基于张量分解的多声道音频恢复方法

为恢复多声道音频在采集过程中丢失的数据,提出基于加权优化的张量分解方法.首先用张量对音频建模,并且根据其尺寸定义一个标识数据丢失位置的加权张量,然后使用加权最小二乘问题描述CANDECOMP/PARAFAC(CP)模型并通过一阶优化算法求解,最终通过获得的因子矩阵恢复音频.通过不同数目通道数据丢失的隐藏参考和基准的多激励测试,说明针对丢失数据采用CP分解方法是有效的,即张量分解能够得到较好的音频恢复效果.      

摩擦型高强螺栓传力性能及缺失的非线性分析

针对钢桁梁桥摩擦型高强螺栓连接出现的螺栓缺失问题,利用ANSYS软件,采用壳单元模拟连接板件,弹簧单元模拟接触面相互作用,对摩擦型高强螺栓连接进行了设计状态和螺栓缺失状态下的非线性有限元分析。结果表明:摩擦型高强螺栓连接发生滑移后,外荷载与螺栓所传剪力之间呈现非线性关系;在无滑移状态,各螺栓所传剪力沿外力方向和其垂直方向均为外侧大,中间小,滑移后趋向均匀;随螺栓缺失个数的增加,螺栓群初始滑移荷载、极限滑移荷载和缺失排螺栓传力比均减小,无滑移和局部滑移时相邻排增大最明显,全面滑移后其余各排则同等增大。因此,在高强螺栓连接的设计中,应对螺栓群受力是否进入非线性进行判断,以确定各排螺栓的实际传力比。     

A high-level synthesis based dual-module redundancy with multi-residue detection （DMR-MRD） fault-tolerant method for on-board processing satellite communication systems

On board processing （OBP） satellite systems have obtained more and more attentions in recent years because of their high efficiency and performance.However,the OBP transponders are very sensitive to the high energy particles in the space radiation environments.Single event upset （SEU） is one of the major radiation effects,which influences the satellite reliability greatly.Triple modular redundancy （TMR） is a classic and efficient method to mask SEUs.However,TMR uses three identical modules and a comparison logic,the circuit size becomes unacceptable,especially in the resource limited environments such as OBP systems.Considering that,a new SEU-tolerant method based on residue code and high-level synthesis （HLS） is proposed,and the new method is applied to FIR filters,which are typical structures in the OBP systems.The simulation results show that,for an applicable HLS scheduling scheme,area reduction can be reduced by 48.26％ compared to TMR,while fault missing rate is 0.15％.     

转型时期农村妇女平等权缺失之原因分析

平等权作为一项基本人权,是人与生俱来的权利,然而在转型时期,我国农村妇女的平等权却依然缺失严重。导致这一缺失的原因是多方面的,农村妇女权利意识的不足则是其中最关键的因素。     

两个二项总体具有部分缺失数据时的估计

文章主要讨论了两个二项总体均具有部分缺失数据时的参数的极大似然估计,并证明了估计的强相合性和渐进正态性,以及给出了两总体参数差的近似置信区间.     

目标变量不完全情形下的最小二乘回归插补

充分利用对辅助变量的回答所提供的变量信息，采用最小二乘方法，给出了目标变量不完全情形下对其的一种回归插补，并对回归差补的期望和方差进行了讨论。     

不完全数据集上的半参组群差异检测

组群差异检测广泛应用于医药、社会网络等领域.现有的组群差异检测都是建立在数据集没有缺失的情况下,因此讨论并给出了在数据集缺失的情况下进行组群差异检测的方法.首先,使用一种新颖的缺失数据填充方法填充缺失数据,然后在半参环境下使用经验似然方法对得到的完全数据集估计出置信区间,进而进行组群差异检测.     

缺失数据下广义线性回归拟似然估计的强相合性

研究了响应变量随机缺失下的广义线性模型,利用处理缺失数据的完全数据方法,构造了广义线性模型中未知参数的拟似然估计.结合随机缺失机制和若干极限定理,证明了该拟似然估计的渐近存在性、强相合性和收敛速度.     

The natural history of the parasitic wasp Trogus pennator (Hymenoptera: Ichneumonidae): Host‐finding behaviour and a possible host countermeasure

The parasitic wasp Trogus pennator (Ichneumonidae) attacks the larvae of swallowtail butterflies (Papilionidae). Female T. pennator were followed in the field as they searched for larvae of the zebra swallowtail butterfly, Eurytides marcellus (Papilionidae), feeding on Asimina (Annonaceae) in central Florida, USA. Predictions of host‐finding theory and interactions with the host were thus investigated in a natural setting. Wasps seldom flew to plants other than Asimina and apparently recognized the plants by visual cues. Plants were attractive regardless of host presence, as the wasps approached plants lacking E. marcellus feeding damage twice as often as they approached damaged plants. However, wasps approached damaged plants at a rate greater than their frequency in the Asimina population, indicating that they can detect host damage before they approach the plants. After approaching within 0.5?m of plants, wasps tended not to land on plants lacking feeding damage. A comparison of the plants they landed on, however, showed no consistent preferences for damaged plants. The weaker trends in the latter data indicate that the presence of feeding damage was not the sole criterion for landing, although it evidently influenced behaviour. After landing, wasps spent more time searching damaged plants than undamaged plants. Butterflies preferred to oviposit on plants shorter than those searched by T. pennator. Possible adaptive consequences of this phenomenon are discussed.     

缺失数据情形下的回归插补及其方差分析

利用无回答所提供的信息采用最小二乘估计给出了缺失数据情形下的目标变量的一种回归插补及其方差估计.在仅目标变量缺失数据情形,得A2中单元关于y对应的回归插补公式(y)i=(b)0+(b)1x12i+…+(b)pxp2i,i=1,…,r3.及其回归插补的协方差阵Cov((Y))=σ2X1(XTX)-1XT1.在辅助变量部分缺失且目标变量缺失的情形,得A2中单元关于y对应的回归插补公式(y)i=(b)0+(b)1x12i+…+(b)p1xp12i,i=1,…,r3.及其回归插补的协方差阵Cov((Y))=σ2X1(X*TX*)-1XT1.