Wireless communication systems that incorporate digital twin(DT)alongside artificial intelligence(AI)are expected to transform 6G networks by providing advanced features for predictive modeling and decision making.The...Wireless communication systems that incorporate digital twin(DT)alongside artificial intelligence(AI)are expected to transform 6G networks by providing advanced features for predictive modeling and decision making.The key component is the creation of DT channels,which form the basis for upcoming applications.However,the existing work of channel predictive generation only considers time dimension,distribution-oriented or multi-step slidingwindow prediction schemes,which is not accurate and efficient for real-time DT communication systems.Therefore,we propose the wireless channel generative adversarial network(WCGAN)to tackle the issue of generating authentic long-batch channels for DT applications.The generator based on convolutional neural networks(CNN)extracts features from both the time and frequency domains to better capture the correlation.The loss function is designed to ensure that the generated channels consistently match the physical channels over an extended period while sharing the same probability distributions.Meanwhile,the accumulating error from the slicing window has been alleviated.The simulation demonstrates that an accurate and efficient DT channel can be generated by employing our proposed WCGAN in various scenarios.展开更多
Tungstated zirconia(WO_(3)/ZrO_(2))solid acid catalysts with different WO_(3) contents were prepared by a hydrothermal method and then used in the catalytic aquathermolysis of heavy oil from Xinjiang.The WO_(3)/ZrO_(2...Tungstated zirconia(WO_(3)/ZrO_(2))solid acid catalysts with different WO_(3) contents were prepared by a hydrothermal method and then used in the catalytic aquathermolysis of heavy oil from Xinjiang.The WO_(3)/ZrO_(2) solid acid catalyst was characterized by a range of characterization methods,including X-ray diffraction,NH3-temperature programmed desorption,and pyridine infrared spectroscopy.The WO_(3) content of the WO_(3)/ZrO_(2) catalysts had an important impact on the structure and property of the catalysts.When the WO_(3) mass fraction was 20%,it facilitated the formation of tetragonal zirconia,thereby enhancing the creation of robust acidic sites.Acidity is considered to have a strong impact on the catalytic performance of the aquathermolysis of heavy oil.When the catalyst containing 20%WO_(3) was used to catalyze the aquathermolysis of heavy oil under conditions of 14.5 MPa,340℃,and 24 h,the viscosity of heavy oil decreased from 47266 to 5398 mPa·s and the viscosity reduction rate reached 88.6%.The physicochemical properties of heavy oil before and after the aquathermolysis were analyzed using a saturates,aromatics,resins,and asphaltenes analysis,gas chromatography,elemental analysis,densimeter etc.After the aquathermolysis,the saturate and aromatic contents significantly increased from 43.3%to 48.35%and 19.47%to 21.88%,respectively,with large reductions in the content of resin and asphaltene from 28.22%to 25.06%and 5.36%to 2.03%,respectively.The sulfur and nitrogen contents,and the density of the oil were significantly decreased.These factors were likely the main reasons for promoting the viscosity reduction of heavy oil during the aquathermolysis over the WO_(3)/ZrO_(2) solid acid catalysts.展开更多
文摘Wireless communication systems that incorporate digital twin(DT)alongside artificial intelligence(AI)are expected to transform 6G networks by providing advanced features for predictive modeling and decision making.The key component is the creation of DT channels,which form the basis for upcoming applications.However,the existing work of channel predictive generation only considers time dimension,distribution-oriented or multi-step slidingwindow prediction schemes,which is not accurate and efficient for real-time DT communication systems.Therefore,we propose the wireless channel generative adversarial network(WCGAN)to tackle the issue of generating authentic long-batch channels for DT applications.The generator based on convolutional neural networks(CNN)extracts features from both the time and frequency domains to better capture the correlation.The loss function is designed to ensure that the generated channels consistently match the physical channels over an extended period while sharing the same probability distributions.Meanwhile,the accumulating error from the slicing window has been alleviated.The simulation demonstrates that an accurate and efficient DT channel can be generated by employing our proposed WCGAN in various scenarios.
基金the financial support from the Open Fund Project of the National Oil Shale Exploitation Research and Development Center,China(No.33550000-22-ZC0613-0255)the Graduate Student Innovation and Practical Ability Training Program of Xi’an Shiyou University(No.YCS23213098)+3 种基金the National Natural Science Foundation of China(No.52274039)the Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2024JC-YBMS-085)the CNPC Innovation Found(No.2022DQ02-0402)The authors also thank the Modern Analysis and Test Center of Xi’an Shiyou University for their help with the characterization of catalysts and analysis of products.
文摘Tungstated zirconia(WO_(3)/ZrO_(2))solid acid catalysts with different WO_(3) contents were prepared by a hydrothermal method and then used in the catalytic aquathermolysis of heavy oil from Xinjiang.The WO_(3)/ZrO_(2) solid acid catalyst was characterized by a range of characterization methods,including X-ray diffraction,NH3-temperature programmed desorption,and pyridine infrared spectroscopy.The WO_(3) content of the WO_(3)/ZrO_(2) catalysts had an important impact on the structure and property of the catalysts.When the WO_(3) mass fraction was 20%,it facilitated the formation of tetragonal zirconia,thereby enhancing the creation of robust acidic sites.Acidity is considered to have a strong impact on the catalytic performance of the aquathermolysis of heavy oil.When the catalyst containing 20%WO_(3) was used to catalyze the aquathermolysis of heavy oil under conditions of 14.5 MPa,340℃,and 24 h,the viscosity of heavy oil decreased from 47266 to 5398 mPa·s and the viscosity reduction rate reached 88.6%.The physicochemical properties of heavy oil before and after the aquathermolysis were analyzed using a saturates,aromatics,resins,and asphaltenes analysis,gas chromatography,elemental analysis,densimeter etc.After the aquathermolysis,the saturate and aromatic contents significantly increased from 43.3%to 48.35%and 19.47%to 21.88%,respectively,with large reductions in the content of resin and asphaltene from 28.22%to 25.06%and 5.36%to 2.03%,respectively.The sulfur and nitrogen contents,and the density of the oil were significantly decreased.These factors were likely the main reasons for promoting the viscosity reduction of heavy oil during the aquathermolysis over the WO_(3)/ZrO_(2) solid acid catalysts.
