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Transmission loss reduction based on FACTS and bacteria foraging algorithm, 4193(2006)222- 231. ], shop scheduling[[] N Zhang.
KPCA feature optimization extraction based on bacterial foraging algorithm When use KPCA to carry out feature extraction, kernel parameters and different kernel functions have a great impact on it, if the kernel parameter is unsuitable, the number of kernel principal opponent which cumulative contribution rate more than 0.85 larger than the dimensions of original data, and not achieve the role of dimensionality reduction at all.
Experiment Data acquisition.
In this experiment, for each type of bearing states, 150 groups of samples are collected, totally 600 groups, composed of 600 × 8 sample data set, and set up primitive feature libraries 1~3.
Selected feature library 1, feature library 2, feature library 3 as test data set, Set the initial population of bacteria is 50, Convergence Threshold is, optimization space: , Fig. 2 ,Fig.3 and Fig.4 shows all stages of spatial distribution of population in the process of optimization by bacterial foraging algorithm (Not units).

On the other hand, the surface roughness data at different planetary gear ratio and polishing time have been measured.
When n0 is too small, part of the ring is destructed at certain position (Figure (a)); (2) Along with the reduction of n0, S shape of the foreside glide lamella of the abrasive flow becomes unobvious gradually, especially in the first quadrant, where the foreside has been in the middle state between the flow and dispersion (Figure (b)); (3) In the upper-half circle, the effect of gravity is evident because of the reduction of the centrifugal force.

In order to offer theoretical basis for the vibration prevention and noise reduction for the area along urban rail traffic line, the propagation laws of environmental vibration are summarized by analyzing observation data in time and frequency domain based on actual field observation data in Beijing.
Prospecting by boring in observed point 3 and 4 were carried out in order to obtain geological condition, the drill exploration data are listed in table 1.
Figure1 Layout of field observation Table 1 Drill exploration data of measuring point 3,4 number depth /m thickness /m Vs/(m·s-1) 1 1 1 130 2 8 7 172 3 10 2 182 4 16 6 230 5 26 10 229 6 31 5 311 Propagation laws of ground environmental vibration Figure2 is a group of typical observed records and analysis results, compare the acceleration time history from figure 2(a), maximum acceleration of the observed points is 38.5gal, 21.5gal, 6.6gal, 3.1 gal and 4.9 gal respectively, the maximum acceleration in general is gradually attenuated with the distance from the centerline of the railway, the reason is energy diffusion and damping of soil.
The coefficients were determined according to the observed data, the ground environmental vibration attenuation formula can be fitted by least-squares criterion from equation(8): A=0.056r -0.313exp(-0.029r) (9) The calculated results from equation(9) were agree with observed results.

By fitting the experimental data, it is observed that the parameters in damage functions obey the linear relation well, and the unknown constants are deduced with finite tests easily.
Damage modeled by stiffness is based on non-destructive tests and a series of data can be measured frequently from one specimen during fatigue experiments; the other approach bewritten by strength requires extensive specimens yet, and only one data is obtained from a specimen.
Theoretically, at least three sets of data will be need and the residual stiffness and strength must be included.
Undoubtedly, if sufficient fatigue data are provided, Eq. 6.a-6.b are the recommended methods to simulate the damage and the Eq. 7.d is needless, the prediction results based on them have more believable precision; reversely, inadequate data are supplied, Eq. 7.d is quite appropriate to estimate the other constants.
Obviously, the more data measured, the more accurately the models reveal the rules.

Titles of the group of records ERI AIS Logbook SRB 1 vessel identification data set X X X X 2 voyage data X X X X 3 information on dangerous goods X X X 4 dynamic information X 5 dimensions of the ship X X 6 data on the position of AIS antenna X 7 dimensions of the ship X X 8 displacement X X 9 AIS and radar auxiliary information X 10 messages transmitted by AIS X 11 general data on ship X X 12 record data on owners, charters, operators X X 13 data on the sizes of the vessel or convoy X X 14 data on tonnage X X 15 data on Engines and Hull X 16 data on goods X X 17 data on the crew and passengers – crew and passenger list X X X 18 name of the commander and number of crew and passengers X X 19 additional data on class/ice X 20 data on voyage incidents X X X 21 data on voyage diary X X X 22 data on the navigation time X X 23 data on the watchman X X 24 modality of ship operation X X 25 working and rest time X X 26 data on the operation of engines
consumptions - - - - 27 data on the engine emissions - - - - We note that AIS data covers almost all groups of records, except the data on crew found in ERI, in the Logbook and in the Service book of each crew member separately.
For automatic calculation of training time, is necessary to combine data from position 17, data on crew-crew list with common data at position 2, voyage dates, and with those specific to AIS from position 4, dynamic information, navigation time.
We also note the repetition of data differently for each type of record.
Thus the identification data of the vessel are default: vessel name, the single European – ENI and MMSI code are common data for each group of records.

Technical data of the austenitic stainless steel AISI Type 316 mesh screen shading devices were collected from the manufactures’ product catalogues [4].
This modeling method can be only combined with spectral optical data type calculation in EnergyPlus.
WINDOW 7.3 can only export Bi-directional Scattering Distribution Function (BSDF) datasets for CFS, which are subsequently read and calculated by the BSDF optical data type in EnergyPlus.
WINDOW incorporates the Klems radiosity-based method to generate BSDF data of multi-layered fenestration systems from the angularly resolved data of single layers [8,9].
Likewise, in the absence of empirical data in the framework of current study, we also selected the WINscr method with BSDF calculation to proceed for assessing the impact of all key-design parameters and discussing the energy and thermal performance of the studied office space.

Small area reduction rolling is one of difficulties for the multi-wedge cross wedge rolling.
Because area reduction of 1 and 2 wedge rolling the middle axle together is only 22.7%, mould can’t easily exhaust metal to axle end and then generate large axial deformation when mould parameters are selected improperly.
(a) effect of forming angle on transverse stress (b) effect of forming angle on at A point transverse stress at B point (c) effect of forming angle on lateral stress at C point Fig. 5 Effect of forming angle on transverse stress at feature points Table 1 The processing parameters for changeable forming angle Stretching angle β（°） Forming angle α（°） Area reduction （%） Rotation angle θ1/θ2（°） the 1st (wedge) the 2nd (wedge) the 3rd (wedge) the 1st the 2nd the 3rd the 1st the 2nd the 3rd the 2nd /the 3rd 8 7.7 4.8 24 24/ 36 22.7 29/ 56 1.83/3.6 30 36 Fig. 6 shows the effect of forming angle on shearing stress at feature points.
Based on the above data, increasing forming angle of 1 and 2 wedge can effectively reduce transverse stress at A,B point and shearing stress at B point ,which is benefit to small area reduction rolling.
Table 3 The processing parameters of model Stretching angle β(°） Forming angle α（°） Area reduction （%） Rotated Angle θ1/θ2 （°） the 1st (wedge) the 2nd (wedge) the 3rd (wedge) the 1st the 2nd the 3rd the 1st the 2nd the 3rd the 2nd /the 3rd 8 7.7 4.8 36 36 24/36 22.7 29/56 1.83/3.6 Conclusions Increasing forming angle of 1 and 2 wedge can reduce transverse stress at A and B point and shearing stress at B point, which is benefit to small area reduction rolling.

In this process, bolted connections may sustain considerable moments and axial forces and failure of these connections may take place due to material strength reduction during the heating stage or large contraction forces produced in the cooling stage.
Furthermore, material property of fillet weld is commonly affected by welding consumables and welding procedures. 2) Accurate prediction of temperature-time variation of connection components relies on thermal conduction in these steel components, radiation/convection boundary conditions and proper definition of fire development models[6]. 3) Strength reduction at elevated temperatures: strength reduction of these components may be varied in fire, arising from various strength reduction factors in different materials as shown in Fig. 1 or non-uniform temperature distribution in a bolted connection. 4) Connection ductility relies mostly on individual elongation of each connection component and differential elongation of various connection components because of increasing temperatures and variation in material properties of the components.
Fig.1 Material strength reduction for connection components Clearly, application of the component-based method in fire is more complicated due to involvement of material properties and strength reduction at high temperatures.
For example, in a flexible endplate connection, bolts and welds has been classified as brittle components, and their strength reduction is obviously faster than steel material at high temperatures as already shown.
World Trade Center Building Performance Study: data collection, preliminary observations, and recommendations.

VIBE models the background using data samples from previous frames.
We can build the similarity degree as the data term; build the spatial information as the smooth term.
The data term is the main proposed part of this paper.
Here we straight give the data terms: Where stands the coefficient of proportionality of the data terms and smooth terms, and Where the mean value of is, is the variance of.
This data set has the advantage of ground truth for all frames through it is a synthetic.

From the house price data of Huanggang city in recent five years, we use this prediction control model to predict the development trend of housing price in the next five years.
In this paper, based on the grey system theory, we present a prediction control model of housing price based on GM(1, 1), and let Huanggang city as an example to predict the housing price in the next five years on the basis of house price data of Huanggang city in recent five years.
The house price data of Huanggang city in recent five years forms the following original sequence x=(1600,1900,2400,3100,3150).
(2) Construct the data matrix B and data vector Y.
Thus, in order to improve the precision, we can modify the value of parameter a, and then we can get the optimal prediction curve which inosculates with the original data.