Advances in biological and medical technologies have been providing us explosive vol- umes of biological and physiological data, such as medical images, electroencephalography, geno- mic and protein sequences. Learnin...Advances in biological and medical technologies have been providing us explosive vol- umes of biological and physiological data, such as medical images, electroencephalography, geno- mic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning展开更多
Big data technology is more and more widely used in modern power systems.Efficient collection of big data such as equipment status,maintenance and grid operation in power systems,and data mining are the important rese...Big data technology is more and more widely used in modern power systems.Efficient collection of big data such as equipment status,maintenance and grid operation in power systems,and data mining are the important research topics for big data application in smart grid.In this paper,the application of big data technology in fast image recognition of transmission towers which are obtained using fixed-wing unmanned aerial vehicle(UAV)by large range tilt photography are researched.A method that using fast region-based convolutional neural networks(Rcnn)convolutional architecture for fast feature embedding(Caffe)to get deep learning of the massive transmission tower image,extract the image characteristics of the tower,train the tower model,and quickly recognize transmission tower image to generate power lines is proposed.The case study shows that this method can be used in tree barrier modeling of transmission lines,which can replace artificial identification of transmission tower,to reduce the time required for tower identification and generating power line,and improve the efficiency of tree barrier modeling by around 14.2%.展开更多
基金supported by the Center for Precision Medicine, Sun Yat-sen University and the National High-tech R&D Program (863 Program Grant No. 2015AA020110) of China awarded to YZ
文摘Advances in biological and medical technologies have been providing us explosive vol- umes of biological and physiological data, such as medical images, electroencephalography, geno- mic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning
基金Technology project of Guangdong power grid(key technologies research on intelligent inspection of Multi-rotor UAVs in transmission lines)under Grant No.GDKJXM20162155(030600KK52160027).
文摘Big data technology is more and more widely used in modern power systems.Efficient collection of big data such as equipment status,maintenance and grid operation in power systems,and data mining are the important research topics for big data application in smart grid.In this paper,the application of big data technology in fast image recognition of transmission towers which are obtained using fixed-wing unmanned aerial vehicle(UAV)by large range tilt photography are researched.A method that using fast region-based convolutional neural networks(Rcnn)convolutional architecture for fast feature embedding(Caffe)to get deep learning of the massive transmission tower image,extract the image characteristics of the tower,train the tower model,and quickly recognize transmission tower image to generate power lines is proposed.The case study shows that this method can be used in tree barrier modeling of transmission lines,which can replace artificial identification of transmission tower,to reduce the time required for tower identification and generating power line,and improve the efficiency of tree barrier modeling by around 14.2%.
