本文在概率密度函数估计的框架下对5种粒子群优化(Particle swarm optimization-PSO)算法的性能进行了验证,它们分别是标准粒子群优化(Standard PSO-SPSO),带约束因子的粒子群优化(PSO with a constriction factor-PSOCF),高斯粒子群优...本文在概率密度函数估计的框架下对5种粒子群优化(Particle swarm optimization-PSO)算法的性能进行了验证,它们分别是标准粒子群优化(Standard PSO-SPSO),带约束因子的粒子群优化(PSO with a constriction factor-PSOCF),高斯粒子群优化(Gaussian PSO-GPSO),带高斯跳跃的高斯粒子群优化(Gaussian PSO with Gaussian jump-GPSOGJ),以及带柯西跳跃的高斯粒子群优化(Gaussian PSO with Cauchy jump-GPSOCJ).基于3种不同的窗口参数(Bandwidth parameter)表达式确定方法,即Bootstrap方法,Least-squares cross-validation(LSCV)方法,以及biased cross-validation(BCV)方法,本文分别使用这5种PSO算法来寻找最优的窗口参数,并在4种常用的概率分布上对它们的优化性能进行了比较,实验的结果表明,带有跳跃的高斯粒子群优化,即GPSOGJ和GPSOCJ,获得了最佳的求解效果.展开更多
A leak detection method based on Bayesian theory and Fisher’s law was developed for water distribution systems. A hydraulic model was associated with the parameters of leaks (location, extent). The randomness of para...A leak detection method based on Bayesian theory and Fisher’s law was developed for water distribution systems. A hydraulic model was associated with the parameters of leaks (location, extent). The randomness of parameter values was quantified by probability density function and updated by Bayesian theory. Values of the parameters were estimated based on Fisher’s law. The amount of leaks was estimated by back propagation neural network. Based on flow characteristics in water distribution systems, the location of leaks can be estimated. The effectiveness of the proposed method was illustrated by simulated leak data of node pressure head and flow rate of pipelines in a test pipe network, and the leaks were spotted accurately and renovated on time.展开更多
For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background mod...For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.展开更多
Traditionally, it is widely accepted that measurement error usually obeys the normal distribution. However, in this paper a new idea is proposed that the error in digitized data which is a major derived data source in...Traditionally, it is widely accepted that measurement error usually obeys the normal distribution. However, in this paper a new idea is proposed that the error in digitized data which is a major derived data source in GIS does not obey the normal distribution but the p-norm distribution with a determinate parameter. Assuming that the error is random and has the same statistical properties, the probability density function of the normal distribution, Laplace distribution and p-norm distribution are derived based on the arithmetic mean axiom, median axiom and p-median axiom, which means that the normal distribution is only one of these distributions but not the least one. Based on this ideal distribution fitness tests such as Skewness and Kurtosis coefficient test, Pearson chi-square chi(2) test and Kolmogorov test for digitized data are conducted. The results show that the error in map digitization obeys the p-norm distribution whose parameter is close to 1.60. A least p-norm estimation and the least square estimation of digitized data are further analyzed, showing that the least p-norm adjustment is better than the least square adjustment for digitized data processing in GIS.展开更多
Fatigue stress measurement has been playing a significant role in the ship structural health monitoring and ship structural safety assessment. The PDF （probability density function） of the measured stress is one of ...Fatigue stress measurement has been playing a significant role in the ship structural health monitoring and ship structural safety assessment. The PDF （probability density function） of the measured stress is one of the essentials for the further studyin this domain. This paper, based on the strain-stress data collected from a container ship, focuses on the spectrum feature of the ship structural fatigue stress. A general analysis procedure for ship hull health estimation was firstly demonstrated. With the guidance of this procedure, the estimation and test of the parameters for the PDF of the stress were conducted, which showed that the stress spectrums fit well with the Weibull distribution. To review the fatigue state, the PDF and distribution function of fatigue damage increment were further developed and examined. The structural healthy assessment of this vessel shown the daily relative fatigue damage increment obeys log-normal or Weibull distribution and the increment of the fatigue damage on steel box girders of the ship hull was very low. Finally, the analyzing results yielded that the girder structure of the ship hull had a very low failure probability, matching well with the actual relative low working load of the ship.展开更多
The paper introduces a new approach to estimating the T-year return-period wave height (TRPW), i.e. the wave height expected to occur in T-year, from two sets of observed extreme data and on the basis of the maximum e...The paper introduces a new approach to estimating the T-year return-period wave height (TRPW), i.e. the wave height expected to occur in T-year, from two sets of observed extreme data and on the basis of the maximum entropy principle. The main points of the approach are as follows. 1) A maximum entropy probability density function (PDF) for the extreme wave height H is derived from a Euler equation subject to some necessary and rational constraints. 2) The parameters in the function are expressed in terms of the mth moment of H. 3) This PDF is convenient to theoretical and practical applications as it is simple and its four parameters are easy to be determined from observed extreme data. An example is given for estimating the TRPW in 50 and 100 years by the present approach and by some currently used methods using observed data at two hydrographic stations.The comparison of the estimated results shows that the present approach is quite similar to the Pearson-Ⅲ and Gumbel methods.展开更多
The probability distribution analysis is per-formed for multi-timescale aerosol optical depth (AOD) using AErosol RObotic NETwork (AERONET) level 2.0 data.The maximum likelihood estimation is employed to determine the...The probability distribution analysis is per-formed for multi-timescale aerosol optical depth (AOD) using AErosol RObotic NETwork (AERONET) level 2.0 data.The maximum likelihood estimation is employed to determine the best-fit probability density function (PDF),and the statement that the fitting Weibull distribution will be light-tailed is proved true for these AOD samples.The best-fit PDF results for multi-site data show that the PDF of AOD samples with longer timescale in most sites tends to be stably represented by lognormal distribution,while Weibull distribution is a better fit for AOD samples with short timescales.The reason for this difference is ana-lyzed through tail characteristics of the two distributions,and an indicator for the selection between Weibull and lognormal distributions is suggested and validated.The result of this research is helpful for determining the most accurate AOD statistics for a given site and a given time-scale and for validating the retrieved AOD through its PDF.展开更多
文摘本文在概率密度函数估计的框架下对5种粒子群优化(Particle swarm optimization-PSO)算法的性能进行了验证,它们分别是标准粒子群优化(Standard PSO-SPSO),带约束因子的粒子群优化(PSO with a constriction factor-PSOCF),高斯粒子群优化(Gaussian PSO-GPSO),带高斯跳跃的高斯粒子群优化(Gaussian PSO with Gaussian jump-GPSOGJ),以及带柯西跳跃的高斯粒子群优化(Gaussian PSO with Cauchy jump-GPSOCJ).基于3种不同的窗口参数(Bandwidth parameter)表达式确定方法,即Bootstrap方法,Least-squares cross-validation(LSCV)方法,以及biased cross-validation(BCV)方法,本文分别使用这5种PSO算法来寻找最优的窗口参数,并在4种常用的概率分布上对它们的优化性能进行了比较,实验的结果表明,带有跳跃的高斯粒子群优化,即GPSOGJ和GPSOCJ,获得了最佳的求解效果.
基金Supported by National Natural Science Foundation of China (No. 50278062 and 50578108)Science and Technology Innovation Funds Project of Tianjin, China (No. 08FDZDSF03200)
文摘A leak detection method based on Bayesian theory and Fisher’s law was developed for water distribution systems. A hydraulic model was associated with the parameters of leaks (location, extent). The randomness of parameter values was quantified by probability density function and updated by Bayesian theory. Values of the parameters were estimated based on Fisher’s law. The amount of leaks was estimated by back propagation neural network. Based on flow characteristics in water distribution systems, the location of leaks can be estimated. The effectiveness of the proposed method was illustrated by simulated leak data of node pressure head and flow rate of pipelines in a test pipe network, and the leaks were spotted accurately and renovated on time.
基金Project(60772080) supported by the National Natural Science Foundation of ChinaProject(3240120) supported by Tianjin Subway Safety System, Honeywell Limited, China
文摘For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.
文摘Traditionally, it is widely accepted that measurement error usually obeys the normal distribution. However, in this paper a new idea is proposed that the error in digitized data which is a major derived data source in GIS does not obey the normal distribution but the p-norm distribution with a determinate parameter. Assuming that the error is random and has the same statistical properties, the probability density function of the normal distribution, Laplace distribution and p-norm distribution are derived based on the arithmetic mean axiom, median axiom and p-median axiom, which means that the normal distribution is only one of these distributions but not the least one. Based on this ideal distribution fitness tests such as Skewness and Kurtosis coefficient test, Pearson chi-square chi(2) test and Kolmogorov test for digitized data are conducted. The results show that the error in map digitization obeys the p-norm distribution whose parameter is close to 1.60. A least p-norm estimation and the least square estimation of digitized data are further analyzed, showing that the least p-norm adjustment is better than the least square adjustment for digitized data processing in GIS.
文摘Fatigue stress measurement has been playing a significant role in the ship structural health monitoring and ship structural safety assessment. The PDF （probability density function） of the measured stress is one of the essentials for the further studyin this domain. This paper, based on the strain-stress data collected from a container ship, focuses on the spectrum feature of the ship structural fatigue stress. A general analysis procedure for ship hull health estimation was firstly demonstrated. With the guidance of this procedure, the estimation and test of the parameters for the PDF of the stress were conducted, which showed that the stress spectrums fit well with the Weibull distribution. To review the fatigue state, the PDF and distribution function of fatigue damage increment were further developed and examined. The structural healthy assessment of this vessel shown the daily relative fatigue damage increment obeys log-normal or Weibull distribution and the increment of the fatigue damage on steel box girders of the ship hull was very low. Finally, the analyzing results yielded that the girder structure of the ship hull had a very low failure probability, matching well with the actual relative low working load of the ship.
基金the Natural Science Foundation of China under Contract No.40706012the Young Scientist Foundation of State Oceanic Administration under Contract No.2008209+1 种基金the Basic Science Operational Fund of the Ministry of Finance assigned to the Third Institute of Oceanography,State Oceanic Administration under Contract No.2007010‘863’program No.2006AA09A301
文摘The paper introduces a new approach to estimating the T-year return-period wave height (TRPW), i.e. the wave height expected to occur in T-year, from two sets of observed extreme data and on the basis of the maximum entropy principle. The main points of the approach are as follows. 1) A maximum entropy probability density function (PDF) for the extreme wave height H is derived from a Euler equation subject to some necessary and rational constraints. 2) The parameters in the function are expressed in terms of the mth moment of H. 3) This PDF is convenient to theoretical and practical applications as it is simple and its four parameters are easy to be determined from observed extreme data. An example is given for estimating the TRPW in 50 and 100 years by the present approach and by some currently used methods using observed data at two hydrographic stations.The comparison of the estimated results shows that the present approach is quite similar to the Pearson-Ⅲ and Gumbel methods.
基金supported by funds from the Chinese Global Change Research Program (Grant No.2010CB951804)the National Natural Science Foundation of China (Grant No.40830103)the China Postdoctoral Science Foundation (Grant No.20100480436)
文摘The probability distribution analysis is per-formed for multi-timescale aerosol optical depth (AOD) using AErosol RObotic NETwork (AERONET) level 2.0 data.The maximum likelihood estimation is employed to determine the best-fit probability density function (PDF),and the statement that the fitting Weibull distribution will be light-tailed is proved true for these AOD samples.The best-fit PDF results for multi-site data show that the PDF of AOD samples with longer timescale in most sites tends to be stably represented by lognormal distribution,while Weibull distribution is a better fit for AOD samples with short timescales.The reason for this difference is ana-lyzed through tail characteristics of the two distributions,and an indicator for the selection between Weibull and lognormal distributions is suggested and validated.The result of this research is helpful for determining the most accurate AOD statistics for a given site and a given time-scale and for validating the retrieved AOD through its PDF.
