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 this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reco...In this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reconstructed by traditional associative imaging methods. Unlike traditional ghost imaging to reconstruct objects from bucket signals, our proposed method can use simple objects (such as EMNIST) as a training set for GAN, and then recognize objects (such as faces) of completely different complexity than the training set. We use traditional ghost imaging and neural network to reconstruct target objects respectively. According to the research results in this paper, the method based on neural network can reconstruct complex objects very well, but the method based on traditional ghost imaging cannot reconstruct complex objects. The research scheme in this paper is of great significance for the reconstruction of complex object-related imaging under low sampling conditions.展开更多
In the existing ghost-imaging-based cryptographic key distribution(GCKD)protocols,the cryptographic keys need to be encoded by using many modulated patterns,which undoubtedly incurs long measurement time and huge memo...In the existing ghost-imaging-based cryptographic key distribution(GCKD)protocols,the cryptographic keys need to be encoded by using many modulated patterns,which undoubtedly incurs long measurement time and huge memory consumption.Given this,based on snapshot compressive ghost imaging,a public network cryptographic key distribution protocol is proposed,where the cryptographic keys and joint authentication information are encrypted into several color block diagrams to guarantee security.It transforms the previous single-pixel sequential multiple measurements into multi-pixel single exposure measurements,significantly reducing sampling time and memory storage.Both simulation and experimental results demonstrate the feasibility of this protocol and its ability to detect illegal attacks.Therefore,it takes GCKD a big step closer to practical applications.展开更多
Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog ...Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.展开更多
[Objective] To study on genetic inactivation bacterial ghosts of Pasteurella multocida based PhiX174 gene E lysis cassette mediated. [ Method ] Recombinant pPBA1100-e was constructed by which the gene E of bacteriopha...[Objective] To study on genetic inactivation bacterial ghosts of Pasteurella multocida based PhiX174 gene E lysis cassette mediated. [ Method ] Recombinant pPBA1100-e was constructed by which the gene E of bacteriophage Phix174 and temperature sensitivity expressing control system hybridized with plasmid pPBA1100 by genetic engineering method. Recombinant was transformed to Pasteurella multocida and lysis gene E expressed by temperature induction. Recombinant was detected by restriction endonuclease. Cell morphology of bacterial ghost of Pasteurella mul- tocida was observed by scanning electron microscopy and inactivation ratio was estimated by CFU analysis. I Result~ The results indicated that the recombination plasmid presented three bands by restriction endonuclease and agarose electrophoresis and that molecular weight of every band ac- corded with theoretical value. The result of SEM observing showed that recombination plasmid expressed successfully in P. multocida and produced bacterial ghost. The result of CFU detecting demonstrated that inactivation ratio of P. multocida reached 99 per cent. ~Conclusion~ This study pro- vided technical bases for the preparation of antigen vaccine of natural bacterial outer membrane protein.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.
文摘In this paper, we improve traditional generative adversarial networks (GAN) with reference to residual networks and convolutional neural networks to facilitate the reconstruction of complex objects that cannot be reconstructed by traditional associative imaging methods. Unlike traditional ghost imaging to reconstruct objects from bucket signals, our proposed method can use simple objects (such as EMNIST) as a training set for GAN, and then recognize objects (such as faces) of completely different complexity than the training set. We use traditional ghost imaging and neural network to reconstruct target objects respectively. According to the research results in this paper, the method based on neural network can reconstruct complex objects very well, but the method based on traditional ghost imaging cannot reconstruct complex objects. The research scheme in this paper is of great significance for the reconstruction of complex object-related imaging under low sampling conditions.
基金supported by the Beijing Natural Science Foundation(Grant No.4222016).
文摘In the existing ghost-imaging-based cryptographic key distribution(GCKD)protocols,the cryptographic keys need to be encoded by using many modulated patterns,which undoubtedly incurs long measurement time and huge memory consumption.Given this,based on snapshot compressive ghost imaging,a public network cryptographic key distribution protocol is proposed,where the cryptographic keys and joint authentication information are encrypted into several color block diagrams to guarantee security.It transforms the previous single-pixel sequential multiple measurements into multi-pixel single exposure measurements,significantly reducing sampling time and memory storage.Both simulation and experimental results demonstrate the feasibility of this protocol and its ability to detect illegal attacks.Therefore,it takes GCKD a big step closer to practical applications.
基金supported by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2022MF249)。
文摘Imaging through fluctuating scattering media such as fog is of challenge since it seriously degrades the image quality.We investigate how the image quality of computational ghost imaging is reduced by fluctuating fog and how to obtain a high-quality defogging ghost image. We show theoretically and experimentally that the photon number fluctuations introduced by fluctuating fog is the reason for ghost image degradation. An algorithm is proposed to process the signals collected by the computational ghost imaging device to eliminate photon number fluctuations of different measurement events. Thus, a high-quality defogging ghost image is reconstructed even though fog is evenly distributed on the optical path. A nearly 100% defogging ghost image is obtained by further using a cycle generative adversarial network to process the reconstructed defogging image.
基金Supported by the Hongkong Fok Ying Tung Ming Yuan Fund(HK314-14591)Guangdong Provincial Agricultural Research Projects(2004B20201019)Shaoguan Science and Technology Innovation Projects(2010140473)
文摘[Objective] To study on genetic inactivation bacterial ghosts of Pasteurella multocida based PhiX174 gene E lysis cassette mediated. [ Method ] Recombinant pPBA1100-e was constructed by which the gene E of bacteriophage Phix174 and temperature sensitivity expressing control system hybridized with plasmid pPBA1100 by genetic engineering method. Recombinant was transformed to Pasteurella multocida and lysis gene E expressed by temperature induction. Recombinant was detected by restriction endonuclease. Cell morphology of bacterial ghost of Pasteurella mul- tocida was observed by scanning electron microscopy and inactivation ratio was estimated by CFU analysis. I Result~ The results indicated that the recombination plasmid presented three bands by restriction endonuclease and agarose electrophoresis and that molecular weight of every band ac- corded with theoretical value. The result of SEM observing showed that recombination plasmid expressed successfully in P. multocida and produced bacterial ghost. The result of CFU detecting demonstrated that inactivation ratio of P. multocida reached 99 per cent. ~Conclusion~ This study pro- vided technical bases for the preparation of antigen vaccine of natural bacterial outer membrane protein.
