本文提出一种基于GraphSAGE(graph sample and aggregate)算法的配电网故障定位方法。以对系统侧母线电压进行形态学黑帽运算的结果启动故障定位算法;利用GSA模型自主挖掘网络拓扑和零序电流特征,根据节点特征和标签建立函数映射,评估...本文提出一种基于GraphSAGE(graph sample and aggregate)算法的配电网故障定位方法。以对系统侧母线电压进行形态学黑帽运算的结果启动故障定位算法;利用GSA模型自主挖掘网络拓扑和零序电流特征,根据节点特征和标签建立函数映射,评估线路运行状态从而实现故障定位。基于PSCAD/EMTDC仿真平台搭建IEEE33节点模型,测试结果表明所提配电网故障定位方法可行且有效。并且配电网拓扑变化时,该方法无需重新训练模型即能获得可靠的故障定位结果,验证了方法的鲁棒性和对拓扑变化的适应性。展开更多
AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hos...AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included,and 13470 infrared pupil images were collected for the study.All infrared images for pupil segmentation were labeled using the Labelme software.The computation of pupil diameter is divided into four steps:image pre-processing,pupil identification and localization,pupil segmentation,and diameter calculation.Two major models are used in the computation process:the modified YoloV3 and Deeplabv 3+models,which must be trained beforehand.RESULTS:The test dataset included 1348 infrared pupil images.On the test dataset,the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils.The DeeplabV3+model achieved a background intersection over union(IOU)of 99.23%,a pupil IOU of 93.81%,and a mean IOU of 96.52%.The pupil diameters in the test dataset ranged from 20 to 56 pixels,with a mean of 36.06±6.85 pixels.The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels,with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm,proven to be highly accurate and reliable for clinical application.展开更多
Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis...Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis of artificial neural network. Deep learning brings new development direction to artificial neural network. Convolution neural network is a new artificial neural network method, which combines artificial neural network and deep learning technology, and this new neural network is widely used in many fields of computer vision. Modern image recognition algorithm requires classifi cation system to adapt to different types of tasks, and deep network and convolution neural network is a hot research topic in neural networks. According to the characteristics of satellite digital image, we use the convolution neural network to classify the image, which combines texture features with spectral features. The experimental results show that the convolution neural network algorithm can effectively classify the image.展开更多
In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary's discriminative power, the reconstruction error, classification error and inhomogeneous representat...In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary's discriminative power, the reconstruction error, classification error and inhomogeneous representation error are integrated into the objective function. The proposed approach learns a single structured dictionary and a linear classifier jointly. The learned dictionary encourages the samples from the same class to have similar sparse codes, and the samples from different classes to have dissimilar sparse codes. The solution to the objective function is achieved by employing a feature-sign search algorithm and Lagrange dual method. Experimental results on three public databases demonstrate that the proposed approach outperforms several recently proposed dictionary learning techniques for classification.展开更多
It is a fairly challenging issue to make image repositories easy to be searched and browsed. This depends on a technique--image clustering. Kernel-based clustering algorithm has been one of the most promising clusteri...It is a fairly challenging issue to make image repositories easy to be searched and browsed. This depends on a technique--image clustering. Kernel-based clustering algorithm has been one of the most promising clustering methods in the last few years, beeanse it can handle data with high dimensional complex structure. In this paper, a kernel fuzzy learning (KFL) algorithm is proposed, which takes advantages of the distance kernel trick and the gradient-based fuzzy clustering method to execute the image clustering automatically. Experimental results show that KFL is a more efficient method for image clustering in comparison with recent renorted alternative methods.展开更多
文摘本文提出一种基于GraphSAGE(graph sample and aggregate)算法的配电网故障定位方法。以对系统侧母线电压进行形态学黑帽运算的结果启动故障定位算法;利用GSA模型自主挖掘网络拓扑和零序电流特征,根据节点特征和标签建立函数映射,评估线路运行状态从而实现故障定位。基于PSCAD/EMTDC仿真平台搭建IEEE33节点模型,测试结果表明所提配电网故障定位方法可行且有效。并且配电网拓扑变化时,该方法无需重新训练模型即能获得可靠的故障定位结果,验证了方法的鲁棒性和对拓扑变化的适应性。
文摘AIM:To establish pupil diameter measurement algorithms based on infrared images that can be used in real-world clinical settings.METHODS:A total of 188 patients from outpatient clinic at He Eye Specialist Shenyang Hospital from Spetember to December 2022 were included,and 13470 infrared pupil images were collected for the study.All infrared images for pupil segmentation were labeled using the Labelme software.The computation of pupil diameter is divided into four steps:image pre-processing,pupil identification and localization,pupil segmentation,and diameter calculation.Two major models are used in the computation process:the modified YoloV3 and Deeplabv 3+models,which must be trained beforehand.RESULTS:The test dataset included 1348 infrared pupil images.On the test dataset,the modified YoloV3 model had a detection rate of 99.98% and an average precision(AP)of 0.80 for pupils.The DeeplabV3+model achieved a background intersection over union(IOU)of 99.23%,a pupil IOU of 93.81%,and a mean IOU of 96.52%.The pupil diameters in the test dataset ranged from 20 to 56 pixels,with a mean of 36.06±6.85 pixels.The absolute error in pupil diameters between predicted and actual values ranged from 0 to 7 pixels,with a mean absolute error(MAE)of 1.06±0.96 pixels.CONCLUSION:This study successfully demonstrates a robust infrared image-based pupil diameter measurement algorithm,proven to be highly accurate and reliable for clinical application.
文摘Artifi cial neural network is a kind of artificial intelligence method to simulate the function of human brain, and deep learning technology can establish a depth network model with hierarchical structure on the basis of artificial neural network. Deep learning brings new development direction to artificial neural network. Convolution neural network is a new artificial neural network method, which combines artificial neural network and deep learning technology, and this new neural network is widely used in many fields of computer vision. Modern image recognition algorithm requires classifi cation system to adapt to different types of tasks, and deep network and convolution neural network is a hot research topic in neural networks. According to the characteristics of satellite digital image, we use the convolution neural network to classify the image, which combines texture features with spectral features. The experimental results show that the convolution neural network algorithm can effectively classify the image.
基金Supported by the National Natural Science Foundation of China(No.61379014)
文摘In this paper, a discriminative structured dictionary learning algorithm is presented. To enhance the dictionary's discriminative power, the reconstruction error, classification error and inhomogeneous representation error are integrated into the objective function. The proposed approach learns a single structured dictionary and a linear classifier jointly. The learned dictionary encourages the samples from the same class to have similar sparse codes, and the samples from different classes to have dissimilar sparse codes. The solution to the objective function is achieved by employing a feature-sign search algorithm and Lagrange dual method. Experimental results on three public databases demonstrate that the proposed approach outperforms several recently proposed dictionary learning techniques for classification.
基金Supported by the National Natural Science Foundation of China (No. 61101159, 60872123), the China Postdoctoral Science Foundation (No. 20100480049) and the Fundamental Research Funds for the Central Universities (No. 201 IZM0033)
文摘It is a fairly challenging issue to make image repositories easy to be searched and browsed. This depends on a technique--image clustering. Kernel-based clustering algorithm has been one of the most promising clustering methods in the last few years, beeanse it can handle data with high dimensional complex structure. In this paper, a kernel fuzzy learning (KFL) algorithm is proposed, which takes advantages of the distance kernel trick and the gradient-based fuzzy clustering method to execute the image clustering automatically. Experimental results show that KFL is a more efficient method for image clustering in comparison with recent renorted alternative methods.