The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the tradit...The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.展开更多
With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),po...With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy.展开更多
目的:探究MR减影技术在MR子宫输卵管造影(magnetic resonance hysterosalpingography,MR-HSG)图像后处理中的应用价值。方法:回顾性分析2019年3月至2022年12月某院收治的36例女性不孕症患者的临床资料及MR-HSG检查资料,最终纳入患者34...目的:探究MR减影技术在MR子宫输卵管造影(magnetic resonance hysterosalpingography,MR-HSG)图像后处理中的应用价值。方法:回顾性分析2019年3月至2022年12月某院收治的36例女性不孕症患者的临床资料及MR-HSG检查资料,最终纳入患者34例。所有患者均采用美国GE Brivo MR3551.5T光纤磁共振成像系统和6通道体部相控阵表面线圈进行检查,利用GE AW 4.6后处理工作站对原始图像进行减影后处理。采用卡方检验比较原始图像、减影图像、联合图像的输卵管显示情况(显示率),采用配对t检验比较运用MR减影技术前后图像信噪比(signal to noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR)的差异。结果:输卵管间质部在原始图像、减影图像、联合图像中的显示率分别为37.88%、48.48%、51.52%,输卵管峡部在原始图像、减影图像、联合图像中的显示率分别为57.58%、62.12%、66.67%,输卵管壶腹部在原始图像、减影图像、联合图像中的显示率分别为77.27%、84.85%、89.39%。在间质部,减影图像与联合图像显示率比较,差异有统计学意义(P<0.01)。在峡部、壶腹部及宫腔轮廓中,将通过原始图像、减影图像、联合图像所得的显示率两两之间进行比较,差异有统计学意义(P<0.05)。减影图像输卵管壶腹部SNR为97.57±43.11、CNR为139.67±62.85,均高于原始图像(SNR为61.23±24.19、CNR为75.37±34.62),差异有统计学意义(P均<0.05)。结论:利用MR减影技术进行MR-HSG图像后处理可明显提升MR-HSG图像SNR、CNR及输卵管可视化效果,提高输卵管检出率,降低阅片误判率,为优化MR-HSG诊断流程提供参考。展开更多
提出T-球形模糊数的减法和除法算子,讨论T-球形模糊数减法和除法算子的性质。提出基于T-球形模糊减法和除法算子的灰色关联分析方法,并将该方法与多准则妥协解排序(vlsekriterijumska optimizacija i kompromisno resenje,VIKOR)方法结...提出T-球形模糊数的减法和除法算子,讨论T-球形模糊数减法和除法算子的性质。提出基于T-球形模糊减法和除法算子的灰色关联分析方法,并将该方法与多准则妥协解排序(vlsekriterijumska optimizacija i kompromisno resenje,VIKOR)方法结合,防止计算过程中T-球形模糊信息的丢失,利用一种新的得分函数完善T-球形模糊数的比较机制。通过实例及对比实验说明所提的基于T-球形模糊减法和除法算子的灰色-VIKOR方法的有效性和优越性,为解决T-球形模糊环境下的多属性决策问题提供新的有效的方法。展开更多
基金supported by National Natural Science Foundation of China(42364008,41804110)in part by Guizhou Provincial Basic Research Program(Natural Science)(ZK[2022]060)+1 种基金in part by China Postdoctoral Science Foundation(2022M723127)in part by Youth Innovation Team Project of Shandong Provincial Education Department(2022KJ141).
文摘The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.
基金Supported by the Technology Project of State Grid Corporation Headquarters(No.5100-202322029A-1-1-ZN)the 2024 Youth Science Foundation Project of China (No.62303006)。
文摘With the large-scale integration of new energy sources,various resources such as energy storage,electric vehicles(EVs),and photovoltaics(PV) have participated in the scheduling of active distribution networks(ADNs),posing new challenges to the operation and scheduling of distribution networks.Aiming at the uncertainty of PV and EV,an optimal scheduling model for ADNs based on multi-scenario fuzzy set based charging station resource forecasting is constructed.To address the scheduling uncertainties caused by PV and load forecasting errors,a day-ahead optimal scheduling model based on conditional value at risk(CVaR) for cost assessment is established,with the optimization objectives of minimizing the operation cost of distribution networks and the risk cost caused by forecasting errors.An improved subtractive optimizer algorithm is proposed to solve the model and formulate day-ahead optimization schemes.Secondly,a forecasting model for dispatchable resources in charging stations is constructed based on event-based fuzzy set theory.On this basis,an intraday scheduling model is built to comprehensively utilize the dispatchable resources of charging stations to coordinate with the output of distributed power sources,achieving optimal scheduling with the goal of minimizing operation costs.Finally,an experimental scenario based on the IEEE-33 node system is designed for simulation verification.The comparison of optimal scheduling results shows that the proposed method can fully exploit the potential scheduling resources of charging stations,improving the operation stability of ADNs and the accommodution capacity of new energy.
文摘目的:探究MR减影技术在MR子宫输卵管造影(magnetic resonance hysterosalpingography,MR-HSG)图像后处理中的应用价值。方法:回顾性分析2019年3月至2022年12月某院收治的36例女性不孕症患者的临床资料及MR-HSG检查资料,最终纳入患者34例。所有患者均采用美国GE Brivo MR3551.5T光纤磁共振成像系统和6通道体部相控阵表面线圈进行检查,利用GE AW 4.6后处理工作站对原始图像进行减影后处理。采用卡方检验比较原始图像、减影图像、联合图像的输卵管显示情况(显示率),采用配对t检验比较运用MR减影技术前后图像信噪比(signal to noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR)的差异。结果:输卵管间质部在原始图像、减影图像、联合图像中的显示率分别为37.88%、48.48%、51.52%,输卵管峡部在原始图像、减影图像、联合图像中的显示率分别为57.58%、62.12%、66.67%,输卵管壶腹部在原始图像、减影图像、联合图像中的显示率分别为77.27%、84.85%、89.39%。在间质部,减影图像与联合图像显示率比较,差异有统计学意义(P<0.01)。在峡部、壶腹部及宫腔轮廓中,将通过原始图像、减影图像、联合图像所得的显示率两两之间进行比较,差异有统计学意义(P<0.05)。减影图像输卵管壶腹部SNR为97.57±43.11、CNR为139.67±62.85,均高于原始图像(SNR为61.23±24.19、CNR为75.37±34.62),差异有统计学意义(P均<0.05)。结论:利用MR减影技术进行MR-HSG图像后处理可明显提升MR-HSG图像SNR、CNR及输卵管可视化效果,提高输卵管检出率,降低阅片误判率,为优化MR-HSG诊断流程提供参考。
文摘提出T-球形模糊数的减法和除法算子,讨论T-球形模糊数减法和除法算子的性质。提出基于T-球形模糊减法和除法算子的灰色关联分析方法,并将该方法与多准则妥协解排序(vlsekriterijumska optimizacija i kompromisno resenje,VIKOR)方法结合,防止计算过程中T-球形模糊信息的丢失,利用一种新的得分函数完善T-球形模糊数的比较机制。通过实例及对比实验说明所提的基于T-球形模糊减法和除法算子的灰色-VIKOR方法的有效性和优越性,为解决T-球形模糊环境下的多属性决策问题提供新的有效的方法。
