Aims The pattern and driving factors of forest fires are of interest for fire occurrence prediction and forest fire management.The aims of the study were:(i)to describe the history of human-caused fires by season and ...Aims The pattern and driving factors of forest fires are of interest for fire occurrence prediction and forest fire management.The aims of the study were:(i)to describe the history of human-caused fires by season and size of burned area over time;(ii)to identify the spatial patterns of human-caused fires and test for the existence of‘hotspots’to determine their exact locations in the Daxing’an mountains;(iii)to determine the driving factors that determine the spatial distribution and the possibility of human-caused fire occurrence.Methods In this study,K-function and Kernel density estimation were used to analyze the spatial pattern of human-caused fires.The analysis was conducted in s-plus and arcgIs environments,respectively.The analysis of driving factors was performed in SPSS 19.0 based on a logistic regression model.The variables used to identify factors that influence fire occurrence included vegetation types,meteorological conditions,socioeconomic factors,topography and infrastructure factors,which were extracted and collected through the spatial analysis mode of arcgIs and from official statistics,respectively.Important Findings The annual number of human-caused fires and the area burnt have declined since 1987 due to the implementation of a forest fire protection act.There were significant spatial heterogeneity and seasonal variations in the distribution of human-caused fires in the Daxing’an mountains.The heterogeneity was caused by elevation,distance to the nearest railway,forest type and temperature.a logistic regression model was developed to predict the likelihood of human-caused fire occurrence in the Daxing’an mountains;its global accuracy attained 64.8%.The model was thus comparable to other relevant studies.展开更多
针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别...针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。展开更多
This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a rand...This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.展开更多
In order to solve the problem of low accuracy of traditional fixed window width kernel density estimation(KDE)in radar cross section(RCS)statistical characteristics analysis,an improved Epanechnikov KDE(K-KDE)algorith...In order to solve the problem of low accuracy of traditional fixed window width kernel density estimation(KDE)in radar cross section(RCS)statistical characteristics analysis,an improved Epanechnikov KDE(K-KDE)algorithm was proposed to analyze the statistical characteristics of the engine’s backward RCS.Firstly,the K-nearest neighbor method was used to calculate the dynamic window width of the K-KDE,and the Euclidean distance of each adjacent sample was used to judge the local density of the sample,and then the window width of the kernel function was adjusted by the distance between the sample point and the nearest neighbor to complete the KDE.Secondly,based on the K-KDE and the traditional KDE algorithm,the cumulative probability density function(CPDF)of four RCS random distribution sample points subject to fixed parameters was calculated.The results showed that the root mean square error of the K-KDE was reduced by 31.2%,38.8%,38.1%and 31.9%respectively compared with the KDE.Finally,the K-KDE combined with the second generation statistical analysis models were used to analyze the statistical characteristics of the engine backward RCS.展开更多
文摘Aims The pattern and driving factors of forest fires are of interest for fire occurrence prediction and forest fire management.The aims of the study were:(i)to describe the history of human-caused fires by season and size of burned area over time;(ii)to identify the spatial patterns of human-caused fires and test for the existence of‘hotspots’to determine their exact locations in the Daxing’an mountains;(iii)to determine the driving factors that determine the spatial distribution and the possibility of human-caused fire occurrence.Methods In this study,K-function and Kernel density estimation were used to analyze the spatial pattern of human-caused fires.The analysis was conducted in s-plus and arcgIs environments,respectively.The analysis of driving factors was performed in SPSS 19.0 based on a logistic regression model.The variables used to identify factors that influence fire occurrence included vegetation types,meteorological conditions,socioeconomic factors,topography and infrastructure factors,which were extracted and collected through the spatial analysis mode of arcgIs and from official statistics,respectively.Important Findings The annual number of human-caused fires and the area burnt have declined since 1987 due to the implementation of a forest fire protection act.There were significant spatial heterogeneity and seasonal variations in the distribution of human-caused fires in the Daxing’an mountains.The heterogeneity was caused by elevation,distance to the nearest railway,forest type and temperature.a logistic regression model was developed to predict the likelihood of human-caused fire occurrence in the Daxing’an mountains;its global accuracy attained 64.8%.The model was thus comparable to other relevant studies.
文摘针对通信中软扩频信号伪码序列盲估计困难的问题,提出一种奇异值分解(singular value decomposition,SVD)和K-means聚类相结合的方法。该方法先对接收信号按照一倍伪码周期进行不重叠分段构造数据矩阵。其次对数据矩阵和相似性矩阵分别进行SVD完成对伪码序列集合规模数的估计、数据降噪、粗分类以及初始聚类中心的选取。最后通过K-means算法优化分类结果,得到伪码序列的估计值。该算法在聚类之前事先确定聚类数目,大大减少了迭代次数。同时实验结果表明,该算法在信息码元分组小于5 bit,信噪比大于-10 dB时可以准确估计出软扩频信号的伪码序列,性能较同类算法有所提升。
基金supported by the National Natural Science Foundation of China(Grant No.12002246 and No.52178301)Knowledge Innovation Program of Wuhan(Grant No.2022010801020357)+2 种基金the Science Research Foundation of Wuhan Institute of Technology(Grant No.K2021030)2020 annual Open Fund of Failure Mechanics&Engineering Disaster Prevention and Mitigation,Key Laboratory of Sichuan Province(Sichuan University)(Grant No.2020JDS0022)Open Research Fund Program of Hubei Provincial Key Laboratory of Chemical Equipment Intensification and Intrinsic Safety(Grant No.2019KA03)。
文摘This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.
文摘In order to solve the problem of low accuracy of traditional fixed window width kernel density estimation(KDE)in radar cross section(RCS)statistical characteristics analysis,an improved Epanechnikov KDE(K-KDE)algorithm was proposed to analyze the statistical characteristics of the engine’s backward RCS.Firstly,the K-nearest neighbor method was used to calculate the dynamic window width of the K-KDE,and the Euclidean distance of each adjacent sample was used to judge the local density of the sample,and then the window width of the kernel function was adjusted by the distance between the sample point and the nearest neighbor to complete the KDE.Secondly,based on the K-KDE and the traditional KDE algorithm,the cumulative probability density function(CPDF)of four RCS random distribution sample points subject to fixed parameters was calculated.The results showed that the root mean square error of the K-KDE was reduced by 31.2%,38.8%,38.1%and 31.9%respectively compared with the KDE.Finally,the K-KDE combined with the second generation statistical analysis models were used to analyze the statistical characteristics of the engine backward RCS.
