This paper studies the problem of robust H∞ control of piecewise-linear chaotic systems with random data loss. The communication links between the plant and the controller are assumed to be imperfect （that is, data ...This paper studies the problem of robust H∞ control of piecewise-linear chaotic systems with random data loss. The communication links between the plant and the controller are assumed to be imperfect （that is, data loss occurs intermittently, which appears typically in a network environment）. The data loss is modelled as a random process which obeys a Bernoulli distribution. In the face of random data loss, a piecewise controller is designed to robustly stabilize the networked system in the sense of mean square and also achieve a prescribed H∞ disturbance attenuation performance based on a piecewise-quadratic Lyapunov function. The required H∞ controllers can be designed by solving a set of linear matrix inequalities （LMIs）. Chua＇s system is provided to illustrate the usefulness and applicability of the developed theoretical results.展开更多
Because the traditional method is difficult to obtain the internal relationshipand association rules of data when dealingwith massive data, a fuzzy clusteringmethod is proposed to analyze massive data. Firstly, the sa...Because the traditional method is difficult to obtain the internal relationshipand association rules of data when dealingwith massive data, a fuzzy clusteringmethod is proposed to analyze massive data. Firstly, the sample matrix wasnormalized through the normalization of sample data. Secondly, a fuzzy equivalencematrix was constructed by using fuzzy clustering method based on thenormalization matrix, and then the fuzzy equivalence matrix was applied as thebasis for dynamic clustering. Finally, a series of classifications were carried out onthe mass data at the cut-set level successively and a dynamic cluster diagram wasgenerated. The experimental results show that using data fuzzy clustering methodcan effectively identify association rules of data sets by multiple iterations ofmassive data, and the clustering process has short running time and good robustness.Therefore, it can be widely applied to the identification and classification ofassociation rules of massive data such as sound, image and natural resources.展开更多
For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density（PSD） data can be obtaine...For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density（PSD） data can be obtained at the stage of flight test. Thus, those conventional evaluation methods cannot be employed when the distribution characteristics and priori information are unknown. In this paper, the fuzzy norm method（FNM） is proposed which combines the advantages of fuzzy theory and norm theory. The proposed method can deeply dig system information from limited data, which probability distribution is not taken into account. Firstly, the FNM is employed to evaluate variable interval and expanded uncertainty from limited PSD data, and the performance of FNM is demonstrated by confidence level, reliability and computing accuracy of expanded uncertainty. In addition, the optimal fuzzy parameters are discussed to meet the requirements of aviation standards and metrological practice. Finally, computer simulation is used to prove the adaptability of FNM. Compared with statistical methods, FNM has superiority for evaluating expanded uncertainty from limited data. The results show that the reliability of calculation and evaluation is superior to 95%.展开更多
We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR wer...We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.展开更多
In this paper the influence of the differently distributed phase-randontized to the data obtained in dynamic analysis for critical value is studied.The calculation results validate that the sufficient phase-randomized...In this paper the influence of the differently distributed phase-randontized to the data obtained in dynamic analysis for critical value is studied.The calculation results validate that the sufficient phase-randomized of the different distributed random numbers are less influential on the critical value . This offers the theoretical foundation of the feasibility and practicality of the phase-randomized method.展开更多
We consider the problem of variable selection for the single-index random effects models with longitudinal data. An automatic variable selection procedure is developed using smooth-threshold. The proposed method share...We consider the problem of variable selection for the single-index random effects models with longitudinal data. An automatic variable selection procedure is developed using smooth-threshold. The proposed method shares some of the desired features of existing variable selection methods: the resulting estimator enjoys the oracle property;the proposed procedure avoids the convex optimization problem and is flexible and easy to implement. Moreover, we use the penalized weighted deviance criterion for a data-driven choice of the tuning parameters. Simulation studies are carried out to assess the performance of our method, and a real dataset is analyzed for further illustration.展开更多
As an industry accepted storage scheme, hafnium oxide（HfO_x） based resistive random access memory（RRAM）should further improve its thermal stability and data retention for practical applications. We therefore fabri...As an industry accepted storage scheme, hafnium oxide（HfO_x） based resistive random access memory（RRAM）should further improve its thermal stability and data retention for practical applications. We therefore fabricated RRAMs with HfO_x/ZnO double-layer as the storage medium to study their thermal stability as well as data retention. The HfO_x/ZnO double-layer is capable of reversible bipolar switching under ultralow switching current（〈 3 μA） with a Schottky emission dominant conduction for the high resistance state and a Poole–Frenkel emission governed conduction for the low resistance state. Compared with a drastically increased switching current at 120℃ for the single HfO_x layer RRAM, the HfO_x/ZnO double-layer exhibits excellent thermal stability and maintains neglectful fluctuations in switching current at high temperatures（up to 180℃）, which might be attributed to the increased Schottky barrier height to suppress current at high temperatures. Additionally, the HfO_x/ZnO double-layer exhibits 10-year data retention @85℃ that is helpful for the practical applications in RRAMs.展开更多
The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare...The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) re</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">presented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are em</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">ployed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the car-</span><span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">following behavior with better performance under multiple performance indicators.展开更多
In this paper, it is discussed that two tests for varying dispersion of binomial data in the framework of nonlinear logistic models with random effects, which are widely used in analyzing longitudinal binomial data. O...In this paper, it is discussed that two tests for varying dispersion of binomial data in the framework of nonlinear logistic models with random effects, which are widely used in analyzing longitudinal binomial data. One is the individual test and power calculation for varying dispersion through testing the randomness of cluster effects, which is extensions of Dean(1992) and Commenges et al (1994). The second test is the composite test for varying dispersion through simultaneously testing the randomness of cluster effects and the equality of random-effect means. The score test statistics are constructed and expressed in simple, easy to use, matrix formulas. The authors illustrate their test methods using the insecticide data (Giltinan, Capizzi & Malani (1988)).展开更多
背景:近期研究表明,肠道菌群可能会影响肌萎缩侧索硬化症的发展进程,然而两者之间的因果关系尚不清楚。目的:利用孟德尔随机化方法探索肠道菌群与肌萎缩侧索硬化症之间的因果关系。方法:从IEU Open GWAS数据库(由英国布里斯托尔大学的...背景:近期研究表明,肠道菌群可能会影响肌萎缩侧索硬化症的发展进程,然而两者之间的因果关系尚不清楚。目的:利用孟德尔随机化方法探索肠道菌群与肌萎缩侧索硬化症之间的因果关系。方法:从IEU Open GWAS数据库(由英国布里斯托尔大学的英国医学研究委员会和遗传流行病学研究所开发,旨在提供与多种疾病相关的全基因组关联研究数据,为开放数据库)中分别获取肠道菌群和肌萎缩侧索硬化症的GWAS数据,以肠道菌群为暴露因素、肌萎缩侧索硬化症为结局变量,使用逆方差加权法、MR-Egger回归法、加权中位数法、加权模型法和简单模型法来探究两者之间的因果关系。使用敏感性分析检验孟德尔随机化结果的可靠性,使用反向孟德尔随机化分析进一步验证两者间的因果关系。结果与结论:(1)正向孟德尔随机化分析结果表明,6种肠道菌群与肌萎缩侧索硬化症之间存在因果关系,其中嗜胆菌属(β=0.206,OR=1.229)、毛螺菌属(β=0.288,OR=1.333)、马文-布莱恩特氏菌属(β=0.196,OR=1.216)、瘤胃球菌UCG010属(β=0.254,OR=1.289)和泰泽氏菌属3型(β=0.128,OR=1.136)可能是肌萎缩侧索硬化症的潜在危险因素,肠杆菌属(β=-0.203,OR=0.816)可能是肌萎缩侧索硬化症的保护因素;(2)在敏感性分析中,未发现显著的异质性和水平多效性(P均> 0.05),反向孟德尔随机化分析亦未揭示肠道菌群与肌萎缩侧索硬化症之间存在反向因果关系;(3)该研究结果不仅为肌萎缩侧索硬化症治疗提供了潜在的生物标志物,还为开发基于肠道菌群的新的干预治疗方案提供了理论依据,对中国基础医学研究具有一定的启示意义。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
基金Project partially supported by the Young Scientists Fund of the National Natural Science Foundation of China(Grant No.60904004)the Key Youth Science and Technology Foundation of University of Electronic Science and Technology of China (Grant No.L08010201JX0720)
文摘This paper studies the problem of robust H∞ control of piecewise-linear chaotic systems with random data loss. The communication links between the plant and the controller are assumed to be imperfect （that is, data loss occurs intermittently, which appears typically in a network environment）. The data loss is modelled as a random process which obeys a Bernoulli distribution. In the face of random data loss, a piecewise controller is designed to robustly stabilize the networked system in the sense of mean square and also achieve a prescribed H∞ disturbance attenuation performance based on a piecewise-quadratic Lyapunov function. The required H∞ controllers can be designed by solving a set of linear matrix inequalities （LMIs）. Chua＇s system is provided to illustrate the usefulness and applicability of the developed theoretical results.
文摘Because the traditional method is difficult to obtain the internal relationshipand association rules of data when dealingwith massive data, a fuzzy clusteringmethod is proposed to analyze massive data. Firstly, the sample matrix wasnormalized through the normalization of sample data. Secondly, a fuzzy equivalencematrix was constructed by using fuzzy clustering method based on thenormalization matrix, and then the fuzzy equivalence matrix was applied as thebasis for dynamic clustering. Finally, a series of classifications were carried out onthe mass data at the cut-set level successively and a dynamic cluster diagram wasgenerated. The experimental results show that using data fuzzy clustering methodcan effectively identify association rules of data sets by multiple iterations ofmassive data, and the clustering process has short running time and good robustness.Therefore, it can be widely applied to the identification and classification ofassociation rules of massive data such as sound, image and natural resources.
基金supported by Aeronautical Science Foundation of China (No. 20100251006)Technological Foundation Project of China (No. J132012C001)
文摘For random vibration of airborne platform, the accurate evaluation is a key indicator to ensure normal operation of airborne equipment in flight. However, only limited power spectral density（PSD） data can be obtained at the stage of flight test. Thus, those conventional evaluation methods cannot be employed when the distribution characteristics and priori information are unknown. In this paper, the fuzzy norm method（FNM） is proposed which combines the advantages of fuzzy theory and norm theory. The proposed method can deeply dig system information from limited data, which probability distribution is not taken into account. Firstly, the FNM is employed to evaluate variable interval and expanded uncertainty from limited PSD data, and the performance of FNM is demonstrated by confidence level, reliability and computing accuracy of expanded uncertainty. In addition, the optimal fuzzy parameters are discussed to meet the requirements of aviation standards and metrological practice. Finally, computer simulation is used to prove the adaptability of FNM. Compared with statistical methods, FNM has superiority for evaluating expanded uncertainty from limited data. The results show that the reliability of calculation and evaluation is superior to 95%.
基金supported by the National Natural Science Foundation of China(Nos.31500518,31500519,and 31470640)
文摘We developed a forest type classification technology for the Daxing＇an Mountains of northeast China using multisource remote sensing data.A SPOT-5 image and two temporal images of RADARSAT-2 full-polarization SAR were used to identify forest types in the Pangu Forest Farm of the Daxing＇an Mountains.Forest types were identified using random forest（RF） classification with the following data combination types： SPOT-5 alone,SPOT-5 and SAR images in August or November,and SPOT-5 and two temporal SAR images.We identified many forest types using a combination of multitemporal SAR and SPOT-5 images,including Betula platyphylla,Larix gmelinii,Pinus sylvestris and Picea koraiensis forests.The accuracy of classification exceeded 88% and improved by 12% when compared to the classification results obtained using SPOT data alone.RF classification using a combination of multisource remote sensing data improved classification accuracy compared to that achieved using single-source remote sensing data.
文摘In this paper the influence of the differently distributed phase-randontized to the data obtained in dynamic analysis for critical value is studied.The calculation results validate that the sufficient phase-randomized of the different distributed random numbers are less influential on the critical value . This offers the theoretical foundation of the feasibility and practicality of the phase-randomized method.
文摘We consider the problem of variable selection for the single-index random effects models with longitudinal data. An automatic variable selection procedure is developed using smooth-threshold. The proposed method shares some of the desired features of existing variable selection methods: the resulting estimator enjoys the oracle property;the proposed procedure avoids the convex optimization problem and is flexible and easy to implement. Moreover, we use the penalized weighted deviance criterion for a data-driven choice of the tuning parameters. Simulation studies are carried out to assess the performance of our method, and a real dataset is analyzed for further illustration.
基金supported by the National Natural Science Foundation of China(Grant Nos.61006003 and 61674038)the Natural Science Foundation of Fujian Province,China(Grant Nos.2015J01249 and 2010J05134)+1 种基金the Science Foundation of Fujian Education Department of China(Grant No.JAT160073)the Science Foundation of Fujian Provincial Economic and Information Technology Commission of China(Grant No.83016006)
文摘As an industry accepted storage scheme, hafnium oxide（HfO_x） based resistive random access memory（RRAM）should further improve its thermal stability and data retention for practical applications. We therefore fabricated RRAMs with HfO_x/ZnO double-layer as the storage medium to study their thermal stability as well as data retention. The HfO_x/ZnO double-layer is capable of reversible bipolar switching under ultralow switching current（〈 3 μA） with a Schottky emission dominant conduction for the high resistance state and a Poole–Frenkel emission governed conduction for the low resistance state. Compared with a drastically increased switching current at 120℃ for the single HfO_x layer RRAM, the HfO_x/ZnO double-layer exhibits excellent thermal stability and maintains neglectful fluctuations in switching current at high temperatures（up to 180℃）, which might be attributed to the increased Schottky barrier height to suppress current at high temperatures. Additionally, the HfO_x/ZnO double-layer exhibits 10-year data retention @85℃ that is helpful for the practical applications in RRAMs.
文摘The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) re</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">presented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are em</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">ployed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the car-</span><span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">following behavior with better performance under multiple performance indicators.
基金The project supported by NNSFC (19631040), NSSFC (04BTJ002) and the grant for post-doctor fellows in SELF.
文摘In this paper, it is discussed that two tests for varying dispersion of binomial data in the framework of nonlinear logistic models with random effects, which are widely used in analyzing longitudinal binomial data. One is the individual test and power calculation for varying dispersion through testing the randomness of cluster effects, which is extensions of Dean(1992) and Commenges et al (1994). The second test is the composite test for varying dispersion through simultaneously testing the randomness of cluster effects and the equality of random-effect means. The score test statistics are constructed and expressed in simple, easy to use, matrix formulas. The authors illustrate their test methods using the insecticide data (Giltinan, Capizzi & Malani (1988)).
文摘背景:近期研究表明,肠道菌群可能会影响肌萎缩侧索硬化症的发展进程,然而两者之间的因果关系尚不清楚。目的:利用孟德尔随机化方法探索肠道菌群与肌萎缩侧索硬化症之间的因果关系。方法:从IEU Open GWAS数据库(由英国布里斯托尔大学的英国医学研究委员会和遗传流行病学研究所开发,旨在提供与多种疾病相关的全基因组关联研究数据,为开放数据库)中分别获取肠道菌群和肌萎缩侧索硬化症的GWAS数据,以肠道菌群为暴露因素、肌萎缩侧索硬化症为结局变量,使用逆方差加权法、MR-Egger回归法、加权中位数法、加权模型法和简单模型法来探究两者之间的因果关系。使用敏感性分析检验孟德尔随机化结果的可靠性,使用反向孟德尔随机化分析进一步验证两者间的因果关系。结果与结论:(1)正向孟德尔随机化分析结果表明,6种肠道菌群与肌萎缩侧索硬化症之间存在因果关系,其中嗜胆菌属(β=0.206,OR=1.229)、毛螺菌属(β=0.288,OR=1.333)、马文-布莱恩特氏菌属(β=0.196,OR=1.216)、瘤胃球菌UCG010属(β=0.254,OR=1.289)和泰泽氏菌属3型(β=0.128,OR=1.136)可能是肌萎缩侧索硬化症的潜在危险因素,肠杆菌属(β=-0.203,OR=0.816)可能是肌萎缩侧索硬化症的保护因素;(2)在敏感性分析中,未发现显著的异质性和水平多效性(P均> 0.05),反向孟德尔随机化分析亦未揭示肠道菌群与肌萎缩侧索硬化症之间存在反向因果关系;(3)该研究结果不仅为肌萎缩侧索硬化症治疗提供了潜在的生物标志物,还为开发基于肠道菌群的新的干预治疗方案提供了理论依据,对中国基础医学研究具有一定的启示意义。
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.
