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For the defect property and features, B.Monemar [6] studied the impacts of defect for LED, discussed the defect reduction of the internal quantum efficiency of InGaN-based multiple quantum.
The FCM clustering makes use of membership degree to determine each data point belongs to a certain cluster.
Let xi is a vector (i=1, 2, ×××, n), it is the ith measured data.
The cj is the center of the cluster, and || × || is any norm that expresses the similarity between the measured data and the center.
Thus, for the grey-scale image segmentation by using FCM, the data set for FCM is the gray value of every pixel, the main task involves the search for the image points that are similar enough to be grouped together.

Introduction In the past decades, hyperbranched polymers have attracted considerable attention due to their remarkable properties such as reduction of melt and solution viscosity, high solubility and ready to be functional in comparison to their linear analogues, which resulted from the large number of reactive end-groups within a molecule, approximately spherical molecular shape and the absence of chain entanglement[1-2].
Before the data gathering, all samples were heated to 200℃ and held in the molten state for 5 min to eliminate the influence of thermal history.
It is clear that the Avrami equation is effective for analyzing the experimental data of the isothermal crystallization kinetics.
However, the non-integer values of the Avrami exponent were obtained from experimental data, ranging from 3.63 to 5.01 and from 2.63 to 2.89, respectively.
The values of t1/2 obtained from experimental data are also given in Table 1.

When failure occurred in the network, it is difficult to guarantee reliable data transmission services when the routing is being re-calculated.
In the best case, shown in the Fig.2, thin arrow links can also be involved in data transmission, achieve multi-next hop forwarding in parallel.
It will not interrupt the normal transmission of data in the route re-calculation process. 2 Algorithm Formulation Let a network topology be represented as a graph G=（N，L）, where N is the set of nodes(routers), number is n, L is the set of edges(links).
The sequence is a strict order that there is no same equivalence class nodes, so that all links can be provided to participate in data packet forwarding.
The complexity of calculation focuses on the, which can be obtained on the phase of offline, so this algorithm can achieve great reduction of complexity. 4 Simulation model Figure 3 shows the topology1 we choose to simulate, which has a higher degree of connection between nodes, in order to provide multiple options for the next hop resources.

The noise data were gathered and saved to the personal computer during this period.
The noise data were gathered and saved to the personal computer during this period.
The noise data were began to collect as soon as the train arrives at the mark point 2, then stopped collecting when the train has completely departed from the mark point 2 [6].
Principle of Data Processing The following principles are obeyed in handling of background noise: a) If the difference between background noise and the measured noise level is more than 10 dB, the noise should not need to be amended; 2) If the difference between background noise and the measured noised level lies in range of 3~9 dB, the principle in table 2 will be adopted to make the amendment.
Kim, “Improvements for reduction of the brake squeal noise at Seoul metro rolling stock on tracks”, Journal of Mechanical Science and Technology,Vol.23, No.8, 2206-2214, 2009

Fig. 1 Micro plastic duplicate gear Computer Vision Inspection System The computer vision inspection system of micro gear is composed of the CCD, optical system, the data acquisition and processing system.
Therefore, by the proportion of relations we can obtain that: The process of detecting gear by the dummy circle scan method contains: To sub-pixel center of the circle (x¢0,y¢0), do a dummy circle of radius r, so the dummy circle and the gear profile have 2z intersetion points, by the judge of searching, we can find out the integer pixel coordinates of these intersection points, take advantage of dimensionality reduction gray moment operator of sub-pixel location, we can work out the sub-pixel coordinates(x¢i, y¢i)of these points and take these horizontal and vertical pixel coordinates of the intersection point into two one-dimensional array respectively.
Experimental Data Analysis In the experiments of checking tooth shape detection algorithm, the parameters of detected small module plastic gear are: z1=50, m1=0.25mm; z2=10, m2=0.3mm, where m1, m2 and z1, z2 are the tooth number and modulus of the large gear and the small gear respectively.
To take the tooth shape detection of small gear for example, and taking virtual round as small gear’s pitch circle: Table 1 Test data of small gear tooth profile NO di,i+1/d1,2(i=3, 5, …, 19) di,i+2/d2z-1,1(i=1, 3, …, 17) 1 d3,4/d1,2=1.0102 d1,3/d19,1=0.9897 2 d5,6/d1,2=0.9707 d3,5/d19,1=1.0344 3 d7,8/d1,2=1.0514 d5,7/d19,1=0.9822 4 d9,10/d1,2=1.0462 d7,9/d19,1=0.9924 5 d11,12/d1,2=0.9705 d9,11/d19,1=1.0327 6 d13,14/d1,2=1.0661 d11,13/d19,1=0.9897 7 d15,16/d1,2=0.9588 d13,15/d19,1=1.0262 8 d17,18/d1,2=1.0114 d15,17/d19,1=1.0381 9 d19,20/d1,2=0.9676 d17,19/d19,1=0.9819 Shown in table 1, (i=3, 5, …,19), (i=1, 3, …, 17), so, the tooth test data in the sub-degree circle is qualified.

The influence law of quenching and tempering temperature on the strength and hardness of 65Mn steel is obtained through data-process of the experimental result, and the mathematical model is established.
Table 1 Test standard of quenching and tempering temperature Factor Quenching temperature/°C Tempering temperature /°C Code Mark of variable X1 X2 Basic standard 780 250 0 Varying range 20 30 Top standard 800 280 +1 Lower standard 760 220 -1 The Experimental Results and Data Processing The phase transition critical temperature test was carried out on a SDTQ600 type differential scanning calorimeter (DSC).
After data processing, the regression equation was obtained as follows: Hardness equation: HRC=58.7+2.2X1-1.5X2- 3.2 2 1x + 0.3 22x - 0.3 X1X2 Tensile strength equation: bs =1151.7+42.2X1-30.4X2-63.0 2 1x + 5.6 22x - 4.88 X1X2 Where：Ｘ1∈[-1，+1]；Ｘ2 ∈[-1，+1]； The variance test of this experiment has been done, and the results show that factors X1 and X2 is significant at α=0.001, 0.005 respectively.
The experimental data is reliable.
Metallographic analysis shows that a small number of ferrite block exists in the sample quenched 760°C (see Fig. 2a), ferrite block has a significant reduction in the hardness and strength.

Methodology 3.1 Process of this study This study was conducted in the following steps in order to compare energy consumption and CO2 emissions of G-SEED certified apartments and non G-SEED certified apartments. 1) Analyze the evaluation points and ratio of G-SEED apartments with excellent grade in energy and environment sectors. 2) Analyze energy consumption and CO2 emissions of G-SEED certified apartments. 3) Compare average energy consumption and CO2 emissions of general apartments in Daegu. 4) Verify the environmental effectiveness in terms of energy consumption and CO2 emissions. 3.2 Data source and collecting data In this study, the Apartment Management Info System of the Ministry of Land, Infrastructure and Transport was used in order to collect information on energy consumption and CO2 emissions [3].
June, July, August, September) and then data on energy consumption and CO2 emissions in both seasons was gathered.
Based on data in Table 3, it can be expected that the subject buildings have positive results for energy consumption and CO2 emissions.
The location of target buildings in this study Table 3.The obtained score of target buildings from G−SEED Area Standard Score of A Score of B Score of C Score of D Energy Energy consumption 7 / 12 7 / 12 8.78 / 12 8.83 / 12 Use ofalternative energy 0 / 3 0 / 3 0 / 3 0 / 3 Environmentalpollution Reduction of CO2emission 3 / 3 3/ 3 2 / 3 2 / 3 Grade Excellent Excellent Excellent Excellent 5.

When the viscosity of PET was 0.54dL/g, the reduction of viscosity difference meant the viscosity of PTT component melt was from high to low.
The potential shrinkage difference between the two components decreased with the reduction of the viscosity difference between the two components, and the potential crimp energy was not significant.
(a)PET/PTT70/30 (b)PET/PTT60/40 (c)PET/PTT50/50 Figure 7 The crimp shapes of the PET/PTT filaments with different weight ratios The effect of weight ratio on the structure.In the different content bicomponent fiber, with reduction of the PTT component concentration, the crystallinity of PTT components increased, and the crystallinity of the PET component reduced.In particular, when the content of PTT components was 30%, its crystallinity increased to 72.04%; but with the content of the component increasing, crystallinity of each single component reduced, and the specific datum were shown in Table 4.
Under the same component and cross-sectional area, the spining tension on the unit mass of component increased because of the reduction of the component content, then strengthened the induced orientation, and the crystallinity increased.

The data obtained from Expedition Widya Nusantara (EWIN) by P2O-LIPI in the territorial of Raja Ampat Sea on November 14-24th 2007, by using 12 out of 33 observation stations.
Method The data obtained from Expedition Widya Nusantara (EWIN) by P2O-LIPI.
For this research we used only 12 stations data which divided into 3 transects: transect A, B and C, as shown in Fig. 1 and the coordinat of each transect shown by Table 1.
TS diagram patterns in the area transect C (Figure-2) is the most different patterns where there has been a reduction in the value of salinity is quite large compared to the area transects A transect and B.

Also, we confirmed that our interaction formula and the residual life are reasonable by comparison with statistical rupture time data from materials that have in fact degraded.
Also, coarse carbide has grown as degradation continued under heat treatment, from this, reduction in strength is inferred.
Moreover, if data can be obtained by setting the degradation time to more detailed conditions, it is expected that the data in Figure 4. can be expressed more precisely so that more accurate lifetime prediction is possible.