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Virtual methods to assess the fitting of a fracture fixation plate were proposed recently, however with limitations such as simplified fit criteria or manual data processing.
However, data collection were performed manually, which requires long processing time and has higher possibility for error by the operator.
By automating the process, accurate and speedy data collection is possible.
Therefore, more time could be spent for extended data analysis to provide further information on an implant's fitting based on quantitative measures.
Second, to investigate the extent of borderline fitting cases for a tibia plate by performing extended analysis on the data collected based on the automated fitting method.

Fig. 2 presents data related to the types of human errors reported in accident reports [11].
Table 1.ENDSLEY’S SA ERROR TAXONOMY Level 1: Failure to correctly perceive information such as： Ø Data not available Ø Data hard to discriminate or detect Ø Failure to monitor or observe data Ø Misperception of data Level 2: Failure to correctly integrate or comprehend information such as: Ø Lack of poor mental model Ø Use of incorrect model Ø Over reliance on default values Ø Memory loss Level 3：Failure to project future actions or state of the system such as: Ø Overprojection of current trends For instance, in an aircraft environment, operators must aware of critical flight parameters, the states of their on-broad system, their own location and the location of important reference points and terrain, and the location of other aircraft along with relevant flight parameters and characteristics.
Thus, although they were aware of low level data (effectively monitoring the system), they has less comprehension of what the data meant in relation to operational goals.
In addition, there is a tendency for some displays to eliminate raw system data, in favor of processed, integrated information.
Important information as to the source of information, its reliability, or the value of constituent data underlying the integrated information may be unavailable.

This device can acquire the fluorescence data by PMT or the chip images by CCD, and 3-dimensional electric moving stage could be controlled to accomplish the auto focusing and auto tracking by image process.
The device could detect or observe the CE chip data in real time.
Graphical interface display, fluorescence signal data display, waveform drawing, CCD image acquiring, image preprocessing, auto focusing, auto tracking, and routine file operation were mainly achieved by the computer process system.
As is shown in Fig. 4, the better detection results have been got for Rhodamine B solution of 10-4 mol/L and 10-5 mol/L, furthermore the fluorescence intensity decreases significantly with the reduction of the solution concentration.
The device could acquire the fluorescence data by PMT or the chip images by CCD, so it could detect or observe the CE chip data in real time and its detection sensitivity could reach up to 10-5 mol/L.

The treatment measure including pollution source control and pollutant reduction was put forward for the sake of water quality improvement.
Based on monitoring data of water quality, the water quality of Erlongshan Reservoir is evaluated totally.
Monitoring data of major pollutants in Erlongshan Reservoir is shown in Table 2.
As the case of the monitoring data in the reservoir in August 2005, the TN Table. 1 The evaluation results of water quality in Erlongshan Reservoi Monitoring points Water quality categories Exceeded target parameters Jul.
Water quality monitoring data in Erlongshan Reservoir Parameters .Positions Time Jul.

Structured model to access effectiveness of training centres 2.1 Data collection After extracting parameters from the literature, the next step was the collection of data.
On the other hand, Bartlett’s test indicates the significance of data.
If the value of significance is less than 0.01, then the data is accurate for factor analysis.
This improvement suggests a better overall fit of the model to the data.
This improvement suggests that the revised model better represents the observed data.

In this case, the Midilli model is given by the following equation: (3) wherea, b, k andnare the parameters to be determined by fitting to experimental data.
The results showed that the Midilli model presented best fit to the experimental data.
Meneghetti et al. [16] set empirical mathematical models to experimental data ofmoisture content rice grains.The effects of intermittency relations and operating efficiency were studied.
In Silva et al. [3] experimental data of continuous and discontinuous pear drying were analyzed.The solution of the diffusion equation in spherical coordinates assuming a convective boundary condition was fitted to the experimental data, allowing the determination of effective diffusion coefficient and the convective mass transfer coefficient at the surface of the product.
Silva et al. [18] have fitted both empirical and diffusive models to experimental data of whole banana drying.

DSPSI possesses the advantage over conventional out-of-plane displacement-sensitive interferometry, that only a single difference of the unwrapped phase map is required to obtain flexural strains, thereby relieving problems with noise and reduction in the field of view.
These two images are called by a Matlab program to process image data.

This system is based on the original of each system, the new construction of the combination forecast, real-time scheduling, scheme optimization is completed, the system has greatly enhanced the flexibility and practicality. 5.2 GIS The application of GIS technology to realize Reservoir location search, layer control, information display and measurement functions, the key technologies as follows: (1)Development of data interface: Application of GOOGLE EARTH API technology, Google map are integrated into the system platform, the user can choose arbitrary positioning of the reservoir, and in a three-dimensional view of the form to be displayed on a map, and in a variety of forms of real-time display of water and rain data and forecasting and dispatching results data
(2)The comprehensive early warning: To achieve the targets of reservoir automatic alarm and warning, including real-time data and daily forecasting data of early warning, alarm level is divided into four levels, system according to the characteristics of the current data and the value of each reservoir and the alarm condition to judge, special display in a different color, can real-time understand what reservoir existence danger. 5.6 The Flood Forecasting Automatic collection of water and rainfall information, calculation of basin precipitation after 8:00 precipitation, runoff and PA, and confluence calculation according to the unit line selected by the user, the final completion of the whole process of reservoir runoff calculation, obtain the net rainfall, reservoir water level, storage and process of information the characteristic values of the flow, the establishment of calibration model for real-time correction of forecasting results, which can analyze and compare with the historical
flood; at the same time, automatic storage reduction calculation.
The key techniques as follows: (1)The pseudo forecast: The automatic acquisition of the next 24 hours rainfall data or custom rainfall information system, pseudo forecast to achieve different flood magnitudes, access to information possible flood process.
At the same time, the system can automatically extract the history of typical rainfall, according to the history of typical rainfall magnitude and rain type characteristic value of the same enlargement or reduction

Thus, data coming from the 22 reliable stations were used to examine the temporal trend, and the data on all stations in 2010 were used to examine the spatial distribution of haze.
In this study, the daily average was used to reconstruct a haze data set.
Estimation of Mexico’s informal economy using DMSP nighttime lights data.
Spatial Restruction of Urbanization in Chinese Mainland in 1990s Using DMSP/ OLS Night Light Data and Statistical Data.
Review on applications of DMSP/OLS night-time emissions data.

The first technique is based on multi-transmitter-multireceiver (MTMR) technique with tomographic methods used for data reconstruction.
In the MTMR, the possibility of SHM using algebraic reconstruction techniques (ART) for tomographic imaging with Lamb wave data measured in realistic materials is examined.
Hence, tomographic reconstruction with limited Lamb wave data must be performed.
Data was collected on Wing box before (reference data) and after Fig. 6 Architecture for a STMR Array Lamb wave based SHM system.
[8] Subbarao, P.M.V., Munshi, P. and Muralidhar, K. (1996) "Performance of Iterative Tomographic Algorithms applied to Non-destructive Evaluation with limited data".