在悬浮磁偶极场约束装置中,姿态控制系线圈(Tilt-Slide-Rotate,TSR)或者由高能量的粒子共振(Resonant Line Field,RLF)激发磁场会破坏背景磁场的拓扑结构,进而影响粒子约束。由于α粒子是DD-3He催化核反应产物之一,能否在TSR线圈和RLF...在悬浮磁偶极场约束装置中,姿态控制系线圈(Tilt-Slide-Rotate,TSR)或者由高能量的粒子共振(Resonant Line Field,RLF)激发磁场会破坏背景磁场的拓扑结构,进而影响粒子约束。由于α粒子是DD-3He催化核反应产物之一,能否在TSR线圈和RLF效应产生的磁场扰动中稳定约束高能量α粒子,对于加热背景等离子体的研究具有非常重要的意义。本研究中背景磁偶极场是通过偶极场平衡代码求解一个交换稳定的平衡得到的,在TSR线圈倾斜和偏移的工作模式下,对粒子投掷位置和TSR线圈工作电流α粒子约束时间和空间特性进行统计,同时在背景磁偶极场中叠加环向磁场方法模拟低极向扰动模数的磁场。由于TSR线圈产生磁场会破坏背景磁偶极场闭合磁场线的结构,使得投掷位置靠近TSR线圈侧的α粒子迅速损失。在RLF效应引发的模数n=0和n=1的极向扰动磁场中,在10μs内,n=0极向扰动磁场比n=1的磁扰动约束粒子份额更高,且当α粒子飞行时间大于10μs后,n=0模式下约束粒子份额迅速减少。展开更多
Taking the natural gas reservoirs of the Sinian Dengying Formation on the east and west sides(Gaoshiti-Moxi area and north slope of central Sichuan paleo-uplift on the east;Weiyuan and Well Datan-1 block on the west)o...Taking the natural gas reservoirs of the Sinian Dengying Formation on the east and west sides(Gaoshiti-Moxi area and north slope of central Sichuan paleo-uplift on the east;Weiyuan and Well Datan-1 block on the west)of the Deyang-Anyue rift trough in the Sichuan Basin,China,as the research object,the geochemical parameters(component,isotopic composition)of natural gas from the Dengying Formation in different areas are compared,and then the differences in geochemical characteristics of Dengying natural gas on the east and west sides of the Deyang-Anyue rift trough and their genesis are clarified.First,the Dengying gas reservoirs on both sides of the rift trough are predominantly composed of oil-cracking gas with high maturity,which is typical dry gas.Second,severely modified by thermochemical sulfate reduction(TSR)reaction,the Dengying gas reservoirs on the east side exhibit high H2S and CO_(2) contents,with an elevated δ^(13)C_(2) value(average value higher than-29‰).The Dengying gas reservoirs in the Weiyuan area are less affected by TSR modification,though the δ^(13)C_(1) values are slightly greater than that of the reservoirs on the east side with partial reversal of carbon isotope composition,likely due to the water-soluble gas precipitation and accumulation mechanism.The Dengying gas reservoir of Well Datan-1 shows no influence from TSR.Third,the Dengying gas reservoirs reflect high helium contents(significantly higher than that on the east side)in the Weiyuan and Datan-1 areas on the west side,which is supposed to attribute to the widespread granites in basement and efficient vertical transport along faults.Fourth,controlled by the paleo-salinity of water medium in the depositional period of the source rock,the δ^(2)HCH_(4) values of the Dengying gas reservoirs on the west side are slightly lighter than those on the east side.Fifth,the Dengying natural gas in the Datan-1 area is contributed by the source rocks of the Sinian Doushantuo Formation and the third member of the Dengying Formation,in addition to the Cambrian Qiongzhusi Formation.展开更多
Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recogn...Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.展开更多
文摘在悬浮磁偶极场约束装置中,姿态控制系线圈(Tilt-Slide-Rotate,TSR)或者由高能量的粒子共振(Resonant Line Field,RLF)激发磁场会破坏背景磁场的拓扑结构,进而影响粒子约束。由于α粒子是DD-3He催化核反应产物之一,能否在TSR线圈和RLF效应产生的磁场扰动中稳定约束高能量α粒子,对于加热背景等离子体的研究具有非常重要的意义。本研究中背景磁偶极场是通过偶极场平衡代码求解一个交换稳定的平衡得到的,在TSR线圈倾斜和偏移的工作模式下,对粒子投掷位置和TSR线圈工作电流α粒子约束时间和空间特性进行统计,同时在背景磁偶极场中叠加环向磁场方法模拟低极向扰动模数的磁场。由于TSR线圈产生磁场会破坏背景磁偶极场闭合磁场线的结构,使得投掷位置靠近TSR线圈侧的α粒子迅速损失。在RLF效应引发的模数n=0和n=1的极向扰动磁场中,在10μs内,n=0极向扰动磁场比n=1的磁扰动约束粒子份额更高,且当α粒子飞行时间大于10μs后,n=0模式下约束粒子份额迅速减少。
基金Supported by the National Natural Science Foundation of China(42272161)PetroChina Science and Technology Major Project(2023ZZ16)Research Institute of Exploration and Development,PetroChina Southwest Oil&Gasfield Company(2024D101-01-06)。
文摘Taking the natural gas reservoirs of the Sinian Dengying Formation on the east and west sides(Gaoshiti-Moxi area and north slope of central Sichuan paleo-uplift on the east;Weiyuan and Well Datan-1 block on the west)of the Deyang-Anyue rift trough in the Sichuan Basin,China,as the research object,the geochemical parameters(component,isotopic composition)of natural gas from the Dengying Formation in different areas are compared,and then the differences in geochemical characteristics of Dengying natural gas on the east and west sides of the Deyang-Anyue rift trough and their genesis are clarified.First,the Dengying gas reservoirs on both sides of the rift trough are predominantly composed of oil-cracking gas with high maturity,which is typical dry gas.Second,severely modified by thermochemical sulfate reduction(TSR)reaction,the Dengying gas reservoirs on the east side exhibit high H2S and CO_(2) contents,with an elevated δ^(13)C_(2) value(average value higher than-29‰).The Dengying gas reservoirs in the Weiyuan area are less affected by TSR modification,though the δ^(13)C_(1) values are slightly greater than that of the reservoirs on the east side with partial reversal of carbon isotope composition,likely due to the water-soluble gas precipitation and accumulation mechanism.The Dengying gas reservoir of Well Datan-1 shows no influence from TSR.Third,the Dengying gas reservoirs reflect high helium contents(significantly higher than that on the east side)in the Weiyuan and Datan-1 areas on the west side,which is supposed to attribute to the widespread granites in basement and efficient vertical transport along faults.Fourth,controlled by the paleo-salinity of water medium in the depositional period of the source rock,the δ^(2)HCH_(4) values of the Dengying gas reservoirs on the west side are slightly lighter than those on the east side.Fifth,the Dengying natural gas in the Datan-1 area is contributed by the source rocks of the Sinian Doushantuo Formation and the third member of the Dengying Formation,in addition to the Cambrian Qiongzhusi Formation.
文摘Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.
