针对k-means算法对于远离群点敏感和k值难以确定等缺陷,在分析已有的k-means改进算法的基础上,引进肘部法则的思想对数据进行优化处理并且根据自适应思想结合误差平方和SSE(sum of squared error),提出一种自适应调整k值的k-means改进...针对k-means算法对于远离群点敏感和k值难以确定等缺陷,在分析已有的k-means改进算法的基础上,引进肘部法则的思想对数据进行优化处理并且根据自适应思想结合误差平方和SSE(sum of squared error),提出一种自适应调整k值的k-means改进算法。选取机器学习库中的真实数据集进行仿真实验,其结果表明,改进后的k-means算法中的剔除远离群点和自适应调整k值的方法均可行,准确性高、聚类效果质量更优。展开更多
Purpose: This study describes a machine-learning approach utilizing patients' anatomical changes to predict parotid mean dose changes in fractionated radiotherapy for head-and-neck cancer, thereby facilitating pla...Purpose: This study describes a machine-learning approach utilizing patients' anatomical changes to predict parotid mean dose changes in fractionated radiotherapy for head-and-neck cancer, thereby facilitating plan adaptation decisions. Methods: Parotid mean dose changes during treatment sessions are assumed to correlate with patients’ anatomical changes, quantified by 65 geometrical features in four sets. SET1 is the parotid volumetric changes;SET2 is the distance changes from the parotid to the PTV;SET3 is the length of beam path changes between the parotid and skin near the neck;SET4 is the distance changes from the parotid to the two bony landmarks—the dens of the C2 and tip of the basilar part of the occipital bone. The introduced landmarks in SET4 are used as surrogates for the PTV in SET2 due to PTV’s unavailability at the simulation stage. Signed Euclidean distance is applied to quantify the distance and beam path length. A decision tree classifier to predict an x% increase in parotid mean dose is developed. In a study involving 18 patients (36 parotids) previously treated with adaptive radiotherapy, a leave-one-out cross-validation combined with enumerating 4 combinations of the 65 geometrical features is used to find a feature subset maximizing classifier’s accuracy. The classifier’s accuracy, with and without SET2’s PTV features inclusion, is evaluated to determine the SET4’s bony landmark surrogate feasibility. Results: Under x = 5% (or x = 10%) parotid mean dose increase: without SET2’s PTV features inclusion, one beam path feature from SET3 and one bony landmark feature from SET4 yield maximal accuracy of 86.1%, which is a 30.5% (19.4%) improvement over prevalence = 55.6% (66.7%);TPR = 87.5% (75%), TNR = 85% (91.7%), PPV = 82.3% (81.8%) and NPV = 89.5% (88%). With SET2’s PTV features inclusion, accuracy increases from 86.1% to 91.6%. Conclusion: Under the current 18 enrolled patients’ data, we found that the introduced SET4’s bony landmarks are feasible surrogates for the SET2�展开更多
自适应波束形成是机载预警雷达数字信号处理的一个关键环节。针对传统最小均方误差(least mean square,LMS)算法在短快拍数条件下的波束形成性能下降以及因迭代震荡易收敛于局部最优值的问题,提出了一种基于机器学习的随机方差减小梯度...自适应波束形成是机载预警雷达数字信号处理的一个关键环节。针对传统最小均方误差(least mean square,LMS)算法在短快拍数条件下的波束形成性能下降以及因迭代震荡易收敛于局部最优值的问题,提出了一种基于机器学习的随机方差减小梯度下降(stochastic variance reduction gradient descent,SVRGD)自适应波束形成方法。首先,建立面阵列接收信号数据模型。其次,基于随机梯度下降原理,引入方差缩减法通过内外循环迭代方式进行梯度修正,以减小随机梯度估计的方差,建立算法模型与实现流程。最后,通过设置平面阵列仿真场景,分析SVRGD自适应波束形成算法在波束形成、抗干扰、收敛速度等方面的性能,验证了该算法在低快拍数、强干扰和强噪声背景下具有的优良能力。展开更多
文摘针对k-means算法对于远离群点敏感和k值难以确定等缺陷,在分析已有的k-means改进算法的基础上,引进肘部法则的思想对数据进行优化处理并且根据自适应思想结合误差平方和SSE(sum of squared error),提出一种自适应调整k值的k-means改进算法。选取机器学习库中的真实数据集进行仿真实验,其结果表明,改进后的k-means算法中的剔除远离群点和自适应调整k值的方法均可行,准确性高、聚类效果质量更优。
文摘Purpose: This study describes a machine-learning approach utilizing patients' anatomical changes to predict parotid mean dose changes in fractionated radiotherapy for head-and-neck cancer, thereby facilitating plan adaptation decisions. Methods: Parotid mean dose changes during treatment sessions are assumed to correlate with patients’ anatomical changes, quantified by 65 geometrical features in four sets. SET1 is the parotid volumetric changes;SET2 is the distance changes from the parotid to the PTV;SET3 is the length of beam path changes between the parotid and skin near the neck;SET4 is the distance changes from the parotid to the two bony landmarks—the dens of the C2 and tip of the basilar part of the occipital bone. The introduced landmarks in SET4 are used as surrogates for the PTV in SET2 due to PTV’s unavailability at the simulation stage. Signed Euclidean distance is applied to quantify the distance and beam path length. A decision tree classifier to predict an x% increase in parotid mean dose is developed. In a study involving 18 patients (36 parotids) previously treated with adaptive radiotherapy, a leave-one-out cross-validation combined with enumerating 4 combinations of the 65 geometrical features is used to find a feature subset maximizing classifier’s accuracy. The classifier’s accuracy, with and without SET2’s PTV features inclusion, is evaluated to determine the SET4’s bony landmark surrogate feasibility. Results: Under x = 5% (or x = 10%) parotid mean dose increase: without SET2’s PTV features inclusion, one beam path feature from SET3 and one bony landmark feature from SET4 yield maximal accuracy of 86.1%, which is a 30.5% (19.4%) improvement over prevalence = 55.6% (66.7%);TPR = 87.5% (75%), TNR = 85% (91.7%), PPV = 82.3% (81.8%) and NPV = 89.5% (88%). With SET2’s PTV features inclusion, accuracy increases from 86.1% to 91.6%. Conclusion: Under the current 18 enrolled patients’ data, we found that the introduced SET4’s bony landmarks are feasible surrogates for the SET2�
