With the widespread use of the internet,there is an increasing need to ensure the security and privacy of transmitted data.This has led to an intensified focus on the study of video steganography,which is a technique ...With the widespread use of the internet,there is an increasing need to ensure the security and privacy of transmitted data.This has led to an intensified focus on the study of video steganography,which is a technique that hides data within a video cover to avoid detection.The effectiveness of any steganography method depends on its ability to embed data without altering the original video’s quality while maintaining high efficiency.This paper proposes a new method to video steganography,which involves utilizing a Genetic Algorithm(GA)for identifying the Region of Interest(ROI)in the cover video.The ROI is the area in the video that is the most suitable for data embedding.The secret data is encrypted using the Advanced Encryption Standard(AES),which is a widely accepted encryption standard,before being embedded into the cover video,utilizing up to 10%of the cover video.This process ensures the security and confidentiality of the embedded data.The performance metrics for assessing the proposed method are the Peak Signalto-Noise Ratio(PSNR)and the encoding and decoding time.The results show that the proposed method has a high embedding capacity and efficiency,with a PSNR ranging between 64 and 75 dBs,which indicates that the embedded data is almost indistinguishable from the original video.Additionally,the method can encode and decode data quickly,making it efficient for real-time applications.展开更多
A new error analysis method is presented via genetic algorithms for high precise heading determination model based on two total positioning stations （TPSs）. The method has the ability to search all possible solution...A new error analysis method is presented via genetic algorithms for high precise heading determination model based on two total positioning stations （TPSs）. The method has the ability to search all possible solution space by the genetic operators of elitist model and restriction. The result of analyzing the error of this model shows that the accuracy of this model is precise enough to meet the need of calibration for navigation systems on ship, and the search space is only 0. 03% of the total search space, and the precision of heading determination is 4＂ in a general dock.展开更多
Each physical process in a numerical weather prediction(NWP)system may have many different parameterization schemes.Early studies have shown that the performance of different physical parameterization schemes varies w...Each physical process in a numerical weather prediction(NWP)system may have many different parameterization schemes.Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated.Thus,it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems.However,it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations.This study applied a simple genetic algorithm(SGA)to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting.The feasibility of SGA was verified with the simulation of Typhoon Mujigae(2015)by using the Weather Research and Forecasting(WRF)model and Typhoon Higos(2020)by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport(COAWST)modeling system.The results show that SGA can efficiently obtain the optimal combination of schemes.For Typhoon Mujigae(2015),the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials.Similar results can be obtained for Typhoon Higos(2020).Compared to the default combination proposed by the COAWST model system,the optimal combination scheme significantly improves the simulation of typhoon track and intensity.This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation.This can help provide references for future development of NWP models,and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds.展开更多
文摘With the widespread use of the internet,there is an increasing need to ensure the security and privacy of transmitted data.This has led to an intensified focus on the study of video steganography,which is a technique that hides data within a video cover to avoid detection.The effectiveness of any steganography method depends on its ability to embed data without altering the original video’s quality while maintaining high efficiency.This paper proposes a new method to video steganography,which involves utilizing a Genetic Algorithm(GA)for identifying the Region of Interest(ROI)in the cover video.The ROI is the area in the video that is the most suitable for data embedding.The secret data is encrypted using the Advanced Encryption Standard(AES),which is a widely accepted encryption standard,before being embedded into the cover video,utilizing up to 10%of the cover video.This process ensures the security and confidentiality of the embedded data.The performance metrics for assessing the proposed method are the Peak Signalto-Noise Ratio(PSNR)and the encoding and decoding time.The results show that the proposed method has a high embedding capacity and efficiency,with a PSNR ranging between 64 and 75 dBs,which indicates that the embedded data is almost indistinguishable from the original video.Additionally,the method can encode and decode data quickly,making it efficient for real-time applications.
文摘A new error analysis method is presented via genetic algorithms for high precise heading determination model based on two total positioning stations （TPSs）. The method has the ability to search all possible solution space by the genetic operators of elitist model and restriction. The result of analyzing the error of this model shows that the accuracy of this model is precise enough to meet the need of calibration for navigation systems on ship, and the search space is only 0. 03% of the total search space, and the precision of heading determination is 4＂ in a general dock.
基金Supported by the National Natural Science Foundation of China(42130605)Shenzhen Science and Technology Program(JCYJ20210324131810029)Guangdong Province Introduction of Innovative Research and Development Team Project China(2019ZT08G669)。
文摘Each physical process in a numerical weather prediction(NWP)system may have many different parameterization schemes.Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated.Thus,it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems.However,it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations.This study applied a simple genetic algorithm(SGA)to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting.The feasibility of SGA was verified with the simulation of Typhoon Mujigae(2015)by using the Weather Research and Forecasting(WRF)model and Typhoon Higos(2020)by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport(COAWST)modeling system.The results show that SGA can efficiently obtain the optimal combination of schemes.For Typhoon Mujigae(2015),the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials.Similar results can be obtained for Typhoon Higos(2020).Compared to the default combination proposed by the COAWST model system,the optimal combination scheme significantly improves the simulation of typhoon track and intensity.This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation.This can help provide references for future development of NWP models,and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds.
