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ardzhouke@x263.net, bwenqiang111222@126.com, cliuna219@yahoo.com.cn, d1410320708@qq.com, elinxiaoxiao1988826@163.com Keywords: Waste combustion, Miniaturization, Cyclic economy, Source reduction.
Small continuous efficient domestic waste disposal system, which has the characteristics such as high degree in safety disposal, reduction effect, initial small investment, low operation cost, small energy consumption, flexible layout and environment friendly, fills the technical blank of domestic waste sourcedisposal in our country successfully.
Measurement Data Dust and fume mg/m3 80 23.9 Smoke blackness Ringelman concentration, level 1 1 CO mg/m3 150 2 NOx mg/m3 400 180 SO2 mg/m3 260 4 HCl mg/m3 75 10.3 Hg mg/m3 0.2 7.6×10-4 Cd mg/m3 0.1 6.9×10-4 Pb mg/m3 1.6 0.317 dioxin ngTEQ/m3 1.0 0.1618 Table2.
In addition, the system use the advanced Internet of things technologies, and can whole-process monitor and record the operating conditions and emissions of harmful substances through pollutants control online monitoring system with full monitoring and recording of the operation of the system and can upload the data in real-time to the relevant administrative departments.

The final section of the paper presents the results of the experimental research, which are compared to data existing in specialty literature.
(2) Experimental measurements led to the following data: ; H-height of the water layer above the FBG; H=0.5 m; ro-inner radius of a nozzle; ro=0.25·10-3m; σ-surface tension coefficient of the water; σ =73·10-3N/m By replacing these values in relation (2), one obtains: (3) The pressure loss established experimentally was obtained for an air flow equal to =600 l/h; this pressure loss will be compared with data from specialty literature [12].
Environmental Protection Agency Office of Research and Development Center for Environmental Research Information Risk Reduction Engineering Laboratory Cincinnati, OH 45268, EPA/625/1-89/023, Design Manual fine pore aeration systems, September 1989

A study on Heat transfer analysis and Experiment of Reinforced concrete columns using 500℃ Isotherm method In-Hwan, Yeo, a,Kyu-Jae, Hwang,bJae-Hong, Anc and Jong-Ho, Lee,d Korea Institute of Construction Technology, Republic of Korea ayeo@kict.re.kr, bhkjthink@kict.re.kr, crehong@kict.re.kr, dljh84@kict.re.kr Keywords:unstressed tests, axial load ratio, assessment of fire resistance performance, RC columns Abstract.This study aims to assess heat transfer analysis of RC columns and unstressed test by applying 500℃ isotherm method, which is the strength deterioration estimation method of Eurocode, inorder to use it as basic data for fire engineering design.
Also, it aims to evaluate strength alleviation of RC column members, which are exposed fire, in order to use it as basic data to establish fire engineering design system, by adopting the simple method described in Eurocode for the axial load ratio affected on column members.
Annex B of EN 1992-1-2, in which fire resistance design method is described, suggests a simple method, which can be easily applied, and use of data by experiments are available for.[1] Figure 1.
The analysis results of the adopted model of RC columns showed an error range of at least 0.2% but no higher than 20%. 2) As the distance from concrete surface grew farther 10mm to 45mm while cross-sectional size increased based on 3 hours of fire resistance time, temperature reduction effect occurred up to 53.2% and 56.2% respectively. 3) Axial load ratio was calculated through remaining strength ratio estimated by experiment and analysis results.

Based on multiple experimental researches the paper presents graphics and mathematical models obtained by data processing using modern software of wear dependence and cumulate thermal deformations according to the number of controlled number of pieces and dimension of the gauge.
Experimental results after 1000 pieces verified Pieces verified External diameter 25h6 [mm] 46.1h6 [mm] 52s6 [mm] 0 25.040353 46.10175 52.058748 50 25.041341 46.1038 52.058749 100 25.040955 46.10862 52.058628 150 25.042708 46.1038 52.058623 200 25.04131 46.10615 52.058745 250 25.040472 46.10007 52.058739 300 25.04309 46.1068 52.058803 350 25.0412 46.10878 52.058783 400 25.040962 46.1024 52.058803 450 25.040986 46.10862 52.058799 500 25.043789 46.10019 52.058793 550 25.042386 46.10387 52.05847 600 25.040802 46.10771 52.058869 650 25.043744 46.10967 52.058867 700 25.041163 46.10316 52.058857 750 25.043973 46.10708 52.058847 800 25.04124 46.10797 52.058843 850 25.041191 46.10985 52.058841 900 25.041266 46.10934 52.058885 950 25.041339 46.10956 52.058901 1000 25.041275 46.10998 52.058904 By processing the data from the Table 1 and by using specific software it was concluded the next graphical dependences, Fig.2, Fig.3, Fig. 4 and mathematical models of the wear and thermal deformation by
Logistic Model: y=a1+be-cx (7) Coefficient Data: a = 3.72256352808E-002 b = 2.38584671457E+001 c = 3.15144287703E-002 .
This methodology permits the optimization of the parameters values z1, y1 by taking into consideration the production plan against minimization of the gauges number, especially the shaft gauge, what gives reduction to the product cost by cutting down the gauges fabrication costs [6].

Figure 3 shows an example of this dependency, where four clusters of data points can be clearly distinguished.
To assign data points to clusters, k-means was used.
The algorithm determined 4 clusters of data points.
Additionally, a cluster analysis were carried out on the recorded data.
Fig. 4: The best result of clustering algorithm for LEM dimensionality reduction method with 3 output components.

ERA is a modal analysis technique which generates a system realization using the frequency response given input and output data.
As a comparison, finite element model was built to obtain the environmental stimulus response data.
The modal parameters are identified precisely only to obtain the response data.
S An Eigensystem Realization Algorithm for Modal Parameter Identification and Model Reduction.
S. (1994b) “Determination of model parameters from ambient vibration data for structural health monitoring.”

The data show that, reverse engineering can shorten the product design and manufacture of the life cycle of more than 40%.
Reverse engineering have very good application in the new parts design, has been part of the copy, damaged or worn parts of reduction, model accuracy, art and archaeological relics replication, product design perfect, digital model checking, etc. we should pay attention to the following contents in the process of using reverse engineering technology to mold optimal design: (1) Data collecting, we improve the scanning data collection point precision and efficiency, which use advanced characteristics identification technology to reduce artificial intervention, realize data acquisition process automation in the scanning process
(2) Data processing, the effective method to point cloud data processing process was improved to eliminate various interference factors, build unified data format conversion standards, and reduce data distortion and lost
Splicing the point cloud data to obtain the point cloud data as shown in Fig. 5.
The Imageware software will take measurement data processing as follows: mixed point and noise point removal, data of the compact, data block and curve surface reconstruction, and then march the suture, cutting, transition in the use of UGNX software.

Little however has been reported on the data from the compression set test itself.
There is good agreement between the two data sources.
This is illustrated by the data in Fig.1 for all temperatures investigated.
This observation cannot be applied to the 25% initial fixed compressive strain data.
As shown in Fig.1 this data is more widely scattered than that obtained from the 50% initial fixed compressive strain tests.

So we consider both types of features. 2.2 Dissimilarity Representation and Data Preprocessing An appropriate representation of samples is based on data.
And 200 examples are used as training data.
All the detection accuracy is low when fewer features is used on both original data and transformed data.
It’s fairly to say that if the original data cannot be described, the structure of these data isn’t exists either.
So there is no ground that the transformed data may absorb the structure information of the data.

Where M is the number of BP neural network training data, N is the smallest scale, namely, to predict the scale coefficient of the first M+1 data by the scale coefficient of the last M data; BP neural network 1 to N can use the same structure, parallel processing the N sets of data at the same time, so generating a new set of scale coefficient of each layer, as well obtaining the scale coefficient prediction output value by means of the trained network.
Step 7:Getting the multiplier by the above step 2 to step 3, then using it for the actual data to initialize the AR model, estimating parameters of AR model, and then according to (11), getting the prediction multiplier; (11) Step 8: Calculating on the basis of prediction multiplier, and to conduct wavelet inverse transformation combining with the scale coefficient, generating prediction data, as shown in fig. 1.
Predicting the first M data by the the scale coefficient prediction value of last M data and multiplier AM + 1, and so on, the first M+2 data can be predicted, etc.
Fig. 1 Predicted data generation method Fig. 2 BP neural network structure Three layers BP neural network can approximate any nonlinear function [4], as shown in fig. 2, the circles represent neurons nodes, number of nodes in each layer references experience value and the experiment value.
We conduct on experiment by "the Silence of the lambs" (from the university of Berlin mpeg-4 video tracking database [5]), As known in fig. 3, it may be observed that when the size of a video frame doesn’t change sharply, the difference is small between the predicted values and the real value; When the amount of data change severely, the difference between the predicted values and the real value also increases, but predicted value can reflect the change trend of real data to a certain degree.