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Economic Losses Research of Seismic Damage Based on Grey Correlation and Genetic Neural Network Xian-zhang LI 1, 2,a, Zhi-tao WANG 1,b, Xiao-dong GUO 1,c 1 Institute of Earthquake Resistance and Disaster Reduction, Beijing University of Technology, Beijing China 100124 2 College of Architecture and Civil Engineering,Beijing University of Technology,Beijing China 100124 axianzhang12@126.com, bwzt@bjut.edu.cn, czxy02@bjut.edu.cn Keywords: earthquake disaster; economic losses; grey correlation; genetic neural network; prediction Abstract.
This article selects the factors influence on seismic losses as far as possible from the macroscopic earthquake losses angle in the light of problems above mentioned, and then go on influencing factor reduction through grey correlation algorithm to confirm the main factors influence on the seismic losses, and use the genetic algorithm to optimize the weight threshold of BP network, and build a prediction model based on grey correlation algorithm and genetic BP network, and it can supply relevant for urban seismic risk analysis and predisaster planning.
(2) The calculation of grey relational grades (2) is grey relational grades of and.n is the length of two arrays(data number) in formula 2.
Considering the parameters which can get in an earthquake and according to literature [7] and the current earthquake resistant code, we select 20 earthquake samples as the training sample from 1995 to 2000,and select the 21 to 23 as the prediction data, as is showed in table 1.
The verification sample data are in the range of training sample data because of the sample is not easy to obtain, therefore, it need further analysis and improve for the model outside the scope of the samples.

To verify the data obtained by these post-mortem measurements, the experiments of the same type were performed in-situ during the boundary motion at a temperature and detected using the synchrotron radiation [11].
Results of measurements of grain boundary migration Annealing of the sample promotes the reduction of the grain boundary energy via reduction of its area.
For comparison, the data obtained by Furtkamp (σ) and Tsurekawa (ν) are also shown [9].
This was confirmed by the existence of an identical linear dependence between ∆H m and ln(Mγ)0 for experimental data on grain boundary migration at different grain boundaries of an Fe-6at.
Conclusions Measured data on migration of individual grain boundaries in an Fe-6at.

Reduction of electrical energy losses Q is compensated by natural and artificial methods of reactive power.
The natural method includes special measures taken in enterprises, reduction of inductive resistances in electrical circuits, saving of electrical energy, etc. implies that.
The automatic control center of "Azerishiq" OJSC calculates and stores monthly and daily comparative data, including the consumption and technical losses of electric energy (Figure 7 and Figure 8).
Monthly comparative data.
Daily comparative data.

Therefore, energy conservation and low-carbon development of the cement industry will be the effective measures to alleviate China's energy and environmental crisis and achieve China's 2020 carbon reduction targets.
Procedures Procedure Content Carbon Footprint Analysis 1 Raw material purchasing Raw material purchasing Carbon footprint included in upstream emission and the data can be accessed from upstream industry directly. 2 Raw material transportation Raw material transportation Carbon footprint caused by fuel combusted in vehicle transportation.. 3 Building material manufacturing Proportionately mix and crush the raw materials such as limestone, iron ore , clay etc., Carbon footprint caused by electricity/fuel consumed for mechanical power equipment Feed raw mix into pre-heater for preheating; Then fed raw mix directly from the preheater to the rotary kiln for high temperature heating; Discharge clinker into the clinker cooler.
Table 3 Calculation Model of Cement Manufacture Carbon Footprint Calculation Model y=ixi i=1,2,3,4,5,6 y：Cement manufacture carbon footprint i： Factors that cause carbon emissions xi： Carbon footprint caused by different factors 1st Level Parameters x1 Carbon footprint of raw material mining (Carbon footprint included in upstream emissions and the data can be accessed from upstream industry directly.) x2 Carbon footprint of raw material transportation (Carbon footprint of the combustion of various fuels consumed in the transportation for cement raw materials).
The data provided by the upstream manufacturers can be used for calculation directly (carbon footprint of waste utilization excluded).
Reference [1] Liu Wei, Cui Suping: Analysis on CO2 emission reduction potential of cement manufacture.

ZigBee transponder seems a better choice in terms of data rate and low power consumption as compared to others.
An ARM7 microcontroller processed images and communicated to neighboring motes via transceiver at data rates up to 76.8 KB per second.
The system is coded accordingly to save battery as the system undergoes a sleep mode when no transmission of communication data is taking place.
The objective of such selection was overall reduction in system power consumption as compared to previous methods.
Gaurav Mathur, Peter Desnoyers, Paul Chukiu, Deepak Ganesan, and Prashant Shenoy, 'Ultra-Low Power Data Storage for Sensor Networks', ACM Transactions on Sensor Networks (TOSN), 5 (2009), 33

The environmental and economic performance is quite important to achieve the goal of 40-45% carbon reduction per GDP for China.
Life cycle inventory data are mainly collected from environmental impact assessment report of Zhejiangyuhua aluminum alloy production site in China [5].
Moreover, the infrastructure of aluminum alloy production plant, chemicals production, wastewater treatment data and relevant background data from European were used [7].
Vol.11(2005),p.1 (In Chinese) [7] Ecoinvent data v2.0 Final Reports Ecoinvent 2000 No.1-15, Swiss Centre for Life Cycle Inventories, Dubendorf, CH (2007) www.ecoinvent.ch [8] Information on http://www.chemcp.com [9] G.
Zuo :Greenhouse gas emissions and reduction potential of primary aluminum production in China.

In particular, concurrent accesses to shared data must be properly synchronized.
The training stage involves generating the machine learning models from sample data set which consists of the four above-mentioned types of atomicity violations with correct labels.
Related Work There are many existing research papers about detecting the various concurrency errors including deadlocks, data races and atomicity violations.
Xu et al. [23] proposed inferring computation units based on data dependence and control dependence, then atomicity is checked on the computation units [20].
Automated type-based analysis of data races and atomicity.

Liquefaction evaluation methods based on CPT have become one of the methods commonly used due to its advantage of good continuity in data, stability, reliability and high accuracy.
The method is on the basis of observed data from past earthquake field investigation, and the evaluation formula is given by statistical analysis.
Liquefaction evaluation methods Chinese method The Chinese method bases on the test data from different intensity zones in Tangshan earthquake, and is derived by means of statistical analysis using evaluation function.
Evaluation of liquefaction resistance (CRR) using Olsen method Olsen (1997), who put forwarded many techniques to assess liquefaction resistance from CPT data, suggested a somewhat different procedure for calculating CRR, which can be simplified by the following equations: (16) (17) Analysis of the evaluation result The criteria to determine the depth and thickness of the liquefied and non-liquefied soil layers are to mainly analyze the CPT data together with the SPT and velocity data.
Then compare these data with calculated results of Chinese method, Robertson and Olsen methods, if they are consistent, the evaluation is successful, otherwise it is failed.

In order to facilitate the comparison of the operating characteristics of the tested engines, as well as to make a reference to various weather conditions, the data obtained was reduced to standard reference state.
Downstream of the gas generator there is a 6-stage power turbine which transmits torque to the line shaft through a reduction gear.
Statistical analysis is the method of processing data (results of measurements performed according to an assumed program of tests) in order to find regularities in the examined phenomena and to interpret them using the methods employed in mathematical statistics.
Observing the trends present in the changes of the operating characteristics and selected diagnostic symptoms facilitates obtaining data which may be used in the decision-making process related to equipment operation in order to ensure naval combat readiness, as well as to maintenance, adjustment or replacement of engine components and finally, to repairs.
That data may also be used to design new engines or upgrade the existing.

Introduction Hydrogen sickness is a well-known phenomenon for its remarkable reduction of mechanical properties of copper [1,2].
Temperature dependence of Q-1 for OFE copper in annealed and hydrogen-charged condition (data points marked by open symbols) and in annealed, pre-strained and hydrogen charged condition (data points marked by filled symbols).
Data points for oxygen free electrolytically refined copper (OFE) are marked by circles.
Data points for oxygen-free phosphorus-alloyed copper (OFP) are marked by squares.
Anderson, Hydrogen embrittlement of pure copper and of dilute copper alloys by alternate oxidation and reduction, Trans.