Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR ...Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.展开更多
直流输电线路故障行波波速不确定、波头提取困难以及噪声干扰等因素制约了直流电网中故障测距技术的应用。为了降低上述因素对定位准确性的影响,提出一种基于局部特征有理样条插值均值分解(LMD based on characteristic rational spline...直流输电线路故障行波波速不确定、波头提取困难以及噪声干扰等因素制约了直流电网中故障测距技术的应用。为了降低上述因素对定位准确性的影响,提出一种基于局部特征有理样条插值均值分解(LMD based on characteristic rational spline,CRS-LMD)和奇异值分解(singular value decomposition,SVD)的故障测距方法。首先,利用特征尺度选取最优极点系数,结合有理样条插值调节拟合曲线的松紧程度,实现对故障电压行波的局部均值分解。其次,采用奇异值分解对故障行波波头进行准确提取。最后,在PSCAD/EMTDC中搭建了张北±500 kV柔性直流电网的仿真模型,模拟各种故障情况并输出故障数据,利用Matlab对故障数据进行处理并验证定位算法。最后,仿真结果表明,所提故障测距算法在不同故障距离和故障类型下均能实现故障测距,且在叠加噪声和过渡电阻的情况下也能保障较高的精确性。展开更多
针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别...针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。展开更多
为了更有效分析管道信号,提出一种基于采用在恶劣环境下的遗传算法(Genetic Algorithms in Harsh Environments,GAHE)优化变分模态分解(Variational Mode Decomposition,VMD)联合奇异值分解(Singular Value Decomposition,SVD)与选择性...为了更有效分析管道信号,提出一种基于采用在恶劣环境下的遗传算法(Genetic Algorithms in Harsh Environments,GAHE)优化变分模态分解(Variational Mode Decomposition,VMD)联合奇异值分解(Singular Value Decomposition,SVD)与选择性累计能量贡献率(Selective Cumulative Energy Contribution,SCEC)的互补去噪方法。首先,提出用GAHE算法优化VMD算法并结合相对熵对信号中的中高频噪声进行初步消噪,解决VMD参数难以确定和传统遗传算法收敛慢的问题。其次,提出采用SCEC算法结合SVD算法对信号中残留的中低频噪声进行消噪,解决非线性、非平稳信号中大数量级的直流分量影响奇异值选择的问题。最后,通过实验与分析表明:GAHE优化算法收敛速度更快;SCEC奇异值选择法的抗直流能力更强;所提算法的处理效果较优且算法两部分具有互补特性。展开更多
航空发动机叶尖间隙是监控其运行状态的有效参数,现有间隙测量方法很难满足超高转速下间隙距离的奈奎斯特采样率,因此无法有效提取精确的叶尖间隙值。本文基于压缩感知原理,针对间隙距离数据特征提出一种采用K-SVD(K-singular value dec...航空发动机叶尖间隙是监控其运行状态的有效参数,现有间隙测量方法很难满足超高转速下间隙距离的奈奎斯特采样率,因此无法有效提取精确的叶尖间隙值。本文基于压缩感知原理,针对间隙距离数据特征提出一种采用K-SVD(K-singular value decomposition)字典训练稀疏基的数据重构方法,该方法首先构建出K-SVD字典稀疏基对数据进行稀疏化表示,然后使用m序列高斯随机矩阵对数据进行压缩观测,最后基于压缩欠采样观测值使用正交匹配追踪算法对数据进行重构,进而精确提取叶尖间隙值。实验结果表明,在欠采样条件下间隙距离数据可精确恢复重构,与高采样率下的间隙数据相比,重构误差不超过0.02 mm。展开更多
基金This research was funded by the National Natural Science Foundation of China(Nos.71762010,62262019,62162025,61966013,12162012)the Hainan Provincial Natural Science Foundation of China(Nos.823RC488,623RC481,620RC603,621QN241,620RC602,121RC536)+1 种基金the Haikou Science and Technology Plan Project of China(No.2022-016)the Project supported by the Education Department of Hainan Province,No.Hnky2021-23.
文摘Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.
文摘直流输电线路故障行波波速不确定、波头提取困难以及噪声干扰等因素制约了直流电网中故障测距技术的应用。为了降低上述因素对定位准确性的影响,提出一种基于局部特征有理样条插值均值分解(LMD based on characteristic rational spline,CRS-LMD)和奇异值分解(singular value decomposition,SVD)的故障测距方法。首先,利用特征尺度选取最优极点系数,结合有理样条插值调节拟合曲线的松紧程度,实现对故障电压行波的局部均值分解。其次,采用奇异值分解对故障行波波头进行准确提取。最后,在PSCAD/EMTDC中搭建了张北±500 kV柔性直流电网的仿真模型,模拟各种故障情况并输出故障数据,利用Matlab对故障数据进行处理并验证定位算法。最后,仿真结果表明,所提故障测距算法在不同故障距离和故障类型下均能实现故障测距,且在叠加噪声和过渡电阻的情况下也能保障较高的精确性。
文摘针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。
文摘为了更有效分析管道信号,提出一种基于采用在恶劣环境下的遗传算法(Genetic Algorithms in Harsh Environments,GAHE)优化变分模态分解(Variational Mode Decomposition,VMD)联合奇异值分解(Singular Value Decomposition,SVD)与选择性累计能量贡献率(Selective Cumulative Energy Contribution,SCEC)的互补去噪方法。首先,提出用GAHE算法优化VMD算法并结合相对熵对信号中的中高频噪声进行初步消噪,解决VMD参数难以确定和传统遗传算法收敛慢的问题。其次,提出采用SCEC算法结合SVD算法对信号中残留的中低频噪声进行消噪,解决非线性、非平稳信号中大数量级的直流分量影响奇异值选择的问题。最后,通过实验与分析表明:GAHE优化算法收敛速度更快;SCEC奇异值选择法的抗直流能力更强;所提算法的处理效果较优且算法两部分具有互补特性。
文摘航空发动机叶尖间隙是监控其运行状态的有效参数,现有间隙测量方法很难满足超高转速下间隙距离的奈奎斯特采样率,因此无法有效提取精确的叶尖间隙值。本文基于压缩感知原理,针对间隙距离数据特征提出一种采用K-SVD(K-singular value decomposition)字典训练稀疏基的数据重构方法,该方法首先构建出K-SVD字典稀疏基对数据进行稀疏化表示,然后使用m序列高斯随机矩阵对数据进行压缩观测,最后基于压缩欠采样观测值使用正交匹配追踪算法对数据进行重构,进而精确提取叶尖间隙值。实验结果表明,在欠采样条件下间隙距离数据可精确恢复重构,与高采样率下的间隙数据相比,重构误差不超过0.02 mm。
