The software in engineering field, even in others filed become most important and more necessary in the 21 century, although there are many toolbox, such as fuzzy, chaos, …, etc., had been developed in the past, the grey toolbox still seldom and not quite completed. Therefore, in this paper, the focus is on the toolbox development of ordinal grey relational grade and cardinal grey relational grade by using Matlab, to development the completed grey relational grade toolbox. Firstly, we preview the whole mathematical foundation of grey relational grade in detail. Secondly, the mathematics model of ten kinds of grey relational grade, and two types of grey relational grades (ordinal and cardinal) are presented. Thirdly, based on the mathematics model, we use Matlab to develop the grey relational grade toolbox. As the results, in this paper, ten kinds of grey relational grade methods, and two types of grey relational grades (ordinal and cardinal) are developed, we can say this kind of completed research is the main contribution and is the first research in grey relational grade. Also can extends in the economy in benefit, for our research, the market price of the whole grey relational grade toolbox with our evaluation; at least will over US 500 dollars. To sum up, we hope this completed grey relational grade toolbox is not only to enhance the depth research of grey relation grade, buy also become a new approach for the applications in grey system theory.
For any system in the real world, it is known that the original data is always random. Therefore, before it can be operational, the data must be retreated; otherwise, there may be errors in the results. In grey system theory, it contains a large amount of data and complex mathematical operation; the application software design of grey system theory in the past was written individually. Though this way could solve the problem for the moment, it is not very sufficient for the generating in using the scope of the aspect. In this paper, we develop the whole grey generating model toolbox in grey system theory, to cooperate with the study and application of in real system. Based on the above-mentioned reasons, we present four grey generating methods. By adopting Matlab, we create the idea of computer toolbox for grey system theory. As the results, in this paper, four kinds of grey generating methods are developed, we can say this completed research could be one of the main contributions and is the first research in grey generating. For our research, we get several important results: This completed grey generating toolbox to enhance the depth research of grey system; In addition to reduce time of analyzing and promote the analyzing level; Guide the grey system theory to the internationalization and reach the research of academy to promote the teaching.
Previous studies have shown that there was a relatively large amount of uncertainty along the major wind direction in the results of locating emission sources using the one-dimensional radial plume mapping (RPM(1D)) technique based on optical remote sensing measurements. This paper proposes setting up an additional monitoring line that is perpendicular to the original scanning beam geometry to reduce this uncertainty. We first conducted a computer simulation study using the Gaussian dispersion model to generate the downwind concentrations of plumes from 400 source locations in a 201 m × 201 m spatial domain under various wind directions (n = 181). The optical remote sensing instrument was assumed to be at (0, 0) with two perpendicular monitoring lines, each of which had three beam segments of equal length. Each pair of the reconstructed downwind concentration profiles was then used to trace back to the source locations. The results showed that the accuracy of the method and its uncertainty were improved by using the proposed two-line RPM(1D) approach rather than the original one-line RPM(1D) approach at most simulated source locations. In a follow-up field experiment, a tracer gas was released at the coordinate of (100, 100). The release location was covered within the 0.25- to 0.5-probability area of the estimated results, and the distance between the actual and estimated source locations was 18.4 m (9.2% of the longest beam path).
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