Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large ove...Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large overlap,high cost and poor real-time performance in photovoltaic panel shadow detection.Firstly,the Ghost CSP module based on Cross Stage Partial(CSP)is adopted in feature extraction network to improve the accuracy and detection speed.Based on extracted features,recursive feature fusion structure ismentioned to enhance the feature information of all objects.We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression,respectively.Finally,in order to achieve fast detection,the Ghost strategy is chosen to lighten the size of the algorithm.The results of the experiment show that the average detection accuracy(mAP)of the algorithm can reach up to 97.17%,the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second(FPS),which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment.The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.展开更多
In the online environment,schools should keep up with the pace of development and strengthen the application of hybrid teaching mode,so as to strengthen the cultivation of students’comprehensive computer application ...In the online environment,schools should keep up with the pace of development and strengthen the application of hybrid teaching mode,so as to strengthen the cultivation of students’comprehensive computer application ability and computer subject literacy,and improve students’learning efficiency.The author explores the actual situation of teaching basic computer courses at this stage and the requirements of applying hybrid teaching mode to carry out computer basic course teaching under the network environment,and puts forward an effective strategy for the application of hybrid teaching mode in computer basic course under the network environment,hoping to contribute to the improvement of the teaching quality and quality of computer basic course.展开更多
Effective methods are urgently required to optimize Raman spectroscopy technology to ameliorate its low detection sensitivity.Here,we superposed two near-concentric cavities to develop a dual near-concentric cavities ...Effective methods are urgently required to optimize Raman spectroscopy technology to ameliorate its low detection sensitivity.Here,we superposed two near-concentric cavities to develop a dual near-concentric cavities group(DNCCG)to assess its effect on gas Raman signal intensity,signal-to-noise ratio(SNR),and limit of detection(LOD).The results showed that DNCCG generally had higher CO_(2) Raman signal intensity than the sum of two near-concentric cavities.Meanwhile,the noise intensity of DNCCG was not enhanced by the superposition of near-concentric cavities.Accordingly,DNCCG increased the SNR.The LOD for CO_(2) was 24.6 parts per million.DNCCG could be an effective method to improve the detection capability of trace gases and broaden the dynamic detection range,which might aid the future development of innovative technology for multicomponent gas detection.展开更多
基金supported by the National Natural Science Foundation of China(No.52074305)Henan Scientific and Technological Research Project(No.212102210005)Open Fund of Henan Engineering Laboratory for Photoelectric Sensing and Intelligent Measurement and Control(No.HELPSIMC-2020-00X).
文摘Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large overlap,high cost and poor real-time performance in photovoltaic panel shadow detection.Firstly,the Ghost CSP module based on Cross Stage Partial(CSP)is adopted in feature extraction network to improve the accuracy and detection speed.Based on extracted features,recursive feature fusion structure ismentioned to enhance the feature information of all objects.We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression,respectively.Finally,in order to achieve fast detection,the Ghost strategy is chosen to lighten the size of the algorithm.The results of the experiment show that the average detection accuracy(mAP)of the algorithm can reach up to 97.17%,the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second(FPS),which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment.The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.
基金Jilin Provincial Education Science Thirteen-Five-Year Plan 2019 Annual Key Topic:Research on the Construction of Stereoscopic Classroom in Higher Education Institutions in the 5G EraSubject No.:ZD19151.
文摘In the online environment,schools should keep up with the pace of development and strengthen the application of hybrid teaching mode,so as to strengthen the cultivation of students’comprehensive computer application ability and computer subject literacy,and improve students’learning efficiency.The author explores the actual situation of teaching basic computer courses at this stage and the requirements of applying hybrid teaching mode to carry out computer basic course teaching under the network environment,and puts forward an effective strategy for the application of hybrid teaching mode in computer basic course under the network environment,hoping to contribute to the improvement of the teaching quality and quality of computer basic course.
基金This work was supported by the High-end Foreign Experts Introduction Plan(No.G2021003003L)the Hebei Province Introduced Foreign Intelligence Projects(No.2022-18).
文摘Effective methods are urgently required to optimize Raman spectroscopy technology to ameliorate its low detection sensitivity.Here,we superposed two near-concentric cavities to develop a dual near-concentric cavities group(DNCCG)to assess its effect on gas Raman signal intensity,signal-to-noise ratio(SNR),and limit of detection(LOD).The results showed that DNCCG generally had higher CO_(2) Raman signal intensity than the sum of two near-concentric cavities.Meanwhile,the noise intensity of DNCCG was not enhanced by the superposition of near-concentric cavities.Accordingly,DNCCG increased the SNR.The LOD for CO_(2) was 24.6 parts per million.DNCCG could be an effective method to improve the detection capability of trace gases and broaden the dynamic detection range,which might aid the future development of innovative technology for multicomponent gas detection.
