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The EPR data were compared with infrared (IR) and photoluminescence (PL) data.
The main effect of the annealing at 500°C (the spectrum c in Fig. 2,) is disappearing of C(sp3)-H band and strong reduction of O-H band.
At the same time from the data presented in Table 2 it is seen that for the SiO2:C sample annealed at 500°C and 700°C the g-factor of the localized carbon spin system is lower than that for the free electron value (ge = 2.0023).
Parameters of non-localized (g1) and localized (g2) paramagnetic systems extracted from the fitting of Eq. (2) with experimental data in annealed SiO2:C samples.
Comparing the EPR and PL data obtained in annealed SiO2:C samples we can suggest that the position of the PL band depends whether the unpaired electron of CRR is located on a C or O atom and its efficiency decrease with carbon clusterization.

The actual data indicates that the impact of design phase on cost achieves by 70%-80%.
And more manpower and material resources are invested into data analysis.
And the historical data indicators of construction which are stored in BIM database are compared.
Under the powerful computing function of database, the data statistics of 4D, 5D and ND is realized.
BIM is used to establish project cost data center.

Nowadays rotating systems are equipped with diagnostic systems that are based on collecting and recording measurement data and that process a huge amount of data during the machine operation.
After the end of tests, a signal of readiness to initiate the drive will be generated; • data collection and recording in real-time after the drive starts its operation (with the rotating shaft), comprising: the start-up, the shut-down, operation at the nominal rotational frequency and operation in the "overspeed" mode; • "on-line" recording and control of parameters of the system operation during each phase and a selection and reduction of saved data files in the non-volatile memory that concern mainly untypical behaviors of the system; signaling and generating different level alarms in cases of exceeding data limit critical values for the operation of the power transmission system or the bearing system.
Fig. 5 presents the wheel of clearances for the model and experimental data obtained during the test.
Diagram of the phase portrait for the model data Fig. 5.
Diagram of the clearance wheel for the model and experimental data During the test procedure, the measurement data for a given number of control points evenly distributed on the periphery of the clearance wheel are registered.

The analysis in this paper is mainly based on the measured data of the collector area key design parameters such as average daily water use, water temperature, the thermal collection efficiency, water tank and pipeline heat loss and other parameters, as well as the problem found in the test of the construction and operation control , put forward reasonable suggestions for energy saving potential, provide reference for the centralized solar hot water system design and operation in future.
Therefore, the design value of daily hot water consumption should be selected base on the average daily amount, adopt proportional coefficient reduction method for the highest daily hot water quota should be more reasonable.
In the design, due to the different engineering pipeline with different length, and selection of insulation materials and thermal insulation properties are different, the heat loss value are different, so take the heat loss value should according to the actual testing data results of the equipment and piping insulation.
The original design requires is basic realization of automatic control to the heat pump system, but in the actual found that, operation people according to the cold and heat data of the system judge the switching in various work conditions and the pump starting or stopping, not fully automatic operation control, if operational errors appear such as operation switch valve and the water pump, cooling water pump, chilled water pump start and stop, it would cause unnecessary waste of energy.
According to the measured data, the collector area calculation result often bigger if in accordance with the norm

The framework incorporated HLCCM is proposed in cloud computing based collaborative design environment and allows users to estimate the product data and other related information on a wide variety of application.
The product attributes strongly correlated with LCC factors are chosen as learning data in the HLCCM.
The cost factors considered would depend on the stage in which we want to use the model, the kind of information to be extracted from the model, the data available as input to the model and the product being designed.
Table 3: The predicted LCC results with 200 sample data using the HLCCS (MAPE) Conventional ANN HLCCS Training Validation Training Validation Life cycle energy cost 19.32% 28.51% 11.13% 16.17% Maintenance cost 7.14% 11.17% 4.27% 7.02% Average 13.23% 19.84% 7.70% 11.60% In Table 3, the HLCCM has higher prediction accuracy than BPNN for the validation data.
This distributed modeling paradigm is very different from centralized data repositories where users conform to a single standard and are required to check in on a regular basis to make sure their models are updated.

The welding parameters such as arc current and voltage were captured by using data logger and the stick-slip effect was analyzed.
The influence of stick-slip effect on welding is observed in the arc waveforms plotted from the data obtained from the data logger.
Welding Parameters kept constant Wire Feed Speed 7.7 m/min Travel Speed 0.7 m/min Arc Length 2 mm Stick Out Distance 10 mm Shielding Gas Ar 80%+CO2 20% Gas Flow Rate 18 litre per minute TVC data logger The Validation Center (TVC) data logger is an system which is able to record and analyse the welding parameters.
Using TVC data logger, though there were enough parameters which can be measured and analysed like temperature, gas flow rate, travel speed etc., for analysis the voltage, currrent and wire feed speed were sufficient.
Arc waveforms After getting the transient data of arc voltage, current and wire feed speed, the arc waveforms are plotted.

The survey data of each criterion is averaged to find the mean of satisfaction score.
The questionnaire surveyed data are analyzed by SPSS indicating the lack of understanding of the construction technique with prefabrication [1].
SPSS® (Statistical Package for the Social Science for Windows) is used to analyze the survey data.
The Levene’s test shows p-value <= 0.05, indicating that the survey data do not follow homogeneity of variance assumption for most criteria.
Welch and Brown-Forsythe tests are conducted when the survey data do not follow homogeneity of variance assumption to compare satisfaction mean score among habitat groups.

Section 4 consists of implementation notes and some synthetic as well as real data examples to prove the robustness of our proposal.
Assuming that our data mapped into F Space is centered such that.
Evidence shows that if the radius of the data set is smaller, the reconstruction error can be reduced (supposed the new coming data and the training data are come from the same distribution).
Fig.1 if is the actual pre-image in input space, but we get it by the combination of training set data and.
Data clustering and learning.

The CSI 2130 portable analyzer was used to carry out data acquisition and signal analysis.
The value of 0.9954 of R-square means that the fit explains 99.54% of the total variation in the data about the average.
The acceleration transmissibility is measured and the curve fitting technique is applied to the experimental data.
The coefficients obtained using the data from the full frequency range are shown in Table 1.
The tests conducted show that such a reduction of the number of experimental data without increasing the standard deviation considerably lead to better curve fitting results.

These include the definition of Non Linear Path Length, where the sub paths may not be the shortest path, having ‘k’ no of shortest paths in a node instead of having only the shortest path, then removing the path dominancy for state space reduction.
Fig. 1 The data flow diagram of the system When we talk about the exact requirement of the path dominancy we need a queue and some values that have been already loaded into the queue since the previous modules of main algorithm.
The data flow diagram (DFD) of the proposed work explains the state changes of modules when data flow take place.
For proper handling of these attributes we need the member functions to read and write data over these attributes.
Summaries and Conclusion The queue is packed as user – defined data in our work and all the modules are packed as member of user defined data types.