Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support v...Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.展开更多
针对全波段光谱技术的生鲜猪肉综合品质快速无损分类存在光谱数据量大、样本数量较少时分类准确率较低等缺点。该文提出了一种基于偏最小二乘(partial least squares,PLS)投影分析算法和支持向量机的生鲜猪肉综合品质分类器。利用基于...针对全波段光谱技术的生鲜猪肉综合品质快速无损分类存在光谱数据量大、样本数量较少时分类准确率较低等缺点。该文提出了一种基于偏最小二乘(partial least squares,PLS)投影分析算法和支持向量机的生鲜猪肉综合品质分类器。利用基于偏最小二乘投影分析算法对全波段光谱数据进行数据降维,选取了13个特征波长。利用粒子群优化算法优化支持向量机惩罚参数和径向基核函数参数,优化后二者最优为4.939和0.01。利用选取的特征波长和优化后的参数建立了生鲜猪肉综合品质支持向量分类器。研究结果表明,分类器对训练集中白肌肉(pale,soft and exudative,PSE)、正常肉(reddish-pink,firm and non-exudative,RFN)和黑干肉(dark,firm and dry,DFD)的回判识别率分别为为88.46%、94.11%和92.31%;测试集中PSE、RFN和DFD预测正确率分别为84.62%、94.11%和84.62%。该分类器满足模型简单、预测准确率高等优点,为生鲜猪肉综合品质在线分级提供参考。展开更多
基金Projects(2013BAB02B01,2013BAB02B03)supported by the National Key Technologies R&D Program of ChinaProjects(41072224,41272347)supported by the National Natural Science Foundation of China
文摘Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.
文摘针对全波段光谱技术的生鲜猪肉综合品质快速无损分类存在光谱数据量大、样本数量较少时分类准确率较低等缺点。该文提出了一种基于偏最小二乘(partial least squares,PLS)投影分析算法和支持向量机的生鲜猪肉综合品质分类器。利用基于偏最小二乘投影分析算法对全波段光谱数据进行数据降维,选取了13个特征波长。利用粒子群优化算法优化支持向量机惩罚参数和径向基核函数参数,优化后二者最优为4.939和0.01。利用选取的特征波长和优化后的参数建立了生鲜猪肉综合品质支持向量分类器。研究结果表明,分类器对训练集中白肌肉(pale,soft and exudative,PSE)、正常肉(reddish-pink,firm and non-exudative,RFN)和黑干肉(dark,firm and dry,DFD)的回判识别率分别为为88.46%、94.11%和92.31%;测试集中PSE、RFN和DFD预测正确率分别为84.62%、94.11%和84.62%。该分类器满足模型简单、预测准确率高等优点,为生鲜猪肉综合品质在线分级提供参考。
