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Thus, the technologies are demanded to integrate all processes from design to manufacturing, so that data should be uniformed.
Moreover, it allows CAD software and simulation software to read and write with the same data format, and it can be directly connected to machining systems using special CAM (V-Cam / Nano V-Cam) software [2].
MD assisted Interpolation of Voxel data Simulation (Tool Pass) Tool Pass NC data Feedback On-machine/ Standard measurement Surface of Workpiece Center of Tool Ultra-precision Machining (ELID Grinding) Simulation based on VCAD (Ray tracing ; V-Opt)CAM with nm level （V-Cam/Nano V-Cam） Products VCAD VCAD Format Interpolation of Voxel data Simulation (Tool Pass) Tool Pass NC data Feedback On-machine/ Standard measurement Surface of Workpiece Center of Tool Ultra-precision Machining (ELID Grinding) Simulation based on VCAD (Ray tracing ; V-Opt)CAM with nm level （V-Cam/Nano V-Cam） Products VCAD VCAD Format Figure 1 V-Cam system fabrication processes fabrication process research is also pursued to be applied to VCAD models.
Because GRIN lens can be designed freely unlike regular lens, advantages that are reduction of the number of lenses and improved optical performance are looked forward to.
The analyze process of 'V-Opt' software mainly consist of (1) reading of VCAD data, (2) setting of analyze conditions, and (3) simulation analysis.

The displacement was measured as a function of time at constant load and data were acquired at the rate of 1 s to 30 seconds per data point, depending on expected duration of the test.
To study the scatter in test data, a test was repeated at 400 N.
SPC data obeying Norton‘s power law.
(3) The steady state deflection rate plotted against rupture life on log-log scale (Fig. 6) shows that SPC data, like conventional creep data, follow Monkman-Grant relationship.
Like conventional creep data, SPC deflection rate obeyed Norton’s power law and Monkman-Grant relationship.

To introduce of cloud computing into power dispatch system, power system can build intelligent cloud use of the power system network,fully comprehensive within the system of computing and storage resources,greatly enhance the data processing and interactive capabilities of network[2].
The basic functions include data collection, data processing, measurement management, control operations, event handling, human-computer interaction, protection and control, and grid control etc[4].
Comprehensive dispatching system of DG based on cloud computing,can to support the platform layer including real-time data service, history data service, graphical interface services, general reporting services etc.
The extended function includes comprehensive data statistical , power generation forecast, planned and remote islanding detection, dispatch and coordination control, web browsing function etc, it can completely replace the existing dispatch system.
It not only saves hardware, and software resources, reduce maintenance workload, but also has many other advantages, such as the comprehensive dispatch system of DG based on cloud collecting widely the data of DG, people can use these data to implement of the source, grid, load in the regional lines.

Table 1 Car Data Car Price in US$ Re-sale Value in US$ Mileage in km/lit.
The collected input data are shown in Table 3.
Table 3 Input data Supplier Total Shipment Received % of Acceptance % on Schedule Cost Reduction Suggestions Price/ Unit A 100 90 80 1 40 B 60 80 90 1 50 C 50 70 100 3 60 AHP: Pairwise comparisons are made between suppliers and also between criteria.
FL provides a method to make definite decisions based on imprecise and ambiguous input data.
Decision making process in the tourism sector such as choosing the location from several alternatives of a hotel using past data and applying one or more ANNs to forecast probable future socio-economic data to arrive at the best alternative or choosing the most convenient marketing plan by creating a projection of the future socio-economic data.

Both of these functions rationalized values for calculating input data outputs.
The main idea behind using neural networks to model a correlation is the ability to learn from past data and generalize when responding to new data.
The following are the steps of the neural network modeling: 2.2.1 Collection of Data The first step in neural network modeling is collection of data.
The data are necessary to train the network and estimate its ability to generalize.
The data points obtained in this case are listed in Table 2.

Data Flow Diagram Figure 2: Data Flow Diagram Figure 3: Music recommendation B.
These are the factors generally by no means that obvious and that we have to be the compelled to deduce the supposed latent factors from the data.
Hybrid systems use client knowledge and similar client data to predict that songs the client can like
● Million Songs knowledge set comes from numerous websites and contains differing types of the music. ● First a part of project is to feature this data and make a knowledge pipeline
● Then merge the 2 datasets and build one massive dataset that contain all the data we have a tendency to need. ● Now we ought to convert that information and glance through it.

The results of current simulation were compared with the experimental data those obtained by forging of hollow spur gear forms.
Results and discussion In order to verify the FEM simulation results of DEFORM-3D software for the forming of the hollow spur gears, the theoretical results and experimental data of Choi et. al [10] are compared with the results of the current simulation.
The load predicted by the current FEM simulation is closer to the experimental data than the prediction by upper-bond method [10] for the hollow spur gear forging.
The maximum punch load is almost the same for different initial billet's height. 0.0 10.0 20.0 30.0 40.0 50.0 Height Reduction [ % ] 0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 Load [ KN * 10 ] Current simulation Experimental data of Choi et. al [10] Theoretical results of Choi et. al [10] 0.00 50.00 100.00 150.00 200.00 Punch Load (KN) 0.00 2.00 4.00 6.00 8.00 10.00 Punch Travel (mm) Di = 0 Do Di = 0.2 Do Di = 0.4 Do N = Number of teeth M = Module u = Friction factor Di = Inner diameter of billet Do = Outer diameter of billet H = Initial height of billet Current simulation N = 15, u = 0.1 M = 2 Do / H = 1.0 0.00 4.00 8.00 12.00 16.00 Punch Travel (mm) 0.00 200.00 400.00 Punch Load (KN) M = 2 M = 3 M = 4 Current simulation N = 15, u = 0.1 Di / Do = 0.2 H / Do = 1.0 N = Number of teeth M = Module u = Frictoin factor Di = Inner diameter of billet Do = Outer diameter of billet H = Initial height of billet 0.00 3.00 6.00 9.00 12.00 15.00
The load predicted by the current FEM simulation is closer to the experimental data than the prediction by the upper-bond method.

The PL data of the gamma irradiated glass samples with 171 nm excitation is presented in figure 3.
Based on the literature data available, the five peaks observed were assigned to various defect centers as follows.The peaks at 687 and 507 nm were assigned to the non-bridging-oxygen hole centers (NBOHC) as shown by Cannas et al.[80, 86].
Fig. 5 Base line corrected and normalized FTIR data for the gamma irradiated Trombay glass as a function of dose Mohapatra et al. [91] have reported similar results for the electron beam irradiated Trombay nuclear waste glasses in the dose range 105 to 108Gy.
Explanation for this type of reduction in the number of defect centers has been given by Debnath [98], who had suggested that a hopping process between ferrous and ferric ions in the glass is mainly responsible for this reduction in defects.
This is in agreement with the overall IR and Raman data which show only marginal changes in the irradiated samples.

The data show that initially the specimens in 50000mg/L magnesium sulfate solution have the highest level of mass gain.
The data indicate that there is no relationship between changes in mass and strength.
The expansion data are shown in Fig. 4.
The data for the change in length show a number of variations to the change in mass.
Meanwhile, the data indicate that mass gain does not immediately result in elongation.

GPS was used for collecting driving data of location, distance, vehicle speed and acceleration.
Driving data and emission data were aligned firstly before data analysis.
The data belong to each road type was selected and separated based on GPS data.
Combining particle emission, exhaust flow data and driving data, the results of particle number and mass emission ratio were calculated.
Results and Discussion Driving Data in Road Types.