Studying the significant impacts on vegetation of drought due to global warming is crucial in order to understand its dynamics and interrelationships with temperature,rainfall,and normalized difference vegetation inde...Studying the significant impacts on vegetation of drought due to global warming is crucial in order to understand its dynamics and interrelationships with temperature,rainfall,and normalized difference vegetation index(NDVI).These factors are linked to excesses drought frequency and severity on the regional scale,and their effect on vegetation remains an important topic for climate change study.East Asia is very sensitive and susceptible to climate change.In this study,we examined the effect of drought on the seasonal variations of vegetation in relation to climate variability and determined which growing seasons are most vulnerable to drought risk;and then explored the spatio-temporal evolution of the trend in drought changes in East Asia from 1982 to 2019.The data were studied using a series of several drought indexes,and the data were then classified using a heat map,box and whisker plot analysis,and principal component analysis.The various drought indexes from January to August improved rapidly,except for vegetation health index(VHI)and temperature condition index(TCI).While these indices were constant in September,they increased again in October,but in December,they showed a descending trend.The seasonal and monthly analysis of the drought indexes and the heat map confirmed that the East Asian region suffered from extreme droughts in 1984,1993,2007,and 2012among the study years.The distribution of the trend in drought changes indicated that more severe drought occurred in the northwestern region than in the southeastern area of East Asia.The drought tendency slope was used to describe the changes in drought events during 1982–2019 in the study region.The correlations among monthly precipitation anomaly percentage(NAP),NDVI,TCI,vegetation condition index(VCI),temperature vegetation drought index(TVDI),and VHI indicated considerably positive correlations,while considerably negative correlations were found among the three pairs of NDVI and VHI,TVDI and VHI,and NDVI and TCI.This ecological and climatic mechani展开更多
Adaptive sampling is an iterative process for the construction of a global approximation model. Most of engineering analysis tools computes multiple parameters in a single run. This research proposes a novel multi-res...Adaptive sampling is an iterative process for the construction of a global approximation model. Most of engineering analysis tools computes multiple parameters in a single run. This research proposes a novel multi-response adaptive sampling algorithm for simultaneous construction of multiple surrogate models in a time-efficient and accurate manner. The new algorithm uses the Jackknife cross-validation variance and a minimum distance metric to construct a sampling criterion function. A weighted sum of the function is used to consider the characteristics of multiple surrogate models. The proposed algorithm demonstrates good performance on total 22 numerical problems in comparison with three existing adaptive sampling algorithms. The numerical problems include several two-dimensional and six-dimensional functions which are combined into singleresponse and multi-response systems. Application of the proposed algorithm for construction of aerodynamic tables for 2 D airfoil is demonstrated. Scaling-based variable-fidelity modeling is implemented to enhance the accuracy of surrogate modeling. The algorithm succeeds in constructing a system of three highly nonlinear aerodynamic response surfaces within a reasonable amount of time while preserving high accuracy of approximation.展开更多
Multi-fidelity Data Fusion(MDF)frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels.How...Multi-fidelity Data Fusion(MDF)frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels.However,most existing MDF frameworks assume a uniform data structure between sampling data sources;thus,producing an accurate solution at the required level,for cases of non-uniform data structures is challenging.To address this challenge,an Adaptive Multi-fidelity Data Fusion(AMDF)framework is proposed to produce a composite surrogate model which can efficiently model multi-fidelity data featuring non-uniform structures.Firstly,the design space of the input data with non-uniform data structures is decomposed into subdomains containing simplified structures.Secondly,different MDF frameworks and a rule-based selection process are adopted to construct multiple local models for the subdomain data.On the other hand,the Enhanced Local Fidelity Modeling(ELFM)method is proposed to combine the generated local models into a unique and continuous global model.Finally,the resulting model inherits the features of local models and approximates a complete database for the whole design space.The validation of the proposed framework is performed to demonstrate its approximation capabilities in(A)four multi-dimensional analytical problems and(B)a practical engineering case study of constructing an F16C fighter aircraft’s aerodynamic database.Accuracy comparisons of the generated models using the proposed AMDF framework and conventional MDF approaches using a single global modeling algorithm are performed to reveal the adaptability of the proposed approach for fusing multi-fidelity data featuring non-uniform structures.Indeed,the results indicated that the proposed framework outperforms the state-of-the-art MDF approach in the cases of non-uniform data.展开更多
In recent years,major cities around the world such as New York in USA,Melbourne in Australia,and Shanghai in China,have planned to boost their nighttime urban vibrancy levels to spur the economy and achieve cultural d...In recent years,major cities around the world such as New York in USA,Melbourne in Australia,and Shanghai in China,have planned to boost their nighttime urban vibrancy levels to spur the economy and achieve cultural diversity.The study of nighttime urban vibrancy from the perspective of spatiotemporal characteristics is increasingly being recognized as part of the essential work in the field of urban planning and geography.This research used mobile phone signaling records to measure urban vibrancy in central Shanghai and revealed its spatiotemporal patterns during nighttime.Specifically,this research explored the changes of urban vibrancy within a day,studied the distribution of urban vibrancy during the nighttime,and visually presented the spatiotemporal changes of nighttime urban vibrancy in central Shanghai.Moreover,on the basis of the behavior pattern of each mobile user,we classified nighttime urban vibrancy into three different types:nighttime working vibrancy,nighttime leisure vibrancy,and nighttime floating vibrancy.We then tried to determine how land use affected nighttime leisure vibrancy.The results showed that urban vibrancy in central Shanghai exhibits a periodic pattern over one-day period.A high-level nighttime urban vibrancy belt is present within central Shanghai.Business offices,hotels,entertainment and recreational districts,wholesale markets,and express services contribute most to the vibrancy at nighttime.In addition,the correlation analysis shows that public and commercial facilities generate high levels of nighttime leisure vibrancy than residential facilities.The mixed land use of public and commercial facilities and residential facilities within 500 m is more critical than the mixed use of a single land lot.The research can be a basis for supporting land use planning and providing evidence for policy-making to improve the level of nighttime urban vibrancy in cities.展开更多
基金the Basic Research Project of Zhejiang Normal University,China(ZC304022952)the China Postdoctoral Science Foundation Funding(2018M642614)the Natural Science Foundation Youth Proj ect of S h andong Provi nce,C hina(ZR2020QF281)。
文摘Studying the significant impacts on vegetation of drought due to global warming is crucial in order to understand its dynamics and interrelationships with temperature,rainfall,and normalized difference vegetation index(NDVI).These factors are linked to excesses drought frequency and severity on the regional scale,and their effect on vegetation remains an important topic for climate change study.East Asia is very sensitive and susceptible to climate change.In this study,we examined the effect of drought on the seasonal variations of vegetation in relation to climate variability and determined which growing seasons are most vulnerable to drought risk;and then explored the spatio-temporal evolution of the trend in drought changes in East Asia from 1982 to 2019.The data were studied using a series of several drought indexes,and the data were then classified using a heat map,box and whisker plot analysis,and principal component analysis.The various drought indexes from January to August improved rapidly,except for vegetation health index(VHI)and temperature condition index(TCI).While these indices were constant in September,they increased again in October,but in December,they showed a descending trend.The seasonal and monthly analysis of the drought indexes and the heat map confirmed that the East Asian region suffered from extreme droughts in 1984,1993,2007,and 2012among the study years.The distribution of the trend in drought changes indicated that more severe drought occurred in the northwestern region than in the southeastern area of East Asia.The drought tendency slope was used to describe the changes in drought events during 1982–2019 in the study region.The correlations among monthly precipitation anomaly percentage(NAP),NDVI,TCI,vegetation condition index(VCI),temperature vegetation drought index(TVDI),and VHI indicated considerably positive correlations,while considerably negative correlations were found among the three pairs of NDVI and VHI,TVDI and VHI,and NDVI and TCI.This ecological and climatic mechani
基金supported by the Konkuk University Brain Pool 2018the National Research Foundation of Korea(NRF)[Grant NRF-2018R1D1A1B07046779]funded by the Korean government(MISP)
文摘Adaptive sampling is an iterative process for the construction of a global approximation model. Most of engineering analysis tools computes multiple parameters in a single run. This research proposes a novel multi-response adaptive sampling algorithm for simultaneous construction of multiple surrogate models in a time-efficient and accurate manner. The new algorithm uses the Jackknife cross-validation variance and a minimum distance metric to construct a sampling criterion function. A weighted sum of the function is used to consider the characteristics of multiple surrogate models. The proposed algorithm demonstrates good performance on total 22 numerical problems in comparison with three existing adaptive sampling algorithms. The numerical problems include several two-dimensional and six-dimensional functions which are combined into singleresponse and multi-response systems. Application of the proposed algorithm for construction of aerodynamic tables for 2 D airfoil is demonstrated. Scaling-based variable-fidelity modeling is implemented to enhance the accuracy of surrogate modeling. The algorithm succeeds in constructing a system of three highly nonlinear aerodynamic response surfaces within a reasonable amount of time while preserving high accuracy of approximation.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2020R1A6A1A03046811).This paper was also supported by Konkuk University Researcher Fund in 2021.
文摘Multi-fidelity Data Fusion(MDF)frameworks have emerged as a prominent approach to producing economical but accurate surrogate models for aerodynamic data modeling by integrating data with different fidelity levels.However,most existing MDF frameworks assume a uniform data structure between sampling data sources;thus,producing an accurate solution at the required level,for cases of non-uniform data structures is challenging.To address this challenge,an Adaptive Multi-fidelity Data Fusion(AMDF)framework is proposed to produce a composite surrogate model which can efficiently model multi-fidelity data featuring non-uniform structures.Firstly,the design space of the input data with non-uniform data structures is decomposed into subdomains containing simplified structures.Secondly,different MDF frameworks and a rule-based selection process are adopted to construct multiple local models for the subdomain data.On the other hand,the Enhanced Local Fidelity Modeling(ELFM)method is proposed to combine the generated local models into a unique and continuous global model.Finally,the resulting model inherits the features of local models and approximates a complete database for the whole design space.The validation of the proposed framework is performed to demonstrate its approximation capabilities in(A)four multi-dimensional analytical problems and(B)a practical engineering case study of constructing an F16C fighter aircraft’s aerodynamic database.Accuracy comparisons of the generated models using the proposed AMDF framework and conventional MDF approaches using a single global modeling algorithm are performed to reveal the adaptability of the proposed approach for fusing multi-fidelity data featuring non-uniform structures.Indeed,the results indicated that the proposed framework outperforms the state-of-the-art MDF approach in the cases of non-uniform data.
基金the National Natural Science Foundation of China(41771170).
文摘In recent years,major cities around the world such as New York in USA,Melbourne in Australia,and Shanghai in China,have planned to boost their nighttime urban vibrancy levels to spur the economy and achieve cultural diversity.The study of nighttime urban vibrancy from the perspective of spatiotemporal characteristics is increasingly being recognized as part of the essential work in the field of urban planning and geography.This research used mobile phone signaling records to measure urban vibrancy in central Shanghai and revealed its spatiotemporal patterns during nighttime.Specifically,this research explored the changes of urban vibrancy within a day,studied the distribution of urban vibrancy during the nighttime,and visually presented the spatiotemporal changes of nighttime urban vibrancy in central Shanghai.Moreover,on the basis of the behavior pattern of each mobile user,we classified nighttime urban vibrancy into three different types:nighttime working vibrancy,nighttime leisure vibrancy,and nighttime floating vibrancy.We then tried to determine how land use affected nighttime leisure vibrancy.The results showed that urban vibrancy in central Shanghai exhibits a periodic pattern over one-day period.A high-level nighttime urban vibrancy belt is present within central Shanghai.Business offices,hotels,entertainment and recreational districts,wholesale markets,and express services contribute most to the vibrancy at nighttime.In addition,the correlation analysis shows that public and commercial facilities generate high levels of nighttime leisure vibrancy than residential facilities.The mixed land use of public and commercial facilities and residential facilities within 500 m is more critical than the mixed use of a single land lot.The research can be a basis for supporting land use planning and providing evidence for policy-making to improve the level of nighttime urban vibrancy in cities.
