基于图论分析的部分相干高斯光束聚焦性质研究

在部分相干高斯光束聚焦性质研究中,聚焦光斑的大小严重影响光束的聚焦质量,而传统的部分相干高斯光束聚焦性质研究方法无法对其详细分析,聚焦质量不高。本文提出一种基于图论分析的部分相干高斯光束聚焦性质研究方法。建立部分相干高斯光束的聚焦模型,对部分相干高斯光束聚焦光强分布、束腰宽度和发散角进行计算,研究部分相干高斯光束聚焦光斑及聚焦性质。实验结果表明,采用基于图论分析的部分相干高斯光束聚焦方法能够提高光束的聚焦质量。

等温衰减电流法的研究

分析了采用等温衰减电流法研究有机固体材料陷阱的优越性 ,其实验过程简单 ,所得结果直观 ,且实验过程由计算机自动控制 ,可大大提高实验速度和精度 .

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.

Prediction Method Study on the Remaining Useful Life of Plant New Varieties Rights Based on Weibull Survival Function and Gaussian Model——Taking Hybrid Rice Variety for Example

In view of the difficulty in determining remaining useful life of plant new variety right in economic analysis, Weibull Survival Analysis Method and Gaussian Model to were used to study how to accurately estimate the remaining useful life of plant new variety right. The results showed that the average life of the granted rice varieties was 10.013 years. With the increase of the age of plant variety rights, the probability of the same residual life Ttreaching x was smaller and smaller, the reliability lower and lower, while the probability of the variety rights becoming invalid became greater. The remaining useful life of a specific granted rice variety was closely related to the demonstration promotion age when the granted rice variety reached its maximum area and the appearance of alternative varieties, and when the demonstration promotion age of the granted rice variety reaching the one with the maximum area, the promotion area would be decreased once a new alternative variety appeared, correspondingly with the shortening of the remaining useful life of the variety. Therefore, Weibull Survival Analysis Method and Gaussian Model could describe the remaining useful life＇s time trend, as well as determine the remaining useful life of a concrete plant variety right, clarify the entire life time of varieties rights, and make the economic analysis of plant new varieties rights more accurate and reasonable.

长周期光纤光栅逐点写入的高斯型折射率谐波分析

由于写入方式的限制,长周期光纤光栅(LPG)的实际折射率分布与经典理论有明显的差异,由此造成写入光栅的特性与经典理论的导出结果不能完全吻合。用傅里叶级数展开的方法对LPG逐点写入造成的高斯型折射率分布进行谐波分析,据此总结了高斯型折射率分布下光束形状与光栅周期的关系对谐波分布的影响,讨论了逐点写入的最佳参数选择,得出了对光栅写入和特性估计有关的结论,并给出了高斯光束逐点写入和振幅掩模板写入长周期光栅的透射谱比较。

存在移动车辆遮挡的VANETs连通性研究

针对高速公路场景下的车流移动特性以及时变的信道状态，着重考虑道路中其他移动车辆作为遮挡物对车间通信链路的影响，深入分析了车流密度、车辆速度均值以及标准误差对车联网链路连通概率的影响，采用双斜率路径损耗模型并结合车辆速度服从截断型高斯分布的车流移动特性以及两状态的Markov信道状态转移模型，推导出车辆平稳阻挡概率，以信号的传播损耗是否超出通信系统容限为依据对车联网连通性进行判决，得到通信链路连通概率。仿真结果表明，在通信距离一定的条件下，速度服从截断型高斯分布的移动遮挡物模型具有更好的连通性，同时，单位时间内达到的车辆越多，车流密度越大，平稳阻挡概率越大，网络连通性越差。

Multi Boost with ENN-based ensemble fault diagnosis method and its application in complicated chemical process

Fault diagnosis plays an important role in complicated industrial process.It is a challenging task to detect,identify and locate faults quickly and accurately for large-scale process system.To solve the problem,a novel Multi Boost-based integrated ENN(extension neural network) fault diagnosis method is proposed.Fault data of complicated chemical process have some difficult-to-handle characteristics,such as high-dimension,non-linear and non-Gaussian distribution,so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features.Then,the affinity propagation(AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time.Afterwards,an integrated ENN classifier based on Multi Boost strategy is constructed to identify fault types.The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters.Finally,a real industrial system—Tennessee Eastman(TE) process is employed to evaluate the performance of the proposed method.And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.

Frequency domain wave equation forward modeling using gaussian elimination with static pivoting

Frequency domain wave equation forward modeling is a problem of solving large scale linear sparse systems which is often subject to the limits of computational efficiency and memory storage. Conventional Gaussian elimination cannot resolve the parallel computation of huge data. Therefore, we use the Gaussian elimination with static pivoting （GESP） method for sparse matrix decomposition and multi-source finite-difference modeling. The GESP method does not only improve the computational efficiency but also benefit the distributed parallel computation of matrix decomposition within a single frequency point. We test the proposed method using the classic Marmousi model. Both the single-frequency wave field and time domain seismic section show that the proposed method improves the simulation accuracy and computational efficiency and saves and makes full use of memory. This method can lay the basis for waveform inversion.

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.

Crest-Height Distribution of Nonlinear Random Waves

The wave crest is an important factor for the design of both fixed and floating marine structures.Wave crest height is a dominant parameter in assessing the likelihood of wave-in-deck impact and resultant severe damage.Many empirical and theoretical distribution functions for wave crest heights have been proposed,but there is a lack of agreement between them.It is of significance to develop a better new nonlinear wave crest height distribution model.The progress in the research of wave crest heights is reviewed in this paper.Based on Stokes' wave theory,an approximate nonlinear wave crest-height distribution formula with simple parameters is derived.Two sets of measured data are presented and compared with various theoretical distributions of wave crests obtained from nonlinear wave models and analysis of the comparison is given in detail.The new crest-height distribution model agrees well with observations.Also,the new theoretical distribution is more accurate than the other methods cited in this paper and has a greater range of applications.

Spatial batch optimal design based on self-learning Gaussian process models for LPCVD processes

Low pressure chemical vapor deposition(LPCVD) is one of the most important processes during semiconductor manufacturing.However,the spatial distribution of internal temperature and extremely few samples makes it hard to build a good-quality model of this batch process.Besides,due to the properties of this process,the reliability of the model must be taken into consideration when optimizing the MVs.In this work,an optimal design strategy based on the self-learning Gaussian process model(GPM) is proposed to control this kind of spatial batch process.The GPM is utilized as the internal model to predict the thicknesses of thin films on all spatial-distributed wafers using the limited data.Unlike the conventional model based design,the uncertainties of predictions provided by GPM are taken into consideration to guide the optimal design of manipulated variables so that the designing can be more prudent Besides,the GPM is also actively enhanced using as little data as possible based on the predictive uncertainties.The effectiveness of the proposed strategy is successfully demonstrated in an LPCVD process.

Bar in Questionnaires

Vougiouklis ＆ Vougiouklis have proposed the replacement of Likert scales, usually used in questionnaires, with a bar. With this proposal a discrete situation is replaced by a fuzzy one. There are identified certain advantages concerning the use of the bar as compared to that of a scale during both the stages of filling-in as well as processing a questionnaire. The main advantage associated with business research requirements is the fact that it is expected to be much quicker to fill in and much easier to explain to participants. The bar provides the potential for different types of processing Likert scales cannot. Therefore the researchers are allowed to ascertain that the given answers follow not only the already suggested and used Gauss distribution but also a parabola distribution as it will be suggested in present paper, and which is expected to give the researcher the opportunity to ＂correct＂ this tendency. Therefore, a possibility of choosing amongst a number of alternatives is offered, by utilizing fuzzy logic in the same way as it has already been done in industry and combining mathematical models with multivalued operations.

Linear Bayes Estimators Applied to the Inverse Gaussian Lifetime Model

For the two-parameter inverse Gaussian distribution denoted by IG（μ,A）, the authors employ a linear Bayes procedure to estimate the parameters μ and A. The superiority of the proposed linear Bayes estimator （LBE） over both the classical UMVUE and the maximum likelihood estimator （MLE） is established in terms of the mean squared error matrix （MSEM） criterion. Compared with the usual Bayes estimator, which is obtained by an MCMC method, the proposed LBE is simple and easy to use. Some numerical results are presented to verify that the LBE performs well.

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.

Non-gaussian Test Models for Prediction and State Estimation with Model Errors

Turbulent dynamical systems involve dynamics with both a large dimensional phase space and a large number of positive Lyapunov exponents. Such systems are ubiqui- tous in applications in contemporary science and engineering where the statistical ensemble prediction and the real time filtering/state estimation are needed despite the underlying complexity of the system. Statistically exactly solvable test models have a crucial role to provide firm mathematical underpinning or new algorithms for vastly more complex scien- tific phenomena. Here, a class of statistically exactly solvable non-Gaussian test models is introduced, where a generalized Feynman-Ka~ formulation reduces the exact behavior of conditional statistical moments to the solution to inhomogeneous Fokker-Planck equations modified by linear lower order coupling and source terms. This procedure is applied to a test model with hidden instabilities and is combined with information theory to address two important issues in the contemporary statistical prediction of turbulent dynamical systems： the coarse-grained ensemble prediction in a perfect model and the improving long range forecasting in imperfect models. The models discussed here should be use- ful for many other applications and algorithms for the real time prediction and the state estimation.

Invariant Measures and Asymptotic Gaussian Bounds for Normal Forms of Stochastic Climate Model

The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high dimensional climate models is an important topic for atmospheric low-frequency variability,climate sensitivity,and improved extended range forecasting.Recently,techniques from applied mathematics have been utilized to systematically derive normal forms for reduced stochastic climate models for low-frequency variables.It was shown that dyad and multiplicative triad interactions combine with the climatological linear operator interactions to produce a normal form with both strong nonlinear cubic dissipation and Correlated Additive and Multiplicative(CAM) stochastic noise.The probability distribution functions(PDFs) of low frequency climate variables exhibit small but significant departure from Gaussianity but have asymptotic tails which decay at most like a Gaussian.Here,rigorous upper bounds with Gaussian decay are proved for the invariant measure of general normal form stochastic models.Asymptotic Gaussian lower bounds are also established under suitable hypotheses.

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.