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Simulation Model Development Having cleared the bank process, we need to collect necessary data to simulate the model.
After obtaining to data sampling, a probability distribution function for each of them should be placed because of the inputs of a simulation model be in the form of the probability distribution.
Simulation model validation Names of Variables Computer Simulation Data Actual Data Achievement Rate % Counter Utilization 0.532 0.6 89% Number out per day 135 150 90% Defining Scenarios Based on the variables, three strategies will be proposed.
Simulation results versus manager and employees comments Strategy Result of Scenarios Bank Manager Comment Bank Employee Comment Best Worst Queue Reduction 5 6 A scenario with lowest queue could be the best.
In Vom Data Warehouse zum Corporate Knowledge Center (pp. 71-88).

Fig.4 Comparison of load-deflection curves with PVB thickness of 2.28mm The results show that no matter the laminated glass with the thickness of 6+1.14+6 or 6+2.28+6, the numerical results agree well with test data.
The results based on Chinese code have an obvious error with the test data, and the results are somewhat conservative, which will cause material waste.
The numerical results agree well with test data, which shows that the finite element analytical method is reliable
The results based on Chinese code have an obvious error with the test data while the results based on European code are close to the test data
For laminated glass with different PVB thickness, the test results show that the revised results are very close to the test data and the proposal formula has theoretical and practical significance for design.

Motivated by this, we introduce the Constrained All-k-Nearest-Neighbor (CAkNN) query that for every data in query data set A, retrieves its k NNs in data set B and located in the restricted region.
Xia et al. [10] present an algorithm termed Gorder which achieves its efficiency by sorting data based on an ordering that enables effective join pruning, data blocks scheduling and distance computation filtering and reduction.
It takes a query data object a, NB, k, R as input.
We have used both real and synthetic data in the experiments.
Su: Efficient index-based KNN join processing for highdimensional data.

The simulation experiment and analysis As the pressure of market competition, the cost data of corporate or companies are usually confidential.
The case data are from the pacific line, IT168 and mobile homes and other sites.
Operation steps: (1) Data standardization.
The result of the feasibility test of the factor analysis both KMO and Bartlett is significant, the measure value of KMO is 0.891, and the value of P of Bartlett is 0, that is to say the data is suitable for the factor analysis ( Tab 1).
By the same rule, the 32 indicators were classified into 6 categories with dimension reduction.

A constitutive equation of the test material expresses the true flow stress and true strain data measured by the high-temperature SHPB apparatus.
The conventional SHPB apparatus consists of two long slender bars that sandwich a short cylindrical specimen between them, a striker bar, a gas gun or an alternate device for accelerating the striker bar, strain gauges mounted on both bars to measure the stress wave propagation and instrumentation and data acquisition system to control, record and analyze wave data.
This was intentionally caused by a reduction in the velocity of the striker bar in order to maintain the same strain-rate in the different tests.
Comparison of the true flow stress–true strain relationship of V-5Cr-5Ti for the experimental data and the Johnson–Cook constitutive equation A comparison of the Johnson–Cook constitutive equation and the experimental data for the V–5Cr–5TiV alloy is shown in Fig. 4.
The figure indicates that the Johnson–Cook constitutive equation is in reasonable agreement with the experimental data from room temperature to 1100˚C Conclusions We introduced a new experimental technique for a high-temperature SHPB test system.

In the paper, the system works in a manual state via a cutting controller, when the system is balanced at a given value, a step signal is suddenly added, and based on the temperature value measured in experiment, parameter identification for data obtained in the experiment is performed by means of the MATLAB system identification toolbox.
Input experimental data and select a model to obtain the following result after identification of the experimental data: K=1, T=300, .
Fig.2 Response curve with interference Fig.3 Structure of control system System realizations The system uses STC89C52 as a processor, having the following functional modules: a keyboard module, a display module, a temperature measurement module, a memory module, a reduction module and a SCM module.
A relative software program [3] Fig.4 Flow chart of main program Fig.5 Experiment temperature curve is programmed by the Keil C51 software development system, the system software mainly includes a main program module, a data collection module, a display module, a keyboard module, a memory module, a control algorithm module, etc.
The main program module is used for establishing an overall framework for other modules and carrying out initialization, and a flow chart is shown in figure 4; the data collection module is used for collecting digital signals converted by DS18B20 and storing them in the memory; the display module is used for displaying setting variables, current variables, running time, input functions, etc.; the keyboard module is used for realizing human-computer interaction of data, etc.; the memory module is used for storing temperature data collected each time in real time; and the control algorithm module is used for completing fuzzy PID operation of the control system and outputting control variables.

Sales data analysis of BMW shows only 1% case is exactly same [2].
Using dynamic data flow function of fault diagnosis instrument can read the dynamic data from every sensors and actuators.
Through the analysis of these data and some data from other tester such as AVO meter, can help to judge fault style and find location.
These parameters are divided into data flow parameters and the rated parameters.
This data fuzzy represented for too high, too low, normal, or with open (on), close (off) represent the switch signal.

Fig. 1 Structure of monitoring system Fig. 2 Fuzzy control principle diagram of the system The monitoring system is based on LonWorks technology structure, the whole system consists of three layers of network structure: the first layer is a field device layer, is mainly the actuators and sensors; The second is the controller layer, namely Lon intelligent node DDC, mainly responsible for data acquisition and control; The third is the monitoring layer, is mainly responsible for PC, the system installation, network and data transmission and display, using the configuration software Advantech WebAccess [4].
Load forecast predict air conditioning system load of the next moment according to the storage of a large number of historical load data, the current load and impact load of the outdoor environment temperature, etc, to predict air conditioning system load of the next moment, transmitted to the fuzzy controller.
LonWorks intelligent node distribution in the industrial field, mainly used for receiving and processing from the sensor input data, implement the communication and control actuators such as the operation task.
Based on the chip intelligent node is by a neuron chip plus transceiver composition site node, able to complete the equipment of communication and some simple data processing tasks, the advantage is simple structure, low cost, easy development.
Fuzzy control table usually do not need to change, and real-time operation data from outside the CPU FlashRom and read from RAM speed difference on the system's performance does not produce obvious influence, so the fuzzy control table in the FlashRom, so still not consume memory.

According to Figure 4, the data solved corresponding to different three-dimensional simulation models, the slope values of torque characteristic corresponding to different speed sum, and calculating the average under the same number of poles.
The slope values of torque characteristic have been calculated in different magnetic poles and magnetic materials, the data shown in Table 3.
The slope values of torque characteristic Magnetic materials pole-pairs one Two Three NNF33M 0.01462 0.02156 0.02063 Sm2Co17 0.01268 0.01754 0.01676 SmCo5 0.01127 0.01478 0.01409 According to the data in Table 3, the curves on torque characteristics slope and number of poles have been drawn under different magnetic materials, shown in Figure 5.
The relative error values at different rotor thickness Reference value The relative error (%) =0.116 =0.738 =0.329 =0.965 =0.412 =0.517 Analysis of the data in Table 4 and Table 5 can see that the damping torque is proportional to the rotor thickness for electromagnetic damper.
References [1] KASARDA , M E F , Mendoza H KIRK R G, et al .Reduction of subsynchronous vibratio-ns in a single-diskrotor using an active magnetic damper [J].

At last, regulation scope should be as wide as possible so that data of different flow patterns can be analyzed.
Control System Design Data Acquisition System.
Testing control and data acquisition system is shown in Fig. 5.
Fig.5 Testing Control and Data Acquisition System Control System.
Then, data collected from each sampling site travel through flow totalizer and control instruments.