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Based on the data obtained during this study, the initial footstep geometry produces data in the form of total deformation (1.383 mm), equivalent stress (21.013 MPa), and safety factor (1.227).
The 10% variation produces data in the form of total deformation (1.4368 mm), equivalent stress (20,564 MPa), and safety factor ( 1.2538).
At the same time, the 25% variation produces data in the form of total deformation (1.3058 mm), equivalent stress (22.27 MPa), and safety factor (1.1577).
This data is entered into the ANSYS Workbench software's Engineering Data section's table.
Based on these data, the best footstep design is a design with a mass reduction of 20% because when compared with other conditions it produces von Mises stress results, the smallest total deformation, while the safety factor is the largest.

The FactSage and Chemkin simulation results have a good agreement with the experiment data, and reveal that the main production reactions are: NO2+O<=>NO+O2, NO2+H<=>NO+OH.
NO Emission of Rice Hull Combustion For easily comparison of NO emission in different combustion process, the experiment data obtained from the flue gas analyzer was amended to the emissions under the excess air coefficient of 1.4.
At the same time, much CO, released by PVC, played an important role in NO-reduction [8].
NO concentration from simulation had a good agreement with experiment data, as shown in Fig.6, which indicated that combining FactSage and Chemkin was feasible.
Fig.6 NO concentration from simulation vs experiment data Fig.7 NO concentration from simulation (rice hull combustion vs co-combustion) Threshold normalized rate of production coefficients (Rop) were used to find out the main production reactions.

Data mining is the most powerful tool which discovers knowledge from large amounts of data.
Data mining functions are used to specify pattern type looked for in data mining task.
The description type data mining describes data sets in concise and synoptic ways and analyzes general attributes of data.
Applying data mining to data analysis in manufacturing.
Wavelet-based data reduction techniques for process fault detection.

We also provide the sample data for further realization of whole machine data processing and sample data database building.
The modular design is adopted for the data management function configuration.
We realize the automatic acquisition of relevant data and information through a variety of ways according to the actual needs, and realize the data processing process and logic rules through the network data analysis system and fault diagnosis system; in response to user service requests, we also build the bridge between the customer service and data service [6].
Then import the collected sample data in text format into the database, so as to carry out the data acquisition analysis and processing.
This system is divided into five parts: data entry systems, data query system, trend display system, data analysis system, and fault knowledge base system.

Finally test data is transmitted though USB interface to upper computer software based on labview, the software’s functions included data reduction, data analysis, data storage etc.
The hardware circuits consist of data conditioning circuit, data acquisition circuit, data transmission and reception circuit and USB communication circuit.
Data Communication.
There are sensor signal acquisition and display, data save, data query etc.
Fig.6 The data acquisition diagram Fig.7 The data save diagram When the “start” button is pressed, the software gets test data, change data into test signals and display test signals.

Then get the reduction of the condition attributes. 4) Build the Support Vector Machine and train it with the training data. 5) Select test data to test the Support Vector Machine.
of the decision table Currently, the most widely used Rough Set data processing tool is ROSETTA, which has many reduction algorithms, such as genetic algorithm, dynamic reduction algorithm, extended law.
Firstly, using the ODBC function of the ROSETTA to import the data in Tab.2.
Otherwise, input the test data into the second SVM automatically and so on.
When the data samples and the type of faults increase, the effect will be more significant.

The flight test data and helicopter characteristics are taken from Ref. [[] R.
The power required of current analysis fits well with the flight test data, implying that this model is sufficient to predict the power required.
The flight data of SA349/2 helicopter is used here to correlate with the load analysis.
The details of blade structural properties, static airfoil data and rotor geometry characteristics can be found in Ref. [[] R.
The first ten harmonics of moment in Fig. 3 shows a close agreement between the present analysis and the flight test data.

Data were then collected through Autolab/PGSTAT101 using cyclic voltammetry and differential pulse voltammetry.
D value of reduction reaction 4 × 10-5 derived by slope.
The scheme of the reduction reaction is shown in Fig. 8.
The potential reduction peak is -0.91 V (vs.
Ag/AgCl) at pH 9 and the potential reduction peak is -0.81 V (vs.

This paper extends the recently developed cross modal strain energy (CMSE) method for damage localization and severity estimation, using limited modal data, with iterative process.
The former aims at updating directly the elements of mass, stiffness, and damping matrices in such a way that the updated model reproduces the measured data as accurately as possible.
Listed in columns 2-5 of Table 1 are the modal data of the first ten bending modes, including the modal frequencies and the modal assurance criteria (MAC) of the undamaged and damaged beams.
Datta, Abhijit Gupta, Maitreya Lagadapati, A direct method for model updating with incomplete measured data and without spurious modes, Mechanical Systems and Signal Processing 21 (2007), 2715–2731
[11] Guyan, R.J., Reduction of Stiffness and Mass Matrices, AIAA J., 3 (1965), 380.

The basic data format for patient cases has listed in Table 4.
So dimensionality reduction is also required to develop a single dimension data mining model.
FP-tree algorithm is used to mine the new single dimension data, after which rule screening is carried out aiming to delete the inappropriate and take appropriate rules.[5] Example of Data Mining Course Table 5 lists the transaction data that need to be mined.
Patient data and mining record is shown in Table 6.
Data Mining: Concepts and Techniques[M].