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
针对移动机器人噪声模型不确定性导致定位算法鲁棒性弱、精度低的问题,提出一种基于奇异值分解(Singular Value Decomposition,SVD)的自适应无迹H_(∞)滤波定位算法。该算法利用无迹H_(∞)滤波融合多传感器数据估计移动机器人位姿,并通...针对移动机器人噪声模型不确定性导致定位算法鲁棒性弱、精度低的问题,提出一种基于奇异值分解(Singular Value Decomposition,SVD)的自适应无迹H_(∞)滤波定位算法。该算法利用无迹H_(∞)滤波融合多传感器数据估计移动机器人位姿,并通过自适应调节滤波器参数γ,提高了移动机器人的定位精度。同时为了提高算法的鲁棒性,采用SVD分解代替常规Cholesky分解,避免了误差协方差矩阵在数值迭代过程中出现负定的情况。实验结果表明:相较于扩展H_(∞)滤波和粒子滤波算法,基于SVD分解的自适应无迹H_(∞)滤波定位算法具有精度高、鲁棒性强的优势。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.
文摘针对移动机器人噪声模型不确定性导致定位算法鲁棒性弱、精度低的问题,提出一种基于奇异值分解(Singular Value Decomposition,SVD)的自适应无迹H_(∞)滤波定位算法。该算法利用无迹H_(∞)滤波融合多传感器数据估计移动机器人位姿,并通过自适应调节滤波器参数γ,提高了移动机器人的定位精度。同时为了提高算法的鲁棒性,采用SVD分解代替常规Cholesky分解,避免了误差协方差矩阵在数值迭代过程中出现负定的情况。实验结果表明:相较于扩展H_(∞)滤波和粒子滤波算法,基于SVD分解的自适应无迹H_(∞)滤波定位算法具有精度高、鲁棒性强的优势。
