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Also, feature size reduction in manufacturing enables high performance and high density ICs to be manufactured.
In the following section, we will discuss our proposed usage of metal fill emulation methodology provided by Synopsys in the design flow to achieve time and effort saving while capable of obtaining accurate parasitic data as soon as we obtain the baseline layout in the design cycle.
Next, we discuss the parasitic data obtained using metal fill emulation and physical metal fill insertion.
By having accurate parasitic data early in the design cycle, designers will have a good reference point where they can prevent over-designing or under-designing.
Table 1 shows the data collected from both runs we have performed.

The objectives that may be attained through the implementation of an integrated system for Tool Management include: ∙ improvement in performance of the Production System; ∙ high levels of machine utilization; ∙ reduction of downtime; ∙ tool's selection on optimum level; ∙ reduction of variety and number of tools used; ∙ optimizing industrial purchasing; ∙ supplying tools, to machines just in time, which involves the engineering department in the concerns of the shop.
Loading the data in active sheet Fig. 5.
Bar codes provide fast, easy, and accurate method of data manipulation.
VBA Variant Software to Planning and Manufacturing Control [8] On the launching base, the user can load in the homepage a file which contained for each parts identification data, like: type, code, order’s number, dimension of semi-finished, number of pieces, etc.
Figure 4 shows the Excel active sheet where the user loads the identification data for each piece who will be manufactured.

The geometrical and physical data of this beam list as follow, , , , and each span length .
Damage in the structure is assumed as a reduction in the stiffness of individual beam element, but the inertia properties are unchanged.
The measured modal data is simulated through the FE model in its damaged state.
Genetic algorithms + data structures = evolution programs.
Structural damage detection via modal data with genetic algorithms.

Take the calculation of fundamental matrix for example, the solving processes are as follows: (1) Take the features of extracting Harris corners as the data to be matched
Γ is the probability of at least one benign sampling subset been achieved, should be greater than 0.95, expresses the proportion of the error matching pairs accounting for data set, p expresses the number of data points in basic subset
(5) ① randomly sample a basic subset in data set with =8. ② A fundamental matrix is calculated based on the basic subset use least-squares algorithm. ③ Appraise the compatibility of: Calculate the distances between each point’s pixel to its corresponding epipolar, being greater than a certain threshold is considered as error matching pairs, less than is considered as correct matching pairs
The switching function is as follows: (10) The average slip ratio is selected as the control target in order to control the slip ratio, which not only has the same effect with the reduction of function max (Ti/Ni),but also solves the insufficient of traction force when the body traversing rough terrain .
Reduction of slip ratio causes the reduction of Ti/Ni preventing soil from failure.

Constant friction factor m was considered, equal to 0.7 (calculated using an inverse approach using data from a preliminary numerical campaign).
In this study, the following process parameters were used: angular velocity to 5.235 rad/sec, reduction 50%.
Table 2: Comparison simulation data for the LFW and ARB processes Concerning the obtained data, it can be observed that in ARB process high pressure and strain rate are reached, while for LFW high temperature values are reached.
As a matter of fact, the reference time is related to the beginning of bonding, i.e. when the axial shortening, namely the reduction in height of the specimen, [4] has not occurred yet.
From the reported data, it can be concluded that pressure and temperature seems to be as the two most influencing variables for effective bonding.

It directly affects the quality of sending data.
The correctness of the rate-matching directly affects the quality of sending data. [1][2][3].
According to the data length of every TrCH under the limit of data length in an CCtrCH, they are processed by the following procedure.
The simulation results semi-static performances the number before multiplexing Punch or repeat The disposed number the number after multiplexing Simulation 1 TrCH01 1 150 punch -106 44 TrCH02 3 150 punch -17 133 TrCH03 5 150 repeat +73 223 Simulation 1 TrCH01 4 150 repeat +50 200 TrCH02 2 150 punch -50 100 TrCH03 2 150 punch -50 100 In simulation1, most data bits of TrCH01 are punched just because of its lower semi-static performances, it has to wait for the next multiplexing, but TrCH03 will be served a higher data quality than TrCH02.In simulation 2, after multiplexing,TrCH01 has a better data quality than that of the other two , but TrCH02 and TrCH03 have the same serving quality.
The results shows that as long as the data length for one CCTrCH are limited to one range , the higher of semi-static performances, the higher quality requirements of the data for one TrCH, which means needing more added bits to be transmitted, and vice versa.

Mechanical model A mechanical model was developed using the data from the simulations.
Fig. 3 Maximum diameter – height reduction (left) and force – height reduction (right) diagrams The variational parameters and the friction factors are not constant during the compression, they are calculated in each state of the compression.
Fig. 4 Variational parameters – height reduction (left) and friction factor – height reduction (right) diagrams The comparison between the plane strain compression test and the cylindrical upsetting test are shown in Fig. 5.

Fig. 1 Total project Maintenance decision theories analysis The main idea of Rough Set Theory is based on some observation and measurement which is not accurate results to classify the data and based on incomplete information and knowledge to deal some fuzzy phenomenon.
Attribution reduction is one of the core contents of Rough Set Theory, on the basis of reduction of decision table attribution information and maintaining the decision quality, decision rules are accessed by looking for leading information from decision table attribution information and eliminating the redundant information in the table information.
If all attribution is divided into condition attribution and decision attribution, then the information system is stated as .Decision rules for special equipment are conducted on the reduction based on decision table.
Through the decision table and its reduction theory, special equipment maintenance rules are formulated and the initial change by correction maintenance and time based maintenance to condition based maintenance is realized.

However, as the coating in the present case is less than 4 µm thick, glancing incidence would be needed to avoid data from being collected from the substrate.
It appears that there is a reduction in grain size with increasing bias as evident from the ion induced secondary electron (ISE) images.
This is consistent with earlier reported results by Sato et al. who report grain size reduction with increasing bias voltage in case of cathodic arc deposited TiAlN coatings [9].
The reduction in grain size has been confirmed by preliminary TEM studies carried out.
A tangible reduction in grain size could be observed between the TiAlN coating deposited at -100 V and the one deposited at -150 V.

This tunnel accommodates the majority of the underground public utilities necessary to supply this business centre with energy, water and data communication.
The SRF is the critical strength reduction factor.
Table 2 Results of numericalanalysis no stonecolumns stonecolumnsspacing(m) 2 1.8 Strength Reduce Factor(SRF) 1 1.2 1 1.2 1 1.2 Absolute horizontal displacement (mm) 0 2.5 0 1.9 0 6 Absolute vertical displacement (mm) 0 93 0 42 0 37.5 Maximum shear strain 0.053 0.067 0.017 0.022 0.0225 0.026 Volumetric strain 0.054 0.06 0.02 0.035 0.007 0.007 Mean stress (MPa) 196 174 270 213 304 210 Replacement area ratio, r(%) 0 25 30.8 Fig.2Numerical model of no stone columns Fig.3 Absolute horizontal displacement versusdistance for no stone columns Fig.4 Absolute vertical displacement versus distance Fig.5 Maximum shear strain versus distance for no stone columns for no stone columns Fig.6 Mean stress versus distance for no stone columns Fig.7 Volumetric strain versus distance for no stone columns Fig.8 Shear strength reduction versus distance Fig.9 Numerical model of stone columns for no stone columns
Fig.14 Volumetric strain versus distance Fig.15 Shear strength reduction versus distance for stone columns with 2m spaces for stone columns with 2m spaces Fig.16 Numerical model of stone columns Fig.17 Absolute horizontal displacement versus with 1.8m spaces distance for stone columns with 1.8m spaces Fig.18 Absolute vertical displacement versus Fig.19 Maximum shear strain versus distance distancefor stone columns with 1.8m spaces for stone columns with 1.8m spaces Fig.20 Mean stress versus distance for stone columns Fig.21 Volumetric strain versus distance for with 1.8m spaces stone columns with 1.8m spaces Fig.22 Total displacement versus distance Fig.23 Shear strength reduction versus distance for stone columns with 1.8m spaces for stone columns with