China has proposed to achieve carbon neutrality by 2060.Although previous studies have assessed net-zero emissions pathways in China,the diversity observed in these studies in terms of model construction and parameter...China has proposed to achieve carbon neutrality by 2060.Although previous studies have assessed net-zero emissions pathways in China,the diversity observed in these studies in terms of model construction and parameter setting has led to inconsistent conclusions on some key issues,such as energy transition.This study employs a multi-model comparison method to examine the significance of China's carbon neutrality target on its energy systems by assessing 67 carbon neutrality scenarios in China that are collected from the ADVANCE database,and identifying the differences between energy transition pathways under BEF60 scenarios(i.e.scenarios realising carbon neutrality by 2060)and AFT60 scenarios(i.e.scenarios realising carbon neutrality after 2060).Results show that China needs a larger deployment of low-carbon electricity,a higher electrification rate and more carbon sequestration amount under BEF60 scenarios than those under AFT60 scenarios.Meanwhile,the magnitude of the difference between the two categories of scenarios varies significantly in terms of different outcome indicators.Those that present significant disparities include the deployment scale of solar power(increasing by 160%),the electrification rate of the building sector(increasing by 27%)and the carbon sequestration amount of biomass power with carbon capture and storage(increasing by 380%).In addition,this study selects six indicators to present the technological and economic characteristics of various energy systems of China at the point of net-zero emissions.Exploring the relationships between characteristics,this research identifies the common features among various net-zero energy systems.A great share of non-biomass renewable power generation is always associated with a relatively high per capita energy use,implying that high renewables penetration may relax restrictions on energy consumption,which should be addressed for China when making efforts to promote energy transition.展开更多
The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of ta...The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of taking the contour geometric features into account,which may lead to mismatching in map boundaries and areas with intensive contours or extreme terrain changes.In light of this,it is put forward that a matching strategy from coarse to precious based on the contour geometric features.The proposed matching strategy can be described as follows.Firstly,the point sequence is converted to feature sequence according to a feature descriptive function based on curvature and angle of normal vector.Then the level of similarity among multi-source contours is calculated by using the longest common subsequence solution.Accordingly,the identical contours could be matched based on the above calculated results.In the experiment for the proposed method,the reliability and efficiency of the matching method are verified using simulative datasets and real datasets respectively.It has been proved that the proposed contour matching strategy has a high matching precision and good applicability.展开更多
Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,th...Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively.In this paper,we propose an improved common spatial pattern(B-CSP)method to extract features for alleviating these adverse effects.First,for different subjects,the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization(ERD)and event-related synchronization(ERS)patterns;then the signals of the optimal frequency band are decomposed into spatial patterns,and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data.The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network(BPNN)classifier to classify single-trial MI EEG.Another two conventional feature extraction methods,original common spatial pattern(CSP)and autoregressive(AR),are used for comparison.An improved classification performance for both data sets(public data set:91.25%±1.77%for left hand vs.foot and84.50%±5.42%for left hand vs.right hand;experimental data set:90.43%±4.26%for left hand vs.foot)verifies the advantages of the B-CSP method over conventional methods.The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively,and this study provides practical and theoretical approaches to BCI applications.展开更多
基金Financial support was obtained from the Key Project of the National Natural Science Foundation of China(72140007)the National Key Research and Development Program of China(2018YFA0606504)Energy Foundation(G-2009-32445).
文摘China has proposed to achieve carbon neutrality by 2060.Although previous studies have assessed net-zero emissions pathways in China,the diversity observed in these studies in terms of model construction and parameter setting has led to inconsistent conclusions on some key issues,such as energy transition.This study employs a multi-model comparison method to examine the significance of China's carbon neutrality target on its energy systems by assessing 67 carbon neutrality scenarios in China that are collected from the ADVANCE database,and identifying the differences between energy transition pathways under BEF60 scenarios(i.e.scenarios realising carbon neutrality by 2060)and AFT60 scenarios(i.e.scenarios realising carbon neutrality after 2060).Results show that China needs a larger deployment of low-carbon electricity,a higher electrification rate and more carbon sequestration amount under BEF60 scenarios than those under AFT60 scenarios.Meanwhile,the magnitude of the difference between the two categories of scenarios varies significantly in terms of different outcome indicators.Those that present significant disparities include the deployment scale of solar power(increasing by 160%),the electrification rate of the building sector(increasing by 27%)and the carbon sequestration amount of biomass power with carbon capture and storage(increasing by 380%).In addition,this study selects six indicators to present the technological and economic characteristics of various energy systems of China at the point of net-zero emissions.Exploring the relationships between characteristics,this research identifies the common features among various net-zero energy systems.A great share of non-biomass renewable power generation is always associated with a relatively high per capita energy use,implying that high renewables penetration may relax restrictions on energy consumption,which should be addressed for China when making efforts to promote energy transition.
基金National Science Foundation of China(Nos.41801388,41901397)。
文摘The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of taking the contour geometric features into account,which may lead to mismatching in map boundaries and areas with intensive contours or extreme terrain changes.In light of this,it is put forward that a matching strategy from coarse to precious based on the contour geometric features.The proposed matching strategy can be described as follows.Firstly,the point sequence is converted to feature sequence according to a feature descriptive function based on curvature and angle of normal vector.Then the level of similarity among multi-source contours is calculated by using the longest common subsequence solution.Accordingly,the identical contours could be matched based on the above calculated results.In the experiment for the proposed method,the reliability and efficiency of the matching method are verified using simulative datasets and real datasets respectively.It has been proved that the proposed contour matching strategy has a high matching precision and good applicability.
基金Project supported by the National Natural Science Foundation of China(Nos.61702454 and 61772468)the MOE Project of Humanities and Social Sciences,China(No.17YJC870018)+1 种基金the Fundamental Research Funds for the Provincial Universities of Zhejiang Province,China(No.GB201901006)the Philosophy and Social Science Planning Fund Project of Zhejiang Province,China(No.20NDQN260YB)
文摘Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively.In this paper,we propose an improved common spatial pattern(B-CSP)method to extract features for alleviating these adverse effects.First,for different subjects,the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization(ERD)and event-related synchronization(ERS)patterns;then the signals of the optimal frequency band are decomposed into spatial patterns,and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data.The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network(BPNN)classifier to classify single-trial MI EEG.Another two conventional feature extraction methods,original common spatial pattern(CSP)and autoregressive(AR),are used for comparison.An improved classification performance for both data sets(public data set:91.25%±1.77%for left hand vs.foot and84.50%±5.42%for left hand vs.right hand;experimental data set:90.43%±4.26%for left hand vs.foot)verifies the advantages of the B-CSP method over conventional methods.The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively,and this study provides practical and theoretical approaches to BCI applications.
