Search results

The natural decrease in accuracy with the use of less data compared to the gain due to the reduction of experimental effort is analysed.
Using the vertices and symmetrical distribution of the data within the full experimental matrix allows a drastic reduction of experimental effort while maintaining the initial accuracy.
The data distribution for reduced data sets achieving the best accuracy can then be optimized inversely.
Fitting with reduced data For the fitting with reduced data two key questions have to be answered, 1) how much data are necessary to maintain the same accuracy as the initial fit and 2) how should the data be distributed within the full experimental matrix.
The material model imposes a variety of conditions on the data distribution.

The process calculation of producing stainless steel crude melts by smelting reduction in a 150 t converter is carried out for the first time by use of the empirical data and calculation method of refining plain carbon steel in a converter, according to the blowing conditions of 185 t smelting reduction converter of No.4 steelmaking shop in Chiba Works of JFE Steel.
Using the self-programming procedure for the processing calculation, the proper coke quantity and a complete set of calculation data for the process of smelting reduction in a converter are obtained, including the batch calculation, the material balance and heat balance calculations.
Using the self-programming procedure for the processing calculation, the proper coke quantity and a complete set of calculation data for the process of smelting reduction in a converter are obtained, including the batch calculation, the material balance and heat balance calculations.
They are calculated respectively according to the base data and set values of processing parameters.
Using the self-programming procedure containing proper coke quantity for the process calculation (shown in Fig. 1), a complete set of calculation data for the process of smelting reduction in a converter are obtained.

According to the results of simulation, it shows good agreement with experimental data.
From the data in the figure, it can be seen in different operating points the error between simulation result and experimental data is very small, generally within 5%.
Comparison of simulation results with experimental data.
Comparison of simulation results with experimental data.
All these simulation results compared with experimental data shows good agreement.

In this work, the RSM, simulated in FORTRAN, is validated with hot rolling experimental data for higher reductions.
The modified model was simulated and validated with hot rolling experimental data for hot rolling schedules at low strain rates and low reductions (<10%).
The modified model was then simulated in FORTRAN and validated with hot rolling experimental data for different hot rolling schedules at high reductions (up to 22.7%).
The required input data were rolling speed, furnace temperature, initial and final height of the specimen, and specimen width.
Results and Discussion The output of the FORTRAN codes shows temperature data for AISI316 specimen with different geometrical forms.

Make Huangbai criterion and sample data as weibull distribution to calculate similar.
Information reduction method description Information reduction method is based on rough set[3, 4].
Rough set theory can be regarded as a new mathematical tool for imperfect data analysis.
Rough set based data analysis starts from a data table called a decision table, columns of which are labeled by attributes, rows---by objects of interest and entries of the table are attribute values.
After calculated, the fuzzy centre data of recommended samples are(0.0694±0.0731, 0.1158±0.3044, 0.0131±0.0144); the fuzzy centre data of no-recommended samples are(0.1684±0.0983, 0.0318±0.0177, 0.0077±0.0083).

Basic Attribute Reduction Algorithm Based on Discriminability Matrix Algorithm Description The general steps of attribute reduction based on discriminability matrix are: firstly obtain the core of attribute reduction set with discriminability matrix and then to calculate attribute reduction set with reduction algorithm.
Improved Attribute Reduction Algorithm The minimum reduction is same as others reduction of an information system, which are both NP complex problems.
References [1] Chan P and Stolfo S. : On the Accuracy of Meta-learning for Scalable Data Mining, Journal of Intelligent Systems, vol. 8, 1997 pp.5-28
[2] Anannd S. : EDM: A General Framework for Data Mining Based on Evidence Theory, Data & Knowledge Engineering, vol. 18, 1996, pp.189-223
[5] Chen M: Data Mining: An Overview from a Database Perspective, IEEE Transactions on Knowledge and Data Engineering, vol. 8, 1996, pp.866-883.

Relevant literatures proposed electricity energy-saving key technical support system architecture, such as energy consumption, pollutant emissions supervision and inspection and data analysis support system, energy saving and emission reduction targets, evaluation and assessment technical support system[5].
Since the specific condition for each area is different, emission capacity can be calculated by substituting corresponding data into the simplified model.
Our data is mainly from Jilin Grid Inc.
To calculate the capacity for 2011, the data we need is as following: coal power for 2011’s Jilin is 5.684×1010kWh, or 1.7×107t for coal consumption, wind power is 3.968×109kWh, or 1.19×106t for coal consumption.
Limited by data, we can only calculate operational emission reduction capability for Jilin grid taking one year as the unit time.

Based on the statistical data of Changsha-Zhuzhou-Xiangtan (CZT) urban agglomeration from the year 2005 to 2010, the environmental learning curves of sulfur dioxide emission per 10 thousand Yuan (RMB) production value and per capita gross domestic product (GDP) were established, and sulfur dioxide emission reduction potential of these cities was analyzed.
Data Source.
All data used in this paper came from Statistical Yearbook of Hunan Province, Changsha Statistical Yearbook, Zhuzhou Statistical Yearbook, Xiangtan Statistical Yearbook and the web site of Hunan Provincial Statistics Bureau.
From the data, it also can be seen that the decrease of sulfur dioxide emission per 10 thousand Yuan production value in Changsha city is the largest, and then in Zhuzhou city.
Sulfur dioxide emission reduction potential.

Analyze and conclude the relationship data between existing-pipeline-controlled rainfall return period and runoff coefficient, by using source control.
Calculate and conclude the design scale of ecological measures and storage pools, draw the corresponding generalized model, by using peak flow reduction, volume reduction and flow reduction measures.
That includes existing sewer discharge return period improving, peak flow reduction, volume reduction, flow reduction etc.
Fig5 The melts generally model of flood volume reduction Determine the scale of flowing reduction mode Flowing reduction measures include stormwater detention zone, vegetation swales, storage pool etc.
Fig.7 The schematic picture of flood rate reduction Conclusion This paper presents several measures of runoff reduction, which includes existing sewer discharge return period improving, peak flow reduction, volume reduction, flow reduction etc. and analyzes the handling facilities scale of these various measures in Zhangjiakou city, which is selected as a study area.

In the course of this ongoing collaboration, the organization and dissemination of information and data play a key role.
Therefore, it is necessary to introduce data filtering function during the transmission of information flow between the various departments.
It has three main functions: (1) collect related data and information of disaster from the public, media and specific experts; (2) organize useful data; (3) send the necessary final filtered metadata, data and information to its subordinate.
In all stages of the disaster management process, the applied information and communication technologies includes the internet, information security, computer databases, integrated data management, intelligent data management, audit tools, multi-agent systems, multi-language support, data filtering, mobile communication technology, wired communication technologies, and wireless communication technologies.
Research on sharing of international disaster data and information, Disaster, vol.3, pp.109-113, 2008