Accelerated Primal-Dual Projection Neurodynamic Approach With Time Scaling for Linear and Set Constrained Convex Optimization Problems

The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates.Most previous studies in this field have primarily concentrated on unconstrained smooth con-vex optimization problems.In this paper,on the basis of primal-dual dynamical approach,Nesterov accelerated dynamical approach,projection operator and directional gradient,we present two accelerated primal-dual projection neurodynamic approaches with time scaling to address convex optimization problems with smooth and nonsmooth objective functions subject to linear and set constraints,which consist of a second-order ODE(ordinary differential equation)or differential conclusion system for the primal variables and a first-order ODE for the dual vari-ables.By satisfying specific conditions for time scaling,we demonstrate that the proposed approaches have a faster conver-gence rate.This only requires assuming convexity of the objective function.We validate the effectiveness of our proposed two accel-erated primal-dual projection neurodynamic approaches through numerical experiments.

高斯混合函数区域匹配引导的Level Set纹理图像分割

基于高斯混合模型颜色匹配及多尺度图像增强,文中提出了有效的边缘停止函数用于引导levelset函数演化,有效地解决了纹理图像的分割问题.文中首先提出基于高斯混合模型颜色分布的边缘停止函数,通过计算levelset演化窄带区域与用户给定交互区域的相似性,根据其相似性来引导levelset快速演化;然后,提出一个定义在多尺度图像梯度上的边缘停止函数,使得levelset能精确地分割出图像的边缘;最后,结合以上两种边缘停止函数的优点,提出一个边缘停止函数的混合模型,根据图像颜色、边缘特征自适应地引导levelset函数演化.实验结果表明,文中提出的算法不仅能有效地检测出纹理目标区域,同时需要计算出纹理区域精确、光滑的边界.

Exact Solutions and Invariant Sets to General Reaction-Diffusion Equation

In this paper, we introduce new invariant sets, and the invariant sets and exact solutions to general reactiondiffusion equation are discussed. It is shown that there exist a class of exact solutions to the equations that belong to the invariant sets.

Deriving the Exact Percentage of Dark Energy Using a Transfinite Version of Nottale’s Scale Relativity

In this paper Nottale’s acclaimed scale relativity theory is given a transfinite Occam’s razor leading to exact predictions of the missing dark energy [1,2] of the cosmos. It is found that 95.4915% of the energy in the cosmos according to Einstein’s prediction must be dark energy or not there at all. This percentage is in almost complete agreement with actual measurements.

Numerical simulation of displacement characteristics of CO_2 injected in pore-scale porous media

Pore structure of porous media, including pore size and topology, is rather complex. In immiscible twophase displacement process, the capillary force affected by pore size dominates the two-phase flow in the porous media, affecting displacement results. Direct observation of the flow patterns in the porous media is difficult, and therefore knowledge about the two-phase displacement flow is insufficient. In this paper, a two-dimensional(2D) pore structure was extracted from a sandstone sample, and the flow process that CO_2 displaces resident brine in the extracted pore structure was simulated using the Navier eStokes equation combined with the conservative level set method. The simulation results reveal that the pore throat is a crucial factor for determining CO_2 displacement process in the porous media. The two-phase meniscuses in each pore throat were in a self-adjusting process. In the displacement process,CO_2 preferentially broke through the maximum pore throat. Before breaking through the maximum pore throat, the pressure of CO_2 continually increased, and the curvature and position of two-phase interfaces in the other pore throats adjusted accordingly. Once the maximum pore throat was broken through by the CO_2, the capillary force in the other pore throats released accordingly; subsequently, the interfaces withdrew under the effect of capillary fore, preparing for breaking through the next pore throat.Therefore, the two-phase displacement in CO_2 injection is accompanied by the breaking through and adjusting of the two-phase interfaces.

Patient-perceived empathy from nurses in Taiwan acute care settings

Purpose: The aim of this study was to understand patient-perceived empathy from nurse among different units of Taiwan acute care settings. Design: A crosssectional study was used with a questionnaire survey in acute care units and intensive care units of a medical center in northern Taiwan. Participants were selected by convenient sampling. A total of 548 patients at 14 acute care units and 4 intensive care units in a large Medical Center participated in this questionnaire survey. Methods: Scale of patient-perceived empathy from nurse including 15 item Likert scale was used in this study. The statistical software SPSS 15.0 for Windows was applied and Cronbach’s α, factor analysis, Pearson’s correlation, t-test and one way ANOVA were conducted to analyze the data. Findings: Patients who were first-time hospitalization had higher scores of patient-perceived empathy from nurse than patients who have had hospitalizing experiences. The patient in surgical unit had the highest score of patient-perceived empathy from nurse. The empathy scale score of patient without ICU experience was significantly higher than patient with ICU experience. Also, patients without depressive history had higher score than patients with depression history. Conclusion: Patients with multiple admissions of hospitalization, ICU experience, and depressive history had lower score than other patients. It is suggested that the perception of patients with complicated disease or condition might be different. Thus, an in-depth caring knowledge of both emotions and disease was needed to support patients’ perception of empathy. Clinical Relevance: The care of complicated patients especially with ICU experience needs strong knowledge based both on emotion and disease.

Invariant Sets and Exact Solutions to Nonlinear Diffusion Equations with x-Dependent Convection and Absorption

In this paper, we introduce a new invariant set Eo={u：ux=f＇（x）F（u）＋ε[g＇（x）-f＇（x）g（x）]F（u）×exp（-∫^u1/F（z）dz）}where f and g are some smooth functions of x, ε is a constant, and F is a smooth function to be determined. The invariant sets and exact sohltions to nonlinear diffusion equation ut = （ D（u）ux）x ＋ Q（x, u）ux ＋ P（x, u）, are discussed. It is shown that there exist several classes of solutions to the equation that belong to the invariant set Eo.

Algorithm for Extracting All Relative-Attribute Reducts in Decision System of Large-Scale Records

A new algorithm based on rough core was proposed to extract all relative-attribute reducts in decision information systems of large-scale records. In the algorithm, the rough core of the decision-making information system is first calculated. Then, an approach based on a top-down strategy is adopted to select the non-core condition attributes and generate candidate relative-attribute reducts. Finally, the set of all relative-attribute reducts is obtained by pruning the candidate relative-attribute reducts. Experimental results show that the proposed algorithm is superior to the other methods such as the algorithm without computing core, the exhaustive method and the discernibility matrix method in extracting all relative-attribute reducts for large-scale data sets.

A Dynamic Active-Set Method for Linear Programming

An efficient active-set approach is presented for both nonnegative and general linear programming by adding varying numbers of constraints at each iteration. Computational experiments demonstrate that the proposed approach is significantly faster than previous active-set and standard linear programming algorithms.

基于RS-Conv的多尺度神经网络LiDAR点云断裂带提取方法

断裂带与地震、滑坡等自然灾害的发生有着密切关系,精准提取断裂带不仅可为地震断层的定量化研究提供指导,还可为地震灾害风险评估及防震减灾决策的制定提供科学依据。针对现有方法中LiDAR点云断裂带提取不完整、连续性差及错误率高等问题,文中提出了一种基于RS-Conv的多尺度神经网络LiDAR点云断裂带提取方法,以便更好地解决复杂地形区域的断裂带自动提取问题。该方法首先构建不同空间尺度的邻域点集,从而更全面地考察点云的局部几何结构特征。考虑到RS-Conv算子能够很好地表征中心点与邻域点的空间关系,文中以RS-Conv算子作为卷积模块构建了多尺度神经网络模型,以提取出LiDAR点云不同尺度的深层次特征,对其进行堆叠并输入到全连接层,以完成对断裂带点的提取。最后,在ISPRS点云数据集、川滇点云数据集和鲜水河数据集上对文中所述方法与张量分解方法和Deep Neural Networks(DNN)方法进行了对比实验,结果表明,文中方法的分类精度最高,分类总误差最低仅为0.3%,较其他方法降低了0.91%~2.79%,证实了该方法在点云断裂带提取方面的优越性。

F-粗糙集的拓展与应用

F-粗糙集是第1个动态粗糙集模型,具有良好的兼容性、表达能力以及动态性,但绝大多数粗糙集模型动态性不足.为了明晰粗糙集模型的动态性,拓展F-粗糙集的应用,综述了F-粗糙集研究的现状和发展,证明了多粒度粗糙集、多粒度邻域粗糙集和多尺度信息系统中的粗糙集可以用F-粗糙集表示.此外,还研究了F-粗糙集的可拓性,并建议将其他粗糙集模型与F-粗糙集相结合,使它们在处理动态数据、增量数据和海量数据时具有更强的能力.研究结果将提高其他粗糙集模型的动态性,有利于粗糙集进一步应用到大数据领域.

广义多尺度决策表最优尺度选择的快速算法

广义多尺度决策表的条件属性和决策属性都具有多个尺度.最优尺度能够将较粗的条件属性与较细的决策属性相结合,达到效率与精度的平衡.然而现有的最优尺度选择算法计算效率较低.为此,提出一种最优尺度选择的快速算法.首先探讨最优尺度的一些性质和边界域的变化情况,给出判断边界域是否相等的条件以及最优尺度的等价定义;然后一种快速算法.最后通过数值实验表明其相较于现有算法速度更快,能有效解决最优尺度选择问题,该算法在计算效率方面取得了显著的提升,实现了在较短的时间内得出最佳结果的目标.

独乐寺建筑与塑像一体化设计模式探析

独乐寺蕴含基于文化理念和整尺思维的一体化设计模式,将院落规划、建筑空间设计和塑像布设通盘考量,以十一面观音面长(1.338m合4.5尺)作为基准长统摄像、阁、寺各级尺度,使各部分具有和谐的比例关系,设计时令观音心部位于阁心并作为原点,综合运用方格网、同心圆、放射线等手段实现核心要素的对位关联,形成具有空间互动性及文化内涵的整体,体现出中国古代的“天圆地方、建极向心”宇宙观、“数形义合一”营造思想、“型范”思维和人本理念。

SVM样本约简算法研究综述

支持向量机(Support Vector Machine, SVM)是基于统计学习理论和结构风险最小化原则发展起来的一种有监督的机器学习算法,它有效克服了局部最小和维数灾难等问题,具有良好的泛化性能,并被广泛应用于模式识别和人工智能领域。但SVM的学习效率随着训练样本数量的增加而显著降低,对于大规模训练集,采用标准优化方法的传统SVM面临着内存需求过大、执行速度慢,有时甚至无法执行的问题。为了缓解SVM在大规模训练集上存储需求高、训练时间长等问题,学者们提出了SVM样本约简算法。文中首先介绍了SVM理论基础,然后从基于聚类、几何分析、主动学习、增量学习和随机抽样5个方面系统综述了SVM样本约简算法的研究现状,讨论了各种SVM样本约简算法的优缺点,最后总结全文并展望未来。

集间两级语义互补的小样本语义分割

现有的小样本语义分割模型通常只提取单一级别的语义特征,然而由于小样本数据的样本量少、各级语义特征具有不同属性等原因,提取单一级别语义特征的网络模型很难在保证分割能力的同时兼具泛化性.针对该问题,提出了一种集间两级语义互补的小样本语义分割方法.该方法使用具有强类别特征的支持集高级语义特征对具备泛化性的查询集中级语义特征加权,在增强查询集各目标类别特征的同时保留查询集中级语义特征的泛化能力;另外,该模型通过最大化支持集潜在语义信息、为查询集构建非参数学习的先验信息等方式增强两集信息之间的交互性以获得更丰富的判别信息.该方法在PASCAL-5i数据集上进行仿真实验,mIoU值在1-shot和5-shot两类设置上分别能达到45.3%和48.8%,其结果超越部分先进主流的小样本语义分割算法,且网络模型的参数量也控制在可以接受的范围之内.

基于日间日内不确定集的中长期电源扩展规划

电源扩展规划(generation expansion planning,GEP)模型常用于为高比例可再生能源电力系统设计可行的规划方案,而对波动性可再生能源(variable renewable energy,VRE)不确定性的合理表征对规划方案的可行性至关重要。随着风光等可再生能源渗透率的增加,波动性可再生能源表现出了不同时间尺度上的不确定性。提出了一种基于日间和日内可再生能源不确定集的长期GEP模型,从而保障不同时间尺度下的电力电量平衡需求。日间不确定集用来描述长期VRE出力的波动性和间歇性,而日内不确定集用作描述日内的典型和极端出力情形,二者共同描述不同时间尺度下VRE出力的不确定性。使用我国电力系统作为算例,将所提方法与典型日法、典型日聚合法和日间电量平衡法进行仿真对比。基于8760 h全年时序生产模拟的结果表明,所提方法能够显著降低不同风光出力情形下电力系统的失负荷和碳排放,但其他方法均无法满足平衡和清洁需求。

多尺度决策系统中测试代价敏感的属性与尺度同步选择

属性与尺度同步选择方法可有效解决涉及代价因素的多尺度决策系统的知识约简问题,然而在现有研究中,少有基于代价进行属性与尺度的同步选择,并且大多数算法只针对协调的多尺度决策系统或不协调的多尺度决策系统.为了解决这一问题,文中以最小化数据处理的总测试代价为目标,提出测试代价敏感的属性与尺度同步选择算法,同时适用于协调的多尺度决策系统和不协调的多尺度决策系统.首先,构造基于粗糙集的理论模型,模型中的概念及性质同时考虑属性因素和尺度因素.其次,基于粗糙集的理论模型,设计启发式算法,能基于测试代价对多尺度决策系统同时进行属性约简与尺度选择,并且不同的属性可选择不同的尺度.最后,在12个数据集上的实验验证文中算法的有效性、实用性及优越性.

柔性直流换流站异常监控数据实时预警研究

为了提高对异常数据的预警性能、维护柔性直流换流站的运行稳定性,提出了柔性直流换流站异常监控数据实时预警方法。首先,利用经验规则消除数据碰撞问题,根据数据信息哈希函数的异常值,准确检测出其中的异常数据。然后,利用小波变换对异常监控数据进行预处理,并从异常数据中提取关键变量信息。最后,根据监控过程中出现异常数据的概率,设定预警阈值,再利用预警函数构建实时预警模型。试验结果表明,应用该方法后,误报率被控制在20%以下,平均绝对误差被控制在0.12以下。相比于传统方法,该方法的预警结果更有效、准确率更高。

基于裕度因子的多风电场站集群频率协调控制策略

为提升高比例电力电子装备电力系统的涉网频率性能,提出了一种基于裕度因子的多风电场站集群频率协调控制方法。对于多风电场站集群系统一次调频功率的分配问题,综合考虑转子转速及变流器容量运行条件后,利用等比例裕度因子的一次调频功率分配方法,将有功功率指令下发给各风电场站。针对各风电场站中虚拟惯量、虚拟阻尼系数的制定问题,研究了一种基于可释放动能的频率调节参数分区设置方法。将各风电场站运行状态通过频率响应的变化趋势进行分区,并在此基础上结合可用调频动能对频率调节参数进行合理灵活的设置。仿真结果表明所提方法充分利用了各风电场站的调频资源,提高了高比例电力电子装备电力系统的频率稳定性,为新型电力系统的频率安全性提供了相应的技术保障。

桥墩柔性自浮式防撞装置基本结构尺度研究

为对柔性自浮式防撞装置的结构尺度进行合理设计,提出了该类装置的桥墩抗撞方向截面总宽度最小保障尺度、中间保障尺度和完全保障尺度3种基本结构尺度的概念,并以此确定其合理取值范围。针对设防等级为3000吨级船舶的柔性自浮式防撞装置,采用放样统计的方法,按85%保证率确定不同截面高度防撞装置的最小保障尺度取值;基于机械能守恒原理及有限元法给出完全保障尺度的经验计算公式;结合完全保障尺度及最小保障尺度的取值,考虑桥墩维修量及桥梁安全,给出中间保障尺度的建议值。提出防撞装置的基本结构尺度不应小于最小保障尺度,宜优先满足完全保障尺度,如无条件实现完全保障尺度时,可采用中间保障尺度。