MULTIVARIATE EXTREME VALUE DISTRIBUTION AND ITS FISHER INFORMATION MATRIX

The paper is concerned with the basic properties of multivariate extreme value distribution (in the Logistic model). We obtain the characteristic function and recurrence formula of the density function. The explicit algebraic formula for Fisher information matrix is indicated. A simple and accurate procedure for generating random vector from multivariate extreme value distribution is presented.

A Class of Distributed Variable Structure Multiple Model Algorithm Based on Posterior Information of Information Matrix

The tracking of maneuvering targets in radar networking scenarios is studied in this paper.For the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking accuracy.An adaptive grid-expected-mode augmentation variable structure multiple model algorithm is proposed.The adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model set.Furthermore,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is proposed.For the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.

Novel Fault Detection Optimization Algorithm for Single Event Effect system Based on Multi-information Entropy Fusion

Fault detection caused by single event effect( SEE) in system was studied,and an improved fault detection algorithm by fusing multi-information entropy for detecting soft error was proposed based on multi-objective detection approach and classification management method. In the improved fault detection algorithm, the analysis model of posteriori information with corresponding multi-fault alternative detection points was formulated through correlation information matrix, and the maximum incremental information entropy was chosen as the classification principle for the optimal detection points. A system design example was given to prove the rationality and feasibility of this algorithm.This fault detection algorithm can achieve the purpose of fault detection and resource configuration with high efficiency.

Developing a Secure Framework Using Feature Selection and Attack Detection Technique

Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.

A Low Resolution Image Restoration Method based on BP Neural Network

In order to investigate the restoration of low resolution images, the linear and nonlinear interpolation methods were applied for the interpolation of the com- mon information matrix obtained from a series of pictures, getting the restructuring matrix. The characteristic block with the best restoration effect was determined by analyzing the pixel difference of the common information of each image at the same position. Then the characteristic blocks and their original blocks were used to build and train neural network. Finally, images were restored by the neural network and the differences between pictures were reduced. Experimental results showed that this method could significantly improve the efficiency and precision of algorithm.

Optimal observation configuration of UAVs based on angle and range measurements and cooperative target tracking in three-dimensional space

This article investigates the optimal observation configuration of unmanned aerial vehicles(UAVs) based on angle and range measurements, and generalizes predecessors' researches in two dimensions into three dimensions. The relative geometry of the UAVs-target will significantly affect the state estimation performance of the target, the cost function based on the Fisher information matrix(FIM) is used to derive the FIM determinant of UAVs' observation in three-dimensional space, and the optimal observation geometric configuration that maximizes the determinant of the FIM is obtained. It is shown that the optimal observation configuration of the UAVs-target is usually not unique, and the optimal observation configuration is proved for two UAVs and three UAVs in three-dimension. The long-range over-the-horizon target tracking is simulated and analyzed based on the analysis of optimal observation configuration for two UAVs. The simulation results show that the theoretical analysis and control algorithm can effectively improve the positioning accuracy of the target. It can provide a helpful reference for the design of over-the-horizon target localization based on UAVs.

Sensor placement for structural damage detection with modal data

A new method is presented for prioritizing sensor locations for structural health monitoring (SHM). In view of the needs of SHM and damage detection,sensor locations are optimized for the purpose of both sensitivity for local damages and independence of the target mode. However,the two different optimization criterions lead to an inconsistency of the optimal result. Considering the structural response changes that result from damage,the relationship between the structural response and damage is deduced from the structural motion equation by a quasi-analytical mode. Based on the harmony between damage identifiability and mode observability,an object function is set up,including the information of mode independence and damage sensitivity. Utilizing the technique of singular value decomposition,an interior algorithm for the optimum sensor placement is proposed with the multiple objective criterions of minimizing the condition number of coefficient matrix and maximizing the fisher information matrix. A numerical example shows that this approach can effectively avoid the contradiction between the two different optimization criterions. Comparing with the result of single object,the result of damage detection from the optical sensor locations is much more accurate.

Geometric Properties of AR(q) Nonlinear Regression Models

This paper is devoted to a study of geometric properties of AR(q) nonlinear regression models. We present geometric frameworks for regression parameter space and autoregression parameter space respectively based on the weighted inner product by fisher information matrix. Several geometric properties related to statistical curvatures are given for the models. The results of this paper extended the work of Bates & Watts(1980,1988)[1.2] and Seber & Wild (1989)[3].

The Alpha-Beta-Gamma Skew Normal Distribution and Its Application

In this paper, a new class of skew multimodal distributions with more flexible than alpha skew normal distribution and alpha-beta skew normal distribution is proposed, which makes some important distributions become its special cases. The statistical properties of the new distribution are studied in detail, its moment generating function, skewness coefficient, kurtosis coefficient, Fisher information matrix, maximum likelihood estimators are derived. Moreover, a random simulation study is carried out for test the performance of the estimators, the simulation results show that with the increase of sample size, the mean value of maximum likelihood estimators tends to the true value. The new distribution family provides a better fit compared with other known skew distributions through the analysis of a real data set.

A Study on Mixture of Exponentiated Rayleigh and Exponentiated Exponential Distributions Based on Order Statistics

Mixtures of lifetime distributions occur when two different causes of failure arc present, each with the same parametric form of lifetime distributions. This paper is considered with the mixture model of exponentiated Rayleigh and exponentiated exponential distributions. The author＇s objectives are finding the statistical properties of the model and estimating the parameters of the model by using point estimation and interval estimation methods. First, some properties of the model with some graphs of the density function are discussed. Next, the maximum likelihood method of estimation is used for estimating scale and shape parameters of the model. Estimating the parameters is studied under complete and type II censored samples for different sample sizes. Asymptotic Fisher information matrix of the estimators for complete samples is founded with different sample sizes. The asymptotic variances of the maximum likelihood estimates are derived. Based on the asymptotic variances of the maximum likelihood estimates, interval estimates of the parameters are obtained. Some of the equations in this paper are solved by using numerical iteration such as Newton Raphson method by using Mathematica 7.0. The performance of findings in the paper is showed by demonstrating some numerical illustrations through Monte Carlo simulation study based on absolute relative bias and mean square error.

基于演化博弈的城市数据中心建设主体分析

针对数字城市背景下城市数据中心建设主体分析的需求,本文提出了一种基于演化博弈的城市数据中心建设主体分析模型,首先采用演化博弈理论针对城市数据中心建设的主体分析需求,对其3个主体市民、信息化主管部门和强势部门进行模型的构建,然后根据市民、信息化主管部门、强势部门之间的相互关系,得出三个主体的期望收益函数,接着根据复制动态方程,得到复制动态微分方程,最后对演化博弈模型进行联立,求得市民、信息化主管部门、信息强势部门三者的局部均衡点,并根据雅克比矩阵的方法,判断市民、信息化主管部门和信息强势部门之间的均衡点的稳定性。仿真试验结果表明,本文提出的基于演化博弈的城市数据中心建设主体分析模型应用效果良好。

高职物流专业信息化教学模式应用研究

"互联网+教育"要求提升高职院校教师信息化教学能力,提升高职院校核心竞争力的应用研究以高职院校物流专业为试点,采用波士顿矩阵法进行信息化教学模式的判别和优化,利用信息化教学模型整合物流专业校内外资源,不断提升信息化教育教学实力,构建专业信息化资源库,提升物流专业信息化教育教学水平。

中小学教育信息化评价指标体系及评价方法探讨

论述了建立中小学教育信息化评价指标体系的目的和原则,对影响中小学信息化评价的主要因素进行了分析,并在此基础上建立信息化评价指标体系,讨论指标权重的确定与标准化处理的问题,并对量化评价进行初步探讨.

网络安全威胁集中分析的研究

随着电子渠道的逐步拓展,来自外部的各种安全威胁日益增多,那么如何在现有软硬件系统基础上,更安全、高效、便捷地应对来自外部网络的安全威胁是需要深入研究的内容。笔者以A单位为例,从构建统一采集信息、统一分析信息、统一告警的网络安全威胁集中分析平台着手,为电子渠道业务的平稳运行提供一个优秀的架构载体。

Pupil design based on Fisher information optimization to extend field depth in practical optical system

Wavefront coding （WFC） is used to extend the field depth of an incoherent optical system by employing a phase mask on the pupil. We uses a Fisher information （FI） metric based optimization method to design a phase mask by taking the modulation transfer function （MTF） of the practical optical system into consid- eration. This method can modulate the wavefront so that the point spread function and optical transfer function are insensitive to the object distance. The simulation results show that the optimized phase mask based on the proposed method can further improve the defocusing image quality while maintaining the focusing image quality.

Fisher Information in Moving Extreme Ranked Set Sampling with Application to Parameter Estimation

In statistical parameter estimation problems,how well the parameters are estimated largely depends on the sampling design used.In the current paper,a modification of ranked set sampling called moving extremes ranked set sampling(MERSS)is considered for the Fisher information matrix for the location-scale family.The Fisher information matrix for this model are respectively derived under simple random sampling and MERSS.In order to give more insight into the performance of MERSS with respect to simple random sampling,the Fisher information matrix for usual locationscale distributions are respectively computed under the two sampling.The numerical results show that MERSS provides more information than simple random sampling in parametric inference.

Quantum Fisher information of triphoton states

Based on the standard angular momentum theory, we create an experiment on preparing maximally path- entangled （[N, 0] ＋ [0, N〉]2 （NOON） states of triphotons. In order to explain the error between the theoretical and experimental data, we consider the background events during the experiment, and observe their effect on the uncertainty in S1- Afterwards, we calculate the quantum Fisher information （QFI） of the states to evaluate their potential applications in quantum metrology. Our results show that by adding the appropriate background terms, the theoretical data of the produced states matches well with the experimental data. In this case, the QFI of the states is lower than maximally entangled NOON states, but still higher than a classical state.

Optimal sensor placement in health monitoring of suspension bridge

Structural health monitoring(SHM) provides an effective approach to ensure the safety of structures.However,with the restriction of the cost of sensor system and data processing,only a small number of sensors could be available in the health monitoring system(HMS).In order to obtain the best identification of structural characteristics,optimal sensor placement(OSP) becomes an inevitable task in the design of HMS.This paper introduces the process for determining the OSP in HMS of a suspension bridge,in which four different OSP methods have been investigated,including the effective independence(EI) method,the effective independence driving-point residue(EFI-DPR) method,the minimized modal assurance criterion(minMAC) method and the principal subset selection-based extended EI(PSS-EI) method.Then,three criteria,which are modal assurance matrix(MAC),condition number(CN) of mode shape matrix and determinant of Fisher information matrix(FIM),were employed to evaluate the effect of the OSP methods respectively.The result showed that the PSS-EI method developed has the ability to guarantee the highest determinant of FIM,a relatively small off-diagonal term of MAC and agreeable CN,as well as the deployment of sensors in a uniform and symmetric fashion for the studied bridge.Finally,the scheme obtained by PSS-EI was adopted in the HMS.

An interval effective independence method for optimal sensor placement based on non-probabilistic approach

This paper presents an interval effective independence method for optimal sensor placement, which contains uncertain structural information. To overcome the lack of insufficient statistic description of uncertain parameters, this paper treats uncertainties as non-probability intervals. Based on the iterative process of classical effective independence method, the proposed study considers the eliminating steps with uncertain cases. Therefore, this method with Fisher information matrix is extended to interval numbers, which could conform to actual engineering. As long as we know the bounds of uncertainties, the interval Fisher information matrix could be obtained conveniently by interval analysis technology. Moreover, due to the definition and calculation of the interval relationship, the possibilities of eliminating candidate sensors in each iterative process and the final layout of sensor placement are both presented in this paper. Finally, two numerical examples, including a five-storey shear structure and a truss structure are proposed respectively in this paper. Compared with Monte Carlo simulation, both of them can indicate the veracity of the interval effective independence method.

Bayesian inference and life testing plans for generalized exponential distribution

Recently generalized exponential distribution has received considerable attentions. In this paper, we deal with the Bayesian inference of the unknown parameters of the progressively censored generalized exponential distribution. It is assumed that the scale and the shape parameters have independent gamma priors. The Bayes estimates of the unknown parameters cannot be obtained in the closed form. Lindley’s approximation and importance sampling technique have been suggested to compute the approximate Bayes estimates. Markov Chain Monte Carlo method has been used to compute the approximate Bayes estimates and also to construct the highest posterior density credible intervals. We also provide different criteria to compare two different sampling schemes and hence to find the optimal sampling schemes. It is observed that finding the optimum censoring procedure is a computationally expensive process. And we have recommended to use the sub-optimal censoring procedure, which can be obtained very easily. Monte Carlo simulations are performed to compare the performances of the different methods and one data analysis has been performed for illustrative purposes.