针对因风电场机组异常数据而导致风电功率预测精度下降的问题,文章提出一种基于密度噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法加上最小绝对残差(least absolute residual,LAR)法的风...针对因风电场机组异常数据而导致风电功率预测精度下降的问题,文章提出一种基于密度噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法加上最小绝对残差(least absolute residual,LAR)法的风电场数据清洗方法。首先利用DBSCAN算法识别分散型异常数据,然后基于LAR方法构建堆积型异常数据识别模型,分别实现对风电场分散型异常数据和堆积型异常数据的清洗,最后通过Pearson相关系数和反向传播神经网络预测模型验证所提方法的效果。结果表明,基于DBSCAN+LAR的风电场数据清洗方法能有效减小风电功率预测误差。展开更多
Inadvertent Lead Malposition in Left Ventricle is a rare and underdiagnosed incident, which may occur during implantation of cardiac electronic devices and may remain asymptomatic. We reported the case of a 71-year-ol...Inadvertent Lead Malposition in Left Ventricle is a rare and underdiagnosed incident, which may occur during implantation of cardiac electronic devices and may remain asymptomatic. We reported the case of a 71-year-old man who was implanted with a ventricular single-chamber pacemaker for a slow atrial fibrillation with syncope and whose routine transthoracic echocardiography 23 months after implantation displayed a malposition of the pacemaker lead into the Left Ventricle through a patent foramen oval. The patient was asymptomatic. The electrocardiogram showed right bundle branch block QRS-paced morphology with a positive QRS pattern in V1, a median paced QRS axis on the frontal plane at -120°, a Precordial transition on V5. At the lateral Chest X-ray the lead curved backwards to the spine. Given the age of this old patient who already received oral anticoagulant for Atrial Fibrillation and the Lead malposition discovered 23 months after pacemaker’s implantation, we decided to maintain the lead in LV and continue anticoagulation.展开更多
针对软件可靠性早期预测中软件复杂性度量属性维数灾难问题,提出了一种基于最小绝对值压缩与选择方法(The Least Absolute Shrinkage and Select Operator,LASSO)和最小角回归(Least Angle Regression,LARS)算法的软件复杂性度量属性特...针对软件可靠性早期预测中软件复杂性度量属性维数灾难问题,提出了一种基于最小绝对值压缩与选择方法(The Least Absolute Shrinkage and Select Operator,LASSO)和最小角回归(Least Angle Regression,LARS)算法的软件复杂性度量属性特征选择方法。该方法筛选掉一些对早期预测结果影响较小的软件复杂性度量属性,得到与早期预测关系最为密切的关键属性子集。首先分析了LASSO回归方法的特点及其在特征选择中的应用,然后对LARS算法进行了修正,使其可以解决LASSO方法所涉及的问题,得到相关的复杂性度量属性子集。最后结合学习向量量化(Learning Vector Quantization,LVQ)神经网络进行软件可靠性早期预测,并基于十折交叉方法进行实验。通过与传统特征选择方法相比较,证明所提方法可以显著提高软件可靠性早期预测精度。展开更多
文摘针对因风电场机组异常数据而导致风电功率预测精度下降的问题,文章提出一种基于密度噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法加上最小绝对残差(least absolute residual,LAR)法的风电场数据清洗方法。首先利用DBSCAN算法识别分散型异常数据,然后基于LAR方法构建堆积型异常数据识别模型,分别实现对风电场分散型异常数据和堆积型异常数据的清洗,最后通过Pearson相关系数和反向传播神经网络预测模型验证所提方法的效果。结果表明,基于DBSCAN+LAR的风电场数据清洗方法能有效减小风电功率预测误差。
文摘Inadvertent Lead Malposition in Left Ventricle is a rare and underdiagnosed incident, which may occur during implantation of cardiac electronic devices and may remain asymptomatic. We reported the case of a 71-year-old man who was implanted with a ventricular single-chamber pacemaker for a slow atrial fibrillation with syncope and whose routine transthoracic echocardiography 23 months after implantation displayed a malposition of the pacemaker lead into the Left Ventricle through a patent foramen oval. The patient was asymptomatic. The electrocardiogram showed right bundle branch block QRS-paced morphology with a positive QRS pattern in V1, a median paced QRS axis on the frontal plane at -120°, a Precordial transition on V5. At the lateral Chest X-ray the lead curved backwards to the spine. Given the age of this old patient who already received oral anticoagulant for Atrial Fibrillation and the Lead malposition discovered 23 months after pacemaker’s implantation, we decided to maintain the lead in LV and continue anticoagulation.
