This paper presents a method for detecting the small infrared target under complex background.An algorithm,named local mutation weighted information entropy(LMWIE),is proposed to suppress background.Then,the grey valu...This paper presents a method for detecting the small infrared target under complex background.An algorithm,named local mutation weighted information entropy(LMWIE),is proposed to suppress background.Then,the grey value of targets is enhanced by calculating the local energy.Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets.Experimental results show that compared with the adaptive Butterworth high-pass filter method,the proposed algorithm is more effective and faster for the infrared small target detection.展开更多
Landslides are a frequent geomorphological hazard in tropical regions,particularly where steep terrain and high precipitation coincide.This study evaluates landslide susceptibility in the Jelapang area of Perak,Malays...Landslides are a frequent geomorphological hazard in tropical regions,particularly where steep terrain and high precipitation coincide.This study evaluates landslide susceptibility in the Jelapang area of Perak,Malaysia,using Shannon Entropy-weighted bivariatemodels(i.e.,Frequency Ratio,Information Value,andWeight of Evidence),in comparison with Logistic Regression.Seven conditioning factors were selected based on their geomorphological relevance and tested for multicollinearity:slope gradient,slope aspect,curvature,vegetation cover,lineament density,terrain ruggedness index,and flow accumulation.Each model generated susceptibility maps,which were validated using Receiver Operating Characteristic curves and Area Under the Curve metrics.Logistic Regression yielded the highest predictive accuracy,reflecting its strength in capturing interactions among variables.Among the bivariate models,Frequency Ratio performed best,slightly outperforming the other two methods.Zones of high susceptibility were consistently located along steep slopes,high lineament density areas,and near built environments.The study demonstrates that incorporating Shannon Entropy improves the performance of conventional bivariate methods and provides a useful framework for spatial susceptibility modeling in data-constrained environments.The comparison with Logistic Regression highlights the advantages ofmultivariate modeling in capturing complex spatial relationships.Limitations of the study include the use of secondary spatial data and the exclusion of dynamic parameters such as rainfall intensity.Future research should incorporate temporal datasets and investigate machine learning techniques to enhance model generalizability and predictive capability.展开更多
In this study, by starting from Maximum entropy (MaxEnt) distribution of time series, we introduce a measure that quantifies information worth of a set of autocovariances. The information worth of autocovariences is m...In this study, by starting from Maximum entropy (MaxEnt) distribution of time series, we introduce a measure that quantifies information worth of a set of autocovariances. The information worth of autocovariences is measured in terms of entropy difference of MaxEnt distributions subject to different autocovariance sets due to the fact that the information discrepancy between two distributions is measured in terms of their entropy difference in MaxEnt modeling. However, MinMaxEnt distributions (models) are obtained on the basis of MaxEnt distributions dependent on parameters according to autocovariances for time series. This distribution is the one which has minimum entropy and maximum information out of all MaxEnt distributions for family of time series constructed by considering one or several values as parameters. Furthermore, it is shown that as the number of autocovariances increases, the entropy of approximating distribution goes on decreasing. In addition, it is proved that information worth of each model defined on the basis of MinMaxEnt modeling about stationary time series is equal to sum of all possible information increments corresponding to each model with respect to preceding model starting with first model in the sequence of models. The fulfillment of obtained results is demonstrated on an example by using a program written in Matlab.展开更多
The accurate calculation of marine environmental design parameters depends on the probability distribution model,and the calculation results of different distribution models are often different.It is very important to...The accurate calculation of marine environmental design parameters depends on the probability distribution model,and the calculation results of different distribution models are often different.It is very important to determine which distribution model is more stable and reasonable when extrapolating the recurrence level of the studied sea area.In this paper,we constructed an evaluation method of the overall uncertainty of the calculation results and a measurement of the uncertainty of the design parameters derivation model,by incorporating the influence of sample information on the model information entropy,such as sample size,degree of dispersion,and sampling error.Results show that the sample data size and the degree of dispersion are directly proportional to the information entropy.Within the same group of data,the maximum entropy distribution model has the lowest overall uncertainty,while the Gumbel distribution model has the largest overall uncertainty.In other words,the maximum entropy distribution model has good applicability in the accurate calculation of marine environmental design parameters.展开更多
汉源县滑坡地质灾害频发,对于未来不同气候情景下的滑坡地质灾害易发性情况的预测研究,可为未来气候变化下的防灾减灾工作提供参考依据。文中基于EC-Earth3模式下的3种未来不同气候情景(SSP1-2.6、SSP2-4.5、SSP5-8.5)数据,在Pearson相...汉源县滑坡地质灾害频发,对于未来不同气候情景下的滑坡地质灾害易发性情况的预测研究,可为未来气候变化下的防灾减灾工作提供参考依据。文中基于EC-Earth3模式下的3种未来不同气候情景(SSP1-2.6、SSP2-4.5、SSP5-8.5)数据,在Pearson相关性分析与多重共线性分析的基础上,最终选取坡度、坡向、剖面曲率、地形湿度、土地利用、距断层距离、降雨量和径流量共8个影响因子作为滑坡易发性评估指标,采用信息量-熵指数模型与支持向量机模型开展不同气候情景下滑坡易发性评估。结果表明:在SSP1-2.6、SSP2-4.5以及SSP5-8.5情景下,信息量-熵指数模型的曲线下面积(area under curve,AUC)值均为0.928,在SSP1-2.6、SSP2-4.5以及SSP5-8.5情景下,支持向量机模型的AUC值分别为0.957、0.967、0.969。支持向量机模型在未来不同气候情景下的滑坡易发性预测精度方面具有更强的鲁棒性,在SSP1-2.6、SSP2-4.5以及SSP5-8.5情景下发生滑坡灾害的区域面积分别为23.02%、21.09%、26.39%,表明在高排放、高发展的情景下,滑坡灾害发生的可能性将会更大。展开更多
基金supported by the National Natural Science Foundation of China(61171194)
文摘This paper presents a method for detecting the small infrared target under complex background.An algorithm,named local mutation weighted information entropy(LMWIE),is proposed to suppress background.Then,the grey value of targets is enhanced by calculating the local energy.Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets.Experimental results show that compared with the adaptive Butterworth high-pass filter method,the proposed algorithm is more effective and faster for the infrared small target detection.
文摘Landslides are a frequent geomorphological hazard in tropical regions,particularly where steep terrain and high precipitation coincide.This study evaluates landslide susceptibility in the Jelapang area of Perak,Malaysia,using Shannon Entropy-weighted bivariatemodels(i.e.,Frequency Ratio,Information Value,andWeight of Evidence),in comparison with Logistic Regression.Seven conditioning factors were selected based on their geomorphological relevance and tested for multicollinearity:slope gradient,slope aspect,curvature,vegetation cover,lineament density,terrain ruggedness index,and flow accumulation.Each model generated susceptibility maps,which were validated using Receiver Operating Characteristic curves and Area Under the Curve metrics.Logistic Regression yielded the highest predictive accuracy,reflecting its strength in capturing interactions among variables.Among the bivariate models,Frequency Ratio performed best,slightly outperforming the other two methods.Zones of high susceptibility were consistently located along steep slopes,high lineament density areas,and near built environments.The study demonstrates that incorporating Shannon Entropy improves the performance of conventional bivariate methods and provides a useful framework for spatial susceptibility modeling in data-constrained environments.The comparison with Logistic Regression highlights the advantages ofmultivariate modeling in capturing complex spatial relationships.Limitations of the study include the use of secondary spatial data and the exclusion of dynamic parameters such as rainfall intensity.Future research should incorporate temporal datasets and investigate machine learning techniques to enhance model generalizability and predictive capability.
文摘In this study, by starting from Maximum entropy (MaxEnt) distribution of time series, we introduce a measure that quantifies information worth of a set of autocovariances. The information worth of autocovariences is measured in terms of entropy difference of MaxEnt distributions subject to different autocovariance sets due to the fact that the information discrepancy between two distributions is measured in terms of their entropy difference in MaxEnt modeling. However, MinMaxEnt distributions (models) are obtained on the basis of MaxEnt distributions dependent on parameters according to autocovariances for time series. This distribution is the one which has minimum entropy and maximum information out of all MaxEnt distributions for family of time series constructed by considering one or several values as parameters. Furthermore, it is shown that as the number of autocovariances increases, the entropy of approximating distribution goes on decreasing. In addition, it is proved that information worth of each model defined on the basis of MinMaxEnt modeling about stationary time series is equal to sum of all possible information increments corresponding to each model with respect to preceding model starting with first model in the sequence of models. The fulfillment of obtained results is demonstrated on an example by using a program written in Matlab.
基金Supported by the National Natural Science Foundation of China(Nos.52071306,51379195)the Natural Science Foundation of Shandong Province(No.ZR2019MEE050)the Graduate Education Foundation(No.HDYA19006)。
文摘The accurate calculation of marine environmental design parameters depends on the probability distribution model,and the calculation results of different distribution models are often different.It is very important to determine which distribution model is more stable and reasonable when extrapolating the recurrence level of the studied sea area.In this paper,we constructed an evaluation method of the overall uncertainty of the calculation results and a measurement of the uncertainty of the design parameters derivation model,by incorporating the influence of sample information on the model information entropy,such as sample size,degree of dispersion,and sampling error.Results show that the sample data size and the degree of dispersion are directly proportional to the information entropy.Within the same group of data,the maximum entropy distribution model has the lowest overall uncertainty,while the Gumbel distribution model has the largest overall uncertainty.In other words,the maximum entropy distribution model has good applicability in the accurate calculation of marine environmental design parameters.
文摘汉源县滑坡地质灾害频发,对于未来不同气候情景下的滑坡地质灾害易发性情况的预测研究,可为未来气候变化下的防灾减灾工作提供参考依据。文中基于EC-Earth3模式下的3种未来不同气候情景(SSP1-2.6、SSP2-4.5、SSP5-8.5)数据,在Pearson相关性分析与多重共线性分析的基础上,最终选取坡度、坡向、剖面曲率、地形湿度、土地利用、距断层距离、降雨量和径流量共8个影响因子作为滑坡易发性评估指标,采用信息量-熵指数模型与支持向量机模型开展不同气候情景下滑坡易发性评估。结果表明:在SSP1-2.6、SSP2-4.5以及SSP5-8.5情景下,信息量-熵指数模型的曲线下面积(area under curve,AUC)值均为0.928,在SSP1-2.6、SSP2-4.5以及SSP5-8.5情景下,支持向量机模型的AUC值分别为0.957、0.967、0.969。支持向量机模型在未来不同气候情景下的滑坡易发性预测精度方面具有更强的鲁棒性,在SSP1-2.6、SSP2-4.5以及SSP5-8.5情景下发生滑坡灾害的区域面积分别为23.02%、21.09%、26.39%,表明在高排放、高发展的情景下,滑坡灾害发生的可能性将会更大。
