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Introduction Monitoring of vibration at relatively high frequencies is a challenging task because as the frequency increases, the vibration amplitude decreases and thus very sensitive techniques must be used to extract meaningful data while rejecting the effect of noise.
As for the specific case of FBG, signal processing helps moving the complexity of high-performance interrogation systems from optics to electronics, with a substantial reduction of costs.
Typically, the spectral analysis of vibrations is carried out by performing a Fast Fourier Transform (FFT) on the acquired data.
After T iteration, the obtained Finite Response Impulse (FIR) filter is then used as noise whitener for the real-data measurement.
In this case it is shown how the use of signal processing mitigates the noise at high frequencies and makes the raw data converging to the actual spectrum.

The process of wood recognition includes data acquisition, image preprocessing, feature extraction, decisions classification and so on[1].
When the data of feature is relatively few, this algorithm is relatively strong.
Data Preparation.
Principal component analysis (Principle Component Analysis, PCA) is a classical linear dimensionality reduction method, it is the sense of minimum mean square error with less number of dimensions that the original data, with features of simple and efficient.

In general, a wide range of processes have been reported to eliminate the Cr(VI) from groundwater such as chemical precipitation, adsorption, electrochemical reduction, sulphide precipitation, ion-exchange, reverse osmosis, elecrodialysis, etc[4-6].
The experimental data were fitted into Eq(3) for linearization by plotting Ce/qe against Ce (figure not given) and good fit of this equation(R=0.9789) reflects monolayer adsorption.
The plot of logqe against logCe shows (figure not given) the isotherm data is well fitted with the Freundlich model (R=0.9852).
The sorption data of synthetic zeolite were well fitted with both the Freundlich and the Langmuir isotherm models.
Consequently, Langmuir linear regression data showed the monolayer adsorption capacity of chromium onto synthetic zeolite is 2.358 mg/g.

The in-plane size effects are examined by comparing the numerically estimated results with the experimentally determined data.
The dominant parameters such as σ1 and D for defining yield condition as well as the f0, fc and ff, which are presented in references [5, 6], for simulation of void growth and coalescence were determined using the finite element analyses results for standard CT specimens and the corresponding fracture toughness test data with respect to SA515 Gr.60 and SA516 Gr.70 carbon steels.
The data obtained from experiments and numerical simulation made clear that the J-integral was high when the crack tip constraint was low.
It should be pointed out that a reduction of crack tip constraint can lead to a relatively high material resistance against ductile fracture and it is too conservative to make use of J-R curve obtained from IP-1T standard specimen for structural integrity evaluation.
If the geometry is similar to a real structure, it is recommended that adoption of tested or estimated fracture resistance data from larger in-plane sized specimen is more realistic.

For PA-6 based materials the foaming process has traditionally been an insurmountable technical challenge or otherwise limitations have existed to the reduction in density possible.
The tensile test was conducted at a strain rate of 0.01s-1 where displacement is data logged via an externally installed LDVT, connected directly to the sample holder.
Expansion of metallic components due to the loading is accounted for in the data analysis.
Results and Discussions All data has been analysed based on a moving point average, covering a range of approximately 20 data points over 0.01 stain range from 0.01 to 0.10 strain.

Data from field study was subjected to one-way ANOVA.
Data from other experiments was subjected to T-test.
This reduction did not relate to concentration of methanol extract.
Under field conditions, M. pigra spray-treated with different concentration of leaf extracts exhibited growth inhibition and injury (data not shown).
Concentration (mg/ ml) Methanol leaf extracts Dichloromethane leaf extracts 0 3.22 ± 0.29 3.22 ± 0.29 0.1 2.62 ± 0.38 1.85 ± 0.29a 1.0 2.78 ± 0.51 1.54 ± 0.50a 5.0 1.41 ± 0.31b 1.60 ± 0.10b Data are Mean ± SE. a p < 0.01, b p < 0.001 Acetogenins, alkaloids, flavonoids, terpenoids, saponins and essential oils were identified in A. squamosa extract.

Results and discussion Rietveld refinements from XRPD data carried out with the FullProf program5 confirmed single phase with I4/mmm space group for all the as-prepared La2−xSrxMnO4±δ (0.7 ≤ x ≤1.0) samples.
Oxygen stoichiometry (according to iodometric titration), manganese valence and unit cell volume (from Rietveld refinements against XRPD data) in as-prepared La2−xSrxMnO4±δ (0.7 ≤ x ≤1.0) at RT.
Indeed according to iodometric titration, the increase of x promotes the reduction of manganese.
Since laboratory XRPD data gave no evidence of additional peaks matching with the Bmab space group, as observed by NPD for x = 0.84, a Fmmm space group has been chosen.
During the first oxygen uptake highlighted by TGA, all samples undergo a drastic expansion of the basal plane and a contraction along the c-axis, as revealed by the analysis of the ex situ XRPD data for the whole set of annealing temperatures (Fig. 4).

The temperature-time data were recorded using a high-speed data acquisition system (EPAD-TH8-K) linked to a computer with DEWESoft 7.5 at a dynamic rate of 100 Hz/ch, as shown in Fig. 1.
FlexPro8.0.31 data analysis software was used for smoothing the curves and plotting the cooling curve and first derivative curve for extracting characteristic data.
The result also corresponds to best refinement morphology observed in Fig. 3. 0.8 wt.% Sb has resulted in biggest average size reduction from 179.4 to 134.65 µm, about 44 µm reduced, similar to aspect ratio which also decreased from 1.2 to 0.7 by 9% and produced high density particles distribution, about 29 particles per unit area in mm2.

In particular, delaminations can be strongly influenced by the fatigue induced material degradation which can trigger delamination growth phenomena at a relatively low number of cycles gradually leading to the increase of the damaged zone and to the reduction of the overall structural stiffness [7].
Table I: Geometrical and material panel’s data Geometry AB [mm] t [mm] t1 [mm] r1 [mm] r2 [mm] r3 [mm] 50 3.37 0.28 2.5 10 20 Material E1=E2 [GPa] E3 [GPa] G12 [GPa] G23=G13 [GPa] ν12= ν13= ν23 GIc [J/m2] GIIc [J/m2] 42.5 14.5 17.4 0.85 0.22 103 456 The numerical results, in terms of delamination growth as a function of the fatigue cycles have been found in excellent agreement with the experimental data taken from [8], as shown in Figure 2.
Figure 2: Delamination growth comparison between numerical and experimental [2] data In Figure 3 the deformed shapes with uz contour plots at delamination buckling at fatigue cycle n° 1 and n° 287039 are shown.
Numerical results, in terms of delaminated area as a function of the number of cycles, have been found in excellent agreement with the literature experimental data, proving the effectiveness of the method.

In order to reduce these deviations a fitting of the numerical model to the experimental data was conducted.
Fitting of the numerical model to the experimental data A fitting of the numerical model to the experimental data was performed with the use of the Mixed Numerical Experimental Technique (MNET) [7].
The experimental data for the model fitting were collected for the case of the structure without any attached mass.
Multiple experimental-based classifiers were estimated, using 70% of the collected experimental data.
Estimated MEPos (a,c) and MEMass (b,d) for multiple classifiers with testing data from a 5 g (a,b) and 15 g (c,d) mass.