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Object Recognition Algorithm Based on an Improved Convolutional Neural Network
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作者 Zheyi Fan Yu Song Wei Li 《Journal of Beijing Institute of Technology》 EI CAS 2020年第2期139-145,共7页
In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted... In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition. 展开更多
关键词 object recognition selective search algorithm improved convolutional neural network(CNN)
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GCN-LSTM spatiotemporal-network-based method for post-disturbance frequency prediction of power systems 被引量:3
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作者 Dengyi Huang Hao Liu +1 位作者 Tianshu Bi Qixun Yang 《Global Energy Interconnection》 EI CAS CSCD 2022年第1期96-107,共12页
Owing to the expansion of the grid interconnection scale,the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly importa... Owing to the expansion of the grid interconnection scale,the spatiotemporal distribution characteristics of the frequency response of power systems after the occurrence of disturbances have become increasingly important.These characteristics can provide effective support in coordinated security control.However,traditional model-based frequencyprediction methods cannot satisfactorily meet the requirements of online applications owing to the long calculation time and accurate power-system models.Therefore,this study presents a rolling frequency-prediction model based on a graph convolutional network(GCN)and a long short-term memory(LSTM)spatiotemporal network and named as STGCN-LSTM.In the proposed method,the measurement data from phasor measurement units after the occurrence of disturbances are used to construct the spatiotemporal input.An improved GCN embedded with topology information is used to extract the spatial features,while the LSTM network is used to extract the temporal features.The spatiotemporal-network-regression model is further trained,and asynchronous-frequency-sequence prediction is realized by utilizing the rolling update of measurement information.The proposed spatiotemporal-network-based prediction model can achieve accurate frequency prediction by considering the spatiotemporal distribution characteristics of the frequency response.The noise immunity and robustness of the proposed method are verified on the IEEE 39-bus and IEEE 118-bus systems. 展开更多
关键词 Synchronous phasor measurement Frequency-response prediction Spatiotemporal distribution characteristics improved graph convolutional network Long short-term memory network Spatiotemporal-network structure
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How to accurately extract large-scale urban land?Establishment of an improved fully convolutional neural network model
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作者 Boling YIN Dongjie GUAN +4 位作者 Yuxiang ZHANG He XIAO Lidan CHENG Jiameng CAO Xiangyuan SU 《Frontiers of Earth Science》 SCIE CSCD 2022年第4期1061-1076,共16页
Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neur... Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable development.In this paper,an improved fully convolution neural network was provided for perceiving large-scale urban change,by modifying network structure and updating network strategy to extract richer feature information,and to meet the requirement of urban construction land extraction under the background of large-scale low-resolution image.This paper takes the Yangtze River Economic Belt of China as an empirical object to verify the practicability of the network,the results show the extraction results of the improved fully convolutional neural network model reached a precision of kappa coefficient of 0.88,which is better than traditional fully convolutional neural networks,it performs well in the construction land extraction at the scale of small and medium-sized cities. 展开更多
关键词 improved fully convolutional neural network remote sensing image classification city boundary precision evaluation
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