The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter,where this affects their capacity for power generation as well as their safety.Accurately identifying the icin...The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter,where this affects their capacity for power generation as well as their safety.Accurately identifying the icing of the blades of wind turbines in remote areas is thus important,and a general model is needed to this end.This paper proposes a universal model based on a Deep Neural Network(DNN)that uses data from the Supervisory Control and Data Acquisition(SCADA)system.Two datasets from SCADA are first preprocessed through undersampling,that is,they are labeled,normalized,and balanced.The features of icing of the blades of a turbine identified in previous studies are then used to extract training data from the training dataset.A middle feature is proposed to show how a given feature is correlated with icing on the blade.Performance indicators for the model,including a reward function,are also designed to assess its predictive accuracy.Finally,the most suitable model is used to predict the testing data,and values of the reward function and the predictive accuracy of the model are calculated.The proposed method can be used to relate continuously transferred features with a binary status of icing of the blades of the turbine by using variables of the middle feature.The results here show that an integrated indicator systemis superior to a single indicator of accuracy when evaluating the prediction model.展开更多
High-throughput sequencing is a revolutionary technological in- novation in DNA sequencing. This technology has an ultra-low cost per base of sequencing and an overwhelmingly high data output. High-throughput sequenci...High-throughput sequencing is a revolutionary technological in- novation in DNA sequencing. This technology has an ultra-low cost per base of sequencing and an overwhelmingly high data output. High-throughput sequencing has brought novel research methods and solutions to the research fields of genomics and post-genomics. Furthermore, this technology is leading to a new molecular breeding revolution that has landmark significance for scientific research and enables us to launch multi-level, multi- faceted, and multi-extent studies in the fields of crop genetics, genomics, and crop breeding. In this paper, we review progress in the application of high-throughput sequencing technologies to plant molecular breeding studies.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61573138.
文摘The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter,where this affects their capacity for power generation as well as their safety.Accurately identifying the icing of the blades of wind turbines in remote areas is thus important,and a general model is needed to this end.This paper proposes a universal model based on a Deep Neural Network(DNN)that uses data from the Supervisory Control and Data Acquisition(SCADA)system.Two datasets from SCADA are first preprocessed through undersampling,that is,they are labeled,normalized,and balanced.The features of icing of the blades of a turbine identified in previous studies are then used to extract training data from the training dataset.A middle feature is proposed to show how a given feature is correlated with icing on the blade.Performance indicators for the model,including a reward function,are also designed to assess its predictive accuracy.Finally,the most suitable model is used to predict the testing data,and values of the reward function and the predictive accuracy of the model are calculated.The proposed method can be used to relate continuously transferred features with a binary status of icing of the blades of the turbine by using variables of the middle feature.The results here show that an integrated indicator systemis superior to a single indicator of accuracy when evaluating the prediction model.
文摘High-throughput sequencing is a revolutionary technological in- novation in DNA sequencing. This technology has an ultra-low cost per base of sequencing and an overwhelmingly high data output. High-throughput sequencing has brought novel research methods and solutions to the research fields of genomics and post-genomics. Furthermore, this technology is leading to a new molecular breeding revolution that has landmark significance for scientific research and enables us to launch multi-level, multi- faceted, and multi-extent studies in the fields of crop genetics, genomics, and crop breeding. In this paper, we review progress in the application of high-throughput sequencing technologies to plant molecular breeding studies.
