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The load cell sensor will send data about the reduction in the weight of the drying material to the microcontroller for further processing.
Load Cell Data Eror.
DS18B20 Data Error.
Table 5 shows the average data error of 3 temperature sensor data.
The average data error rate is 0,099% for load cell data and 0,28% for temperature sensor data.

Achieving operational excellence through systematic complexity reduction in manufacturing system design Dominik T.
Reduction of Time-Independent Complexity in Manufacturing Systems.
Reduction of Time-Dependent Complexity in Manufacturing Systems.
Reduction of Periodic Complexity.
The sinus interval of the organizational periodicity was determined empirically (c=5 years) on the basis of the analysis of historical data and events as shown in Fig. 2.

Drag Coefficient Identification from Flight Data via Optimal Observer Rongjun Yang 1, Yao Ye2 1Institute of Systems Engineering, China Academy of Engineering Physics, Mianyang, China 2School of Economics and Management, Nanjing University of Information Science and Technology E-mail: rongjun802@163.com Keywords: parameter identification; flight data processing; Kalman filter; smoother.
For effectively using flight test data to extract drag coefficient, an optimal observer based on parameter estimation technique is proposed.
Radar measurement data processing utilizes UKF and URTSS respectively, to reconstruct the flight states, which are compared with the actual parameter.
Chapman-Kirk reduction of free-flight range data to obtain nonlinear aerodynamic coefficients.
Trajectory reconstruction using radar measured data.

According to chemical data, the amount of iron in the waste is high (52.6%), and the most effective solution for extracting iron from the waste is the reduction process.
Khojiev, The technology for the reduction of metal oxides using waste polyethene materials.
Berdiyarov, et al., Low-Temperature Reduction Processing of Copper Slag.
Low-temperature reduction processing of copper slag doi:10.1007/978-981-19-5395-8_15 Retrieved from www.scopus.com [8] S.T.
Berdiyarov, et al., Carbothermal Reduction of Copper Slag for Processing into Pig Iron.

Rough set and neural network have been used in fault diagnosis for many years, rough set is used to make the reduction and neural network is utilized to learn rules and approximate the ideal data.
In this paper, the fault sample reduction is done by the steps are as follows: The binary relation is established and judged according to the sample data, and then the suit rough set model could be founded.
One reduction is selected as the inputs of the network from the three reductions, and the decision is the output.
Table 3 Contrast of the data trained by the network Name Attributes Training time Epochs MSE Before reduction C1-C10 1.43759s 8 0.0275 After reduction C1-C5,C8-C10 1.06484s 8 0.0275 As is shown in Table 3, the change is not obvious in epochs and mean square error (MSE), however, the difference between before and after reduction is the training time, obviously, the training time is decreased greatly after reduction.
The particle swarm scale is configured to 80; the particle dimension is configured to 16; the maximum iterating times is configured to G=200; parameter c1=c2=2; self-adapting inertia =0.5; the maximum speed Vmax=0.3, the network optimized by the PSO is trained with the reduction data, and the test is simulated by the other 40 fault samples, the contrast is listed in Table 4.

Analysis data show that the addition of Ce improves the reduction performance of Fe-Mn species.
Manganese and Cerium compounds are also effective in selective catalytic reduction with NH3 [3, 4].
Analysis data from XRD, BET, XPS, TPR and TEM have provided the insight into the surface and structure properties of Ce-promoted Fe-Mn oxide catalysts compared with the undoped catalyst.
The peak at 405 ˚C can be related to a process of the reduction of Fe2O3 to Fe3O4, while the other two H2-consumption peaks with the reduction of Fe3O4 and FeO, respectively.
Effect of Ce doping on surface area for Fe–Mn oxide Sapmles BET surface area (m2 /g) Pore volume （cm3/g） Fe-Mn oxide Fe-Mn-Ce oxide 58 64 0.212 0.243 From the listed data, it is deduced that Ce-doping result in the increasing of surface area, pore volume of the Fe-Mn samples.

Introduction The weather forecast has a very important role in disaster prevention and reduction, economic construction, social development and national defense construction.
System processing The meteorological data sharing processing data flow is as shown in Figure 1, and the meteorological database storage data is for query platform use, and the data query platform can form drawing according to different types of meteorological data.
System data analysis The meteorological data material is mainly divided into: original meteorological observation data and numerical forecast products.
Reading file header and data.
Reading data from buffer, analyzing data and obtaining data.

Research background Data reported by the Ministry of Housing and Urban-Rural Development of the People’s Republic of China(MOHURD) shows that 3513 wastewater treatment plants have been put into operation in China by September 2013[1].
Literature search and on-the-spot investigation show that the study on the way of concentrated activated sludge reflow used in energy saving and consumption reduction at home and abroad is still back.
Feasibility of concentrated activated sludge reflow process used in energy saving and consumption reduction of small and medium-sized wastewater treatment plant is verified through comparing water quality and technical indicators.
Pilot effect of concentrated sludge reflow instead of secondary settling tank reflow used in energy saving and consumption reduction Based on laboratory study, the author selected two wastewater plants as the pilot unit.
The data of operation for 20 days shows that effluent quality initially fluctuates significantly and tends to be stable in later period.

The Dimension Reduction Method of Face Feature Parameters Based on Modular 2DPCA and PCA Li Qiao-yan1, a, Quan Hai-yan1,b 1Institute of Information Engineering and Automation,KunMing University of Science and Technology,Kunming,China a137352883@qq.com, bquanhaiyan@163.com Keywords: Modular 2DPCA, feature parameters, dimension reduction, PCA, face recognition Abstract.
The Dimension Reduction method based on M2DPCA and PCA The dimension reduction method based on M2DPCA and PCA is implemented as follows: The Training Phase: At the beginning of the M2DPCA,we can divide the sample of the face which the size of the sample is m×n into several parts, and the number of the part is p×q.
Ci composes the vector C which is saved in the face recognition data library.
And it can increase the redundancy of the data .It can result that when we use the minimum distance classifier, the minimum will be changed.
The data in table 1 indicates that the recognition rate of the method of three kinds of block have no difference.

The EQCM data were represented as plots △m versus E.
The EQCM data were represented as graphs of △m versus E.
So the optical data analyzed by the software WVASE32 used for fitting is among wavelength range 400–900nm.
By fitting data from ellipsometric measurements with a single-layer model, the thickness of Mn layers was obtained (Fig. 6c).
The EQCM data were represented as plots △m versus E.