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The furnace (reduction furnace) is used to simulate the heating process in iron ore reduction measurement system.
Principle of Reduction Measuration The iron ore reduction performance measuartion in high temperature is shown in Figure 1.
The sample is restored into the reduction tube.
N2 is poured into the reduction tube, standard-state flow 5L/min.
At the beginning of 15 min, the sample quality should be recorded at least once every 3 min, then every 10 min record the data, until the end of test, 180min.

Then, the pretreated sample data using rough set attribute reduction method to delete redundant attributes.
Rough Set theory is based on a simplified indistinguishability of ideas and knowledge methods, while maintaining the ability to classify the same premise, through knowledge reduction, the logical inference rules from the data in the system as a model of knowledge.
Using rough sets to avoid the loss of information caused by discrete data in a data processing process, through the rough set attribute reduction, greatly reducing the training process of neural networks[3-4].
Fault diagnosis method proposed neighborhood rough sets and neural networks using neighborhood rough set to avoid the loss of information caused by discrete data in a data processing process, through the rough set attribute reduction, greatly reducing the neural network training process.
[8] Yulig Y, Ye SH, Rough set reduction for hybrid data based on genetic algorithm, Journal of harbin institute of technology. 2008,40(5):683-687

Aerodynamic Drag Reduction Design of Concept Sports Car Ying-chao Zhang1, a, Fang Zhao1, b, Shu-xin Shao1, c and Guang-yin Jin2, d 1State Key Laboratory of Automotive Simulation and Control, Jilin University, China 2China FAW Company Limited R&D Center, Jilin, China ayingchao@jlu.edu.cn, bzfzhaofang@gmail.com, cshaoshuxinjlu@163.com, dgabrielking@126.com Keywords: Sports car, shape optimization, CFD, Aerodynamics, drag reduction.
The aim is to provide the experimental data for researches of drag reduction and aerodynamics theory. [2] 2 Model 2.1 Basic Theories.
(3) Comparison with the foregoing analysis, you can see basically the fingerprint consistent with tabular data analysis.
All show that the optimization of spoiler and body results obviously, the reduction of grill wind resistance coefficient mainly due to the reduced area of small grid on both sides.

An experimentally verified FE model provides a baseline that can be compared with data collected from an in-service testing on the same structure to determine whether the structure is in a healthy condition.
In recent years, many methods have been proposed to locate and assess structural damage from measured data.
A reduction coefficient for each finite element is defined and later found to indicate stiffness reduction in that particular element.
For an undamaged structure, all reduction coefficients are zeroes, i.e. δj = 0, j = 1, …, l.
After only 7 iterations, all damaged elements and the extent of their stiffness reductions are clearly identified.

Therefore, this paper presents a modified algorithm based on Nesta algorithm to reduce the amount of data sampled of power quality signal, the complexity of the algorithm to improve the algorithm’s speed.
Compressed sensing algorithm is in the process of data sampling is completed while the compression process, then the resulting signal is then compressed in the terminal for data reduction process.
The core content of the compressed sensing algorithm can be seen in three aspects: First, the sparse representation, its main role is the traditional sparse sampling data base by converting a sparse signal in order to remove redundant data signals traditional, with less the amount of data representation signal; Second, the measurement matrix, and its main role is to mapping x N-dimensional signal to the M-dimensional signal y, mainly to ensure that information is not lost; Third reconstruction algorithm, the main role is from M-dimensional signal y matrix through a non-linear projection regain N-dimensional signal.
Fig. 1 1600 bit timing simulation data image, (a) the voltage sag signal, (b) the voltage liter signal simulation, (c) the voltage interruption signal, (d) the voltage pulse signal Power Quality Signal Compression Sensing Algorithm.
Nesta Reduction Algorithm.

By integration analysis of monitoring data of typical section, curves of horizontal and vertical displacement with time were drawn, shown in Figure 3.
Based on the observation results of typical section and the cumulative horizontal and vertical displacement variation, as well as integration analysis of the data, curves of horizontal and vertical displacement rate with time are drawn, shown in Figure 4.
By integration analysis of monitoring data of typical inclinometer hole, deep displacement curves with depth were drawn, shown in Figure 5.
Fig.11 Plastic strain contours Fig.12 Changing curves of side deformation with reduction factor (3) Based on the penalty function contact of pile soil and the surface of rock mass, the three-dimensional numerical model of coupling of seepage and strain has been established, the measured value of 55.3mm, numerical simulation of 56.1mm through analyzing and comparing with field test data of slope.
Slope Stability Analysis by Strength Reduction FEM [J].

Based on actual data of vehicle loads on Guangzhou-Shenzhen Expressway and relevant statistical results, mid-span bending moments of long-span virtual simple-supported beams are calculated.
Design vehicle load in General Code for Design of Highway Bridges and Culverts (JTG D60-2004) [1] is based on measured data of vehicle loads in 1990.
Those data were obtained from four representative highway survey stations, daily traffic volume of each station was about 3646 ~ 7114 vehicles and the total data were more than 60000 vehicles [2].
Based on the measured data of vehicle loads in GSE and relevant statistical results, a calculation model of load effects of bridges under random fleet is created.
Based on vehicle load data of different lanes of Guangzhou-Shenzhen Expressway, each-lane load effects of a 1600m simple-supported beam are calculated.

Obtaining the sample data of fault feature, then the data is divided into training data and test sample data.
Equipment running status data is high-dimensional information obtained by lifting wavelet packet transform method.
In low-dimensional manifold failure feature extracting method, the vibration data is picked from the low-dimensional manifold of high-dimensional euclidean space.
Then divided the data in to training data and test sample data. 3) Training neural network learning algorithm. 4) Solving state trend prediction problem using the trained network.
This paper proposed data-based multi-transform domains non-linear fault trend forecasting method.

Results show a reduction in system energy losses for the optimized values, without violating any power system requirements.
Two case studies will be conducted by utilizing the data from a typical 132kV main intake substation; and simulated using DIgSILENT® PowerFactory software [8].
Instead, the 24-hour energy loss (in kWh) of the feeder is to be minimized, which is calculated as below: Eloss= t = 024 - ∆tPloss(t) × ∆t (1) Ploss(t) is the active power loss of the network (in kW), returned by PowerFactory after the load flow for corresponding data at time t is executed.
∆t is the time period, taken as 0.05 hour (or 3 minutes), which is equal to the smallest available duration of data sampling.
The middle penetration appears to give the most loss reduction for this case, which is somewhat similar to the total base load of the feeder.

In the present work, new data for transitional and turbulent flows and for different types of polymers and concentration are presented.
The Insight 3G software (TSI) was used for data acquisition and generation of the velocity vectors while the data analysis was done using a MatLab code developed in-house.
Reduction of the diameter of the polymer injection point.
Edomwonyi-Otu, “Drag Reduction in Oil-Water Flows,” PhD Thesis.
Gyr, “Heterogeneous drag reduction concepts and consequences,” J.