High-precision polar motion prediction is of great significance for deep space exploration and satellite navigation.Polar motion is affected by a variety of excitation factors,and nonlinear prediction methods are more...High-precision polar motion prediction is of great significance for deep space exploration and satellite navigation.Polar motion is affected by a variety of excitation factors,and nonlinear prediction methods are more suitable for polar motion prediction.In order to explore the effect of deep learning in polar motion prediction.This paper proposes a combined model based on empirical wavelet transform(EWT),Convolutional Neural Networks(CNN)and Long Short Term Memory(LSTM).By training and forecasting EOP 20C04 data,the effectiveness of the algorithm is verified,and the performance of two forecasting strategies in deep learning for polar motion prediction is explored.The results indicate that recursive multi-step prediction performs better than direct multi-step prediction for short-term forecasts within 15 days,while direct multi-step prediction is more suitable for medium and long-term forecasts.In the 365 days forecast,the mean absolute error of EWT-CNN-LSTM in the X direction and Y direction is 18.25 mas and 15.78 mas,respectively,which is 23.5% and 16.2% higher than the accuracy of Bulletin A.The results show that the algorithm has a good effect in medium and long term polar motion prediction.展开更多
Based on the monitoring data of ambient air quality and meteorological observation data,the characteristics and meteorological influencing factors of air pollution in Luojiang District of Deyang City from 2018 to 2022...Based on the monitoring data of ambient air quality and meteorological observation data,the characteristics and meteorological influencing factors of air pollution in Luojiang District of Deyang City from 2018 to 2022 were analyzed.The results show that from 2018 to 2022,the main air pollutants affecting the air quality of Luojiang District of Deyang City were PM_(2.5) and PM_(10),and the primary pollutant on heavy pollution days was basically PM_(2.5).PM_(2.5) and PM_(10) pollution showed obvious seasonal differences,and PM_(2.5) concentration exceeded the limit mainly in spring and winter,among which it was the most serious in early spring,especially in January and February,followed by December.PM_(10) exceeding the standard had a high seasonal correlation with PM_(2.5) exceeding the standard,mainly in spring and winter,among which it was the most serious in winter,especially in December,followed by January.PM_(2.5) and PM_(10) pollution showed an overall weakening trend.PM_(2.5) and PM_(10) concentration were closely related to meteorological factors such as temperature,relative humidity,wind speed,precipitation and air pressure,and were mainly affected by rainfall.展开更多
A perimeter security system based on ultra-weak fiber Bragg grating high-speed wavelength demodulation was proposed. The demodulation system for signal acquisition and high-speed wavelength calculation was designed ba...A perimeter security system based on ultra-weak fiber Bragg grating high-speed wavelength demodulation was proposed. The demodulation system for signal acquisition and high-speed wavelength calculation was designed based on field programmable gate array (FPGA) platform. The principle of ultra-weak fiber Bragg grating high-speed demodulation and signal recognition method were analyzed theoretically, and the Support Vector Machine model was introduced to optimize the event recognition accuracy of the system. A perimeter security experimental system containing 1000 ultra-weak fiber Bragg gratings, ultra-weak fiber Bragg grating sense optical cables with a diameter of 2.0 mm and a reflectivity of 0.01%, steel space frames and demodulation equipments was built to recognize four typical events such as knocking, shaking, wind blowing and rainfall. The experimental resulted show that the system has a spatial resolution of 1m and an acquisition frequency of 200 Hz. The joint time-frequency domain detection method is used to achieve 99.2% alarm accuracy, and 98% recognition accuracy of two intrusion events, which has good anti-interference performance.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.42304044the Natural Science Foundation of Henan,China under grant No.222300420385。
文摘High-precision polar motion prediction is of great significance for deep space exploration and satellite navigation.Polar motion is affected by a variety of excitation factors,and nonlinear prediction methods are more suitable for polar motion prediction.In order to explore the effect of deep learning in polar motion prediction.This paper proposes a combined model based on empirical wavelet transform(EWT),Convolutional Neural Networks(CNN)and Long Short Term Memory(LSTM).By training and forecasting EOP 20C04 data,the effectiveness of the algorithm is verified,and the performance of two forecasting strategies in deep learning for polar motion prediction is explored.The results indicate that recursive multi-step prediction performs better than direct multi-step prediction for short-term forecasts within 15 days,while direct multi-step prediction is more suitable for medium and long-term forecasts.In the 365 days forecast,the mean absolute error of EWT-CNN-LSTM in the X direction and Y direction is 18.25 mas and 15.78 mas,respectively,which is 23.5% and 16.2% higher than the accuracy of Bulletin A.The results show that the algorithm has a good effect in medium and long term polar motion prediction.
文摘Based on the monitoring data of ambient air quality and meteorological observation data,the characteristics and meteorological influencing factors of air pollution in Luojiang District of Deyang City from 2018 to 2022 were analyzed.The results show that from 2018 to 2022,the main air pollutants affecting the air quality of Luojiang District of Deyang City were PM_(2.5) and PM_(10),and the primary pollutant on heavy pollution days was basically PM_(2.5).PM_(2.5) and PM_(10) pollution showed obvious seasonal differences,and PM_(2.5) concentration exceeded the limit mainly in spring and winter,among which it was the most serious in early spring,especially in January and February,followed by December.PM_(10) exceeding the standard had a high seasonal correlation with PM_(2.5) exceeding the standard,mainly in spring and winter,among which it was the most serious in winter,especially in December,followed by January.PM_(2.5) and PM_(10) pollution showed an overall weakening trend.PM_(2.5) and PM_(10) concentration were closely related to meteorological factors such as temperature,relative humidity,wind speed,precipitation and air pressure,and were mainly affected by rainfall.
文摘A perimeter security system based on ultra-weak fiber Bragg grating high-speed wavelength demodulation was proposed. The demodulation system for signal acquisition and high-speed wavelength calculation was designed based on field programmable gate array (FPGA) platform. The principle of ultra-weak fiber Bragg grating high-speed demodulation and signal recognition method were analyzed theoretically, and the Support Vector Machine model was introduced to optimize the event recognition accuracy of the system. A perimeter security experimental system containing 1000 ultra-weak fiber Bragg gratings, ultra-weak fiber Bragg grating sense optical cables with a diameter of 2.0 mm and a reflectivity of 0.01%, steel space frames and demodulation equipments was built to recognize four typical events such as knocking, shaking, wind blowing and rainfall. The experimental resulted show that the system has a spatial resolution of 1m and an acquisition frequency of 200 Hz. The joint time-frequency domain detection method is used to achieve 99.2% alarm accuracy, and 98% recognition accuracy of two intrusion events, which has good anti-interference performance.
