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A parameter reduction method based on fuzzy rough sets was proposed.
Fundamentals on Fuzzy Rough Sets A structural data used for classification learning can be written as an information system, denoted by , where U is a nonempty and finite set of samples {x1, x2 ,..., xn}, called a universe, A is a set of attributes (also called features, inputs or variables) {a1, a2, ... , am} to characterize the samples.
The objective of rough set based attribute reduction is to find a subset of attributes which has the same discriminating power as the original data and has not any redundant attribute.
Fig.2 Dependency Function There are 6 parameters in the reduction set.
A forward greedy search algorithm is proposed for attribute reduction.

When the diameter is small, narrow band noise reduction is gotten.
The larger the diameter increases, the broader noise reduction would be.
When it got to 80mm, a wide band of the noise reduction is reached.
However, wave propagation is due to the combined effect of inertia and elasticity of the medium in the presence of flow in the ducts (Zhenlin Ji, Engineering Acoustics and Noise Control, Harbin, China, unpublished data), and therefore a wave moves relative to the particles of the medium.
Using non-flow simulation data, the simulations of the acoustic performance of the angle between the main duct and the bypass duct and the changes of the diameters with mean flow were taken.

These data were interpreted to find the potential region for adsorption or desorption of the fluorosurfactant and its influence on metal deposition.
An EcoChemie µ-Autolab II potentiostat with NOVA 1.7 software was used to carry out potential sweeps and record data.
Figure 2 shows that the addition of surfactant has cause a shift in reduction potential.
The CV trace shows that Cu reduction now occurs at -0.43 V.
These data show that the surfactant adsorption blocks the Cu discharge.

Optimal Uncorrelated Unsupervised Discriminant Projection Yu’e LIN 1,a ， Yongcun GUO2,b 1School of Computer Science and Engineering, Anhui University of Science and Technology Huainan, 232001, China 2College of Mechanical Engineering, Anhui University of Science and Technology Huainan, 232001, China alinyu_e@126.com, bguoyc@aust.edu.cn Keywords: manifold-based；dimensionality reduction；face recognition；small sample size problem；uncorrelated Abstract.Unsupervised Discriminant Projection (UDP) is a typical manifold-based dimensionality reduction method, and has been successfully applied in face recognition.
PCA constructs a low-dimensional representation of the original data via minimizing the reconstruction error.
But both PCA and LDA fail to explore the essential structure of the data.
A lot of researchers are attracted to straightforwardly find the inherent nonlinear structure of the data and then many manifold-based learning algorithms are proposed.
Defining affinity matrix is as follows (1) Where represents the neighborhood relation between data sampleand .

The digging spring tooth for corn stubble harvester including self-exited vibration S-shaped spring handle and curved chisel-shaped bionic tooth is designed based on the mechanism of drag reduction of self-excited vibration and bionic drag reduction for reduce digging resistance and power consumption, and the statics analysis of digging spring tooth is done by ANSYS software, the stress and strain distribution diagrams show the design is reasonable.
Design of self-exited vibration S-shaped spring handle In order to reduce the engine power consumption and the digging resistance, the self-exited vibration S-shaped spring handle is designed by the research of drag reduction mechanism of self-excited vibration, as the figure 1 shows.
When the handle is forced, it can generate restoring force and can make the whole spring-tooth vibration to achieve the purpose of reduction tractor-drawn resistance.
Based on the above analysis and the depth research of bionic drag reduction mechanism, the bionic tooth of the excavating spring tooth is designed by the measuring results of claw curved contour shape and the prototype of the front foot middle toe of field mice, as the figure 3 shows.
Table .2 Pressure datum of soil to two teeth with different α（KPa） wedge angle α pressure 25° 560 35° 721 Table .3 Pressure limit data of excavating spring-tooth MPa stress wedge angle α=25° wedge angle α=35° Max. value 570 573 Min. value 0.177 0.182 Table .4 Distortion limit data of excavating spring-tooth （mm） displacement wedge angleα=25° wedge angleα=35° Max. value 17.498 17.172 Min. value 0 0 Fig.6 Stress contour of excavating spring tooth when is 35° Fig.5 Stress contour of excavating spring tooth when is 25° As can be seen from the overall stress distribution of excavating spring tooth, the stress is mainly distributed in the elastic handle and bionic tooth engagement end the circular position of radius 30mm, and stress increases with wedge angle increasing; the maximum stress value is 573 MPa when the α is 35°, and the maximum ultimate strength of excavating spring tooth is 735 MPa, so the spring tooth strength meet the design requirements.

Then the high-dimensional space data are reflected into low-dimensional space data by means of SLLE in order to extract the features of the tool wear state.
It is usually used in data finding and data visualization, in which the classification information of the data and their relationship is unknown.
The example of SLLE dimension reduction is shown in Pic1.The data of the three-dimensional space(Pic1(a)) is reflected to two-dimensional space(Fig1(c)) by LLE.
The three groups of data is locally linear embedding dimension reduction.
The dimension reduction data is used as the feature vectors.

LCA eBalance software (version 4.0) was used for LCA modeling and the background data were obtained from Chinese Core Life Cycle Database (CLCD) implemented in eBalance.
The foreground data of supply chain input were then converted into units of greenhouse gas emissions.
Each instance of foreground input data was multiplied by the relevant GWP value and then summed across the product life cycle.
The internal or external project executives only need to collect foreground data for the most commonly used materials and processes which are relevant for the company and can also avoid companies from investing much in data collection.
Ltd. for providing supply chain information during data collection process.

The Laser Polishing tests results have been used as the input data for a design of experiments (DoE), therefore the optimum operation parameters for the process as well as its degree of influence in the melted surface have been defined.
Maximum roughness reduction parameters with CO2 laser.
Maximum experimental roughness reduction with HPDL laser.
Therefore, for the same material and similar roughness reduction rates, the influence of the laser type is relevant.
Conclusions The laser polishing process, in tool steel DIN 1.2379, allows roughness reductions over the 80 percent.

It aims to create an opportunity to develop own patent, alongside cost reduction on the benchmarked systems.
This indirectly reflects the cost reduction outcome on the material consumption related to the bracket design.
The component was fabricated as a prototype and the data of weight reduction was measured.
Meanwhile, the optimization method using TRIZ achieved 75.3% of weight reduction against the existing design.
In the same financial year (2011/2012), the organization really shows significant results in cost reduction performance.

Compression pre-cracking methods [3] were used to initiate cracks at crack-starter notches with constant-amplitude and load-reduction methods used to generate data from threshold to fracture.
Figure 1(a) shows the ΔK-rate test data, and Figure 1(b) shows the ΔKeff-rate data using a plane-strain constraint factor (α) of 1.8.
The term q = 2 was selected to fit high-rate data.
Figure 3(b) shows the test data (symbols) and the predicted results.
The symbols show test data and the curves show life predictions.