基于背景差法的几种背景建模方法的研究

目前最常用也最有效的运动目标检测方法是背景差法,其中背景提取是背景差法的核心。主要比较了均值滤波法、中值滤波法、混合高斯分布法三种常用的背景建模的方法,说明了各算法在不同情况下的性能优劣。通过实验发现,均值滤波法和中值滤波法适合特定的场合,混合高斯法在场景复杂的情况下效果更好。

基于背景差法的几种背景建模方法的研究

目前最常用也最有效的运动目标检测方法是背景差法,其中背景提取是背景差法的核心。主要比较了均值滤波法、中值滤波法、混合高斯分布法三种常用的背景建模的方法,说明了各算法在不同情况下的性能优劣。通过实验发现,均值滤波法和中值滤波法适合特定的场合,混合高斯法在场景复杂的情况下效果更好。

X射线衍射法测定纳米氧化铝的平均晶粒尺寸

以六硼化镧粉末作为标准样品,根据衍射拟合结果分离出任意角度下的仪器宽化值。对焙烧后的氧化铝样品进行X射线衍射分析,结果表明该氧化铝样品为α晶型结构。对样品因素(晶粒细化、微观应变)引起的宽化值分别使用高斯分布法和Hall法进行拟合,计算出氧化铝样品的平均晶粒尺寸。将计算结果与谢乐公式计算出的各晶面晶粒尺寸值进行对比,表明Hall法测试平均晶粒尺寸更加准确,衍射峰晶粒细化和微观应变引起的宽化函数遵循柯西函数关系,确认该氧化铝样品的平均晶粒尺寸为155 nm。

基于背景差法的几种背景建模方法的研究

目前最常用也最有效的运动目标检测方法是背景差法,其中背景提取是背景差法的核心。主要比较了均值滤波法、中值滤波法、混合高斯分布法三种常用的背景建模的方法,说明了各算法在不同情况下的性能优劣。通过实验发现,均值滤波法和中值滤波法适合特定的场合,混合高斯法在场景复杂的情况下效果更好。

基于大数据分析的一流电网评价指标体系研究

输电网和配电网在电力系统中承担的任务不同,往往难以对电网进行综合的评价,通过一流电网评价指标体系,采用关联度分析法以及高斯分布法对电网评价指标体系进行分析,并对评价指标体系进行区间以及数量的筛选、优化,得到一个实用、有效的指标评价体系。提出了指标偏离度的概念,对所有的指标进行分级评价。

Gaussian mixture model clustering with completed likelihood minimum message length criterion

An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

Adaptive Local Outlier Probability for Dynamic Process Monitoring

Complex industrial processes often have multiple operating modes and present time-varying behavior. The data in one mode may follow specific Gaussian or non-Gaussian distributions. In this paper, a numerically efficient movingwindow local outlier probability algorithm is proposed, lies key feature is the capability to handle complex data distributions and incursive operating condition changes including slow dynamic variations and instant mode shifts. First, a two-step adaption approach is introduced and some designed updating rules are applied to keep the monitoring model up-to-date. Then, a semi-supervised monitoring strategy is developed with an updating switch rule to deal with mode changes. Based on local probability models, the algorithm has a superior ability in detecting faulty conditions and fast adapting to slow variations and new operating modes. Finally, the utility of the proposed method is demonstrated with a numerical example and a non-isothermal continuous stirred tank reactor.

A Novel High Spectral Efficiency Waveform Coding-OVFDM

Based on the Overlapped Multiplexing Principle[12],a frequency domain OVFDM(Overlapped Frequency Domain Multiplexing) Coding is proposed.By the data weighted shift overlapped version of any band-limited Multiplexing Transfer Function H(f) the coding gain and spectral efficiency are both achieved.The heavier the overlap of the data weighted Multiplexing Transfer Function H(f),the higher the coding gain and spectral efficiency as well as the closer the output to the optimum complex Gaussian distribution.The bit error probability performance is estimated.The time domain OVTDM(Overlapped Time Domain Multiplexing) Coding,the dual of OVFDM in time domain is incidentally proposed as well.Both theoretical analysis and testified simulations show that OVFDM(OVTDM) is suitable for high spectral efficiency application and its spectral efficiency is only roughly linear to SNR rather than the well-known logarithm to SNR.

Robust SLAM localization method based on improved variational Bayesian filtering

Aimed at the problem that the state estimation in the measurement update of the simultaneous localization and mapping(SLAM)method is incorrect or even not convergent because of the non-Gaussian measurement noise,outliers,or unknown and time-varying noise statistical characteristics,a robust SLAM method based on the improved variational Bayesian adaptive Kalman filtering(IVBAKF)is proposed.First,the measurement noise covariance is estimated using the variable Bayesian adaptive filtering algorithm.Then,the estimated covariance matrix is robustly processed through the weight function constructed in the form of a reweighted average.Finally,the system updates are iterated multiple times to further gradually correct the state estimation error.Furthermore,to observe features at different depths,a feature measurement model containing depth parameters is constructed.Experimental results show that when the measurement noise does not obey the Gaussian distribution and there are outliers in the measurement information,compared with the variational Bayesian adaptive SLAM method,the positioning accuracy of the proposed method is improved by 17.23%,20.46%,and 17.76%,which has better applicability and robustness to environmental disturbance.

IDFT Numerical Simulation Method for Gaussian Rough Surface with Relatively Large Correlation Length

A numerical simulation method based on inverse discrete Fourier transform(IDFT)is presented for generating Gaussian rough surface with a desired autocorrelation function(ACF). The probability density function of the height distribution of the generated Gaussian surface and the root-mean-square height of the rough surface are also considered. It is found that the height distribution of the generated surface follows the Gaussian distribution, the deviation of the root-mean-square height of the modeled rough surface from the desired value is smaller than that of Patir's method, and the autocorrelation function of the modeled surface is also in good agreement with the desired autocorrelation function. Compared with Patir's method, the modeled surface generated by the IDFT method is in better agreement with the desired autocorrelation function, especially when the correlation length is relatively large.

Towards quantum simulation of Sachdev-Ye-Kitaev model

We study a simplified version of the Sachdev-Ye-Kitaev(SYK) model with real interactions by exact diagonalization. Instead of satisfying a continuous Gaussian distribution, the interaction strengths are assumed to be chosen from discrete values with a finite separation. A quantum phase transition from a chaotic state to an integrable state is observed by increasing the discrete separation. Below the critical value, the discrete model can well reproduce various physical quantities of the original SYK model,including the volume law of the ground-state entanglement, level distribution, thermodynamic entropy,and out-of-time-order correlation(OTOC) functions. For systems of size up to N=20, we find that the transition point increases with system size, indicating that a relatively weak randomness of interaction can stabilize the chaotic phase. Our findings significantly relax the stringent conditions for the realization of SYK model, and can reduce the complexity of various experimental proposals down to realistic ranges.

Light focusing through strongly scattering media by binary amplitude modulation

Based on the angular spectrum method and the circular Gaussian distribution(CGD) model of scattering media,we numerically simulate light focusing through strongly scattering media.A high contrast focus in the target area is produced by using feedback optimization algorithm with binary amplitude modulation.It is possible to form the focusing with one focus or multiple foci at arbitrary areas.The influence of the number of square segments of spatial light modulation on the enhancement factor of intensity is discussed.Simulation results are found to be in good agreement with theoretical analysis for light refocusing.