Polar码在图像传输中的性能研究

文中研究Polar码在图像传输系统中的应用,讨论Polar码的译码迭代次数、码长、码率等因素对恢复图像的影响,并与等同条件下的低密度奇偶校验码（Iow Density Parity Check Code,LDPC）在图像传输中的性能进行比较.数值仿真表明：随着Polar码译码迭代次数增加、码长增长和码率减小,图像传输的误块率（Block Error Rate,BER）减小,峰值信噪比（Peak Signal to Noise Ratio,PSNR）增大.同时在码长为2 048,码率为0.5时,Polar码在图像传输中的性能比LDPC码的性能有明显地提高,在输入信噪比为3 dB时,Polar码的误块率有102增强,PSNR值有4倍的提高.     

基于随机加速对偶下降算法的分布式网络流量优化

传统的分布式网络流量优化问题大都通过对偶梯度下降算法来解决,虽然该算法能够以分布式方式来实现,但其收效速度较慢。加速对偶下降（accelerated dual descent,ADD)算法通过近似牛顿步长的分布式计算,提高了对偶梯度下降算法的收敛速率。但由于通信网络的不确定性,在约束不确定时,该算法的收敛性难以保证。基于此,提出了一种随机形式的ADD算法来解决该网络优化问题。理论上证明了随机ADD算法在不确定性的均方误差有界时,能以较高概率收敛于最优值的一个误差邻域;当给出更严格的不确定性的约束条件时,算法则可以较高概率收敛于最优值。实验结果表明,随机ADD算法的收敛速率比随机梯度下降算法快2个数量级。     

基于机器学习的图像块效应盲评价算法

图像失真不仅能反映在图像的真实结构上,也能反映在图像的伪结构上,利用图像伪结构,基于支持向量机,提出一种图像块效应盲评价方法,在该方法中,使用支持向量机对联合图像专家小组（joint photographic experts group, JPEG)图像的伪结构特征进行训练以实现对图像进行质量评价。这些伪结构特征包括稀疏特征、边缘特征和颜色特征,利用支持向量机对图像的伪结构特征和主观评价结果进行训练,对未训练的图像进行预测。实验证明,该方法能够正确评价JPEG图像的质量且与图像的主观评价结果保持很高的一致性。     

LTE系统中DRX机制的分析与优化设计

非连续接收机制（discontinuous reception,DRX）是长期演进（long term evolution, LTE）系统中终端省电的关键技术之一。针对LTE系统中各种新兴业务和更高的数据传输速率快速发展导致终端能耗加剧的问题,在对DRX机制的原理进行详细分析的基础上,提出了一种长周期可调整的DRX机制。在DRX马尔科夫模型的基础上,根据欧洲电信标准化协会（european telecommunications standards institute,ETSI）数据流量模型,对长周期可调整DRX机制的2个性能指标功率节省因子和平均等待时延进行了理论分析。仿真分析在不同DRX定时器参数下的能耗和时延变化情况,并与固定周期的DRX机制在能耗和时延2个方面进行了对比分析。仿真结果表明,长周期可调整DRX机制在降低终端功耗方面有明显改善。     

基于偏最小二乘分析和稀疏表示的目标跟踪算法

经典的基于子空间学习的跟踪方法通过主成分分析(principal component analysis, PCA)建立并更新目标的特征模型,只考虑目标的特征信息而忽视模型中每个样本的类别特征,从而降低了目标的跟踪精度。为此,提出一种基于偏最小二乘分析(partial least squares analysis, PLS)和稀疏表示的目标跟踪算法。通过PLS去分析关于目标与背景中纹理特征和类别信息之间的相关性,建立一个可区分的低维特征空间。将目标模板线性表示为所有目标候选的线性组合,当存在与目标模板相似的候选时,线性表示的系数满足稀疏性约束,通过L1范数最优化求解稀疏表示系数,根据最小的重构误差得到最优的目标位置。在多个视频场景下的实验结果展示了改进的跟踪算法具有较好的跟踪性能。     

Ba（Ti1-xHfx）O3的电子结构及光学性能的第一性原理研究

利用第一性原理赝势方法和剪刀近似操作计算了Ba（Ti 1-x Hf x ）O 3（x=0,0.125,0.5,0.75）的电子能带结构、态密度和光学性质.计算结果表明,随着Hf掺杂量的增加,Ba（Ti1-xHfx）O3结构发生扭曲的程度越来越严重,导致带隙发生变化,介电常数减小,降低了Ba（Ti 1-x Hf x ）O 3的绝缘性能,反射率有了很大程度的变化,折射率也在降低,这使Ba（Ti 1-x Hf x ）O 3在制作光学元器件时扩大了使用范围.     

Revised Fault Free Analysis Method for Reliability Evaluation of Smart Grids

Smart grid was proposed as a practical form of future power distribution system. Evaluating the reliability of smart grids was of great importance and significance. A revised fault tree model was proposed to distinguish and separate grid-connected operation mode and islanded operation mode of smart grids,focusing on the perspective of the consumers. A hierarchical Monte Carlo simulation method for reliability evaluation was also proposed based on the proposed fault tree model. A case of reliability evaluation for the future renewable electric energy delivery and management（ FREEDM） system was carried out and analyzed. The proposed methods can be applicable to other forms of smart grids.     

Influence of Mica Particles on the Rheological Properties and Thermal Stability of Poly（ lactic acid）

The influence of mica particles on the rheological and thermal properties of poly（ lactic acid）（ PLA） / mica composites were investigated by capillary rheometer and thermogravimetric（ TG）analysis. The results show that the PLA / mica blends are nonNewtonian pseudoplastic and display shear-thinning. The value of non-Newtonian index of the blends melt decreased obviously with the addition of mica particles but somehow even increased when shear rate exceeded 4 500 s- 1. In this work,it could be indicated that appropriate amount of mica particles could somehow enhance the resistance of PLA melt under high shear rate to deviate from Newtonian fluid. TG analysis shows that the thermal stability of PLA decreases a little after the incorporation of the mica particles.As mica particles decompose in a completely different way in contrast to PLA,this abnormal decrease of thermal stability of PLA / mica composite may be attributed to moisture stored between mica layers released at high temperature.     

Research and Implementation of the Enterprise Evaluation Based on a Fusion Clustering Model of AHP-FCM

Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering with fuzzy C-means （FCM） clustering will be advanced. In the method, the initial cluster number and cluster center can be obtained using subtractive clustering. On this basis, clustering result will be further optimized with FCM. In addition, the data dimension will be reduced through the analytic hierarchy process （AHP） before clustering calculating. In order to verify the effectiveness of fusion algorithm, an example about enterprise credit evaluation will be carried out. The results show that the fusion clustering algorithm is suitable for classifying high-dimension data, and the algorithm also does well in running up processing speed and improving visibility of result. So the method is suitable to promote the use.     

Global Mission Sensitivity Study for Phased MissionSystem with Analysis of Variance Method

A comprehensive mission sensitivity analysis index based on Sobol＇s index called global mission sensitivity（ GMS） was proposed in this paper which focused on analyzing the mission sensitivity of components of phased mission systems（ PMS）. The simulation strategy of GMS based on a Petri net and Monte Carlo method was presented which had broad applicability. Finally,the GMS and Birnbaum＇s sensitivity of components in a PMS example were compared. The GMS of component is demonstrated to be more adaptable to reflect the component mission sensitivity when the rated reliability parameters of components cannot be obtained, and components have state dependency or the system is subjected to common cause failure.