The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was need...The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measured are, the more accura te the MRA is, too more temperatures will hinder the analysis calculation. So it is necessary to identify the key temperatures of the machine tool. The selectio n of key temperatures decides the efficiency and precision of MRA. Because of th e complexities and multi-input and multi-output structure of the relationships , the exact quantitative portions as well as the unclear portions must be taken into consideration together to improve the identification of key temperatures. I n this paper, a fuzzy cluster analysis was used to select the key temperatures. The substance of identifying the key temperatures is to group all temperatures b y their relativity, and then to select a temperature from each group as the repr esentation. A fuzzy cluster analysis can uncover the relationships between t he thermal field and deformations more truly and thoroughly. A fuzzy cluster ana lysis is the cluster analysis based on fuzzy sets. Given U={u i|i=0,...,N}, in which u i is the temperature measured, a fuzzy matrix R can be obta ined. The transfer close package t(R) can be deduced from R. A fuzzy clu ster of U then conducts on the basis of t(R). Based on the fuzzy cluster analysis discussed above, this paper identified the k ey temperatures of a horizontal machining center. The number of the temperatures measured was reduced to 4 from 32, and then the multiple regression relationshi p models between the 4 temperatures and the thermal deformations of the spindle were drawn. The remnant errors between the regression models and measured deform ations reached a satisfying low level. At the same time, the decreasing of tempe rature variable number improved the efficiency of measure and analysis greatly.展开更多
Identification of plant-pathogenic fungi is time-consuming due to cultivation and microscopic examination and can be influenced by the interpretation of the micro-morphological characters observed.The present investig...Identification of plant-pathogenic fungi is time-consuming due to cultivation and microscopic examination and can be influenced by the interpretation of the micro-morphological characters observed.The present investigation aimed to create a simple but sophisticated method for the identification of plant-pathogenic fungi by Fourier transform infrared(FTIR)spectroscopy.In this study,FTIR-attenuated total reflectance(ATR)spectroscopy was used in combination with chemometric analysis for identification of important pathogenic fungi of horticultural plants.Mixtures of mycelia and spores from 27fungal strains belonging to nine different families were collected from liquid PD or solid PDA media cultures and subjected to FTIR-ATR spectroscopy measurements.The FTIR-ATR spectra ranging from 4 000to 400cm-1 were obtained.To classify the FTIRATR spectra,cluster analysis was compared with canonical vitiate analysis(CVA)in the spectral regions of3 050~2 800and 1 800~900cm-1.Results showed that the identification accuracies achieved 97.53%and99.18%for the cluster analysis and CVA analysis,respectively,demonstrating the high potential of this technique for fungal strain identification.展开更多
为了提高电力负荷监控的准确性,研究融合主成分含噪密度聚类(density-based spatial clustering of applications with noise with principal component analysis,PCADBSCAN)的混合非侵入式负荷辨识方法。首先,针对原始负荷特征维度较...为了提高电力负荷监控的准确性,研究融合主成分含噪密度聚类(density-based spatial clustering of applications with noise with principal component analysis,PCADBSCAN)的混合非侵入式负荷辨识方法。首先,针对原始负荷特征维度较高的问题,采用主成分分析算法对原始特征数据降维,构建负荷特征模板库,同时,获取负荷电流波形,构建负荷电流模板库。其次,采用基于密度的聚类算法对负荷特征模板库内的样本进行非监督聚类,提取各聚类簇中心。然后,计算待辨识负荷与各特征模板库聚类中心的欧式距离,完成负荷特征匹配,并计算待辨识负荷的电流波形与电流模板库内各电流波形的综合关联度,完成负荷电流波形匹配。最后,混合两次匹配结果,综合判断待辨识负荷,从而实现高可靠辨识。基于用电数据测试数据集的仿真结果显示,该方法各项指标均超过96%。展开更多
文摘The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measured are, the more accura te the MRA is, too more temperatures will hinder the analysis calculation. So it is necessary to identify the key temperatures of the machine tool. The selectio n of key temperatures decides the efficiency and precision of MRA. Because of th e complexities and multi-input and multi-output structure of the relationships , the exact quantitative portions as well as the unclear portions must be taken into consideration together to improve the identification of key temperatures. I n this paper, a fuzzy cluster analysis was used to select the key temperatures. The substance of identifying the key temperatures is to group all temperatures b y their relativity, and then to select a temperature from each group as the repr esentation. A fuzzy cluster analysis can uncover the relationships between t he thermal field and deformations more truly and thoroughly. A fuzzy cluster ana lysis is the cluster analysis based on fuzzy sets. Given U={u i|i=0,...,N}, in which u i is the temperature measured, a fuzzy matrix R can be obta ined. The transfer close package t(R) can be deduced from R. A fuzzy clu ster of U then conducts on the basis of t(R). Based on the fuzzy cluster analysis discussed above, this paper identified the k ey temperatures of a horizontal machining center. The number of the temperatures measured was reduced to 4 from 32, and then the multiple regression relationshi p models between the 4 temperatures and the thermal deformations of the spindle were drawn. The remnant errors between the regression models and measured deform ations reached a satisfying low level. At the same time, the decreasing of tempe rature variable number improved the efficiency of measure and analysis greatly.
基金the National Natural Science Foundation of China(31201473)the Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences(CAAS-ASTIP-IVFCAAS)funded by the Key Laboratory of Biology and Genetic Improvement of Horticultural Crops,Ministry of Agriculture,P.R.China
文摘Identification of plant-pathogenic fungi is time-consuming due to cultivation and microscopic examination and can be influenced by the interpretation of the micro-morphological characters observed.The present investigation aimed to create a simple but sophisticated method for the identification of plant-pathogenic fungi by Fourier transform infrared(FTIR)spectroscopy.In this study,FTIR-attenuated total reflectance(ATR)spectroscopy was used in combination with chemometric analysis for identification of important pathogenic fungi of horticultural plants.Mixtures of mycelia and spores from 27fungal strains belonging to nine different families were collected from liquid PD or solid PDA media cultures and subjected to FTIR-ATR spectroscopy measurements.The FTIR-ATR spectra ranging from 4 000to 400cm-1 were obtained.To classify the FTIRATR spectra,cluster analysis was compared with canonical vitiate analysis(CVA)in the spectral regions of3 050~2 800and 1 800~900cm-1.Results showed that the identification accuracies achieved 97.53%and99.18%for the cluster analysis and CVA analysis,respectively,demonstrating the high potential of this technique for fungal strain identification.
文摘为了提高电力负荷监控的准确性,研究融合主成分含噪密度聚类(density-based spatial clustering of applications with noise with principal component analysis,PCADBSCAN)的混合非侵入式负荷辨识方法。首先,针对原始负荷特征维度较高的问题,采用主成分分析算法对原始特征数据降维,构建负荷特征模板库,同时,获取负荷电流波形,构建负荷电流模板库。其次,采用基于密度的聚类算法对负荷特征模板库内的样本进行非监督聚类,提取各聚类簇中心。然后,计算待辨识负荷与各特征模板库聚类中心的欧式距离,完成负荷特征匹配,并计算待辨识负荷的电流波形与电流模板库内各电流波形的综合关联度,完成负荷电流波形匹配。最后,混合两次匹配结果,综合判断待辨识负荷,从而实现高可靠辨识。基于用电数据测试数据集的仿真结果显示,该方法各项指标均超过96%。
文摘为了提高辨识稳定图中真实模态的准确性与自动化程度,首先,从稳定点定义方式的角度论述了聚类算法效果欠佳的原因,并采用异阶系统非等权重的定义方式输出稳定点;其次,基于数据挖掘思想,采用改进的辨识聚类结构的有序点(ordering points to identify the clustering structure,简称OPTICS)算法自动清洗稳定点集,通过遍历性搜索的方式确定输入参数;然后,提出结合度矩阵去噪的自适应局部密度谱聚类(local density adaptive spectral clustering,简称SC-DA)算法分析稳定点集,并以簇中值作为模态参数的代表值,实现模态参数的自动化识别;最后,将含有密集模态的外滩大桥作为识别对象进行试验验证。试验结果表明:所提出方法具有较高的精度,与频域分解(frequency domain decomposition,简称FDD)法的频率结果最大相差仅为0.012 3 Hz,且在线识别的准确率达到82.86%,显著高于基于层次聚类的自动识别方法,实现了无人工干预下模态参数的自动、准确识别,具有一定的工程应用前景。
