With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-base...With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.展开更多
A new class of near-infrared(NIR)fluorescent organoboron AIEgens was successfully developed for latent fingerprints(LFPs)imaging.They exhibit real-time and in situ high-resolution imaging performance at 1-3 levels of ...A new class of near-infrared(NIR)fluorescent organoboron AIEgens was successfully developed for latent fingerprints(LFPs)imaging.They exhibit real-time and in situ high-resolution imaging performance at 1-3 levels of LFPs by spraying method.In addition,we systematically elucidate the fingerprint imaging mechanism of these AIEgens.Significantly,the excellent level 3 structural imaging capabilities enable the application of them for analyzing incomplete LFPs and identifying individuals in different scenarios.展开更多
文摘With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.
基金supported by the Topnotch Talents Program of Henan Agricultural University(30501032)the National Natural Science Foundation of China(52003228 and 52273197)+2 种基金the Science,Technology and Innovation Commission of Shenzhen Municipality(JCYJ2021324134613038)the Shenzhen Key Laboratory of Functional Aggregate Materials(ZDSYS20211021111400001)Shenzhen Peacock Team Project(KQTD20210811090142053).
文摘A new class of near-infrared(NIR)fluorescent organoboron AIEgens was successfully developed for latent fingerprints(LFPs)imaging.They exhibit real-time and in situ high-resolution imaging performance at 1-3 levels of LFPs by spraying method.In addition,we systematically elucidate the fingerprint imaging mechanism of these AIEgens.Significantly,the excellent level 3 structural imaging capabilities enable the application of them for analyzing incomplete LFPs and identifying individuals in different scenarios.
