The construction of a pest detection algorithm is an important step to couple"ground-space"characteristics,which is also the basis for rapid and accurate monitoring and detection of pest damage.In four exper...The construction of a pest detection algorithm is an important step to couple"ground-space"characteristics,which is also the basis for rapid and accurate monitoring and detection of pest damage.In four experimental areas in Sanming City,Jiangle County,Sha County and Yanping District in Fujian Province,sample data on pest damage in 182 sets of Dendrolimus punctatus were collected.The data were randomly divided into a training set and testing set,and five duplicate tests and one eliminating-indicator test were done.Based on the characterization analysis of the host for D.punctatus damage,seven characteristic indicators of ground and remote sensing including leaf area index,standard error of leaf area index(SEL)of pine forest,normalized difference vegetation index(NDVI),wetness from tasseled cap transformation(WET),green band(B2),red band(B3),near-infrared band(B4)of remote sensing image are obtained to construct BP neural networks and random forest models of pest levels.The detection results of these two algorithms were comprehensively compared from the aspects of detection precision,kappa coefficient,receiver operating characteristic curve,and a paired t test.The results showed that the seven indicators all were responsive to pest damage,and NDVI was relatively weak;the average pest damage detection precision of six tests by BP neural networks was 77.29%,the kappa coefficient was 0.6869 and after the RF algorithm,the respective values were 79.30%and 0.7151,showing that the latter is more optimized,but there was no significant difference(p>0.05);the detection precision,kappa coefficient and AUC of the RF algorithm was higher than the BP neural networks for three pest levels(no damage,moderate damage and severe damage).The detection precision and AUC of BP neural networks were a little higher for mild damage,but the difference was not significant(p>0.05)except for the kappa coefficient for the no damage level(p<0.05).An"over-fitting"phenomenon tends to occur in BP neural networks,while RF method is more robust,providing a detection effect that is better than the BP neural networks.Thus,the application of the random forest algorithm for pest damage and multilevel dispersed variables is thus feasible and suggests that attention to the proportionality of sample data from various categories is needed when collecting data.展开更多
On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random in...On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering （EKF） and the unscented Kalman filtering （UKF）, which were shortened as GEKF and CUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network （RBFNN） prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent.展开更多
由于网络环境攻击手段的多样性,导致误报率较高,设计一种基于改进随机森林算法的风电场通信网络攻击预警方法。融合卷积神经网络与随机森林算法提取风电场通信网络攻击特征。引入攻击频次指标和滑动窗口来动态评估实际攻击次数占比,并...由于网络环境攻击手段的多样性,导致误报率较高,设计一种基于改进随机森林算法的风电场通信网络攻击预警方法。融合卷积神经网络与随机森林算法提取风电场通信网络攻击特征。引入攻击频次指标和滑动窗口来动态评估实际攻击次数占比,并量化攻击频率指数(Attack Frequency Index,AFI)作为预警阈值,结合所构建的预警指标体系与预警等级,实现风电场通信网络攻击预警。实验结果表明,设计方法的平均误报率仅为7.93%,平均响应时间为29.67 ms,且波动较小,显示出更高的稳定性和可靠性。展开更多
Experiments were performed on a high-speed online random neutron analyzing system (HORNA system) with a 252Cf neutron source (up to 1 GHz sampling rate and 3 input data channel),to obtain timeand frequency dependent s...Experiments were performed on a high-speed online random neutron analyzing system (HORNA system) with a 252Cf neutron source (up to 1 GHz sampling rate and 3 input data channel),to obtain timeand frequency dependent signatures which are sensitive to changes in the composition,fissile mass and configuration of the fissile assembly.The data were acquired by three high-speed synchronized acquisition cards at different detector angles,source-detector distances and block sizes.According to the relationship between 252Cf source and the ratio of power spectral density,Rpsd,all the signatures were calculated and analyzed using correlation and periodogram methods.Based on the results,the simulated autocorrelation functions were utilized for identifying different fissile mass with Elman neural network.The experimental results show that the Rpsd almost remains at constant amplitude in frequency range of 0-100 MHz,and is only related to the angle and source-detector distance.The trained Elman neural network is able to distinguish the characteristics of autocorrelation function and identify different fissile mass.The average identification rate reached 90% with high robustness.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.41501361,41401385,30871965)the China Postdoctoral Science Foundation(No.2018M630728)+2 种基金the Open Fund of Fujian Provincial Key Laboratory of Resources and Environment Monitoring&Sustainable Management and Utilization(No.ZD1403)the Open Fund of Fujian Mine Ecological Restoration Engineering Technology Research Center(No.KS2018005)the Scientific Research Foundation of Fuzhou University(No.XRC1345)
文摘The construction of a pest detection algorithm is an important step to couple"ground-space"characteristics,which is also the basis for rapid and accurate monitoring and detection of pest damage.In four experimental areas in Sanming City,Jiangle County,Sha County and Yanping District in Fujian Province,sample data on pest damage in 182 sets of Dendrolimus punctatus were collected.The data were randomly divided into a training set and testing set,and five duplicate tests and one eliminating-indicator test were done.Based on the characterization analysis of the host for D.punctatus damage,seven characteristic indicators of ground and remote sensing including leaf area index,standard error of leaf area index(SEL)of pine forest,normalized difference vegetation index(NDVI),wetness from tasseled cap transformation(WET),green band(B2),red band(B3),near-infrared band(B4)of remote sensing image are obtained to construct BP neural networks and random forest models of pest levels.The detection results of these two algorithms were comprehensively compared from the aspects of detection precision,kappa coefficient,receiver operating characteristic curve,and a paired t test.The results showed that the seven indicators all were responsive to pest damage,and NDVI was relatively weak;the average pest damage detection precision of six tests by BP neural networks was 77.29%,the kappa coefficient was 0.6869 and after the RF algorithm,the respective values were 79.30%and 0.7151,showing that the latter is more optimized,but there was no significant difference(p>0.05);the detection precision,kappa coefficient and AUC of the RF algorithm was higher than the BP neural networks for three pest levels(no damage,moderate damage and severe damage).The detection precision and AUC of BP neural networks were a little higher for mild damage,but the difference was not significant(p>0.05)except for the kappa coefficient for the no damage level(p<0.05).An"over-fitting"phenomenon tends to occur in BP neural networks,while RF method is more robust,providing a detection effect that is better than the BP neural networks.Thus,the application of the random forest algorithm for pest damage and multilevel dispersed variables is thus feasible and suggests that attention to the proportionality of sample data from various categories is needed when collecting data.
基金Project supported by the State Key Program of the National Natural Science of China (Grant No. 60835004)the Natural Science Foundation of Jiangsu Province of China (Grant No. BK2009727)+1 种基金the Natural Science Foundation of Higher Education Institutions of Jiangsu Province of China (Grant No. 10KJB510004)the National Natural Science Foundation of China (Grant No. 61075028)
文摘On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering （EKF） and the unscented Kalman filtering （UKF）, which were shortened as GEKF and CUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network （RBFNN） prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent.
文摘由于网络环境攻击手段的多样性,导致误报率较高,设计一种基于改进随机森林算法的风电场通信网络攻击预警方法。融合卷积神经网络与随机森林算法提取风电场通信网络攻击特征。引入攻击频次指标和滑动窗口来动态评估实际攻击次数占比,并量化攻击频率指数(Attack Frequency Index,AFI)作为预警阈值,结合所构建的预警指标体系与预警等级,实现风电场通信网络攻击预警。实验结果表明,设计方法的平均误报率仅为7.93%,平均响应时间为29.67 ms,且波动较小,显示出更高的稳定性和可靠性。
基金Supported by Natural Science Foundation Project of CQ (CSTC2009BB2188)Fundamental Research Funds for Central Universities (No. CDJXS10120013)
文摘Experiments were performed on a high-speed online random neutron analyzing system (HORNA system) with a 252Cf neutron source (up to 1 GHz sampling rate and 3 input data channel),to obtain timeand frequency dependent signatures which are sensitive to changes in the composition,fissile mass and configuration of the fissile assembly.The data were acquired by three high-speed synchronized acquisition cards at different detector angles,source-detector distances and block sizes.According to the relationship between 252Cf source and the ratio of power spectral density,Rpsd,all the signatures were calculated and analyzed using correlation and periodogram methods.Based on the results,the simulated autocorrelation functions were utilized for identifying different fissile mass with Elman neural network.The experimental results show that the Rpsd almost remains at constant amplitude in frequency range of 0-100 MHz,and is only related to the angle and source-detector distance.The trained Elman neural network is able to distinguish the characteristics of autocorrelation function and identify different fissile mass.The average identification rate reached 90% with high robustness.
