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Introduction Magnesium alloy has received increased interest as possible construction material in the automotive industry due to its low mass density, the possible benefit of weight reduction and consequently fuel saving.
Specimen thickness required for a valid K1C test is very high for light alloys, and reduction in thickness allows the decrease of costs in quality control tests.
The clip gauge was connected to the data acquisition system through the 4-strain gauge leg and connected to the computer equipped with power spectrum density (PCD) software that records the strain at 1 kHz sampling data.
The maximum force, is recorded with CTOD data.
The data tested along with their calculated values of based on Eq. 2 are presented in Table 2.

Introduction Image compression addresses the problem of reducing the amount of data required to represent a digital image.
Wavelet transform exploits both the spatial and frequency correlation of data by dilations (or contractions) and translations of mother wavelet on the input data. [16,17] It supports the multi-resolution analysis of data which allows progressive transmission and zooming of the image without the need of extra storage.
Any such super position decomposes f in to different scale levels, where each level is then further decomposed with a resolution adapted to the level .This technique exploits the psycho-visual as well as statistical redundancies in the image data, enabling bit rate reduction.
To reduce the data loss during the reconstruction, we propose these soft computing techniques.
S.James Walker, Wavelet-Based Image Compression, The Transform and Data Compression Handbook, Ed.

The advantage of using nano-structured light metals is possible weight reduction of manufactured components.
In the case of automotive parts, this may contribute to a reduction in CO2 emissions - one of the most important global challenges - due to the significantly higher strength of nano-structured metals and alloys compared to their microcrystalline counterparts.
However, experimental data presented in Table 1 indicate that for pure aluminium such a value has not been reached yet.
A particular advantage is in the case of vehicles (for both air and ground transportation), for which weight reduction due to a significantly enhanced strength of nanostructured metals and alloys may contribute to the desired reduction of CO2 emissions.

So another category of damage detection techniques based on vibrational data from nonlinear systems are developed.
For example, damage detection in nonlinear systems using system augmentation [1], identification of damage based on attractor changes [2], enhancing nonlinear dynamics for accurate identification of stiffness loss [3], using attractor dimension in structural health monitoring [4, 5], and structural health monitoring through chaotic interrogation [6] are some of these methods that use vibrational data of a nonlinear system for damage detection.
For example, for the healthy region would be 1 and shows a 10% stiffness reduction for the damaged portion of the panel.
In all damage scenarios, the length of the damaged portion is set to 1.25% of total length and only the damage extent (stiffness reduction percent) and damage location differ.
Eigenvalues of 20 different damage scenarios while only 2 of them are independent In each location ten damage extents were assumed (from 1% to 10% stiffness reduction).

Instruction of UCI dataset Dataset (class amount) Dimension Training Sample Test Sample Heart(2) 13 190 86 Iris(3) 4 80 50 Wine(3) 13 105 65 Glass(6) 9 99 95 Experiment Study Data Acquisition.
Noise Reduction and Feature Extraction.
Before the threat events classification by the proposed method, noise reduction and feature extraction must be implemented.
The signals before and after noise reduction are shown in Fig.2.
[2] Tinoco, J., Correia, G, Cortez, P.: A Novel Approach to Predicting Young's Modulus of Jet Grouting Laboratory Formulations over Time Using Data Mining techniques.

Because no energy is consumed during the cooling period of the wood, this leads to a significant reduction of its spending [7, 10] and reducing the cost of drying process for 24% [7].
Reduction of process in 1,5 - 2 times for beech and oak wood is noted in [8].
The inconsistency of the hypothesis of temperature change according to the sinusoidal law with experimental data is established.
Fexp Ftest heating 1.0 2.07 1.15 2.82 cooling 0.14 2.07 1.30 2.82 The obtained experimental data of the kinetics of temperature change (Fig. 2) showed that the heating period is polynomial, and the cooling period is exponential.
Given the acceleration of the drying process in the case of using oscillating schedules and reducing energy consumption by 1.53 times due to the cooling cycle, you can achieve a cost reduction of 3.3 times.

The validity of the proposed approach is demonstrated by comparing the predicted results for the compression force and bulge profile under some reductions with the results obtained by experiment, Finite Element Method (FEM) simulations, and SM analysis.
Generally, the following simulation setting data (see Table 2) have to be inputted to the software for the rotating cases.
Table 2 The simulation setting data inputs for FEM simulation Forming Machine Hydraulic Press Simulation Module 3D Rotating Speed for the Lower Die 0.2rad/sec Constant Shear Friction at Interface 0.3/0.1 Reduction Ratio 18.67~32.67% Moving Speed for the Upper Die 2mm/sec Flow Stress of Layer 2 Cylinder SUS304 : Flow Stress of Layer 1 Cylinder SAE1039 : Geometry of Layer 2 Cylinder Geometry of Layer 1 Cylinder Fig. 4 presents the corresponding results including the effective stress, the effective strain and material flow of the double-layer clad cylinder after compression whether considering the rotating effect.
The compression forces obtained by the Slab analysis and FEM simulation under the given reduction ratios for various clad height ratios can be compared whether considering the rotating effect.
Fig. 6 shows the compression forces from experiment and UBM, SM, FEM three methods with the geometrical and frictional conditions, that is Di=10mm, Hi=15mm, m2=0.3, m1=0.1, under three reduction rates 19%, 23%, 28%, and angle velocity.

Tensile strength σt (MPa) Yield strength σy (MPa) Elongation ε (%) Reduction of area Ψ (%) Hardness (HV) Cu1Cr0.5Zr 420 240 24.1 35 160 STS316L 675 344 49.0 73 230 The friction welder (Model: TOHO TH-25) used is a continuous drive brake type, with ø25 mm rotation chuck and ø40 mm fixed one.
(Cu-alloy = 420MPa) Tensile strength, σt (MPa) Heating time, t1 (sec) 0 1 2 3 4 5 6 0 20 40 60 80 100 Reduction of area Elongation Reduction of area, ψ (%) Elongation, ε (%) Heating time, t1 (sec) 0 50 100 150 200 Ot1Z Absorption of energy Absorption energy, Ea (kN.mm) Fig. 3 Tensile strength vs. heating time Fig. 4 Toughness vs. heating time Relationship between Heating Time and Toughness Fig. 4 shows the relationship between frictional heating time and toughness (elongation, reduction of area and tensile absorption of energy).
The reduction of area is all the same tendency, but turns 20∼26% at 1(sec) and 3(sec) heating time.
Table 4 Data by creep tests of Cu1Cr0.5Zr/STS316L joints Materials Cu1Cr0.5Zr/STS316L Temperature Stress Initial strain Rupture time σ/σt ×100 T ( o C) σ (MPa) ε0(%) tr(h) (%) Fracture position 140 0.387 1.3 40 Cu alloy HAZ 120 0.286 6.2 34.3 〃 100 0.208 21.4 28.6 〃 300 (σt =350) 90 0.155 72.2 25.7 〃 80 0.466 1.7 26.7 Cu alloy HAZ 60 0.324 5.1 20 〃 50 0.243 16.6 16.7 〃 400 (σt =300) 40 0.174 47.8 13.3 〃 40 0.803 1.8 16.7 Cu alloy HAZ 30 0.545 4.9 12.5 〃 20 0.281 18.1 8.3 〃 500 (σt =240) 15 0.184 40.1 6.3 〃 Creep Life Prediction by LMP The variation of metal structure occurs within a shorter time at high temperature.
Without any slope variation of the curve in checking the stress (σ)-rupture time (tr) at the high temperature with more hundreds degree than the actually tested temperature, the Larson-Miller parameter (LMP) for expecting a long time life prediction by a short time life data extrapolation is defined as the following [6].

Liu et al. statistically analyzed air pollution data and birth data during the period 1985-1998 in Vancouver, Canada and found that low birth weight was related to the exposure to sulfur dioxide in the first month of pregnancy.
Many studies have found that the birth weight, length, head circumference reductions were related to prenatal exposure to PAHs, which also could increase the risk of preterm birth, fetal growth restriction [11, 12].
Table 1: The effects of environmental pollutants on pregnancy outcome in different countries and regions environmental pollutants adverse pregnancy outcomes countries or regions carbon monoxide(CO) intrauterine growth retardation preterm birth congenital heart disease Canada Britain United States sulfur dioxide(SO2) intrauterine growth retardation low birth weight preterm birth congenital heart disease nervous system abnormalities Canada Britain United States Britain nitrogen dioxide(NO2) intrauterine growth retardation Canada ozone(O3) oral cleft Taiwan polycyclic aromatic hydrocarbons(PAHs) preterm birth low birth weight length reduction head circumference reduction fetal growth restriction intrauterine growth retardation neural tube defects Poland, United States, Dominica United States, Dominica Czech China polychlorinated biphenyls(PCBs) low birth weight the birth rate of men and women United States United States chlorpyrifos low birth weight
length reduction United States o¸p´-dichlorodiphenyltrichloroethane (DDT) neural tube defects China α-hexachlorocyclohexane (α-HCH) neural tube defects China dioxins the birth rate of men and women Italy cadmium low birth weight length reduction Japan Italy lead congenital malformations damage to the nervous system development impact cognitive ability United States Mexico PM2.5 preterm birth United States United States Conclusions Some negative environmental factors such as contamination of drinking water, agricultural pesticides, air pollution, industry pollution, food contamination and disaster [32] have influenced human health, especially the pregnancy outcomes.

Among them, the relevant data about operational capacity evaluation mainly including regional load, electricity and its growth rate and capacity-load ratio, we choose the transformer capacity-load ratio, the average rate of the transformer load and the maximum load utilization hours to reflect capacity-load ratio, regional load and electricity.
Similarly, we can obtain the index of grid structure evaluation, safety reliability evaluation and energy loss reduction evaluation.
Table 3 Multi-level extension assessment results for function effects post-evaluation of substation construction project Index Correlation Degree Assessment Level (-0.585,-0.581) (-0.343,-0.341) (0.069,0.105) (-0.109,-0.107) 3 (-0.576,-0.487) (-0.274,-0.229) (0.155,0.185) (-0.301,-0.255) 3 (-0.777,-0.656) (-0.597,-0.505) (0.101,0.121) (0.026,0.028) 3 (-0.667,-0.566) (-0.418,-0.359) (-0.110,-0.097) (0.076,0.078) 4 (-0.533,-0.481) (-0.225,-0.202) (0.351,0.389) (-0.328,-0.296) 3 According to the data shown in table 3, we can get following conclusions: 1.
The substation project comprehensive evaluation, operational capacity evaluation, grid structure evaluation and energy loss reduction evaluation level are, assessment conclusion is "good", its shows that the planning target has basic realization. 2.
In order to further enhance the function effects of the substation construction project, we should improve its operational capacity, grid structure and energy loss reduction.