Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Consi...Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Considering thissituation, this paper proposes a power grid fault diagnosismethod based on a deep pyramid convolutional neural networkfor the alarm information set. This approach uses the deepfeature extraction ability of the network to extract fault featureknowledge from alarm information texts and achieve end-to-endfault classification and fault device identification. First, a deeppyramid convolutional neural network model for extracting theoverall characteristics of fault events is constructed to identifyfault types. Second, a deep pyramidal convolutional neuralnetwork model for alarm information text is constructed, thetext description characteristics associated with alarm informationtexts are extracted, the key information corresponding to faultsin the alarm information set is identified, and suspicious faultydevices are selected. Then, a fault device identification strategythat integrates fault-type and time sequence priorities is proposedto identify faulty devices. Finally, the actual fault cases and thefault cases generated by the simulation are studied, and theresults verify the effectiveness and practicability of the methodpresented in this paper.展开更多
While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection ...While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network(TDBU-FPN),which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes(PASCAL VOC)dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps.展开更多
Object detection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale...Object detection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale objects faces significant challenges. We would introduce a new feature pyramid framework called Dual Attention based Feature Pyramid Network(DAFPN), which is designed to avoid predicament about multi-scale object recognition. In DAFPN, the attention mechanism is introduced by calculating the topdown pathway and lateral pathway, where the spatial attention, as well as channel attention, would participate, respectively, such that the pyramidal feature maps can be generated with enhanced spatial and channel interdependencies, which bring more semantical information for the feature pyramid. Using the COCO data set, which consists of a considerable quantity of small-scale objects, the experiments are implemented. The analysis results verify the optimized performance of DAFPN compared with the original Feature Pyramid Network(FPN) specifically for the identification on a small scale. The proposed DAFPN is promising for object detection in an era full of intelligent machines that need to detect multi-scale objects.展开更多
The value of grape cultivars varies.The use of a mixture of cultivars can negate the benefits of improved cultivars and hamper the protection of genetic resources and the identification of new hybrid cultivars.Classif...The value of grape cultivars varies.The use of a mixture of cultivars can negate the benefits of improved cultivars and hamper the protection of genetic resources and the identification of new hybrid cultivars.Classifying cultivars based on their leaves is therefore highly practical.Transplanted grape seedlings take years to bear fruit,but leaves mature in months.Foliar morphology differs among cultivars,so identifying cultivars based on leaves is feasible.Different cultivars,however,can be bred from the same parents,so the leaves of some cultivars can have similar morphologies.In this work,a pyramid residual convolution neural network was developed to classify images of eleven grape cultivars.The model extracts multi-scale feature maps of the leaf images through the convolution layer and enters them into three residual convolution neural networks.Features are fused by adding the value of the convolution kernel feature matrix to enhance the attention on the edge and center regions of the leaves and classify the images.The results indicated that the average accuracy of the model was 92.26%for the proposed leaf dataset.The proposed model is superior to previous models and provides a reliable method for the fine-grained classification and identification of plant cultivars.展开更多
针对轻量化网络结构从特征图提取有效语义信息不足,以及语义信息与空间细节信息融合模块设计不合理而导致分割精度降低的问题,本文提出一种结合全局注意力机制的实时语义分割网络(global attention mechanism with real time semantic s...针对轻量化网络结构从特征图提取有效语义信息不足,以及语义信息与空间细节信息融合模块设计不合理而导致分割精度降低的问题,本文提出一种结合全局注意力机制的实时语义分割网络(global attention mechanism with real time semantic segmentation network,GaSeNet)。首先在双分支结构的语义分支中引入全局注意力机制,在通道与空间两个维度引导卷积神经网来关注与分割任务相关的语义类别,以提取更多有效语义信息;其次在空间细节分支设计混合空洞卷积块,在卷积核大小不变的情况下扩大感受野,以获取更多全局空间细节信息,弥补关键特征信息损失。然后重新设计特征融合模块,引入深度聚合金塔池化,将不同尺度的特征图深度融合,从而提高网络的语义分割性能。最后将所提出的方法在CamVid数据集和Vaihingen数据集上进行实验,通过与最新的语义分割方法对比分析可知,GaSeNet在分割精度上分别提高了4.29%、16.06%,实验结果验证了本文方法处理实时语义分割问题的有效性。展开更多
基金the National Natural Science Foundation of China(51877079).
文摘Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Considering thissituation, this paper proposes a power grid fault diagnosismethod based on a deep pyramid convolutional neural networkfor the alarm information set. This approach uses the deepfeature extraction ability of the network to extract fault featureknowledge from alarm information texts and achieve end-to-endfault classification and fault device identification. First, a deeppyramid convolutional neural network model for extracting theoverall characteristics of fault events is constructed to identifyfault types. Second, a deep pyramidal convolutional neuralnetwork model for alarm information text is constructed, thetext description characteristics associated with alarm informationtexts are extracted, the key information corresponding to faultsin the alarm information set is identified, and suspicious faultydevices are selected. Then, a fault device identification strategythat integrates fault-type and time sequence priorities is proposedto identify faulty devices. Finally, the actual fault cases and thefault cases generated by the simulation are studied, and theresults verify the effectiveness and practicability of the methodpresented in this paper.
基金supported by the Program of Introducing Talents of Discipline to Universities(111 Plan)of China(B14010)the National Natural Science Foundation of China(31727901)
文摘While moving ahead with the object detection technology, especially deep neural networks, many related tasks, such as medical application and industrial automation, have achieved great success. However, the detection of objects with multiple aspect ratios and scales is still a key problem. This paper proposes a top-down and bottom-up feature pyramid network(TDBU-FPN),which combines multi-scale feature representation and anchor generation at multiple aspect ratios. First, in order to build the multi-scale feature map, this paper puts a number of fully convolutional layers after the backbone. Second, to link neighboring feature maps, top-down and bottom-up flows are adopted to introduce context information via top-down flow and supplement suboriginal information via bottom-up flow. The top-down flow refers to the deconvolution procedure, and the bottom-up flow refers to the pooling procedure. Third, the problem of adapting different object aspect ratios is tackled via many anchor shapes with different aspect ratios on each multi-scale feature map. The proposed method is evaluated on the pattern analysis, statistical modeling and computational learning visual object classes(PASCAL VOC)dataset and reaches an accuracy of 79%, which exhibits a 1.8% improvement with a detection speed of 23 fps.
基金supported by the National Natural Science Foundation of China(No.61901016)the special fund for basic scientific research in central colleges and universities-Youth talent support program of Beihang University。
文摘Object detection could be recognized as an essential part of the research to scenarios such as automatic driving and pedestrian detection, etc. Among multiple types of target objects, the identification of small-scale objects faces significant challenges. We would introduce a new feature pyramid framework called Dual Attention based Feature Pyramid Network(DAFPN), which is designed to avoid predicament about multi-scale object recognition. In DAFPN, the attention mechanism is introduced by calculating the topdown pathway and lateral pathway, where the spatial attention, as well as channel attention, would participate, respectively, such that the pyramidal feature maps can be generated with enhanced spatial and channel interdependencies, which bring more semantical information for the feature pyramid. Using the COCO data set, which consists of a considerable quantity of small-scale objects, the experiments are implemented. The analysis results verify the optimized performance of DAFPN compared with the original Feature Pyramid Network(FPN) specifically for the identification on a small scale. The proposed DAFPN is promising for object detection in an era full of intelligent machines that need to detect multi-scale objects.
基金This work was financially supported by the National Key Research and Development Project(Grant No.2020YFD1100601)。
文摘The value of grape cultivars varies.The use of a mixture of cultivars can negate the benefits of improved cultivars and hamper the protection of genetic resources and the identification of new hybrid cultivars.Classifying cultivars based on their leaves is therefore highly practical.Transplanted grape seedlings take years to bear fruit,but leaves mature in months.Foliar morphology differs among cultivars,so identifying cultivars based on leaves is feasible.Different cultivars,however,can be bred from the same parents,so the leaves of some cultivars can have similar morphologies.In this work,a pyramid residual convolution neural network was developed to classify images of eleven grape cultivars.The model extracts multi-scale feature maps of the leaf images through the convolution layer and enters them into three residual convolution neural networks.Features are fused by adding the value of the convolution kernel feature matrix to enhance the attention on the edge and center regions of the leaves and classify the images.The results indicated that the average accuracy of the model was 92.26%for the proposed leaf dataset.The proposed model is superior to previous models and provides a reliable method for the fine-grained classification and identification of plant cultivars.
文摘针对轻量化网络结构从特征图提取有效语义信息不足,以及语义信息与空间细节信息融合模块设计不合理而导致分割精度降低的问题,本文提出一种结合全局注意力机制的实时语义分割网络(global attention mechanism with real time semantic segmentation network,GaSeNet)。首先在双分支结构的语义分支中引入全局注意力机制,在通道与空间两个维度引导卷积神经网来关注与分割任务相关的语义类别,以提取更多有效语义信息;其次在空间细节分支设计混合空洞卷积块,在卷积核大小不变的情况下扩大感受野,以获取更多全局空间细节信息,弥补关键特征信息损失。然后重新设计特征融合模块,引入深度聚合金塔池化,将不同尺度的特征图深度融合,从而提高网络的语义分割性能。最后将所提出的方法在CamVid数据集和Vaihingen数据集上进行实验,通过与最新的语义分割方法对比分析可知,GaSeNet在分割精度上分别提高了4.29%、16.06%,实验结果验证了本文方法处理实时语义分割问题的有效性。
