Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to...Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.展开更多
In order to suppress the influence of temperature changes on the performance of accelerometers,a digital quartz resonant accelerometer with low temperature drift is developed using a quartz resonator cluster as a tran...In order to suppress the influence of temperature changes on the performance of accelerometers,a digital quartz resonant accelerometer with low temperature drift is developed using a quartz resonator cluster as a transducer element.In addition,a digital intellectual property(IP) is designed in FPGA to achieve signal processing and fusion of integrated resonators.A testing system for digital quartz resonant accelerometers is established to characterize the performance under different conditions.The scale factor of the accelerometer prototype reaches 3561.63 Hz/g in the range of -1 g to +1 g,and 3542.5 Hz/g in the range of-10 g to+10 g.In different measurement ranges,the linear correlation coefficient R~2 of the accelerometer achieves greater than 0.998.The temperature drift of the accelerometer prototype is tested using a constant temperature test chamber,with a temperature change from -20℃ to 80℃.After temperature-drift compensation,the zero bias temperature coefficient falls to 0.08 mg/℃,and the scale factor temperature coefficient is 65.43 ppm/℃.The experimental results show that the digital quartz resonant accelerometer exhibits excellent sensitivity and low temperature drift.展开更多
基金funded by the Social Science Foundation of Shandong(No.20CXWJ08).
文摘Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.
文摘In order to suppress the influence of temperature changes on the performance of accelerometers,a digital quartz resonant accelerometer with low temperature drift is developed using a quartz resonator cluster as a transducer element.In addition,a digital intellectual property(IP) is designed in FPGA to achieve signal processing and fusion of integrated resonators.A testing system for digital quartz resonant accelerometers is established to characterize the performance under different conditions.The scale factor of the accelerometer prototype reaches 3561.63 Hz/g in the range of -1 g to +1 g,and 3542.5 Hz/g in the range of-10 g to+10 g.In different measurement ranges,the linear correlation coefficient R~2 of the accelerometer achieves greater than 0.998.The temperature drift of the accelerometer prototype is tested using a constant temperature test chamber,with a temperature change from -20℃ to 80℃.After temperature-drift compensation,the zero bias temperature coefficient falls to 0.08 mg/℃,and the scale factor temperature coefficient is 65.43 ppm/℃.The experimental results show that the digital quartz resonant accelerometer exhibits excellent sensitivity and low temperature drift.
