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Evaluation Index System of the Designed Underground Container Lines According to the functional localization and own characteristics of underground container transportation system[7,8], four evaluation elements are determined for the optimal underground container transportation system through data collection and survey on relative research combined with practical situations of Shanghai city.
(4) Sustainability, the sustainability of the project should be evaluated by the perspective of land saving, energy saving and waste reduction.
(2) Annual running cost, including the annual maintenance cost of underground container transportation system, this cost is scaled by the data
(3) The reduction effect of carbon emission: environment problems have become the key social concerns.
If we substitute the obtained data after zero dimension processing in Membership Function, evaluation matrix can be obtained.

To quantify various index and design building energy efficiency evaluation score sheet, we have discuss adequately with a number of building energy efficiency experts, see Tab.l. the score sheet is to determine each index based on the actual situation of the building, The score value act on data basis for further evaluation.
~505 90 700~770 70 56.63~66.72 85 0.45~0.3 75 4~6 75 505~515 80 770~1000 60 66.72~66.72 75 0.3~0.2 60 >6 60 515~530 60 Time limit for a project (Except remove basic） Score Quantity of energy saving /kg Mtce/m2 Score Steel consumption /kg/m2 Score Consumption of wood/M/m2 Score Cement consumption/kg/m2 Score <120 d 100 >20.12 100 <19 100 <0.013 100 <145 100 120~150 80 19.97~20.12 95 19~21 95 0.013~0.015 80 145~154 95 150~180 70 9.68~19.97 75 21~26 75 0.015~0.017 70 154~157 85 180~210 60 <9.68 60 26~29 60 0.017~0.021 60 157~166 70 Recycling the waste Score Quantity of soil saving/Brick Quantity/m2 Score Figure coefficient Score Use area index Score Structural safety Score Lightweight slab 100 Lightweight slab 100 <0.24 100 >0.79 100 Lightweight slab 100 Building block 80 Building block 85 0.24~0.28 85 0.75~0.79 90 Building block 80 Porous brick 70 Porous brick 65 0.28~0.32 70 0.73~0.75 75 Porous brick 75 Brick-masonry 60 Brick-masonry 0 0.32~0.35 60 0.71~0.73 60 Brick-masonry 65 Sound reduction
In the actual application, each index data must be obtained by concrete value through to the building construction technique material and the use operation investigation, each index data are shown in Tab. 2.
Tab.2 A energy-saving index of certain building Deadweight /(kg/m2) Score Using energy consumption /kg/ (m2.year) Score Material thermal resistance/m2.k/w Score Work efficiency /Number/(D.m2) Score Cost/yuan/m2 Score 630 85 66.4 85 0.43 75 3.5 90 512 80 Time limit for a project (Except remove basic） Score Quantity of energy saving /kg Mtce/m2 Score Steel consumption /kg/m2 Score Consumption of wood/M/m2 Score Cement consumption/kg/m2 Score 120~150 80 15.46 75 23.6 75 0.0146 80 156.8 85 Recycling the waste Score Quantity of soil saving/Brick Quantity/m2 Score Figure coefficient Score Use area index Score Structural safety Score Building block 80 Building block 85 0.26 85 0.784 90 Building block 80 Sound reduction index Score Energy consumption of envelope structure Score Building block 80 Building block 80 On the basis of model formulas (5) and (6), the building is evaluated quantitatively, the score is 78.63.

With the enrichment of the drilling, geology, logging data and all kinds of examination data, and the development of the theory of sedimentology, constantly in recent years some scholars put forward the existence of shallow lakes, lake delta lakes sedimentary facies in Neogene of Bohai Bay Basin [2-4].On the basis of the reference to previous research results, and fully appliance of all kinds of information, we carried an delicate study of the sedimentary characteristics and depositional model of Guantao formation in Zhanhua sag.
At the same time we analyzed the well logging, well log, paleontology, geochemistry and granularity data of this field.
This reflects the shallow water, relieved lake basin, lake level fluctuation and the alternating oxidation reduction environment characteristics of the shallow lake.
The shallow lakes have the characteristic of oxidation-type with alternating oxidation reduction environment.

Significant improvement in reduction of intermetallic compound size is evidenced for PM material.
Some data for pure aluminum and commercial aluminum 6xxx series alloy are shown for comparison purposes.
For comparison, some data for pure aluminum (Al) and aluminum alloy 6xxx series (PA4: Al-1Mg-1Si-0.8Mn) are also shown.
Therefore, some reduction of the hardness for Al-4Fe-4Ni PM samples deformed above ~650 K can be attributed to recovery process and grain growth rather than the particle coarsening.
Martienssen „Landolt-Bornstein, Numerical Data and Functional Relationships in Science and Technology”, Group IV, Vol. 5 (1998), Springer [7] F.

Loland(1976) diagnosed different structure, and obtained the frequency change from experimental modal parameters; Cawley(1979) confirmed that we can identify the damage existence according to reducing the structure frequency and increasing damping; Biswas(1990) studied using the structure changes of natural frequency and vibration mode to locate the damage of structure; Mannan(1990) studied using the measured frequency response function of structure to diagnose damage; Cassiots(1995) introduced a method using only frequency changes to identify local stiffness reduction.
Method of damage identification based on vibration test combined with interdisciplinary technology like vibration theory, vibration testing technology and data processing technology, considered the most promising method for structural damage detection of the whole.
(2) Model updating and system identification method Model updating and system identification method uses the dynamic test data, the basic equations of motion and the finite element model to structure,to optimize and to restrain problems, and corrects quality,stiffness, and damping distribution of structure model,in order to make the response maximum close to the measured dynamic response,and compare the correction model matrix with the baseline model, to realize the diagnosis of structural damage.
The advantages of neural network and genetic algorithm in data processing and nonlinear system identification, has a wide application prospect in the aspects of damage identification and diagnosis of structure
Identification of stiffness reduction using natural frequencies[J].

In addition to aforementioned incentives that polymers bring, i.e. low specific weight and intrinsic lubricity, the engineering community benefits from other merits of polymers including noise reduction, corrosion-free and shock resistance.
Moreover, parts integration as additional gain could reduce assembling time and number of parts, leading to eventual cost reduction.
Table 1 shows the gear data used in the example.
Gear data The model is a plane strain model and simulated as a quasi-static motion of the gear mesh.
Material data Results & Discussion The results are evaluated at 100 evenly distributed points in time and that means 0.05 rad of the input gear wheel.

The ways to evaluate braking performance of electric bicycle are usually divided into three categories: (l)Obtaining data of Electric bicycle in the practical road test to evaluate the electric bicycle brake performance.
To simulate a variety of road conditions of various parameter data and the electric bicycle brake performance assessment
Simulation of dynamic process of electric bicycle braking simulation method by use of computer, then evaluating electric bicycle braking performance through the simulation data.
The brake is loaded in pure flywheel inertia test platform, the relationship between its braking torque and inertia fly wheel and the rotational speed is as follows: (3) Where αj is the test platform frame spindle angular speed reduction.
If replace fly wheel stored kinetic energy with motor output energy, and keep the relationship between braking torque and speed reduction, can be used for simulating the mechanical inertia, called electrical inertia simulation.

Reference [2] had done some researches on collection and management about crane reliability data.
Loading coefficients like hoisting impact factor, acceleration of gravity, wind-force coefficient, reduction coefficient of wind, etc are certain values and can be chosen from Reference [5] by designers.
Reliability Analysis for Crane Boom Relevant Data of Crane Boom.
Table 2 Loading Coefficients Items Values Items Values hoisting impact factor 1.1 structural substantial ratio 0.3 acceleration of gravity 9.8 [N/Kg] min value of hoisting dynamic loading factor 1.05 reduction coefficient of wind 0.63 hoisting state level factor 0.17 wind-force coefficient 1.2 multiplying factor of pulley group 9 Mean values [5] and variation coefficients of calculation loadings are shown in Table 3.
Data inside frame is reliability of crane boom, and the value is 98.0121% when confidence level is 95.00%.

Secondly, the non-referential approaches verify the board based on the design specification data.
In addition, each printed board is analyzed, according to the availability of the artwork data.
The diagram for product inspection processes Methodologies Images Preprocessing Image Preprocessing consists of two steps – to wit: original image’s gray scale transformation and noise reduction: Step I.
Noise reduction Average Filter is employed to reduce noises from images, piling up noises η(x,y) on original image f(x,y) in order to produce a noise image g(x,y), shown as following equation: g(x,y)= f(x,y)+ η(x,y) For every coordinate (x,y), all noises are unrelated to one another and their means are zero.
A lens and an image measurement system are sufficient to read all data and save millions of dollars for equipment purchase and maintenance.

It was observed that increase in particle size brought about a reduction in the physical properties (TS and WA) of the board.
Results from Table 1 and Fig 2 show that the MOR data ranged from 11.11 to 14.12N/mm2.
The range of data in the MOE from Table 1and Fig. 1 was from 1590 to 1958 N/mm2.
The IB data from Table 1 and Fig 3 ranged from 0.28 to 0.44N/mm2.
Results showed that an increase in particle size brought about a reduction in the mechanical properties (Modulus of elasticity, modulus of rupture and Internal bond strength) and dimensional stability (Thickness swell and water absorption) of the produced particleboard.