Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

A HIGH RESOLUTION TIME DELAY ESTIMATION BASED ON THE MAXIMUM ENTROPY POWER SPECTRUM ESTIMATON

Multipath time delay estimation is constrained by the width of the signal correlation function when using correlation based methods. This paper obtains a high resolution time delay estimation by introducing Burg algorithm and Marple algorithm of the maximum entropy power spectral estimation to non-resolvable multipath time delay estimatoin. The principles, the performaces and the results of computer simulation are given.

Information entropy-based estimation of hand and elbow movements using ECoG signals

A method of estimating hand and elbow movements using electrocorticogram （ECoG） signals is proposed. Using multiple channels, surface electromyogram （EMG） signals and ECoG signals were obtained from patients simultaneously. The estimated movements were those to close and then open the hand and those to bend the elbow inward. The patients were encouraged to perform the movements in accordance with their free will instead of after being induced by external stimuli. Surface EMG signals were used to find movement time points, and ECoG signals were used to estimate the movements. To extract the characteristics of the individual movements, the ECoG signals were divided into a total of six bands （the entire band and the ~, 0, a ,fl and 7 bands） to obtain the information entropy, and the maximum likelihood estimation method was used to estimate the movements. The results of the experiment show that the performance averages 74 % when the ECoG of 7 band is used, which is higher than that when other bands are used, and higher estimation success rates are shown in the 7 band than in other bands. The time of the movements is divided into three time sections based on movement time points, and the ＂Before＂ section, which includes the readiness potential, is compared with the ＂Onset＂ section. In the ＂Before＂ section and the ＂Onset＂ section, estimation success rates are 66 % and 65 %, respectively, and thus it is determined that the readiness potential could can be used.

GRADIENT ESTIMATES AND ENTROPY FORMULAE FOR WEIGHTED p-HEAT EQUATIONS ON SMOOTH METRIC MEASURE SPACES

Let （M,g, e^-fdv） be a smooth metric measure space. In this paper, we con- sider two nonlinear weighted p-heat equations. Firstly, we derive a Li-Yau type gradient estimates for the positive solutions to the following nonlinear weighted p-heat equationand f is a smooth function on M under the assumptionthat the m-dimensional nonnegative Bakry-Emery Ricci curvature. Secondly, we show an entropy monotonicity formula with nonnegative m-dimensional Bakry-Emery Ricci curva- ture which is a generalization to the results of Kotschwar and Ni [9], Li [7].

Estimator of Scale Parameter in a Subclass of the Exponential Family under Symmetric Entropy Loss

In this paper we investigate the estimator for the rth power of the scale parameter in a class of exponential family under symmetric entropy loss L（θ, δ） = v（θ/δ ＋ δ/θ - 2）. An exact form of the minimum risk equivariant estimator under symmetric entropy loss is given, and the minimaxity of the minimum risk equivariant estimator is proved. The results with regard to admissibility and inadmissibility of a class of linear estimators of the form cT（X） ＋ d are given, where T（X） Gamma（v, θ）.

A Microcomputer Fitted Method for Determination of the True ODF of Hexagonal Materials according to the Maximum-Entropy Estimate

A method which is especially suitable for microcomputer calculation of the true orientation distribution function (ODF) according to the maximum-entropy estimate is proposed for hexagonal system polycrystalline materials with physical symmetry.The resultant computational software system has been also designed and first carried out in a microcomputer PANAFACOM-U1200 being on line with the X-ray diffractometer D/max-3A.The simu- lated calculation shows that the method is concisely pragmatic and easily popularized,while the results obtained are trust worthy.

A Maximum-Entropy Method for Estimating the Spectrum

Based on the maximunl-entropy （ME） principle, a new power spectral estimator for random waves is derived in the form of S（ω）=a/8H^2^-（2π）^（d＋2）exp[-b（2π/ω）^n],1）y solving a variational problem subject to some quite general constraints. This robust method is comprehensive enough to describe the wave spectra even in extreme wave conditions and is superior to periodogranl method that is not suit＇able to process comparatively short or intensively unsteady signals for its tremendous boundary effect and some inherent defects of FKF. Fortunately, the newly derived method for spectral estimation works fairly well, even though the sample data sets are very short and unsteady, and the reliability and efficiency of this spectral estimator have been preliminarily proved.

Extraction of Information from Crowdsourcing: Experimental Test Employing Bayesian, Maximum Likelihood, and Maximum Entropy Methods

A crowdsourcing experiment in which viewers (the “crowd”) of a British Broadcasting Corporation (BBC) television show submitted estimates of the number of coins in a tumbler was shown in an antecedent paper (Part 1) to follow a log-normal distribution ∧(m,s2). The coin-estimation experiment is an archetype of a broad class of image analysis and object counting problems suitable for solution by crowdsourcing. The objective of the current paper (Part 2) is to determine the location and scale parameters (m,s) of ∧(m,s2) by both Bayesian and maximum likelihood (ML) methods and to compare the results. One outcome of the analysis is the resolution, by means of Jeffreys’ rule, of questions regarding the appropriate Bayesian prior. It is shown that Bayesian and ML analyses lead to the same expression for the location parameter, but different expressions for the scale parameter, which become identical in the limit of an infinite sample size. A second outcome of the analysis concerns use of the sample mean as the measure of information of the crowd in applications where the distribution of responses is not sought or known. In the coin-estimation experiment, the sample mean was found to differ widely from the mean number of coins calculated from ∧(m,s2). This discordance raises critical questions concerning whether, and under what conditions, the sample mean provides a reliable measure of the information of the crowd. This paper resolves that problem by use of the principle of maximum entropy (PME). The PME yields a set of equations for finding the most probable distribution consistent with given prior information and only that information. If there is no solution to the PME equations for a specified sample mean and sample variance, then the sample mean is an unreliable statistic, since no measure can be assigned to its uncertainty. Parts 1 and 2 together demonstrate that the information content of crowdsourcing resides in the distribution of responses (very often log-normal in form), which can be obtained empirically or by appropriate modeling.

Entropy Bayesian Analysis for the Generalized Inverse Exponential Distribution Based on URRSS

This paper deals with the Bayesian estimation of Shannon entropy for the generalized inverse exponential distribution.Assuming that the observed samples are taken from the upper record ranked set sampling(URRSS)and upper record values(URV)schemes.Formulas of Bayesian estimators are derived depending on a gamma prior distribution considering the squared error,linear exponential and precautionary loss functions,in addition,we obtain Bayesian credible intervals.The random-walk Metropolis-Hastings algorithm is handled to generate Markov chain Monte Carlo samples from the posterior distribution.Then,the behavior of the estimates is examined at various record values.The output of the study shows that the entropy Bayesian estimates under URRSS are more convenient than the other estimates under URV in the majority of the situations.Also,the entropy Bayesian estimates perform well as the number of records increases.The obtained results validate the usefulness and efficiency of the URV method.Real data is analyzed for more clarifying purposes which validate the theoretical results.

Entropy损失函数下两参数指数威布尔分布尺度参数的Bayes估计及其性质

在Entropy损失函数下,利用构造多层先验分布的方法求出了指数威布尔分布参数的多层Bayes估计,然后根据经验Bayes估计的思想,利用密度函数的核估计方法,构造了参数的经验Bayes估计并证明了该估计的渐进最优性和可容许性,最后运用随机模拟,将其与平方损失函数下的Bayes估计以及极大似然估计(MLE)进行了比较,结果表明:Entropy损失下的Bayes估计较后两种估计好。

Characteristics Extraction of Vehicle State Information Based on Entropy Calculation

A method of extracting and detecting vehicle stability state characteristics based on entropy is proposed.The vehicle’s longitudinal and lateral dynamics models are established for complex driving and maneuver conditions.The corresponding state observer is designed by adopting the moving horizon estimation algorithm,which realizes the observation of the vehicle stability state considering the global state information.Meanwhile,the Shannon entropy is modified to approximate entropy,and the approximate entropy value of the observed vehicle state is calculated.Furthermore,the optimal controller is designed to further validate the reliability of the entropy value as the reference of control system.Simulation results demonstrate that this method can quickly detect the instability state of the system during the process of vehicle driving,which provides a reference for risk prediction and active control.

Re-Formulation of Mean King’s Problem Using Shannon’s Entropy

Mean King’s problem is formulated as a retrodiction problem among noncommutative observables. In this paper, we reformulate Mean King’s problem using Shannon’s entropy as a first step of introducing quantum uncertainty relation with delayed classical information. As a result, we give informational and statistical meanings to the estimation on Mean King problem. As its application, we give an alternative proof of nonexistence of solutions of Mean King’s problem for qubit system without using entanglement.

新质生产力三维创新生态系统及水平测度分析

新质生产力是由技术颠覆性突破、生产要素创新性配置、产业深度转型升级而催生的当代先进生产力;一切利用新技术提升生产力水平驱动高质量发展的领域,都属于新质生产力的应用范畴。从创新生态系统角度构建新质生产力的评价指标体系,利用熵值法、Kernel密度估计法和空间Markov链法进行实证分析。研究结论如下:首先,新质生产力在微观层面表现为新质人才、新质生产单元和新质企业组织;在中观层面表现为新质产业部门、新质产业链、新质创新网络、新质产业集群;在宏观层面表现为国家科技创新政策体系。其次,基于新质生产力的三个维度,构建新质生产力的综合指标体系,通过熵值法测度发现,中国新质生产力总体水平较低,但呈逐年增长趋势,东部地区新质生产力水平远高于中部、西部和东北地区。最后,利用Kernel密度估计法和空间Markov链法分析可知,新质生产力发展不均衡问题较为凸显,空间效应也是影响新质生产力发展的重要因素。

中国乡村振兴水平测度、动态演进与空间收敛性研究

根据二十字总要求构建乡村振兴评价指标体系,运用熵值法、核密度估计、空间β收敛模型测度和考察2012—2021年中国乡村振兴及5个维度发展水平、动态演进和空间收敛特征。研究结论表明:基于熵值法测度结果,中国乡村振兴发展水平整体较低但考察期内平稳上升,不同地区乡村振兴发展水平存在较为显著的空间分异特征,东部地区发展水平高于中西部地区,5个维度指数大小依次为产业兴旺、治理有效、乡风文明、生活富裕、生态宜居;基于核密度估计结果,全国与中部地区呈现高水平集聚、西部地区呈现高低水平集聚、东部地区未呈现明显的高低水平集聚现象,不同地区乡村振兴水平绝对差异逐步缩小,全国分布极化现象不断改善,不同地区内部存在一定程度的两极分化;基于空间β收敛结果,全国及东、中、西部地区乡村振兴存在明显的空间β收敛,并存在显著的正向空间溢出,所控制的差异化经济特征变量在一定程度上影响着乡村振兴发展水平的空间收敛。

写给物理学家的生成模型

科学研究的本质在于创造。生成式人工智能为更有创意的科学探索打开了无尽的想象空间。作为生成式人工智能的核心,生成模型学习数据样本背后的概率分布,并据此随机采样生成新的样本。生成模型和统计物理在本质上是同一枚硬币的两面。文章从物理的视角介绍扩散模型、自回归模型、流模型、变分自编码器等现代生成模型。生成模型在原子尺度物质结构的生成与设计中展现出巨大的潜力。不仅如此,基于和统计物理的内在联系,生成模型对于优化“大自然的损失函数”——变分自由能具有独特的优势,这为求解困难的统计物理和量子多体问题提供了新的可能。同时,物理学的洞察也在推动生成模型的发展和创新。通过借鉴物理学原理和方法,还可以设计出更加高效、更加统一的生成模型,以应对人工智能领域中的挑战。

北极村国家级自然保护区狍栖息地适宜性及活动节律

掌握野生动物栖息地适宜性及活动节律对科学保护森林生态系统具有重要意义。狍(Capreolus pygargus)作为森林生态系统中重要组成部分,其生物学习性对于植被更新及同域物种分布具有指示作用,掌握其栖息地适宜性及活动节律是恢复生物多样性的基础。于2022年6月—2023年5月在北极村国家级自然保护区采用样线调查和相机监测相结合的方法收集狍活动信息,通过随机布设方法在保护区内共布设35条样线和140台红外相机,并利用最大熵(MaxEnt)栖息地建模及核密度估计法分析狍的栖息地适宜性与活动节律,结果表明:(1)MaxEnt模型的ROC曲线评价结果的平均AUC值为0.839,能够较好的反映研究区域狍栖息地适宜性的实际分布情况;(2)研究利用的14个变量中,距离河流越远出现概率越低是影响狍栖息地选择的关键变量,贡献率达34%。其次针阔混交林、常绿针叶林、林间小路、海拔、草地和居民区是影响狍栖息地质量的次要变量;(3)保护区内狍适宜栖息地和次适宜栖息地面积分别为240.44km^(2)和490.64 km^(2),占研究区域总面积的17.48%和35.67%,主要分布于保护区中部和东部,西部适宜栖息地面积较少且破碎化较为严重;(4)活动节律分析表明,狍在9:00左右达到日活动高峰;不同季节狍活动节律存在差异,暖季狍活动节律具有晨昏性,冷季狍上午和下午的活动高峰期分别延迟和提前。基于研究结果提出以下保护建议:(1)加大保护区周边人类活动的管控力度,根据不同人为干扰强度针对性地对狍种群进行保护;(2)通过加强栖息地连通性管理、构建栖息地廊道等方法使破碎的栖息地连接成整体,促进保护区狍个体间交流。研究获得的结果为分布于我国最北端狍种群的保护与恢复提供了基础数据,对于推动该地区生物多样性保护具有重要意义。

采用改进最大相关熵自适应迭代容积卡尔曼滤波算法的锂离子电池荷电状态估计

针对非高斯噪声干扰下传统滤波算法在估计锂离子电池荷电状态(SOC)时存在不稳定以及精度低的问题,提出一种改进的最大相关熵自适应迭代容积卡尔曼滤波(IMCC-AICKF)算法,用于估计锂离子电池荷电状态。所提算法将加权最小二乘方法与最大相关熵准则(MCC)相结合,定义了一种新的代价权函数作为优化准则,通过优化噪声最小协方差矩阵来减小滤波误差,保证长时间滤波的收敛性和稳定性;再与自适应迭代容积卡尔曼滤波(AICKF)算法相结合,对过程噪声协方差和测量噪声协方差进行更新来提高估计的准确性和鲁棒性。基于两种电池数据,在非高斯噪声干扰下,运用所提算法对电池SOC进行估计,仿真结果表明:与容积卡尔曼滤波(CKF)算法和最大相关熵容积卡尔曼滤波(IMCC-CKF)算法相比,IMCC-AICKF算法对荷电状态估计的最大绝对误差、平均绝对误差和均方根误差都是最小的,且平均绝对误差和均方根误差均小于1%;在给定初始值错误的情况下,IMCC-AICKF算法可以准确收敛到真实值,具有较好的鲁棒性。所提算法在非高斯噪声下能实现更准确的估计,是一种估计精度高且鲁棒性好的SOC估计方法。

中国新经济新动能的测度评价及动态分布特征研究

从新产业、新业态、新技术、新资本4个维度构建中国新经济新动能评价指标体系,利用熵权TOPSIS测度中国新经济新动能发展指数,并借助核密度估计、泰尔指数和收敛模型分析中国新经济新动能的综合发展水平、地区差异及收敛性。结果表明,中国新经济新动能整体呈稳步上升趋势,子系统呈现“新产业>新业态>新技术>新资本”的分布格局;三大区域新经济新动能差异显著,呈现“东部>中部>西部”的发展态势。从地区差异角度而言,中国新经济新动能发展非均衡性逐步凸显,但未发生两极分化现象;中国新经济新动能总体差异呈下降趋势,主要来源为地区间差异。从收敛性看,全国以及三大区域新经济新动能具有显著的σ收敛和绝对β收敛趋势;不同区域新经济新动能条件β收敛具有差异。

地方高校高质量发展水平测度、区域差异及分布动态演进

在构建地方高校高质量发展评价指标体系的基础上,利用全国31个省份的2004—2021年面板数据,使用熵权TOPSIS法测度地方高校高质量发展水平并分析我国地方高校高质量发展的时空特征、区域差异以及分布动态演进,研究结果表明:地方高校高质量发展水平整体不高但呈波动上升趋势;我国区域地方高校高质量发展不平衡,呈现东部-东北部-中部-西部依次递减态势;地方高校高质量发展水平存在显著的区域差异,主要来源于区域间差异,但有缩小的趋势,且西部和东北地区伴有极化情况出现。

数字经济引领广东省域乡村振兴的实现路径

基于2013-2022年广东省的数据,主要采用熵权TOPSIS法和核密度估计方法对乡村振兴进行综合测度,通过建立长期均衡模型和门槛回归模型,以数字经济视角实证研究广东省域乡村振兴现状。实证研究发现,2013-2022年广东省乡村振兴水平呈现不断增长的趋势,说明乡村振兴水平有了明显的提高和改善;2013-2022年核密度估计分布曲线均呈现向右移的趋势,显示广东省域乡村振兴水平呈现提升趋势;门槛回归和长期均衡模型的结果都表明,数字经济和乡村振兴之间存在稳定的关联关系,尤其是数字经济的发展正向推进乡村振兴。