Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart d...Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart disease,but more remains to be accomplished.The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches.By using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single feature.We processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data quality.Furthermore,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the feature.To reduce the dimensionality,we subsequently used PCA with 95%variation.To identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble models.The model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested approach.This illustrates how interaction-focused feature analysis can produce precise and useful insights for heart disease diagnosis.展开更多
为研究谐波成分对电-热工作环境下干式阀侧套管内部绝缘用环氧树脂材料的绝缘特性影响,采用多阶谐波叠加负极性直流偏置电压模拟干式阀侧套管运行波形,在80~120℃的温度范围内研究了不同谐波成分对环氧树脂绝缘劣化特性的影响。研究结...为研究谐波成分对电-热工作环境下干式阀侧套管内部绝缘用环氧树脂材料的绝缘特性影响,采用多阶谐波叠加负极性直流偏置电压模拟干式阀侧套管运行波形,在80~120℃的温度范围内研究了不同谐波成分对环氧树脂绝缘劣化特性的影响。研究结果表明,谐波与高温均对环氧树脂的绝缘劣化起到促进作用,在电-热工作环境下多阶谐波成分的引入环氧树脂劣化的引发电压降低了40.0%,试样的绝缘失效时间降低了64.8%,同时谐波成分导致绝缘劣化面积扩大,基于场驱动电树枝生长模型(field-driven tree growth,FDTG)的分析,高阶谐波成分的引入使电树生长速率系数增高了190.9%,电树生长临界场强降低到17.88 kV/mm,最后通过仿真计算说明多谐波情况下材料介质损耗发热加剧,针对长时工作设备需进一步关注局部过热问题。展开更多
针对目前电子式电流互感器谐波计量中谐波成分复杂,使实际测量中受噪声和异常值干扰,导致谐波计量结果准确性下降的问题,通过引入DBSCAN(dens ity-based spatial clustering of applications with noise)聚类,提出一种基于DBSCAN聚类的...针对目前电子式电流互感器谐波计量中谐波成分复杂,使实际测量中受噪声和异常值干扰,导致谐波计量结果准确性下降的问题,通过引入DBSCAN(dens ity-based spatial clustering of applications with noise)聚类,提出一种基于DBSCAN聚类的电子式电流互感器谐波计量算法,以有效识别并排除数据集中的噪声点和异常值,从而提高谐波成分检测的准确性.首先,采集电子式电流互感器电流信号,根据时频能量峰值获取互感器中各电流信号峰值频率;其次,通过DBSCAN聚类算法计算各电流信号峰值频率之间的距离,根据距离确定噪声信号、非谐波信号以及不同类型的谐波信号,排除数据集中的噪声点和异常值;最后,利用最小二乘法对各类谐波信号的幅值和相位进行计量,得到电流互感器谐波计量结果.实验结果表明,当时间为2 s时,谐波相位的实际值为18°,该算法的谐波相位为18°,始终与实际结果一致,对谐波幅值和相位的计量准确性均较高,表明该算法能有效提升谐波计量精度,避免受噪声和异常值干扰.展开更多
基金supported by the Competitive Research Fund of the University of Aizu,Japan(Grant No.P-13).
文摘Heart disease includes a multiplicity of medical conditions that affect the structure,blood vessels,and general operation of the heart.Numerous researchers have made progress in correcting and predicting early heart disease,but more remains to be accomplished.The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches.By using data fusion from several regions of the country,we intend to increase the accuracy of heart disease prediction.A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern in the data,which cannot be adequately captured by a single feature.We processed the data using techniques including feature scaling,outlier detection and replacement,null and missing value imputation,and more to improve the data quality.Furthermore,the proposed feature engineering method uses the correlation test for numerical features and the chi-square test for categorical features to interact with the feature.To reduce the dimensionality,we subsequently used PCA with 95%variation.To identify patients with heart disease,hyperparameter-based machine learning algorithms like RF,XGBoost,Gradient Boosting,LightGBM,CatBoost,SVM,and MLP are utilized,along with ensemble models.The model’s overall prediction performance ranges from 88%to 92%.In order to attain cutting-edge results,we then used a 1D CNN model,which significantly enhanced the prediction with an accuracy score of 96.36%,precision of 96.45%,recall of 96.36%,specificity score of 99.51%and F1 score of 96.34%.The RF model produces the best results among all the classifiers in the evaluation matrix without feature interaction,with accuracy of 90.21%,precision of 90.40%,recall of 90.86%,specificity of 90.91%,and F1 score of 90.63%.Our proposed 1D CNN model is 7%superior to the one without feature engineering when compared to the suggested approach.This illustrates how interaction-focused feature analysis can produce precise and useful insights for heart disease diagnosis.
文摘为研究谐波成分对电-热工作环境下干式阀侧套管内部绝缘用环氧树脂材料的绝缘特性影响,采用多阶谐波叠加负极性直流偏置电压模拟干式阀侧套管运行波形,在80~120℃的温度范围内研究了不同谐波成分对环氧树脂绝缘劣化特性的影响。研究结果表明,谐波与高温均对环氧树脂的绝缘劣化起到促进作用,在电-热工作环境下多阶谐波成分的引入环氧树脂劣化的引发电压降低了40.0%,试样的绝缘失效时间降低了64.8%,同时谐波成分导致绝缘劣化面积扩大,基于场驱动电树枝生长模型(field-driven tree growth,FDTG)的分析,高阶谐波成分的引入使电树生长速率系数增高了190.9%,电树生长临界场强降低到17.88 kV/mm,最后通过仿真计算说明多谐波情况下材料介质损耗发热加剧,针对长时工作设备需进一步关注局部过热问题。
文摘针对不同磁密幅值、频率、谐波组合等复杂激励工况下磁致伸缩建模面临的精准性问题,该文利用空间注意力机制(spatial attention mechanism,SAM)对传统的卷积神经网络(convolutional neural network,CNN)进行改进,将SAM嵌套入CNN网络中,建立SAMCNN改进型网络。再结合双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络,提出电工钢片SAMCNN-BiLSTM磁致伸缩模型。首先,利用灰狼优化算法(grey wolf optimization,GWO)寻优神经网络结构的参数,实现复杂工况下磁致伸缩效应的准确表征;然后,建立中低频范围单频与叠加谐波激励等复杂工况下的磁致伸缩应变数据库,开展数据预处理与特征分析;最后,对SAMCNN-BiLSTM模型开展对比验证。对比叠加3次谐波激励下的磁致伸缩应变频谱主要分量,SAMCNN-BiLSTM模型计算值最大相对误差为3.70%,其比Jiles-Atherton-Sablik(J-A-S)、二次畴转等模型能更精确地表征电工钢片的磁致伸缩效应。
文摘针对目前电子式电流互感器谐波计量中谐波成分复杂,使实际测量中受噪声和异常值干扰,导致谐波计量结果准确性下降的问题,通过引入DBSCAN(dens ity-based spatial clustering of applications with noise)聚类,提出一种基于DBSCAN聚类的电子式电流互感器谐波计量算法,以有效识别并排除数据集中的噪声点和异常值,从而提高谐波成分检测的准确性.首先,采集电子式电流互感器电流信号,根据时频能量峰值获取互感器中各电流信号峰值频率;其次,通过DBSCAN聚类算法计算各电流信号峰值频率之间的距离,根据距离确定噪声信号、非谐波信号以及不同类型的谐波信号,排除数据集中的噪声点和异常值;最后,利用最小二乘法对各类谐波信号的幅值和相位进行计量,得到电流互感器谐波计量结果.实验结果表明,当时间为2 s时,谐波相位的实际值为18°,该算法的谐波相位为18°,始终与实际结果一致,对谐波幅值和相位的计量准确性均较高,表明该算法能有效提升谐波计量精度,避免受噪声和异常值干扰.
