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Specifically, the labeled data points, combined with the unlabeled data points, are used to build graphs incorporating neighborhood information of the data set.
After projecting the data points into a lower dimensional subspace, the relevant data points get closer, thus ,the irrelevant data points get far.
The weight matrix evaluates the similarity between data points, while the weight matrix evaluates the dissimilarity between data points.
For each data point , the set can naturally be split into two subsets: and .
Knowledge and data engineering.

In a second step, using the thermal field as input data, a methodology for surface roughness prediction was developed.
The process parameters were selected considering previously obtained experimentation data [5].
From this point the measured and predicted roughness reductions diverge.
Although in some cases the error is close to 50%, the information provided by the simulation is useful for process parameter tuning because gives information about relative variations in roughness values .In both cases, with data obtained from the profilometer and with data from simulation, the roughness measurement was carried out using the same software, Talymap© from Taylor Hobson.
So, the methodology can be successfully used to process parameter adjustment .The roughness average values and peak-valley distances have some differences compared with the experimental values, but the simulated profiles reflect the same trend found on experimental data.

The data of urban earthquake damage prediction virtual simulation system is characterized by huge quantity and wide coverage, as described in data layer, including: (1) Basic data.
(3) Three-dimensional model data means the data from three-dimensional solid simulation modeling of acquired data, including general layout of a city, scene three-dimensional data of blocks, green lands, etc., scene three-dimensional data of buildings (structures), terrain and landform data, scene two-dimensional image of buildings (structures) and others
(5) Typical earthquake damage data.
(6) Scenario edition data.
The data calling is realized via dynamic stored data management to improve system operation performance.

The fatigue vulnerability estimation method including the effects induced by the corrosion rate and the traffic variation with time has been proposed to evaluate the reduction of fatigue strength in steel bridge members.
Monte-Carlo simulation is used for reliability analysis which provides the data used to obtain fatigue vulnerability curves.
Therefore, it is necessary to evaluate the fatigue strength of steel bridge members considering corrosion rate with time in order to reflect the interacting reduction effects between fatigue and corrosion.
To introduce the corrosion effect into the conventional fatigue limit state function, the critical number of stress cycle considering corrosion effect, cfN is defined as, fcccf KNN /= , (1) where cN is the number of stress cycle and fcK is the fatigue strength reduction factor caused by corrosion and can be expressed in terms of the depth of average corrosion as follows [5]: Strain History Data for a Detail Strain History Data for a Detail Stress Range Spectrum Stress Range Spectrum Equivalent Stress Range Equivalent Stress Range Calculate the Cumulative stress cycles (N) Calculate the Cumulative stress cycles (N) Limit State Function Limit State Function Calculate the Fatigue Vulnerability (V) Calculate the Fatigue Vulnerability (V) Stress History Data Stress History Data Rainflow cycle-counting Stress Spectrum
The measured variable stress data obtained from Korea Infrastructure Safety & Technology Corporation, and traffic information is provided by Seoul Metropolitan Government as well as Ministry of Construction & Transportation.

According to the information of water quality, hydrology data and the discharge distribution of the river, we chose COD, ammonia nitrogen and volatile phenol as the main control factors, and some formulas were used to calculate the water environmental capacity of COD, ammonia nitrogen and volatile phenol.
According to monitoring data of water quality, among pollutants discharged into the river, the proportion of COD, NH3-N and volatile phenol is relatively larger, so we select them as evaluation factors of the water environmental capacity, the limit value of water environmental quality standards as Table Ⅱ{TTP}8545 .
According to comparison with data of Fenhe River, the results are relatively reasonable.
Hydrological parameters selection The water environmental capacity is different on different reaches or it has different flow, 1980—2008 series’ data is used in this paper.
Pollutants reduction calculating.

Introduction The rapid increase of data traffic in next-generation data center indicates that the infrastructure needs will be synchronized growth.
The next-generation data center commonly used virtualization technology, can support more servers than the traditional data center.
In addition to the end-user access to data, applications generate large amounts of data flow between the cloud computing servers, so that data communication is busier.
The next-generation data center using simple two-Layer architecture.
From the one hand, the TOR makes fiber increased use of copper usage reduction. [3] In TOR mode, each server in the data center cabinet internal IT equipment has more uniform properties.

Data preparation.
Data preparation is broadly divided into three steps : data integration, data selection, data transformation.
While also cleaning data, including noise data, such as missing data and abnormal data processing.
Data selection.
Data reduction and transformation.

Data.Data used In thIs study are maInly from the envIronmental statIstIcs Issued In 2012, IncludIng data of all areas and IndustrIal enterprIsesIn LIaonIng provInce, LIao RIver basIn.
It was conservatIvely estImated that more than 20% reductIon of the pollutants productIon IntensIty could be achIeved.
Research of IndustrIal Pollutant EmIssIon ReductIon PotentIal In LIao RIver BasIn [J].
PollutIon GeneratIon IntensIty of Key IndustrIes and Water-SavIng and PollutIon ReductIon OrIented and Clean ProductIon PotentIal In LIao RIver BasIn [J].
Research on PotentIal of EmIssIon ReductIon of Cleaner ProductIon, 2011, 34(12H): 318-321

When performing the damage detection algorithms, the measured datas are often acquired from the modal testing.
One must treat and to be known quantities available from the measured data, that is and .
The response data are measured at where the accelerometers are placed, thus the master coordinates can be determined accordingly.
Hu: Using incomplete modal data for damage detection in offshore jacket structures, Ocean Engineering(2008), 35, p. 1793–1799 [3] D.
Guyan: Reduction of stiffness and mass matrices.

To implement such an analysis, energy data has to be related to the material flow states working, waiting and failed.
The needed energy data can be estimated or measured.
In the waiting state, the measured data showed a different pattern.
The corresponding data is illustrated in Table I.
Based on real measured energy data, the main energy consumers of the system were identified.