Radar sensor network for target detection using Chernoff information and relative entropy

In this paper, we propose to apply information theory to Ultra wide band (UWB) radar sensor network (RSN) to detect target in foliage environment. Information theoretic algorithms such as Maximum entropy method (MEM) and mutual information are proven methods, that can be applied to data collected by various sensors. However, the complexity of the environment poses uncertainty in fusion center. Chernoff information provides the best error exponent of detection in Bayesian environment. In this paper, we consider the target detection as binary hypothesis testing and use Chernoff information as sensor selection criterion, which significantly reduces the processing load. Another strong information theoretic algorithm, method of types, is applicable to our MEM based target detection algorithm as entropy is dependent on the empirical distribution only. Method of types analyzes the probability of a sequence based on empirical distribution. Based on this, we can find the bound on probability of detection. We also propose to use Relative entropy based processing in the fusion center based on method of types and Chernoff Stein Lemma. We study the required quantization level and number of nodes in gaining the best error exponent. The performance of the algorithms were evaluated, based on real world data.     

基于遗传算法参数优化的PCNN红外图像分割

构造一种基于遗传算法参数优化的脉冲耦合神经网络(PCNN)红外图像分割算法。该算法首先利用PCNN的全局耦合性和脉冲同步性对输入图像进行点火处理,根据PCNN的输出结果计算熵作为遗传算法的适应度函数,并利用熵的变化量作为遗传算法的收敛依据,对PCNN模型中影响图像分割的参数进行组合优化,结合PCNN生物视觉特性和遗传算法解空间随机搜索能力来寻找关键参数的最优值。将遗传算法和PCNN进行结合可充分发挥二者优势,将本文方法与最大类间方差法(OTSU)、最大熵直方图分割算法和PCNN分割方法进行对比,通过交叉熵、区域对比度等客观指标对分割后的图像进行定量分析,结果表明无论从主观视觉还是客观指标,本文方法分割效果优于其他对比方法。     

Closure modeling in bridging regions of variable-resolution (VR) turbulence computations

Optimal utilization of computational resources mandates spatio-temporal variation in resolution for computing complex engineering flows. Closure modeling in regions bridging between different resolutions is rendered difficult due to changing interactions between resolved and unresolved fields. We develop a closure model for the bridging region based on energy conservation principles. Then we proceed to provide a proof of concept in decaying isotropic turbulence with temporally varying resolution. The simplicity of the flow permits a thorough examination of various aspects of the proposed closure not feasible in more complex flows. The results demonstrate the potential promise of the approach, but more validation studies need to be performed.While the present development is in the context of partially averaged Navier–Stokes (PANS) method, the closure principle should apply for other variable-resolution (VR) approaches.     

Research on Multi-Dimensional Optimal Location Selection of Maintenance Station Based on Big Data of Vehicle Trajectory

In order to rationally lay out the location of automobile maintenance service stations, a method of location selection of maintenance service stations based on vehicle trajectory big data is proposed. Taking the vehicle trajectory data as the demand points, the demand points are divided according to the region by using the idea of zoning, and the location of the second-level maintenance station is selected for each region. The second-level maintenance stations selected in the whole country are set as the demand points of the first-level maintenance stations. Considering the objectives of the two dimensions of cost and service level, the location model of the first-level maintenance stations under two-dimensional programming is established, and the improved particle swarm optimization algorithm and immune algorithm, respectively, are used to solve the problem. In this way, the first-level maintenance stations in each region are obtained. The example verification shows that the location selection results for the maintenance stations using the vehicle trajectory big data are reasonable and closer to the actual needs.     

连接时序分类准则声学建模方法优化

对基于连接时序分类准则(connectionist temporal classification,CTC)的端到端声学建模方法进行研究和优化。研究分析了不同声学特征、建模单元以及神经网络结构对CTC声学模型性能的影响,针对CTC模型中blank符号共享导致的建模缺陷提出了建模单元相关的非共享blank方法进行改进,并引入融合建模单元关联信息的模型初始化方法进一步提高CTC模型的性能。在300小时标准英文数据集Switchboard的实验结果显示,结合非共享blank、时延神经网络以及融合建模单元关联信息的初始化方法,CTC声学模型相对于基线系统在词错误率上取得绝对1.1%的下降,同时在训练速度上取得3.3倍的提高,实验结果证明本文针对端到端声学建模提出的优化方法是有效的。      

Entropy considerations in numerical simulations of non-equilibrium rarefied flows

Non-equilibrium rarefied flows are encountered frequently in supersonic flight at high altitudes, vacuum technology and in microscale devices. Prediction of the onset of non-equilibrium is important for accurate numerical simulation of such flows. We formulate and apply the discrete version of Boltzmann’s H-theorem for analysis of non-equilibrium onset and accuracy of numerical modeling of rarefied gas flows. The numerical modeling approach is based on the deterministic solution of kinetic model equations. The numerical solution approach comprises the discrete velocity method in the velocity space and the finite volume method in the physical space with different numerical flux schemes: the first-order, the second-order minmod flux limiter and a third-order WENO schemes. The use of entropy considerations in rarefied flow simulations is illustrated for the normal shock, the Riemann and the two-dimensional shock tube problems. The entropy generation rate based on kinetic theory is shown to be a powerful indicator of the onset of non-equilibrium, accuracy of numerical solution as well as the compatibility of boundary conditions for both steady and unsteady problems.     

Belief Entropy Tree and Random Forest: Learning from Data with Continuous Attributes and Evidential Labels

As well-known machine learning methods, decision trees are widely applied in classification and recognition areas. In this paper, with the uncertainty of labels handled by belief functions, a new decision tree method based on belief entropy is proposed and then extended to random forest. With the Gaussian mixture model, this tree method is able to deal with continuous attribute values directly, without pretreatment of discretization. Specifically, the tree method adopts belief entropy, a kind of uncertainty measurement based on the basic belief assignment, as a new attribute selection tool. To improve the classification performance, we constructed a random forest based on the basic trees and discuss different prediction combination strategies. Some numerical experiments on UCI machine learning data set were conducted, which indicate the good classification accuracy of the proposed method in different situations, especially on data with huge uncertainty.     

Using relative entropy to find optimal approximations: An application to simple fluids

We develop a maximum relative entropy formalism to generate optimal approximations to probability distributions. The central results consist of (a) justifying the use of relative entropy as the uniquely natural criterion to select a preferred approximation from within a family of trial parameterized distributions, and (b) to obtain the optimal approximation by marginalizing over parameters using the method of maximum entropy and information geometry. As an illustration we apply our method to simple fluids. The “exact” canonical distribution is approximated by that of a fluid of hard spheres. The proposed method first determines the preferred value of the hard-sphere diameter, and then obtains an optimal hard-sphere approximation by a suitably weighed average over different hard-sphere diameters. This leads to a considerable improvement in accounting for the soft-core nature of the interatomic potential. As a numerical demonstration, the radial distribution function and the equation of state for a Lennard-Jones fluid (argon) are compared with results from molecular dynamics simulations.     

Entropy Estimation Using a Linguistic Zipf–Mandelbrot–Li Model for Natural Sequences

Entropy estimation faces numerous challenges when applied to various real-world problems. Our interest is in divergence and entropy estimation algorithms which are capable of rapid estimation for natural sequence data such as human and synthetic languages. This typically requires a large amount of data; however, we propose a new approach which is based on a new rank-based analytic Zipf–Mandelbrot–Li probabilistic model. Unlike previous approaches, which do not consider the nature of the probability distribution in relation to language; here, we introduce a novel analytic Zipfian model which includes linguistic constraints. This provides more accurate distributions for natural sequences such as natural or synthetic emergent languages. Results are given which indicates the performance of the proposed ZML model. We derive an entropy estimation method which incorporates the linguistic constraint-based Zipf–Mandelbrot–Li into a new non-equiprobable coincidence counting algorithm which is shown to be effective for tasks such as entropy rate estimation with limited data.     

A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy

The health status of the momentum wheel is vital for a satellite. Recently, research on anomaly detection for satellites has become more and more extensive. Previous research mostly required simulation models for key components. However, the physical models are difficult to construct, and the simulation data does not match the telemetry data in engineering applications. To overcome the above problem, this paper proposes a new anomaly detection framework based on real telemetry data. First, the time-domain and frequency-domain features of the preprocessed telemetry signal are calculated, and the effective features are selected through evaluation. Second, a new Huffman-multi-scale entropy (HMSE) system is proposed, which can effectively improve the discrimination between different data types. Third, this paper adopts a multi-class SVM model based on the directed acyclic graph (DAG) principle and proposes an improved adaptive particle swarm optimization (APSO) method to train the SVM model. The proposed method is applied to anomaly detection for satellite momentum wheel voltage telemetry data. The recognition accuracy and detection rate of the method proposed in this paper can reach 99.60% and 99.87%. Compared with other methods, the proposed method can effectively improve the recognition accuracy and detection rate, and it can also effectively reduce the false alarm rate and the missed alarm rate.     

Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).     

二维直方图θ划分最大Shannon熵图像阈值分割

鉴于常用二维直方图区域直分法存在错分,最近提出的斜分法不具普遍性,提出了适用面更广的基于二维直方图θ划分和最大Shannon熵的图像阈值分割算法.首先给出了二维直方图θ划分方法,采用四条平行斜线及一条其法线与灰度级轴成θ角的直线划分二维直方图区域,按灰度级和邻域平均灰度级的加权和进行阈值分割,斜分法可视为该方法中θ=45°的特例;然后导出了二维直方图θ-划分最大Shannon熵阈值选取公式及其快速递推算法;最后给出了θ取不同值时的分割结果及运行时间,θ取较小值时,边界形状准确性较高,θ取较大值时,抗噪性较强,应用时可根据实际图像特点及需求合理选取θ的值.与常规二维直方图直分最大Shannon熵法相比,本文提出的方法所得分割结果更为准确,抵抗噪声更为稳健,且所需运行时间及存储空间也大为减小.     

On the Regression Model for Generalized Normal Distributions

The traditional linear regression model that assumes normal residuals is applied extensively in engineering and science. However, the normality assumption of the model residuals is often ineffective. This drawback can be overcome by using a generalized normal regression model that assumes a non-normal response. In this paper, we propose regression models based on generalizations of the normal distribution. The proposed regression models can be used effectively in modeling data with a highly skewed response. Furthermore, we study in some details the structural properties of the proposed generalizations of the normal distribution. The maximum likelihood method is used for estimating the parameters of the proposed method. The performance of the maximum likelihood estimators in estimating the distributional parameters is assessed through a small simulation study. Applications to two real datasets are given to illustrate the flexibility and the usefulness of the proposed distributions and their regression models.     

一种光学层析图像的多准则重建方法

光学层析成像是一个病态重建问题,为克服重建过程的病态性,提出将多准则优化理论引入到图像重建中。利用了三个用于光学层析图像重建的准则:平方误差函数、图像熵函数和局部平滑函数。采用向量优化方法将多准则优化问题转化为单准则优化问题求解。为了确定各个目标函数间的权重系数,提出一种动态权重系数求解方法。重建过程目标函数关于光学参量的梯度计算是关键,因此提出一种基于梯度树的计算方法。实验过程中对多准则重建结果和基于平方误差函数的单准则重建结果做了比较,证明该方法能够克服传统的偏重单一目标的单准则重建的不足,有效地重建光学层析图像,提高图像重建质量。     

Statistical-Mechanical Foundations for a Generalized Thermodynamics of Dissipative Processes

A statistical-mechanical formalism for nonequilibrium systems, namely the nonequilibrium statistical operator method, provides microscopic foundations for a generalized thermodynamics of dissipative processes. This formalism is based on a unifying variational approach that is considered to be encompassed in Jaynes' Predictive Statistical Mechanics and principle of maximization of the statistical-informational entropy. Within the framework of the statistical thermodynamics that follows from the method, we demonstrate the existence of generalized forms of the theorem of minimum (informational) entropy production, the criterion for evolution, and the thermodynamic (in)stability criterion. The formalism is not restricted to local equilibrium but, in principle, to general conditions (its complete domain of validity is not yet fully determined). A H-theorem associated to the formalism is presented in the form of an increase of the informational entropy along the evolution of the system. Some of the results are illustrated in an application to the study of a model for a photoexcited direct-gap semiconductor.     

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.     

Modeling spatio-temporal field evolution

Prediction of spatio-temporal field evolution is based on the extraction of a physical law from joint experimental data. This extraction is usually described by a set of differential equations. If the only source of information is a field record, a method of field generators based on nonparametric modeling by conditional average can successfully replace differential equations. In this article we apply the method of field generators to a two-dimensional chaotic field record that describes the asynchronous motion of high-amplitude striations. We show how to choose the model structure in order to optimize the quality of the prediction process.     

一种混沌伪随机序列复杂度分析法

分析了已有的序列线性复杂度分析方法，提出了用近似熵算法计算混沌运动的测度熵，作为衡量混沌伪随机序列复杂度的标准.理论研究表明，利用较短的观察序列，该方法能够准确地反映混沌系统和混沌伪随机序列复杂度的大小，可以作为判断利用混沌系统产生的伪随机序列的复杂度准则.实验结果表明该方法的有效性和理论结果的正确性. 关键词： 混沌 伪随机序列 熵     

Modeling of atmospheric radiative transfer with polarization and its application to the remote sensing of tropospheric ozone

Light reflected or transmitted by a planetary atmosphere contains information about particles and molecules in the atmosphere. Therefore, accurate modeling of the radiation field may be used to retrieve information on atmospheric composition. In this paper, a multi-layer model for a vertically inhomogeneous atmosphere is implemented by using the doubling-adding method for a plane-parallel atmosphere. By studying the degree of linear polarization of the transmitted and reflected solar light in the Huggins bands, we find significant differences between tropospheric ozone and stratospheric ozone. The effects of tropospheric ozone change on the linear polarization are 10 times more than that of the same amount of stratospheric ozone change. We also show the aerosol effect on the linear polarization, but this effect is wavelength independent as compared to that caused by the tropospheric ozone change. The results provide a theoretical basis for the retrieval of tropospheric ozone from measurement of linear polarization of the scattered sunlight both from the ground and from a satellite.     

基于高斯拟合的条纹管成像激光雷达目标重构

为得到更为精细的三维重构,提出一种基于信赖域法高斯拟合的峰值探测法,对每帧图像单时间通道的回波进行高斯建模,建模结果可有效减小噪声干扰,具有唯一峰值,该结果作为新的回波波形,再对其进行峰值探测,并基于空中目标实验数据对本文方法进行验证.实验结果表明,基于高斯拟合的峰值探测法可将特征数据的提取误差降至10%以内;利用该方法提取特征数据进行目标三维重构,可实现探测深度方向上最小30cm的空间分辨力,提高了条纹管激光雷达的三维成像精度.