Low-Complexity Rate Compatible Modulation with Hybrid Weight Set

Rate Compatible Modulation(RCM)is an efficient technique for high spectral efficiency seamless transmission over highly dynamic wireless channels.However,its high decoding complexity at the receiver prevents it from being applied in scenarios where computation resources are limited.To alleviate this problem,an RCM with variable weight sets(RCM-VWS)was presented in the literature to significantly reduce its complexity by employing weight sets of different complexities for channels at different signal-tonoise-ratio(SNR).However,RCM-VWS has to introduce an undesired feedback channel for the transmission of SNR information that is estimated at the receiver and transmitted back to the transmitter for weight set selection.To achieve a low computational complexity while avoiding feedback transmission at the same time,a novel RCM scheme with hybrid weight set(RCM-HWS)is introduced in this paper.A low complexity of decoding is allowed by gradually reducing the complexity of weight sets based on the number symbols already sent.It also avoids the feedback of SNRs since we can deduce SNRs from the number of the symbols transmitted and use a hybrid weight set we have designed.The theoretical analysisand simulation results show that the proposed scheme has the advantages of low demodulation complexity and not requiring feedback channel while maintaining the same transmission throughput as that of the conventional RCM.Therefore,the proposed scheme has a wider range of applications,especially in the case that feedback channel is not available.

Generalized ordered weighted averaging operators based methods for MADM in intuitionistic fuzzy set setting

Multiattribute decision making（MADM） problems, in which the weights and ratings of alternatives are expressed with intuitionistic fuzzy（IF） sets, are investigated.Firstly, the relative degrees of membership and the relative degrees of non-membership are formulated as IF sets, the weights and values of alternatives on both qualitative and quantitative attributes may be expressed as IF sets in a unified way.Then a MADM method based on generalized ordered weighted averaging operators is proposed.The proposed method is illustrated with a numerical example.

Dynamic weighted voting for multiple classifier fusion:a generalized rough set method

To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.

Weight strings and weight sets of modules over generalized Kac-Moody algebras

Borcherds first introduced the so-called generalized Kac-Moody (GKM) algebras. Most of the results about ordinary Kac-Moody algebras can be generalized to these new algebras (cf. ref. [1] and §11.13 in ref. [2]). In the present note we discuss some properties about weight strings and weight sets of the module L(Λ) over a generalized Kac-Moody algebra.

A Simple Weighted Integration Method for Calculating Surface Tension Force to Suppress Parasitic Flow in the Level Set Approach

Parasitic flows may occur in the numerical simulation of incompressible multiphase flow due to errors in the calculation of surface tension terms, specifically for the curvature and unit normal vector. An improved method for calculating the surface tension based on the level set approach is proposed, in which the contribution of not only the center node but also the rest area of a control volume to the calculation of surface tension is considered in a balanced manner. The weighted integration method (WIM) is more consistent with the concept of a banded in- terface in the level set method. It is applied to the temporal evolution of a two-dimensional neutrally buoyant liquid drop and a buoyancy driven deformable bubble in an immiscible fluid for the validation of WIM. The results show that the parasitic flows are evidently suppressed by the weighted integration method. The weight factors for WIM in 3-D cases are also suggested.

Contour tracking using weighted structure tensor based variational level set

A novel contour tracking method using weighted structure tensor based variational level set is proposed in this paper.The image is first converted to weighted structure tensor field by extracting apositive definite symmetric covariance matrix for each pixel.Then,a level set method is employed to represent object contour implicitly which separates the image domain into two areas each modeled by tensor field based Gaussian mixture model separately.By solving agradient flow equation of energy functional with respect to the level set,the object contour will converge to its real profile in the newly arrived frame.Experimental results on several video sequences demonstrate the better performance of our method than the other two contour tracking algorithms.

A Study to Improve the Method of Ascertaining Attribute Weight Based on Rough Sets Theory

There are some shortages to ascertain attribute weight based on rough set in current studies. In this paper,attribute importance represented by rough set is studied deeply. Aiming at the existing problems,algebra presentation of rough sets is proved to be more comprehensive than its information presentation,then a new method of ascertaining attribute weigh is put forward based on rough set conditional entropy. Finally,it is shown that the new method is more reasonable than the old one by an example.

基于云模型的水光互补清洁能源基地容量配置方案优选

为优选水光互补清洁能源基地容量配置方案,从安全性、环保性、经济性、技术性4个方面选取14个指标构建了综合评价指标体系,采用改进云模型方法对容量配置方案进行评价。改进云模型方法以基于层次分析法改进的云模型求解主观权重,以熵权法求解客观权重,组合赋权得到指标权重,再利用条件云发生器计算各指标的隶属度,最后根据级别特征值法计算容量方案等级评分,确定最优方案。将该方法应用于我国西南某清洁能源基地水光互补系统容量配置方案的优选,优选结果与集对分析法优选结果和实际选择方案均相符,证明了该方法的有效性与适用性。

PI control based on fuzzy set-point weighting tracking for hydraulic crane boom system

A PI control strategy based on fuzzy set-point weighting following was proposed for the active damping control of a hydraulic crane boom system （HCBS）. Two valve-controlled PI controllers, which include a proportional feedforward controller based on fuzzy set-point weighting following and a limited semi-integrator（LSI）, are designed respectively. LSI is used to limit output signal and to prevent wind up at the low frequency of the spectrum. By using a range camera and an electronic feedback control, the tip damping on the HCBS can be adjusted artificially. A collaborative control simulation technique of HOPSAN and MATLAB/SIMULINK is applied to the controller design. Simulation results show that the proposed PI control system has less overshoot as well as faster response. The tip damping on the HCBS during operation is improved.

小脑模型关节机器人自适应近似最优鲁棒控制

为了提升控制性能,降低训练时间与稳态误差,提出了一种小脑模型关节机器人自适应近似最优鲁棒控制方法。首先找到克服诸如重力等非线性的最佳权重集,控制函数保持不变,从而消除存在偏差项时的稳态误差。然后寻找实现原点近似最优控制的附加权重集,从而在搜索优化时惩罚控制作用不会导致由于重力引起的任何稳态误差。无功权值和无功权值之和提供了一个鲁棒权值更新的监督终端,Lyapunov方法保证了信号的一致最终有界性,保证了权值漂移和突发不发生。最后通过柔性关节机器人的实验验证了提出方法的有效性。

基于集对分析和证据理论的电能表运行状态评估

为及时准确地评估电能表的运行状态,提出一种基于集对分析和证据理论的电能表运行状态评估方法。首先根据电能表自身基础、在线监测及现场检测信息,建立电能表运行状态评估指标体系。然后运用模糊层次分析法和熵权法分别确定各指标的主客观权重,运用博弈论和变权理论确定各指标的组合权重与变权重。之后运用集对分析法确定各指标与各状态等级间的联系度,采用证据理论对各指标的联系度进行合成。最后利用最大隶属度原则与信度准则共同判定电能表的运行状态等级。实例分析结果表明,该方法与电能表的实际运行情况相符,且评估结果准确直观,与模糊综合评价法和集对分析法相比,该方法在状态等级区分度上达到了0.5256,明显高于模糊综合评价法的0.3024及集对分析法的0.1483,具有较好的区分度,为电能表的状态评估提供了一种新的思路。

基于Vague集犹豫量化的机舱团队协作式评估方法

针对现有机舱团队协作评估方法不统一且主观随意性太强的缺陷,提出一种基于Vague集犹豫量化的新型评估方法。从机舱团队协作的角度对影响评估的各因素进行提取,采用集合相似系数对重要协作指标量化,并采用C-OWA算子赋权法和变异系数法确定主客观权重,利用最小鉴别信息原理求得组合权重,结合Vague集的犹豫量化特点,通过机舱团队实际操作数据与Vague集评估标准得到各指标的定性评估Vague集,并利用集合相似度分析实现机舱团队协作的综合评估。实例计算结果表明:相对于传统的评估方法,在个体完成度的基础上加入具有犹豫团队协作因素的方法,能对评估结果的犹豫度进行较好地刻画,也能很好地实现定量与定性之间的转化,是一种有效且优越的机舱团队协作式评估方法。

加权概率犹豫模糊集多属性群决策算法及应用

对于概率犹豫模糊集中的不同隶属度值赋予了决策专家的综合权重,由此构造出一种蕴含信息量更大、更方便解决决策问题的概率犹豫模糊集合——加权概率犹豫模糊集.针对加权概率犹豫模糊集中的加权概率犹豫模糊元,在使用三维点坐标进行描述的基础上建立加权概率犹豫模糊元的三维得分值模型、三维离差值模型、2个加权概率犹豫模糊元的几何距离模型以及大小判别准则.结合决策专家的综合权重与属性的综合权重提出一种确定正理想方案与负理想方案的方法,基于TOPSIS思想给出一种新的贴近度公式,并利用新的贴近度公式来鉴别各方案优劣.最后,通过一个数值算例说明文中理论与方法的可行性.

加权区间值犹豫模糊集的群决策方法

为了在区间值犹豫模糊集(IVHFS)中体现出决策专家的重要性,把所有评审专家的综合权重注入IVHFS中,进而定义了加权区间值犹豫模糊集(WIVHFS).将WIVHFS用三维点坐标(由区间值隶属度的中位数、区间值隶属度的清晰度以及评审专家综合权重构成)进行刻画,在此基础上给出了加权区间值犹豫模糊元(WIVHFE)的相关运算;基于WIVHFE的相关运算采用熵值法计算测评准则客观权重,利用0-1优先关系矩阵来获取任意2个备选方案的总测评准则权重优势值与总测评准则权重劣势值,并依据二者之间的大小排序方案.最后,通过一个具体案例说明了文中理论与方法的可行性.

复杂光机系统综合性能评估方法

作为涉及光、机、电、控、热、气、液等多个领域的复杂光机系统,如何对其进行综合性能评估一直是难以解决的关键技术问题。针对该问题,通过分层分析并结合熵值、灰色和模糊评估等方法,提出了能够直观反映复杂光机系统的评估因子,即G因子。通过G因子,可直观地对复杂光机系统的综合性能进行评估,并可对比不同光机系统的综合性能;同时在评估过程中,也可对复杂光机系统的某个或几个关键参数进行对比分析。G因子提供了一种能够反映复杂光机系统综合性能的直观参数,为复杂光机系统的综合性能评估及相关系统研制提供了技术支撑。

基于Rasch模型的船舶舾装效率影响要素分析

针对现有提高舾装效率的研究局限于某一环节,缺乏全面系统性分析,难以客观准确评价各因素影响程度的问题,基于实地考察、专家访谈、问卷调查等方法,综合考虑舾装作业离散型特征,从策划和实施2个阶段分析涉及设计、设备、计划以及人机料法环等8个方面的38个影响舾装效率的因素,采用Vague集-熵权法进行主客观评估,筛选出28个关键因素,并采用Rasch模型方法对影响舾装效率的多重要素进行影响程度定量分析。研究发现:策划阶段对舾装效率的影响程度较大,其中因不可控要素供应链受阻是影响最大的要素。研究表明企业应对策划阶段重点改进;实施阶段对舾装效率影响较小,企业无需投入过多资源改进。

随机多属性子空间的ReliefF加权邻域粗糙集与属性约简

属性约简是一种重要的数据降维预处理方法,然而现有的属性约简方法大多没有考虑信息系统中属性权重的信息。ReliefF算法是一种实现简单且运算效率高的属性权重评估方法,提出一种随机多属性子空间的ReliefF加权邻域粗糙集和属性约简算法。该方法生成了多组具有相同大小随机子空间的属性集划分,并对每组划分的随机子空间利用ReliefF算法计算得到属性的局部权重,将所有组得到的属性局部权重求取平均值,得到了信息系统每个属性最终的全局权重;基于属性权重的结果,提出一种新的加权邻域粗糙集模型,并证明了相关理论和性质;在该模型的基础上通过加权邻域依赖度提出一种信息系统的属性约简算法。在公开数据集上的属性约简实验结果表明,所提出的属性约简算法比已有的同类型算法整体上具有更优的约简性能。

一种基于粗糙熵的改进K-modes聚类算法

K-modes聚类算法被广泛应用于人工智能、数据挖掘等领域。传统的K-modes聚类算法有不错的聚类效果,但是存在迭代次数多、计算量大、容易受到冗余属性的干扰等问题,且仅采用简单的0-1匹配的方法来定义2个样本属性值之间的距离,没有充分考虑每个属性对聚类结果的影响。针对上述问题,该文将粗糙熵引入K-modes算法。首先利用粗糙集属性约简算法消除冗余属性,确定各属性的重要程度;然后利用粗糙熵确定每个属性的权重,从而定义新的类内距离。将该文所提算法与传统的K-modes聚类算法分别在4组公开数据集上进行对比试验。试验结果表明,该文所提算法聚类准确率比传统的K-modes聚类算法更高。

基于集对-组合赋权的填埋场失稳风险评价方法

我国填埋场具有高水位、高气压的特征,现有规范和研究中虽提出了不同的稳定性评价指标和分级标准,但对指标权重和综合风险评估缺乏系统性研究。为此,建立了涵盖填埋场几何构型、垃圾土抗剪强度、液气赋存情况和降雨4类7个指标的填埋场失稳风险评价指标体系,通过参数敏感性分析实现5级分类(稳定、较稳定、基本稳定、不稳定、极不稳定),并采用集对分析构建指标联系度函数。采用指标重要性排序法,通过参数敏感性分析、工程失稳事故、文献关注度统计和博弈论组合赋权,确定了评价指标的综合权重。最后,将指标综合权重与指标联系度函数耦合,建立了填埋场失稳风险综合评价模型,并应用于一实际填埋场工程中,与现场实际监测数据结果一致,验证了评价模型的准确性和有效性。

基于椭球计算的集员滤波传感器故障检测方法

为了解决未知故障信号诊断问题,提出了一种基于椭球计算的集员滤波传感器故障检测方法。为了防止数据冲突和降低能量消耗,通过加权一次丢弃调度来控制传感器单元和滤波器之间的信息分发。然后有效将分布式集员滤波方法应用于处理WTOD协议下未知但有界的故障检测问题。进一步通过处理具有一定不等式约束的优化问题,对两个最小迹意义下的椭球进行了优化。最后仿真案例结果证明了提出方法能够有效实现传感器网络的未知故障信号检测,且不需要了解所有状态,极大地扩展了适用范围。