煤炭是我国能源资源安全的压舱石,“双碳”背景下实现煤炭清洁加工与高效利用意义重大,而灰分检测对煤炭清洁化和智能化发展尤为重要。针对现有灰分检测存在的检测精度有待提高的突出问题,以两淮矿区典型煤样为研究对象,通过慢灰和X射...煤炭是我国能源资源安全的压舱石,“双碳”背景下实现煤炭清洁加工与高效利用意义重大,而灰分检测对煤炭清洁化和智能化发展尤为重要。针对现有灰分检测存在的检测精度有待提高的突出问题,以两淮矿区典型煤样为研究对象,通过慢灰和X射线荧光(X ray fluorescence,XRF)测试系统地探究了煤样的灰分和元素组成分布规律,并结合机器学习理论构建了灰分-元素特征数据集;结合灰色系统理论和新陈代谢算法,构建了自适应的GM(1,N)动态网络灰分拟合优化模型,并详细设计了动态网络算法流程;提出了GM(1,N)动态模型的关键超参数,并通过与常规拟合方法对比,全面地评价了模型拟合性能。结果表明:两淮矿区煤可视为由可燃元素和成灰元素共同构成,且成灰元素中质量分数占比最高为Si和Al,次之为S、Fe和Ca等,最少为P和Cl等,并且煤中成灰元素总含量与灰分呈正相关,而可燃元素与之相反;以灰分为标签值、以组成元素为特征值,形成了煤的灰分-元素特征数据集;以样本数据划分→动态网络灰分拟合→模型评价机制→动态拟合模型自适应优化→鲁棒性提升→多轮迭代优化为主线设计了GM(1,N)动态网络灰分拟合模型及其算法流程,有效提升了数据集稳定性和新鲜度,并且迭代收敛速度快,灰分误差阈值5%时其准确率达100%;对比经典GM(1,N)模型和常规多元线性回归模型,证明了新模型的灰分拟合性能得到显著提升,其相对误差为0.16%~4.96%、误差均值仅2.29%。展开更多
To create a new prediction model, the unbiased GM （1,1） model is optimized by the five-point slide method in this paper. Then, based on the occurrence areas of dce blast in Enshi District during 1995 -2004, the new ...To create a new prediction model, the unbiased GM （1,1） model is optimized by the five-point slide method in this paper. Then, based on the occurrence areas of dce blast in Enshi District during 1995 -2004, the new model and unbiased GM （1, 1 ） model are applied to predict the occurrence areas of rice blast during 2005 -2010. Predicting outcomes show that the prediction accuracy of five-point unbiased sliding optimized GM （1, 1 ） model is higher than the unbiased GM （1,1） model. Finally, combined with the prediction results, the author provides some suggestion for Enshi District in the prevention and control of rice blast in 2010.展开更多
This paper expresses the efficient outputs of decisionmaking unit（DMU） as the sum of ＂average outputs＂ forecasted by a GM（1,N） model and ＂increased outputs＂ which reflect the difficulty to realize efficient ou...This paper expresses the efficient outputs of decisionmaking unit（DMU） as the sum of ＂average outputs＂ forecasted by a GM（1,N） model and ＂increased outputs＂ which reflect the difficulty to realize efficient outputs.The increased outputs are solved by linear programming using data envelopment analysis efficiency theories,wherein a new sample is introduced whose inputs are equal to the budget in the issue No.n ＋ 1 and outputs are forecasted by the GM（1,N） model.The shortcoming in the existing methods that the forecasted efficient outputs may be less than the possible actual outputs according to developing trends of input-output rate in the periods of pre-n is overcome.The new prediction method provides decision-makers with more decisionmaking information,and the initial conditions are easy to be given.展开更多
The Coefficient of Variation(CV)of hectometer yarn's weight is one of the guidelines to evaluate its intrinsic quality.In the spinning manufacturing,the control of cotton yarn's weight unevenness is accomplish...The Coefficient of Variation(CV)of hectometer yarn's weight is one of the guidelines to evaluate its intrinsic quality.In the spinning manufacturing,the control of cotton yarn's weight unevenness is accomplished mainly in terms of a spot-check on semi-product and a succedent adjust in process parameters during spinning based on technicians' experience.However,it is theoretically believed among manufacturers that with fixed technical levels and parameters in the spinning process,the quality parameters of assorted cotton have a certain influence on the CV.In order to find out a rule of the influence that assorted cotton has on the CV,a GM(1,N)model,correlated raw cotton's quality parameter with the CV,has firstly been developed according to the modeling theory of grey system,and then been applied in the designing step to predict the CV.It has been approved by practical modeling and validation that the model could fit preferably an accrual CV value,and provide a method of quantitative predicting analysis for textile manufacturers to design cotton yarn's quality.展开更多
文摘煤炭是我国能源资源安全的压舱石,“双碳”背景下实现煤炭清洁加工与高效利用意义重大,而灰分检测对煤炭清洁化和智能化发展尤为重要。针对现有灰分检测存在的检测精度有待提高的突出问题,以两淮矿区典型煤样为研究对象,通过慢灰和X射线荧光(X ray fluorescence,XRF)测试系统地探究了煤样的灰分和元素组成分布规律,并结合机器学习理论构建了灰分-元素特征数据集;结合灰色系统理论和新陈代谢算法,构建了自适应的GM(1,N)动态网络灰分拟合优化模型,并详细设计了动态网络算法流程;提出了GM(1,N)动态模型的关键超参数,并通过与常规拟合方法对比,全面地评价了模型拟合性能。结果表明:两淮矿区煤可视为由可燃元素和成灰元素共同构成,且成灰元素中质量分数占比最高为Si和Al,次之为S、Fe和Ca等,最少为P和Cl等,并且煤中成灰元素总含量与灰分呈正相关,而可燃元素与之相反;以灰分为标签值、以组成元素为特征值,形成了煤的灰分-元素特征数据集;以样本数据划分→动态网络灰分拟合→模型评价机制→动态拟合模型自适应优化→鲁棒性提升→多轮迭代优化为主线设计了GM(1,N)动态网络灰分拟合模型及其算法流程,有效提升了数据集稳定性和新鲜度,并且迭代收敛速度快,灰分误差阈值5%时其准确率达100%;对比经典GM(1,N)模型和常规多元线性回归模型,证明了新模型的灰分拟合性能得到显著提升,其相对误差为0.16%~4.96%、误差均值仅2.29%。
基金Supported by Science Research Project of Department of Education of Hubei Province (B20092901)~~
文摘To create a new prediction model, the unbiased GM （1,1） model is optimized by the five-point slide method in this paper. Then, based on the occurrence areas of dce blast in Enshi District during 1995 -2004, the new model and unbiased GM （1, 1 ） model are applied to predict the occurrence areas of rice blast during 2005 -2010. Predicting outcomes show that the prediction accuracy of five-point unbiased sliding optimized GM （1, 1 ） model is higher than the unbiased GM （1,1） model. Finally, combined with the prediction results, the author provides some suggestion for Enshi District in the prevention and control of rice blast in 2010.
基金supported by the Research Start Funds for Introducing High-level Talents of North China University of Water Resources and Electric Power
文摘This paper expresses the efficient outputs of decisionmaking unit（DMU） as the sum of ＂average outputs＂ forecasted by a GM（1,N） model and ＂increased outputs＂ which reflect the difficulty to realize efficient outputs.The increased outputs are solved by linear programming using data envelopment analysis efficiency theories,wherein a new sample is introduced whose inputs are equal to the budget in the issue No.n ＋ 1 and outputs are forecasted by the GM（1,N） model.The shortcoming in the existing methods that the forecasted efficient outputs may be less than the possible actual outputs according to developing trends of input-output rate in the periods of pre-n is overcome.The new prediction method provides decision-makers with more decisionmaking information,and the initial conditions are easy to be given.
基金Hunan Provincial Basic Science Foundation of China(No.2007FJ3046)Key Scientific Research Fundof Hunan Provincial Education Department,China(No.07A048)
文摘The Coefficient of Variation(CV)of hectometer yarn's weight is one of the guidelines to evaluate its intrinsic quality.In the spinning manufacturing,the control of cotton yarn's weight unevenness is accomplished mainly in terms of a spot-check on semi-product and a succedent adjust in process parameters during spinning based on technicians' experience.However,it is theoretically believed among manufacturers that with fixed technical levels and parameters in the spinning process,the quality parameters of assorted cotton have a certain influence on the CV.In order to find out a rule of the influence that assorted cotton has on the CV,a GM(1,N)model,correlated raw cotton's quality parameter with the CV,has firstly been developed according to the modeling theory of grey system,and then been applied in the designing step to predict the CV.It has been approved by practical modeling and validation that the model could fit preferably an accrual CV value,and provide a method of quantitative predicting analysis for textile manufacturers to design cotton yarn's quality.
