Boundedness of Hausdorff operators on the power weighted Hardy spaces

In this paper, we consider the two-dimensional Hausdorff operators on the power weighted Hardy space H;（R;） （ -1 ≤α≤0）, defined by H;f（x）=∫R;Φ（u）f(A（u）x)du,where Φ∈L;oc;（R;）,A（u） = (α;（u）);is a 2×2 matrix, and each α;is a measurablefunction.We obtain that HΦ,A is bounded from H;（R;） （ -1≤α≤0） to itself, if∫R2|Φ（u）‖det A;（u）|‖A（u）‖;ln(1+‖A;（u）‖;/|det A;（u）|)du<∞.This result improves some known theorems, and in some sense it is sharp.

Endpoint Estimates for Hardy Operator's Conjugate Operator with Power Weight on n-Dimensional Space

In this paper, we establish two integral inequalities for Hardy operator＇s conjugate operator at the endpoint on n-dimensional space. The operator Hn is bounded from Lxα1 （Gn） to Lxβq （Gn） with the bound explicitly worked out and the similar result holds for Hn＊.

LDPC-Coded OFDM Transmission Based on Adaptive Power Weights in Cognitive Radio Systems

In this paper, we propose a new scheme to improve the performance of an LDPC-coded OFDM based cognitive radio (CR) link by applying adaptive power weights. To minimize estimation errors of detected signals in all the CR subcarriers, power weights are allocated to the CR subcarriers at the secondary transmitter. Some constraints for the power weights are considered, such as keeping the interference power introduced by the CR to primary users below a given interference threshold and also keeping sum of transmission powers in all CR subcarriers within a total transmission power. The LDPC decoder applies these power weights in the Log Likelihood Ratios (LLRs) used in message passing scheme at the secondary receiver to achieve more reliable communications. So, the received signal in each CR subcarrier will be decoded with the knowledge of transmission power weights, which come from the cognitive feedback channel without additional cost. Simulation results demonstrate that our proposed scheme achieves a lower bit error rate and a higher transmission rate compared with those of the same scheme without applying power weights.

L^2 NORM INEQUALITY WITH POWER WEIGHTS FOR THE MAXIMAL RIESZ SPHERICAL MEANS

We have pointed out in [1] that so far the L^2 norm inequalities with power weights for the Riesz means σ_R~δ(g)(x) of multiple Fourier integrals have been obtained only by Hirschman and J. L. Rubio de Francia respectively:

Explicit constants for Hardy’s inequality with power weight on n-dimensional product spaces

In this paper, Hardy operator H on n-dimensional product spaces G = (0, ∞)n and its adjoint operator H* are investigated. We use novel methods to obtain two main results. One is that we characterize the sufficient and necessary conditions for the operators H and H* being bounded from Lp(G, xα) to Lq(G, xβ), and the bounds of the operators H and H* are explicitly worked out. The other is that when 1 < p = q < +∞, norms of the operators H and H* are obtained.

Generalizations of mixed weighted power mean inequality

Tarnavas established mixed weighted power mean inequality in 1999. A separation of weighted power mean inequslity was derived in this paper. As its applications, some separations of other inequalities were given.

三维精确power图的GPU并行计算

power图(加权Voronoi图)的计算是计算机图形学和计算几何等领域的一项基础任务.针对求解三维power图的传统串行方法所需的时间成本较高,且现有并行算法所得结果为近似解,提出一种新颖的GPU并行计算方法.首先给出power图与高一维受限Voronoi图的等价构造方法,将Voronoi图的无网格方法直接推广到power图的计算.因此,给定的加权种子点被置于更高一维空间中的一组方格内,据此快速搜索每个种子点的邻居关系,进而使用各个种子点与其若干个邻居的中垂面对各自的power胞元进行并行裁剪,以快速地获取三维空间中的精确power图.对比不同求解域下和5万个种子点的计算耗时,比现有方法具有超过3倍的加速比.

INVERSE POWER METHOD AND WEIGHTED SOBOLEV SPACES

1. Introduction This paper is a continuation of the paper [5]. Now we want to treat the problem

基于幂加权最小二乘拟合的红外相机辐射定标方法

分析了现有线性度计算方法的不足,提出基于偏差/测量值的新线性度评价方式,通过该方式最小二乘拟合的低端温度偏差比高端大一个数量级,并采用加权拟合提高系统响应线性度。在分析不同光谱的温度和辐亮度拟合函数基础上,提出一种基于辐亮度倒数幂加权最小二乘线性拟合的地面和星上辐射定标方法,确认最佳权重幂次,建立了幂次n=1的加权最小二乘拟合外黑体线性定标方程、内黑体线性定标方程、内外黑体辐亮度转换模型。针对星上内黑体高精度两点定标建立了基于加权最小二乘拟合内外黑体辐亮度转换模型;针对星上黑体发射率退化提出了基于恒星定标的红外相机星上内黑体辐亮度定标修正方法,建立了外黑体辐亮度、内黑体辐亮度和恒星辐亮度转换模型。采用加权线性拟合后,积分时间10 ms的低温端外黑体反演偏差由1.63 K下降到0.52 K,内黑体偏差反演温度偏差由0.83 K下降到0.39 K,系统响应线性度明显提升。

Differentiation degree combination weighting method for investment decision-making risk assessment in power grid construction projects

Power grid construction projects are distinguished by their wide variety,high investment,long payback period,and close relation to national development and human welfare.To improve the investment accuracy in such projects and effectively prevent investment risks,this paper proposes an investment optimization decision-making method for multiple power grid construction projects under a certain investment scale.Firstly,an in-depth analysis of the characteristics and development requirements of China’s power grid projects was performed.Thereafter,the time sequence and holographic method was adopted to conduct multi-dimensional,multi-perspective risk assessment of different parts of power grid projects,and a holographic risk assessment index system was developed.Moreover,an investment decision model considering the comprehensive risk based on combination weighting was developed according to the output and input of power grid construction projects.A new combination weighting optimization method that takes into account the investment willingness of enterprises was designed to improve the current weighting evaluation methods.Finally,the validity and applicability of the proposed evaluation method were verified by case examples.

Apply the Double-Weight Neural Network to Dynamic Power Management

This paper expounds a data-fitting algorithm for the double-weight neural network,and presents a new algorithm for the system's power management on the base of that.The double-weight neural network learns knowledge from the past idle periods of the system,and predicts the lengths of the coming idle periods.As a result of that,the system can switch its running states and re- duce the power dissipation according to the predictive values.The results of the experiments prove that this algorithm shows a better performance in increasing the right rate of shutting down and reducing the power consumption than other traditional ones.

Properties, Inference and Applications of Inverse Power Two-Parameter Weighted Lindley Distribution

We proposed “a new extension of three-parametric distribution” called the inverse power two-parameter weighted Lindley (IPWL) distribution capable of modeling a upside-down bathtub hazard rate function. This distribution is studied to get basic structural properties such as reliability measures, moments, inverse moments and its related measures. Simulation studies are done to present the performance and behavior of maximum likelihood estimates of the IPWL distribution parameters. Finally, we perform goodness of fit measures and test statistics using a real data set to show the performance of the new distribution.

Wide-Area Delay-Dependent Adaptive Supervisory Control of Multi-machine Power System Based on Improve Free Weighting Matrix Approach

The paper demonstrates the possibility to enhance the damping of inter-area oscillations using Wide Area Measurement (WAM) based adaptive supervisory controller (ASC) which considers the wide-area signal transmission delays. The paper uses an LMI-based iterative nonlinear optimization algorithm to establish a method of designing state-feedback controllers for power systems with a time-varying delay. This method is based on the delay-dependent stabilization conditions obtained by the improved free weighting matrix (IFWM) approach. In the stabilization conditions, the upper bound of feedback signal’s transmission delays is taken into consideration. Combining theoriesof state feedback control and state observer, the ASC is designed and time-delay output feedback robust controller is realized for power system. The ASC uses the input information from Phase Measurement Units (PMUs) in the system and dispatches supplementary control signals to the available local controllers. The design of the ASC is explained in detail and its performance validated by time domain simulations on a New England test power system (NETPS).

Dynamic Equivalent Method of Motor Loads for Power Systems Based on the Weighted

Dynamic equivalence can not only largely reduce the system size and the computation time but also stress the dominant features of the system [1]-[3]. This paper firstly recommends the basic concept of dynamic equivalent and the status of both domestic and abroad development in this area. The most existing equivalent methods usually only deal with static load models and neglect the dynamic characteristics of loads such as induction motors. In addition, the existing polymerization method which is based on the frequency domain algorithm of induction electric machines parameters takes a long time to equivalent for the large system, then the new method based on the weighted is proposed. Then, the basic steps for dynamic equivalence with the weighted method are introduced as follows. At first, the clustering criterion of motor loads based on time domain simulation is given. The motors with similar dynamic characteristics are classified into one group. Then, the simplication of the buses of motors in same group and network is carried out. Finally, parameters of the equivalent motor are calculated and the equivalent system is thus obtained based on the weighted. This aggregation method is applied to the simple distribution system of 4 generators. Simulation results show that the method can quickly obtain polymerization parameters of generator groups and the aggregation model retains the dynamic performance of the original model with good accuracy, the active and reactive power fitting error is smaller as well.

双层耦合振子电网的弹性能力分析

基于简化的二阶耦合振子模型,考虑网络的权重特征,构建了双层耦合振子电网的物理模型,并从能量流的角度分析了电网面向不同属性的输电线失效时的网络弹性能力.以一个2层电网为研究对象进行数值模拟,结果表明,异质传输线失效时,网络弹性能力较弱;同质传输线失效时,网络弹性能力较强.

考虑保供电需求的光储微电网优化配置及电能质量评估

针对用户日益迫切的保供电需求以及光伏出力与负荷用电的随机性、波动性引发的电能质量的问题,建立了考虑保供电需求的光储微电网双层优化模型,并对规划配置的微电网电能质量进行评估。该模型以等效净负荷标准差和弃光率最小为上层优化目标,以微电网有功功率损耗最小为下层优化目标,以储能满足保供电负荷的保供电需求作为附加约束条件,采用遗传算法求解。然后设计源、网、荷三类指标,采用组合赋权法确定各指标权重,实现对微电网电能质量的综合评估。最后以改进的IEEE-33系统验证了所提模型的有效性。结果表明考虑保供电需求后能够提高微电网电能质量,并在一定范围内保供电负荷越大时越能提高微电网电能质量和用户满意度。

基于改进KNN算法的新能源发电单元运行状态识别

目前识别发电单元运行状态的研究较少,数据来源以数据采集与监控系统为主,采集速度较慢。为此,提出了一种基于发电单元机端电气量数据并融合改进k近邻(KNN)算法的新能源发电单元状态识别方法,直接采集机端电气量数据用于快速判断发电单元状态。提出KNN算法的改进策略,克服了传统KNN算法准确度低、识别速度慢的缺点。利用电力系统分析综合程序获取用于状态识别的发电单元机端电气量数据,利用改进策略对数据进行预处理,并对比传统KNN算法、逐条使用改进策略的KNN算法对新能源发电单元状态识别的耗时与准确度。结果表明所提算法较传统算法的识别准确度和速度明显提升,能满足稳定控制过程中对新能源发电单元的状态感知需求。

基于VC-综合赋权法的海上风电APF配置优化方法研究

大容量海上风电机组的接入改变了传统电力系统结构,给电网带来了谐波等问题,影响了电能质量。为抑制海上风电机组产生的低次谐波,文章首先建立了海上风电机组并网电流的低次谐波理论模型;然后,在仿真软件ETAP上搭建海上风电机组仿真模型,验证不同出力情况下风电场的输出谐波特性;最后,基于风电场输出谐波特性,提出变异系数(Variation Coefficient,VC)综合赋权法对风电场有源滤波器(APF)进行优化配置,提升了风电场谐波的治理效果。基于实际算例验证了所提方法的有效性。

乡村振兴战略下农网综合效益评价模型研究

农村地区之间存在着显著的地区差异,为了指导农村电网投资,提出一种基于组合赋权和多属性边界逼近区域比较法(multi-attributive border approximation area comparison,MABAC)的差异化农网综合效益评价模型。首先,综合考虑农村地区差异及乡村振兴战略对农网发展的要求,从农网投资经济效益、供电能力、基础设施情况、地区发展带动、生产生活质量、清洁低碳发展等6个方面构建农网综合效益评价指标体系;其次,基于最小化主客观权重异质性原则构建基于图模型—熵权法的组合赋权模型,并引入MABAC方法用于解决农网综合效益评价问题;最后,通过算例分析验证了所提出模型的有效性。

计及SOC自恢复的混合储能平抑风电功率波动控制

混合储能系统能够较好地应对复杂的风电波动,有效地提高电网的稳定性和安全性。在混合储能平抑风电功率波动的典型应用场景下,该文首先提出一种计及荷电状态(SOC)自恢复的混合储能平抑风电功率波动控制方法,在满足风电平抑需求的情况下,通过模型预测控制快速调节储能在平抑功率过程中的荷电状态,提高储能持续稳定运行能力;然后,为提高混合储能系统协调运行能力,设计了加权滑动平均(WMA)-模糊控制策略对超级电容和蓄电池功率进行动态分配;最后,结合实际风电功率数据,通过仿真验证了所提策略能有效平衡储能寿命和平抑风电波动的矛盾,能充分考虑两种储能设备的特性差异并提高功率分配的合理性。