture content and VS content variation of each treatment Fig.2 Water removal variation of each treatment Water reduction.
Considering with energy consumption (T3 turning one more time than T2), the turning strategy of T2 was recommended to achieve better quantitative reduction efficiency in composting process.

Those kinematic data was carried out through three major steps: wavelet transformation, Principle Component Analysis (PCA) -based dimensionality reduction and k-fold cross-validation, followed by implementing a best classifier to distinguish 6 difference actions.
Capturing acceleration data.
The data coming from volunteers 1# to 5# and 12#, 13# are arranged used for train, the data coming from volunteers 6# to 11# are arranged used for test.
So that, a total of 492 kinematic data were recorded from 13 volunteers, 420 data of them will be used in train from 7 volunteers, and 72 data which any data from subjects did not been used by train will be used in test from 6 volunteers.
First, raw data are pre-processing by segmenting interest single action from original kinematic data, i.e., the action signal data, through shifting; cropping and size normalizations to save processing time and increase processing speed.

As deduced from laboratory studies, these CWL almost exclusively were adjusted to promote sulfate reduction, concurrently aiming at U reduction and precipitation of U minerals.
In order to contribute to the limited data set, about 20 small-sized natural wetlands in the former German uranium milling area nearby Ronneburg, Thuringia, were investigated hydrogeochemically by means of U accumulation processes.
The statistical evaluation of the data set may reveal primary elemental relations when normalized to refractory Si in the pore water depth profiles, as deduced in one of the wetlands from similar concentration distribution e.g.
On the other hand, in evaluating substrate elemental composition with respect to causal relations, both with measured data and data normalized to refractory Zr, clearly prevailing processes for U retention couldn't be elucidated.
In this position, U reduction and biomineralization of U(IV) species may provide the best conservation, unless forming minerals are nanometer-sized, compromising colloidal transportability.

Then the accepted model is measured and the CAD and CAM programs are compiled based on the measured data.
Sampling Strategy and Data Reconstruction Sampling strategy is important to obtain all the required data with high accuracy in a short time.
Due to huge amount of sampled data a data reduction [6] algorithm based on distance criterion is applied before data reconstruction [7].
Pm will be kept for further data processing if 0δ δ> , and Pm will be used as the new end point.
All the data points are projected to ww wx O y plane of the world coordinate system.

The most potential method of selective catalytic reduction (SCR) to remove NOx from diesel engine emissions is very effective in NOx reduction with an efficiency up to 95%.
From the principles of physisorption measurement with nitrogen, the BET surface area of catalysts was calculated in a combination with use of the data obtained from the isothermal lines at a pressure ranged from 0.1-1.0 bar.
The measured data of the BET surface are shown in Table 1.
X-Ray Diffraction (XRD) is an effective method in identifying structures and constitutes of a catalyst where the measured data is compared with a standard XRD spectrum.
Data acquisition was carried out with a measurement step of 0.8 eV.

Currently, there are various methodologies for helicopter vibration reduction.
During the learning process, the thresholds and decay as follows: (24) where is the total number of the learning data. defines the desired accuracy of an output of GD-FNN, represents the predefined maximum error. and represent the smallest length of interest and the largest length of the input space, respectively.
For the th observation , calculate the out of GD-FNN of all the existing data , where and .
At each frequency, 500 observation data points are selected to train the networks and parameters of the GD-FNN.
Helicopter Vibration Reduction with Trailing-Edge Flaps.

Converted sequences have the features of discrete, non-real numbers and dimensionality reduction, etc., so a reasonable symbolic method can improve the efficiency of time series data mining.
Experimental Evaluation In this experiment, we chose the satellite’s remote sensing data as the test data, whose characters are introduced in section 2.
A Distributed Approach to Detect Outliers in Very Large Data Sets.
A survey of Data-Driven prognostics.
Efficient Algorithms for Mining Outliers from Large Data Sets.

It can reduce the data dimension, and also retain the key messages of the data set.
For data point xi, if data point xk make R(i,k)+A(i,k) meet the maximum of R(i,j)+A(i,j), j=1,…,N, then data point xk is the clustering center of xi.
By this way, the data dimension is reduced, and most of the data informations are also retained.
Silhouette reflects the tightness of data points within one class and the divisibility of data points from different classes.
So after dimension reduction we get a better clustering quality.

These data were finally used to obtain parametric process maps.
Parameter Value(s) Heating rate [°C/s] 10 ±0.5 Soaking time [s] 60 ±0.1 Testing temperature [°C] 300, 350, 400 ±2 Testing strain rate [1/s] 0.5, 5, 100 Maximum strain [-] 1 Repeatability 3 Material flow curves.The force-displacement data recorded by the acquisition system were elaborated to obtain the material flow curves, following Eq. 1 and 2: (1) (2) where s is theflow stress, e the equivalent strain, h0the initial specimen height and s the testing machine stroke.
The data about the material flow curves for the highest and the lowest testing temperatures are shown in Fig.2.
Fig.7 – Shifting of the load peak as a function of thickness reduction.
Fig.8 - Cartesian force components as a function of feed ratio and thickness reduction.