Spectrum prediction is one of the new techniques in cognitive radio that predicts changes in the spectrum state and plays a crucial role in improving spectrum sensing performance.Prediction models previously trained i...Spectrum prediction is one of the new techniques in cognitive radio that predicts changes in the spectrum state and plays a crucial role in improving spectrum sensing performance.Prediction models previously trained in the source band tend to perform poorly in the new target band because of changes in the channel.In addition,cognitive radio devices require dynamic spectrum access,which means that the time to retrain the model in the new band is minimal.To increase the amount of data in the target band,we use the GAN to convert the data of source band into target band.First,we analyze the data differences between bands and calculate FID scores to identify the available bands with the slightest difference from the target predicted band.The original GAN structure is unsuitable for converting spectrum data,and we propose the spectrum data conversion GAN(SDC-GAN).The generator module consists of a convolutional network and an LSTM module that can integrate multiple features of the data and can convert data from the source band to the target band.Finally,we use the generated target band data to train the prediction model.The experimental results validate the effectiveness of the proposed algorithm.展开更多
This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained mode...This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.展开更多
Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce ...Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.展开更多
The rock mechanical properties and elastic anisotropy of terrestrial shale oil reservoirs are affected by various factors,such as lithology,structure,pores,fractures,and fluids.The experimental study of dynamic and st...The rock mechanical properties and elastic anisotropy of terrestrial shale oil reservoirs are affected by various factors,such as lithology,structure,pores,fractures,and fluids.The experimental study of dynamic and static elastic properties can provide important mechanism analysis for the prediction of geological and engineering “sweet spots” in shale reservoirs.There are a large number of studies on the measurement of static mechanical properties of shale,but the experiments on dynamic crossband elastic anisotropy of terrestrial shale have not yet been conducted thoroughly.Therefore,we report the anisotropic dispersion mechanism of favorable lithofacies(lamellar dolomitic shale,with vertical and horizontal bedding) in the inter-salt shale oil reservoir of the Qianjiang Formation for different confining pressures and fluid saturation conditions.The experiments were conducted by the cross-band rock physics measurement technology that comprised low-frequency stress-strain measurements and a high-frequency ultrasonic test.The experimental results indicated that:(1) The elastic property dispersion of the terrestrial shale was stronger than that of marine shale due to the high viscosity of the medium oil in the terrestrial shale.The lamellar structures and interbedded fractures were the main factors that determined the strong anisotropy of the terrestrial shale.(2) The dispersion of elastic properties from low to high frequencies in a partial oil saturation state ranged from strong to weak;the wave-induced fluid flow or intrinsic dissipation of viscoelastic inclusions may be the dominant mechanisms that caused the seismic dispersion.(3) The elastic parameters measured in the direction vertical to the bedding plane had stronger dispersion and pressure sensitivity than those measured in the direction parallel to the bedding plane,and the anisotropy and pressure sensitivity at seismic frequencies were higher than those at the ultrasonic frequencies.(4) Fluid filling reduced the pressure sensitivity of the elastic parameters along the direction vertical to the bedding plane,whereas the opposite trend was observed along the direction parallel to the bedding plane.(5) The anisotropic Gassmann theory could explain the P-wave velocity well at an extremely low frequency,but the prediction of S-and P-wave velocities at a relatively high frequency remained insufficient.Overall,our study can serve as a reliable mechanism reference for the study of frequency-dependent properties of azimuthal anisotropy,and provide important guidance for the seismic prediction of “sweet spots” in shale oil reservoirs.展开更多
基金supported by the fund coded,National Natural Science Fund program(No.11975307)China National Defence Science and Technology Innovation Special Zone Project(19-H863-01-ZT-003-003-12).
文摘Spectrum prediction is one of the new techniques in cognitive radio that predicts changes in the spectrum state and plays a crucial role in improving spectrum sensing performance.Prediction models previously trained in the source band tend to perform poorly in the new target band because of changes in the channel.In addition,cognitive radio devices require dynamic spectrum access,which means that the time to retrain the model in the new band is minimal.To increase the amount of data in the target band,we use the GAN to convert the data of source band into target band.First,we analyze the data differences between bands and calculate FID scores to identify the available bands with the slightest difference from the target predicted band.The original GAN structure is unsuitable for converting spectrum data,and we propose the spectrum data conversion GAN(SDC-GAN).The generator module consists of a convolutional network and an LSTM module that can integrate multiple features of the data and can convert data from the source band to the target band.Finally,we use the generated target band data to train the prediction model.The experimental results validate the effectiveness of the proposed algorithm.
基金This work was supported by the Science and Technology Innovation 2030-Key Project of“New Generation Artificial Intelligence”of China under Grant 2018AAA0102303the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province(No.BK20190030)the National Natural Science Foundation of China(No.61631020,No.61871398,No.61931011 and No.U20B2038).
文摘This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.
基金supported by the National Key R&D Program of China under Grant 2018AAA0102303 and Grant 2018YFB1801103the National Natural Science Foundation of China (No. 61871398 and No. 61931011)+1 种基金the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province (No. BK20190030)the Equipment Advanced Research Field Foundation (No. 61403120304)
文摘Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.
基金supported by the National Natural Science Foundation of China (Grant Nos.U20B2015,41574103,41974120,41804104,and U19B600304)。
文摘The rock mechanical properties and elastic anisotropy of terrestrial shale oil reservoirs are affected by various factors,such as lithology,structure,pores,fractures,and fluids.The experimental study of dynamic and static elastic properties can provide important mechanism analysis for the prediction of geological and engineering “sweet spots” in shale reservoirs.There are a large number of studies on the measurement of static mechanical properties of shale,but the experiments on dynamic crossband elastic anisotropy of terrestrial shale have not yet been conducted thoroughly.Therefore,we report the anisotropic dispersion mechanism of favorable lithofacies(lamellar dolomitic shale,with vertical and horizontal bedding) in the inter-salt shale oil reservoir of the Qianjiang Formation for different confining pressures and fluid saturation conditions.The experiments were conducted by the cross-band rock physics measurement technology that comprised low-frequency stress-strain measurements and a high-frequency ultrasonic test.The experimental results indicated that:(1) The elastic property dispersion of the terrestrial shale was stronger than that of marine shale due to the high viscosity of the medium oil in the terrestrial shale.The lamellar structures and interbedded fractures were the main factors that determined the strong anisotropy of the terrestrial shale.(2) The dispersion of elastic properties from low to high frequencies in a partial oil saturation state ranged from strong to weak;the wave-induced fluid flow or intrinsic dissipation of viscoelastic inclusions may be the dominant mechanisms that caused the seismic dispersion.(3) The elastic parameters measured in the direction vertical to the bedding plane had stronger dispersion and pressure sensitivity than those measured in the direction parallel to the bedding plane,and the anisotropy and pressure sensitivity at seismic frequencies were higher than those at the ultrasonic frequencies.(4) Fluid filling reduced the pressure sensitivity of the elastic parameters along the direction vertical to the bedding plane,whereas the opposite trend was observed along the direction parallel to the bedding plane.(5) The anisotropic Gassmann theory could explain the P-wave velocity well at an extremely low frequency,but the prediction of S-and P-wave velocities at a relatively high frequency remained insufficient.Overall,our study can serve as a reliable mechanism reference for the study of frequency-dependent properties of azimuthal anisotropy,and provide important guidance for the seismic prediction of “sweet spots” in shale oil reservoirs.
