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In more practical terms neural networks are non-linear statistical data modeling or decision making tools.
They can be used to model complex relationships between inputs and outputs or to find patterns in data.
Observed data and calculated data in Huyuankou Station Figure 5.
Observed data and calculated data in Jiahetan Station Figure 6.
Observed data and calculated data in Gaocun Station 6 Conclusion Chaos BP neural network model is a mathematical methodology which describes relations between the input and output data irrespective of processes behind and without the need for making assumptions considering the nature of the relations.

The reduction is more important at elevated temperature.
By turning a hard steel (AISI 4130), Ding [11] obtained a reduction of Kc about 20%.
This reduction became significant only when heating temperature exceeds 500°C.
Indeed, Kc reduction values reach from 13 to 34% for a temperature range of 500 to 750°C.
Tounsi, “ Identification of Material Constitutive Laws for Machining-Part II: Generation of the Constitutive Data and Validation of the Constitutive Law”, Journal of Manufacturing Science and Engineering, Vol. 132, N°5, 2010

When a BPN or PNN is trained and fed with the input vector which is computed from measurement data of the structure before and after damage, the damaged story(s) will be found from the output of the BPN or PNN.
When a BPN is trained and fed with the input vector that is computed from measurement data of the structure before and after damage, the damaged extent will be identified.
Two finite element models based on this structure were developed to generate the simulated response data.
Beck, Phase I IASC-ASCE Structural Health Monitoring Benchmark Problem Using Simulated Data.
Natural Excitation Technique and Eigensystem Realization Algorithm for Phase I of the IASC-ASCE Benchmark Problem: Simulated Data.

Its specific steps are as follows: (1) Data dimensionless processing [6, 7]
Financial data are collected from Website of Sina Finance.
In this paper, the SAV Genetic Reduce (Genetic algorithm.) is used to execute attribute reduction in the collected data based on the ROSETTA software.
Table 2 Standardized sample data No.
Jian, Method on Data Element Standardization (5)—Standardization Stages of Data Element, Information Technology & Standardization. (6)(2008)53-55

Statistical method is employed to cope with gradient data-gradient projection in the horizontal and vertical direction to compute the average gradient value.
Multi-level B-Spline interpolation is employed to smooth gradient data.
In the first stage, a noise reduction process is performed.
At last, three sub-arraies, which contain the gradient data of every pair of edges, are chosen.
It is immune to all inverse factors and protects edge data to maximum.

The sintering temperature and composition are chosen according to phase equilibria data.
The present work gives guidelines based on a materials selection tool and on physico-chemical data for the design of two-layer parts made of tough steel and hard cemented carbides layers.
During sintering, the reduction of the superficial oxides by C with release of CO and CO2 depletes the C content and promotes the M6C formation.
This is consistent with phase equilibria data [5] even after only 2 minutes at the sintering temperature.
Conclusion Multimaterials with specific properties can be selected from materials properties data bases.

The technical hurdle of collecting internal cavity surface data is solved.
Acquisition Data of Valve Body Surface Duplicating Internal Cavity.
Surface Data collection: Data collection of prototype surface spot is usually carried out by contact measuring equipment or non-contact measuring equipment[7].
The data of spot-cloud can be processed by many methods of spot-cloud reduction introduced in reference[10,11].
It means that the data of spot-cloud would be processed twice.

Following this introduction, the model will be described; next we will introduce the data and sources for the study.
Model structure and data The objective function is to minimize the total discounted, cumulated cost of generation and interregional transmission for all six regions throughout the whole planning horizon.
In this study, emission reduction targets for 2030 are set according to relevant literature [4].China Electricity Council (CEC) proposed carbon intensity reduction targets in 2015 and 2020 according the “12th Five-Year "plan.
Data and source Regional electricity demand, generation and installed capacity are collected from power sector statistics, industrial report and other study [7] .Resource restrictions, nuclear and other renewable energy utilization restrictions are based on each province’s governmental statistics, development planning and techno-economic evaluations of renewable energy power technologies [8].
(In Chinese) [3] The notice of “12th five-year” plan for energy conservation and emissions reduction by The State Council of China: 2012 http://www.gov.cn/zwgk/2012-08/21/content_2207867.htm

Introduction By considering all possible variables in an actual trial when you input data by direct systems analysis, you should design a virtual try out, which is very close to the actual system, and then finish the optimal die layout reflecting the technical measures concerning the analysis result of the forming analysis system regarding the initial manufacturing plan.
Fig. 2 The 1st input condition Fig. 3 Draw Shape Fig. 4 1st draw Thickness reduction In Fig. 4, one inappropriate part due to the reduction in thickness is found.
The new operation plan provided a remarkable reduction in the winkle trend.
Fig. 15 The 1nd input condition Fig. 16 Draw Shape Fig. 17 1st draw Thickness reduction In Fig. 17, three inappropriate parts due to thickness reduction are found.
In more detail, thickness reduction in -<1> is 50% above and in <2> thickness reduction is 31.4%, and in , thickness reduction is also 34% in <3>.

The model integrates concepts of Big Data and Industry 4.0 and is currently in the experimental phase at the company.
Once the data is collected, it is sent to an AI system to analyse the data and provide insights that can be used to make informed decisions quickly.
For example, the AI system may be able to identify when a particular machine is likely to fail soon, based on the data it receives.
Using the collected and validated data, a limit point was established in production to prevent waste and reduce energy expenditures.
Wang, "The use of big data for sustainable development in motor production line issues," Sustainability, vol. 12, no. 13, p. 5323, 2020