3D joint inversion of surface and borehole gravity data using zeroth-order minimum entropy regularization

Surface and borehole gravity data contain complementary information.Thus,the joint inversion of these two data types can help retrieve the real spatial distributions of density bodies.When a sharp boundary exists between an anomalous density body and its surrounding rock,the interface recovered by smooth inversion with Tikhonov regularization is not clear,leading to difficulties in the subsequent geological interpretation.In this work,we develop a joint inversion of surface and borehole gravity data using zeroth-order minimum entropy regularization.The method takes advantage of the complementary information from surface and borehole gravity data to enhance the imaging resolution of density bodies.It also produces a focused imaging of bodies through the zeroth-order minimum entropy regularization without requiring a preselection of a proper focusing parameter.We apply the developed joint inversion approach to three diff erent synthetic data sets.Inversion results show that the focusing inversion with the zeroth-order minimum entropy regularization provides a good description of the true spatial extent of anomalous density bodies.Meanwhile,the joint focusing inversion reconstructs a more reliable density model with a relatively high resolution when a density body is passed through by one or more boreholes.

Wavelets and Entropy for Power Quality Assessment

In this paper, wavelet transform and entropy are evaluated using the mathematical analysis concepts of reflexibility, regularity and series obtention, these concepts remark the reason to make a selective reference framework for power quality applications. With this idea the paper used the same treatment for the two algorithms (Multiresolution and Multiscale Entropy). The wavelet is denoted to have the most power full consistence to the light off the reflexibility, regularity and series obtention. The paper proposes a power quality technique namely MpqAT.

Spectral entropy analysis of different alpha band rhythms in relation to hand motor imagery

The event-related desynchronization/synchronization(ERD/ERS) time courses of lower and upper alpha band rhythms during hand motor imagery are investigated respectively by Fourier Sectral Entropy (FSE) in this paper. By analyzing one group of BCI competition data, it was found that FSE within upper alpha band displays a pronounced increase and decrease over contralateral and ipsilateral brain areas respectively at the onset of hand motor imagery, which is corresponding to the antagonistic ERD/ERS patterns in previous studies. Different from the upper alpha activity pattern, FSE within lower alpha band displays a consistent increase over both two hemispheres hand representative areas. The preliminary results show that FSE could disclose the different behaviors of the upper and lower alpha band rhythms so that a new idea with the complexity measure is provided to characterize functional dissociation of lower and upper frequency alpha rhythms in relation to hand motor imagery.

Assessment of Time Series Complexity Using Improved Approximate Entropy

Approximate entropy(ApEn),a measure quantifying complexity and/or regularity,is believed to be an effective method of analyzing diverse settings.However,the similarity definition of vectors based on Heaviside function may cause some problems in the validity and accuracy of ApEn.To overcome the problems,an improved approximate entropy(iApEn)based on the sigmoid function is proposed.The performance of iApEn is tested on the independent identically distributed(IID)Gaussian noise,the MIX stochastic model,the Rossler map,the logistic map,and the high-dimensional Mackey–Glass oscillator.The results show that iApEn is superior to ApEn in several aspects,including better relative consistency,freedom of parameter selection,robust to noise,and more independence on record length when characterizing time series with different complexities.

一种复杂曲面无基准轮廓度的ER-BFGS评定方法

针对大量测点导致曲面轮廓度计算耗时倍增的问题,提出一种基于熵正则化和BFGS算法的曲面轮廓度评定方法。该方法在点到曲面的最小距离函数的基础上,通过熵正则化原理将轮廓度最小区域评定模型的极大极小问题转化为无约束可微优化问题,并利用快速收敛的BFGS算法进行求解,实现了复杂曲面无基准轮廓度的快速评定。实验表明该方法在计算耗时方面比序列二次规划方法缩短约5%~19%,能有效提高在机测量效率。

On Utilization of K-Means for Determination of <i>q</i>-Parameter for Tsallis-Entropy-Maximized-FCM

In this paper, we consider a fuzzy c-means (FCM) clustering algorithm combined with the deterministic annealing method and the Tsallis entropy maximization. The Tsallis entropy is a q-parameter extension of the Shannon entropy. By maximizing the Tsallis entropy within the framework of FCM, membership functions similar to statistical mechanical distribution functions can be derived. One of the major considerations when using this method is how to determine appropriate q values and the highest annealing temperature, Thigh?, for a given data set. Accordingly, in this paper, a method for determining these values simultaneously without introducing any additional parameters is presented. In our approach, the membership function is approximated by a series of expansion methods and the K-means clustering algorithm is utilized as a preprocessing step to estimate a radius of each data distribution. The results of experiments indicate that the proposed method is effective and both q and Thigh can be determined automatically and algebraically from a given data set.

Entropy Production in a Non-Isolated Thermodynamic System Taking into Account Regular Factors of Nonrandom Nature

The work illustrates the impossibility of decreasing entropy in a strictly random thermodynamic process in a non-isolated system using the example of heating a planet by solar radiation flux without and taking into account its rotation around its own axis. That is, the second law of thermodynamics formulated for isolated systems continues to govern such systems. We have shown that in order to achieve a stationary state at lower values of temperature and entropy far from thermodynamic equilibrium at a maximum of temperature and entropy, it is necessary to have regular factors of nonrandom nature, one of which in this example is the rotation of the planet around its own axis. This means that the reason for the appearance of ordered structured objects in non-isolated thermodynamic systems is not the random process itself, but the action of dynamic control mechanisms, such as periodic external influences, nonlinear elements with positive feedback, catalysts for chemical reactions, etc. We present the plots with dependences of temperature and entropy versus time in non-isolated systems with purely random processes and in the presence of a control factor of non-random nature-rotation.

Entropy of Regular Black Holes in Einstein's Gravity

We calculate the entropy of spherically symmetric regular black holes by the path-integral method in Einstein's gravity. This method provides evidence that the entropy of spherically symmetric regular black holes is proportional to a quarter of horizon area, indicating no violation of the entropy-area law.

Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.

熵视角下我国ERE复合系统协调有序发展研究

基于经济系统、资源系统、环境系统三大维度构建ERE复合系统有序度评价指标体系,运用灰关联熵模型对我国30个省域在2000~2012年的ERE复合系统以及两两系统要素组合的有序度进行测算,并从时序演变和空间分异两个视角,对各组合间的协调有序发展水平进行深入解析。结果显示：时序演变上,我国ERE复合系统整体有序度较低,灰关联熵在0.103~0.353上下徘徊波动且呈窄幅退化之势,期末比期初降低5.81%,熵变频率相对较大,经济系统与资源系统、经济系统与环境系统之间也呈类似特征,下降幅度分别为7.66%、9.02%,而资源系统与环境系统的有序度则在波动中上升2.68%,且熵变频率比较平稳,由相对无序转向相对有序。空间分异上,我国ERE复合系统有序度空间分布均衡性有所增强,空间集聚水平在0.046~0.022之间波动性下降,但区域间有序度的绝对差距却不容乐观。其中,有序度最高的浙江与最低的云南有序度值相差2.024倍,极差数值较大,两级分化特征较为显著。到2012年,我国ERE复合系统有序度在东、中、西部呈梯度式分布,东部较高,中部次之,西部靠后,但西部某些省份的ERE复合系统有序度仍表现出较高水平的协调,如宁夏、青海等。

随机正则3-可满足性问题的解簇结构分析

正则3-可满足性(3-SAT)问题是一个NP难问题,研究正则3-SAT问题解簇结构变化,旨在深入理解该问题的判定难度和可满足性解的分布情况。然而,现有分析模型只研究了接近簇集相变点的几个离散值,在不同约束密度下,缺乏统一的分析模型来描述解簇的结构演变。为了解决这一问题,提出解簇结构相变分析模型(PMSS)。该模型主要思想是采用WalkSAT算法和信息传播算法求得正则3-SAT问题可满足的初始解,再利用随机游走构造该初始解的解簇,并对解簇进行分析。用模块度和社区度量解簇社区结构,用结构熵度量解簇结构复杂性。实验结果表明,PMSS能够准确分析解簇结构演变过程,并且正则3-SAT问题实例的可满足相变点位于13~14,与使用Zchaff求解器得到的相变点一致,进一步验证了PMSS的有效性。

基于Whittaker修匀的多目标修匀模型

本文基于Whittaker修匀,提出了一个新的死亡率修匀模型,并采用该模型对两组不同年龄段人群的死亡率进行了修匀计算。该模型的优点一是模型可保证修匀结果的光滑度和拟合度。二是模型具有普遍适用性,可适用于不同年龄段人群的修匀。三是解决了Whittaker修匀中需要主观选取拟合算子和光滑算子之间组合系数的难题。四是解决了Whittaker修匀模型拟合算子中权重选取的难题。五是通过熵正则化法和中心法,将模型转化为一个单目标的凸规划模型,保证了模型计算的可行性。

基于数据挖掘技术研究中医治疗自发性气胸的组方配伍规律

目的:研究中医治疗自发性气胸的组方用药规律,为临床治疗提供有益参考。方法:收集中国知网、维普中文期刊服务平台、万方数据库、中国生物医学数据库等4个数据库建库至2023年2月28日发表的中医治疗自发性气胸的相关文献,采用中医传承辅助平台V2.5统计用药频次、常用药对及药物组合,采用关联规则、改进的互信息法、复杂系统熵聚类分析对药物关联规则及核心药物组合进行分析,采用无监督的熵层次聚类进行新处方分析。结果:共筛选出符合条件的方剂187首,涉及中药237味,使用频次≥10的中药有甘草、黄芪、茯苓、五味子、白术等55味,主要由补虚药、化痰止咳平喘药、活血化瘀药、理气药、清热药及利水渗湿药组成;由关联规则分析得到白术-茯苓、甘草-茯苓、黄芪-当归、党参-黄芪、麦冬-甘草等24种高频药物组合,其中甘草、黄芪、茯苓、白术、五味子、当归等是关联网络的核心;熵聚类分析得到药物核心组合30个、潜在的新处方15个。结论:中医治疗自发性气胸围绕虚、痰、瘀、郁的特点,以益气护肺、祛痰止咳、活血化瘀、行气利水等为核心治法,体现了肺、肝、脾、肾同治及虚实兼顾的治疗思路。

采用多任务特征融合的脑电情绪识别方法

特征选择与融合是提升脑电信号情绪解码精度的重要手段之一。然而,当前脑电情绪解码中的特征选择方法常忽略了脑电信号内在数据结构的隐含信息。该文提出一种基于近邻传播聚类的多任务特征融合方法,通过L_(2,1)范数约束实现稀疏特征选择,同时利用图拉普拉斯正则化保持不同子类间的潜在关系。该算法在不揭示真实样本标签的情况下,在子任务空间有效融合脑网络空间拓扑结构信息和微分熵信息,为高精度脑电信号情绪解码提供具有更高情绪表征能力的特征。DEAP和SEED数据集以及本实验室数据集的分析结果表明,该文提出的方法能显著提高脑电情绪解码的精度。

融合空谱特征的MR-KRVFL高光谱地物识别模型研究

针对高光谱图像复杂空谱特性影响地物识别模型分类精度的问题,提出一种融合空谱特征的流形正则化核随机向量函数连接网络(MR-KRVFL)高光谱图像地物识别方法。首先,对高光谱图像进行熵率超像素分割(ERS),获取对应的同质区域;其次,利用主元分析(PCA)对同质区域进行降维并提取其空谱联合特征;最后,基于空谱特征信息,构造核随机向量函数连接网络(KRVFL)地物识别模型,并对模型进行流形正则化约束,提高高光谱图像地物识别模型的泛化性能。将该模型应用于Indian Pines和Pavia University高光谱数据集,分类精度达到了96.84%和98.83%,证明所提模型的有效性。

改进TD3算法在电机PID控制器中的应用

本文针对永磁同步电机(PMSM)在工业领域中的多变量、非线性和强耦合特性以及传统PID控制在应对其变化时可能导致参数调整困难、响应延迟、鲁棒性差以及适应性问题,提出一种结合双延迟深度确定性策略梯度(TD3)算法和PID控制的创新方案,以优化PID参数调整,实现更精确的电机速度控制.在本文的方法中,融合双向长短期记忆网络(BiLSTM)到Actor和Critic网络中,极大增强了对PMSM动态行为的时间序列数据处理能力,使得系统不仅能够准确捕捉当前状态,还能预测未来趋势,从而实现对PID参数更精确和适应性强的自整定.此外,通过整合熵正则化和好奇心驱动的探索方法进一步增强策略的多样性,避免过早收敛到次优策略,并鼓励模型对未知环境进行深入探索.为验证方法的有效性,设计了一个永磁同步电机的仿真模型,并将本文提出的方法BiLSTM-TD3-ICE与传统的TD3以及经典的Ziegler-Nichols (Z-N)方法进行对比.实验结果充分证明了本文提出的策略在控制性能上具有显著的优势.

一类拟线性抛物方程组非负entropy解的不存在性(英文)

本文在有界正则域内研究了一类加权拟线性抛物方程组.由单个抛物方程相关的已知结论得到此类方程组的非负entropy解的正下界,然后利用一般的Picone恒等式并构造适当的检验函数,证明此类方程组的非负entropy解不存在.

SDCN中基于深度强化学习的移动边缘计算任务卸载算法研究

随着网络技术的不断发展,基于Fat-Tree的网络拓扑结构分布式网络控制模式逐渐显露出其局限性,软件定义数据中心网络(software-defined data center network,SDCN)技术作为Fat-Tree网络拓扑的改进技术,受到越来越多研究者的关注。首先搭建了一个SDCN中的边缘计算架构和基于移动边缘计算(mobileedge computing,MEC)平台三层服务架构的任务卸载模型,结合移动边缘计算平台的实际应用场景,利用同策略经验回放和熵正则改进传统的深度Q网络(deep Q-leaning network,DQN)算法,优化了MEC平台的任务卸载策略,并设计了实验对基于同策略经验回放和熵正则的改进深度Q网络算法(improved DQN algorithm based on same strategy empirical playback and entropy regularization,RSS2E-DQN)和其他3种算法在负载均衡、能耗、时延、网络使用量几个方面进行对比分析,验证了改进算法在上述4个方面具有更优越的性能。

基于深度强化学习的改进移动边缘计算任务卸载算法研究

大数据时代下移动终端用户规模不断扩大,万物互联在给人们带来极大便利的同时,也存在大量数据地理位置分散的问题,给用户服务质量QoS带来了极大挑战。首先,搭建一个基于移动边缘计算平台三层服务架构的任务卸载模型。其次,结合MEC平台实际应用场景,利用同策略经验回放和熵正则改进深度强化学习算法,优化了MEC平台的任务卸载策略,并设计了实验对3种传统算法和改进算法的能耗、时延、网络使用量进行对比分析。实验结果表明,改进算法在降低能耗、时延和网络使用量方面具有更优越的性能。

Measuring System Regularity Using Fuzzy Similarity-based Approximate Entropy

Approximate entropy (ApEn), a measure quantifying regularity and complexity, is believed to be an effective analyzing method of diverse settings that include both deterministic chaotic and stochastic processes, particularly operative in the analysis of physiological signals that involve relatively small amount of data. However, the similarity definition of vectors based on Heaviside function, of which the boundary is discontinuous and hard, may cause some problems in the validity and accuracy of ApEn. To overcome these problems, a modified ApEn based on fuzzy similarity (mApEn) was proposed. The performance on the MIX stochastic model, as well as those on the Logistic map and the Hennon map with noise, shows that the fuzzy similarity-based ApEn gets more satisfying results than the standard ApEn when characterizing systems with different regularities.