@article {438,
	title = {A leakage monitoring system of pipeline in heating network},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {462 - 469},
	abstract = {Supervisory control and data acquisition system (SCADA) is used to monitor the operating data of heating network. But it can do data acquisition and some simple operations only. So in this paper with the data collected by SCADA a leakage monitoring system of pipeline in heating network is designed by the Matble graphical user interface program (GUI). The theoretical basis of this monitoring is the heating network pressure graph theory and the theory of genetic algorithm. With the theory of genetic algorithm the data col-lected by SCADA could be optimized. The optimization avoid the erroneous data which due to measurement error. With the heating network graph theory the leakage monitoring system of pipeline could identify if there is a leakage in the operating conditions in heating pipe network. In the leakage monitoring system, the first step is to collect the initial data which optimized by genetic algorithm as a standard data and these data are stored as a fixed value. After that this system collects the real-time data of heating network and optimizes these data. Finally, the system compares the optimizing real-time data and the fixed value to determine whether there is a leakage. With the experiment the monitoring system which designed in this paper is proved to be very useful in leakage discrimination.},
	keywords = {Genetic Algorithm, Heating Network, MATLAB, Pressure Graph},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84892175513\&partnerID=40\&md5=1c08ed8b399411798f88e31b5dd14cec},
	author = {Xianliang, Y. and Lianlian, J. and Songling, W.}
}
@article {388,
	title = {Production line production planning model of iron and steel enterprise},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {131 - 135},
	abstract = {At present, iron and steel enterprise develops towards the direction with many procedure, many process, many variety and many specification, and reaches hundreds and thousands of product series and product mix, how to plan, organize and control steel production, production schedule of its product line are key issues. According to the main characteristics of production line of iron and steel enterprise, how to make aggregate production planning of iron and steel enterprise are analyzed. An aggregate production plan model of product line of iron and steel enterprise is proposed, which is calculated by using standard library function of the Matlab7.0 genetic algorithm toolbox to program. Penalty function was adopted in the course of getting solution. These parameters of scale of father population, crossover probability, mutation probability and penalty factor were combined and optimized. The result obtained is in accordance with the actual conditions of this enterprise basically that the optimization solution to production plans of real iron and steel enterprise by using the algorithm.},
	keywords = {Aggregate production plan model, Genetic Algorithm, Index terms -iron and steel enterprise, MATLAB, Production line},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879110969\&partnerID=40\&md5=f4274b55fafe675f163de08cb3f49a87},
	author = {Jiang, G. and He, E. and Li, G. and Kong, J. and Xie, L.}
}
@article {90,
	title = {An algorithm for generating the design matrix for multivariate polynomial least squares fitting},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {225 - 230},
	abstract = {This paper presents an algorithm for generating the design matrix that is usually used for multivariate polynomial least square fitting. The design matrix is used in least squares fitting algorithm to construct a set of linear equations whose solution is the required polynomial coefficients. The developed algorithm was coded in MATLAB. The coded function named mv∝lyfit(X,Y,ord) accepts as inputs two matrices: the first argument is a 2D matrix of the independent variable X, the second argument is the dependent variable vector Y, and the last argument is the required degree of the fitting polynomial. The function returns the coefficient vector C and the design matrix A. Number of data points (k) needed for the function should be large enough for the solution of the set of linear equations to exist.},
	keywords = {Curve fitting, MATLAB, Multivariate polynomial fitting, Numerical algorithms},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-44949259846\&partnerID=40\&md5=70df949826a1c381c4c629a148ad7e1e},
	author = {Al-Hamdan, S.F.}
}
