以电采暖为代表的新兴负荷灵活运行能力强,利用其可调能力提升中高压配电网运行安全水平成为“源荷互动”在配电网场景下的重要需求。面对中高压配电网安全运行中网络参数辨识困难与源荷协同复杂度高的双重挑战,提出一种基于数据挖掘的...以电采暖为代表的新兴负荷灵活运行能力强,利用其可调能力提升中高压配电网运行安全水平成为“源荷互动”在配电网场景下的重要需求。面对中高压配电网安全运行中网络参数辨识困难与源荷协同复杂度高的双重挑战,提出一种基于数据挖掘的电采暖负荷优化调度方法。首先,建立融合设备热动态特性和用户舒适度约束的电采暖负荷精细化调节模型,量化分析其调控成本;其次,构建计及光伏出力时序特性和网络潮流安全约束的多时段协同优化模型,实现源荷双侧资源的动态匹配;进而,提出基于历史运行数据挖掘的功率转移分布因子(power transfer distribution factor,PTDF)矩阵在线辨识算法,突破传统物理建模对网络参数精度的依赖;最后,设计基于二次规划的高效求解策略,生成兼顾电网安全和用户需求的最优调控方案。基于IEEE 30系统的仿真结果表明:所提方法可有效避免关键线路和变压器重过载,同时可在不影响用户供暖情况下尽量降低调节代价,实现源网荷高效互动协同。展开更多
Agglomeration supports the high-quality development of the manufacturing industry,and its associated resource and environmental effects play a crucial role in driving green economic development.Based on data from pref...Agglomeration supports the high-quality development of the manufacturing industry,and its associated resource and environmental effects play a crucial role in driving green economic development.Based on data from prefecture-level cities in China from 2005 to 2019,this study employs the inverse distance weighting method,the bivariate local indicator of spatial association model,the spatial Durbin model,and other techniques to explore the relationship between manufacturing agglomeration and PM_(2.5)concentrations,and to assess the impact of its manufacturing agglomeration.Four correlation patterns are observed:high-high,low-low,high-low,and low-high.Among these,high-high and low-low patterns dominate in terms of number of cities.These correlation patterns demonstrate strong temporal stability,with a clear“Matthew effect”.The effect of manufacturing agglomeration on PM_(2.5)levels is significantly negative and helps reduce concentrations regionally,indicating the need to further enhance agglomeration levels regionally.However,it can increase PM_(2.5)levels in neighboring areas due to a siphon effect,and the impact of varies across regions.Compared with levels in 2005-2013,the significance of the relationship between manufacturing agglomeration and PM_(2.5)weakened in the 2013-2019 period.Accordingly,this study proposes countermeasures and policy recommendations aimed at strengthening regional collaborative governance and inspiring differentiated agglomeration strategies to support sustainable economic development in China.展开更多
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo...Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.展开更多
We propose that the core mass function(CMF)can be driven by filament fragmentation.To model a star-forming system of filaments and fibers,we develop a fractal and turbulent tree with a fractal dimension of 2 and a Lar...We propose that the core mass function(CMF)can be driven by filament fragmentation.To model a star-forming system of filaments and fibers,we develop a fractal and turbulent tree with a fractal dimension of 2 and a Larson's law exponent(β)of 0.5.The fragmentation driven by convergent flows along the splines of the fractal tree yields a Kroupa-IMF-like CMF that can be divided into three power-law segments with exponentsα=-0.5,-1.5,and-2,respectively.The turnover masses of the derived CMF are approximately four times those of the Kroupa IMF,corresponding to a star formation efficiency of 0.25.Adoptingβ=1/3,which leads to fractional Brownian motion along the filament,may explain a steeper CMF at the high-mass end,withα=-3.33 close to that of the Salpeter IMF.We suggest that the fibers of the tree are basic building blocks of star formation,with similar properties across different clouds,establishing a common density threshold for star formation and leading to a universal CMF.展开更多
文摘以电采暖为代表的新兴负荷灵活运行能力强,利用其可调能力提升中高压配电网运行安全水平成为“源荷互动”在配电网场景下的重要需求。面对中高压配电网安全运行中网络参数辨识困难与源荷协同复杂度高的双重挑战,提出一种基于数据挖掘的电采暖负荷优化调度方法。首先,建立融合设备热动态特性和用户舒适度约束的电采暖负荷精细化调节模型,量化分析其调控成本;其次,构建计及光伏出力时序特性和网络潮流安全约束的多时段协同优化模型,实现源荷双侧资源的动态匹配;进而,提出基于历史运行数据挖掘的功率转移分布因子(power transfer distribution factor,PTDF)矩阵在线辨识算法,突破传统物理建模对网络参数精度的依赖;最后,设计基于二次规划的高效求解策略,生成兼顾电网安全和用户需求的最优调控方案。基于IEEE 30系统的仿真结果表明:所提方法可有效避免关键线路和变压器重过载,同时可在不影响用户供暖情况下尽量降低调节代价,实现源网荷高效互动协同。
基金supported by the National Natural Science Foundation of China“Research on the Multi-scale Regional Industrial Spatial Evolution Mechanism,Resource and Environmental Effects,and Green Transformation in the Yellow River Basin”[Grant No.42371194]Taishan Scholar Foundation of Shandong Province[Grant Nos.tsqn202408148 and tstp20240821].
文摘Agglomeration supports the high-quality development of the manufacturing industry,and its associated resource and environmental effects play a crucial role in driving green economic development.Based on data from prefecture-level cities in China from 2005 to 2019,this study employs the inverse distance weighting method,the bivariate local indicator of spatial association model,the spatial Durbin model,and other techniques to explore the relationship between manufacturing agglomeration and PM_(2.5)concentrations,and to assess the impact of its manufacturing agglomeration.Four correlation patterns are observed:high-high,low-low,high-low,and low-high.Among these,high-high and low-low patterns dominate in terms of number of cities.These correlation patterns demonstrate strong temporal stability,with a clear“Matthew effect”.The effect of manufacturing agglomeration on PM_(2.5)levels is significantly negative and helps reduce concentrations regionally,indicating the need to further enhance agglomeration levels regionally.However,it can increase PM_(2.5)levels in neighboring areas due to a siphon effect,and the impact of varies across regions.Compared with levels in 2005-2013,the significance of the relationship between manufacturing agglomeration and PM_(2.5)weakened in the 2013-2019 period.Accordingly,this study proposes countermeasures and policy recommendations aimed at strengthening regional collaborative governance and inspiring differentiated agglomeration strategies to support sustainable economic development in China.
基金supported by the Gansu Provincial Natural Science Foundation(grant number 25JRRA074)the Gansu Provincial Key R&D Science and Technology Program(grant number 24YFGA060)the National Natural Science Foundation of China(grant number 62161019).
文摘Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.
基金support of the Strategic Priority Research Program of the Chinese Academy of Sciences under grant No.XDB0800303the National Key R&D Program of China under grant No.2022YFA1603100the National Natural Science Foundation of China(NSFC,Grant No.12203086)。
文摘We propose that the core mass function(CMF)can be driven by filament fragmentation.To model a star-forming system of filaments and fibers,we develop a fractal and turbulent tree with a fractal dimension of 2 and a Larson's law exponent(β)of 0.5.The fragmentation driven by convergent flows along the splines of the fractal tree yields a Kroupa-IMF-like CMF that can be divided into three power-law segments with exponentsα=-0.5,-1.5,and-2,respectively.The turnover masses of the derived CMF are approximately four times those of the Kroupa IMF,corresponding to a star formation efficiency of 0.25.Adoptingβ=1/3,which leads to fractional Brownian motion along the filament,may explain a steeper CMF at the high-mass end,withα=-3.33 close to that of the Salpeter IMF.We suggest that the fibers of the tree are basic building blocks of star formation,with similar properties across different clouds,establishing a common density threshold for star formation and leading to a universal CMF.
