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Test on synthetic data showed that the new approach is simple and effective.
Introduction In mineral exploration, data processing and interpretation of magnetic data, such as reduction to pole, forward modeling and inversion, usually require known magnetization direction.
Synthetic data test is carried out to test the feasibility of the new approach.
Can we estimate total magnetization directions from aeromagnetic data using Helbig’s formulas.
Identifying remanent magnetization effects in magnetic data.

The simulated results show a very good agreement with the measured data.
In order to improve the model predictive accuracy, the FMEP model used in this model based on the actual friction data on bench.
The results of engine performance has been plotted and compared with actual testing data.
The data show a good agreement between simulation result and actual test as illustrated in Figure 8.
The discrepancy of between simulation data versus actual measurement recorded at approximately 0.8%.

This study has demonstrated that, indicatively, it is possible to achieve up to 86% reduction in GHG emissions by increasing the amount of timber specified in buildings.
· At first, the research is normally conducted by gathering technical information including material safety data sheets, data tests of indoor air quality, product warranties, source material characteristics, recycled content data, environmental statement, and durability information
(WM) Waste Reduction(WR) Pollution Prevention(PP) Recycled(RC) Embodied Energy Reduction(EER) Natural Materials(NM) Energy Efficiency(EE) Water Treatment & Conservation(WTC) Non Toxic(NT) Renewable Energy Source(RES) Longer Life(LL) Biodegradable(B) Recyclable(R) Reusable(RU) Others(O) .
Pre-Building Phase: Manufacture · Waste Reduction The waste reduction feature indicates that the manufacturer has taken steps to make the production process more efficient, by reducing the amount of scrap material that results.
Building Phase: Use · Reduction in Construction Waste Designing a building with these standard sizes in mind can greatly reduce the waste material created during the installation process.

It plays a very important role in the area of energy conservation and reduction of pollutant emissions.
Foundation of design.The proposal of single-stage reduction gear is applied in electric vehicle, and then the higher performance of drive motor is required.
（2） nN means the rated speed of motor; ig means ratio of transmission；i0 means main reduction ratio；uN means conventional velocity of EV, and its international unit is km/h；means rolling radius of wheel.
The parameters calculated by formulas 1-5 meet the following basin principles[1]: (1) The max velocity can be calculated by rated data of motor; (2) The max grade ability can be calculated by short time characteristic curve; (3) The conventional velocity of vehicle should correspond with the rated speed of motor; (4) The power balance point of the vehicle max velocity should correspond with equal power section of motor continuous working characteristic curve.
Lieu, “Reduction of torque ripple in brushless dc motors,” IEEE Trans.

The microstructural analysis and porosity measurements suggest that the tensile property enhancement resulting from applied pressure should be attributed to microstructure refinement and porosity reduction of the squeeze cast alloy.
Introduction Weight reduction of automobiles for increased fuel efficiency and performance has stimulated interest in using magnesium alloys for various automotive applications.
Tensile Testing.The mechanical properties of the squeeze cast Mg-Al-Sr alloys were evaluated by tensile testing, which was performed at room temperature on an Instron machine equipped with a data acquisition system.
The improvement in tensile properties by the pressure increase should be attributed to materials densification and porosity reduction Figure 5: Effect of pressure levels on uts, ys and elongation of ams605 alloy.
The material densification and porosity reduction should be responsible for the increase in tensile properties.

Considering the engineering design margin, noise reduction is amplified relevantly, the actual noise reduction of air outlet is determined as 85 dB(A).
Flow area of the third stage silencer: S3=9×11×2.6×0.145=37.3 m2, air volume measured on site is 169.11 m3/s, so flow velocity of the third stage muffler: Placing whose value is 4.5 into Eq. 9: By the data calculated upon, the regenerative noise is within the target range in the end of noise control, the design is reasonable.
The Calculation of the Total Amount of Noise Reduction.
The values of noise reduction of the muffler and the target values of noise reduction are shown in the Table 5.
Table 5 The Values of Noise Reduction of The Muffler And The Target Values of Noise Reduction Center frequency [Hz] 125 250 500 lK 2K 4K Amount of noise reduction [dB] First stage 4.4 4.4 4.4 4.4 4.4 4.4 Second stage 17.5 19.3 17.5 15.1 9.6 6.8 Third stage 11.5 24.3 22.1 18.8 17.7 13.9 Total amount of noise reduction [dB] 33.4 48 44 38.3 31.7 25.1 Minimum value of noise reduction[dB] 25.6 26.5 27.5 20.9 13.5 10.4 According to Table 5, the amount of noise reduction of each center frequency is more than the minimum value of noise reduction, so muffler design can satisfy the requirement of noise reduction.

Borehole data of CZ1, CZ5, and CZ18 of Suzhou rail transit line NO.3 were selected for research.
The drilling data are shown in Tab. 1; shear wave velocity can be seen in Fig.1.
However, reduction coefficient of each soil layer along depth exhibit a decreasing trend as well as discreteness with the increasing input peak acceleration.
On the same earthquake fortification level, PGA along depth under Suzhou artificial waves appear a data concentration phenomenon and reduction coefficient generally outweigh that under far-field ground motions of great earthquake.
PGA reduces along soil depth, at the range of less than 20m depth, the reduction rate is faster and declines straightly.

Fig.1(b) shows EBSD map of CR samples after 35% thickness reduction, where the grains are deformed in the rolling direction.
Fig.3 (a) shows the TEM micrograph of CR 90% thickness reduction sample which heavily deformed microstructure.
In the kernel maps the degree of misorientation correlated to the neighboring data points are indicated by a colour scheme.
Red colours indicate a high degree of misorientation compared to the surrounding data points; blue colours indicate a low misorientation and thus low strains.
From the KAM analysis, the average misorienation of CR 90% + SA was 0.8 degrees which lies in between ST and CR 35 % thickness reduction.

The reliability of both acquired displacement data and their subsequent processing was verified through some preliminary analyses.
Firstly, data were processed to restore the fluctuation of measurements around a mean value equal to zero (linear-base correction).
Furthermore, an additional comparison of these data in the frequency domain confirmed their complete agreement.
The limited deviation of these results verified the reliability of data and confirms that any relevant information was lost.
The overall behaviour of each storey was considered equal to that represented by data obtained at the corresponding floor level.

A Decorrelation-based Nonnegative Matrix Factorization Algorithm for Face Recognition Dan Zhoua, Haiyan Gaob and Yunjie Zhang*c Department of Mathematics, Dalian Maritime University, Dalian, 116026, China awwwzhoudan88@126.com, bhaiyan19890120@qq.com, cyjzhang@dlmu.edu.cn Keywords: Nonnegative matrix factorization, face recognition, dimension reduction, decorrelation in spatial domain.
The proposed algorithm first takes into account the dimension reduction of the original matrix by preprocessing of decorrelation in spatial domain, and then uses nearest neighbor classifier on the reduced subspace.
NMF approximately factorizes the initial data matrix, which represents the whole training face images, into two non-negative matrix factors and consequently produces a parts-based representation of face images.
All the compared methods take the same training and test data.