应用于视频编码的实时多测度联合突变场景切换检测算法

提出一种与视频编码器共用运动搜索模块的实时场景切换检测算法.通过分析原始图像与其运动补偿图像的一阶与二阶差值信号统计特性,分别构造对应于不同场景切换类型的两种测度函数,并联合使用该测度函数检测突变场景切换,同时自适应调节每个测度函数输出值的判决门限.分析和实验结果表明,该算法在视频编码位移估值基础上只增加少量的计算,而对视频序列中的突变场景切换检测的查全率和查准率均优于已有的几种实时检测算法.     

应用于视频编码的实时多测度联合突变场景切换检测算法

提出一种与视频编码器共用运动搜索模块的实时场景切换检测算法.通过分析原始图像与其运动补偿图像的一阶与二阶差值信号统计特性,分别构造对应于不同场景切换类型的两种测度函数,并联合使用该测度函数检测突变场景切换,同时自适应调节每个测度函数输出值的判决门限.分析和实验结果表明,该算法在视频编码位移估值基础上只增加少量的计算,而对视频序列中的突变场景切换检测的查全率和查准率均优于已有的几种实时检测算法.     

紫外可见吸收光谱检测pH值对光聚合脂质体的影响

利用紫外可见吸收光谱法考察脂质溶液pH值对光聚合脂质体的影响.通过水浴超声法制备光聚合脂质体,然后以脂质体的光学特性作为主要指标,利用光谱扫描测量各体系在640nm、540nm处的吸光值,利用比色响应值(CR)来对不同pH值下光聚合脂质体的颜色变化进行定量分析.结果表明,随着pH值的增大,光聚合脂质体溶液颜色由蓝色逐渐变成深红色.     

粒子群及遗传算法在相对论返波管中的应用

在全三维粒子模拟软件CHIPIC平台上,分别开发了粒子群及基因算法模块.以相对论返波管为例,采用三种不同类型的参数(连续参数、离散参数、混合参数),对粒子群及基因算法进行比较.优化结果表明:粒子群算法的收敛速度更快,在有限的迭代步数内得到的目标结果也更优良,总体表现优于基因算法.     

基于量子蚁群算法的旅行商问题求解及算法评估

量子蚁群算法是一种将量子理论与传统蚁群算法结合的高效生物进化算法,主要应用于故障诊断、路径规划、图像分割等问题的优化.基于传统蚁群算法的流程,介绍量子蚁群算法中的量子理论基础以及量子理论如何应用于蚁群算法.通过若干旅行商问题实例分析量子蚁群算法较传统蚁群算法的优势.针对目前研究多以离散指标来评估不同算法、难以直观显示不同算法综合差别的问题,提出一种综合评估算法搜索效率的方法,成功应用于量子蚁群算法和传统蚁群算法的对比,具有一定的实践意义.     

垂直入射时无限长分层柱电磁散射的改进算法及应用

结合非均匀球粒子对平面波散射的散射场计算的改进算法,提出了平面波垂直入射无限长分层圆柱散射场快速稳定而有效的改进电磁散射算法。与已有算法相比,改进算法所能计算的无限长非均匀介质圆柱的尺寸参量突破10000,计算层数达到106,并且计算时间很短,最多仅为几秒。该算法可以用于不同的波段以及不同领域的任意无耗或吸收无限长圆柱体散射场的计算。最后将该算法应用于梯度折射率聚合物光纤(GI-POF)散射特性的研究,为非接触、在线测量聚合物光纤折射率分布提供了理论依据。     

宽带电磁图像卷积神经网络盲恢复方法研究

在利用抛物反射面对电磁干扰源成像过程中,由于系统衍射受限及成像频带较宽,导致干扰源成像模糊,分辨率低,难以分辨,不同频率不同区域干扰源所成图像分辨率不同,采用已有超分辨算法难以提高分辨率。为了实现宽带电磁图像的盲复原, 应用卷积神经网络的方法。网络训练是直接输入模糊图像,不假设任何特定的模糊和噪声模型情况下,重建出高质量图像。实验和仿真结果证明了卷积神经网络盲恢复方法在宽频带不同成像区域下表现了优于其他盲恢复算法的优势。     

量子势阱粒子群优化算法的改进研究

为提高量子势阱粒子群优化算法的优化能力, 通过分析目前量子势阱粒子群优化算法的设计过程, 提出了改进的量子势阱粒子群优化算法. 首先, 分别基于Delta势阱、谐振子和方势阱 提出了改进的量子势阱粒子群优化算法, 并提出了基于统计量均值的控制参数设计方法. 然后, 在势阱中心的设计方面, 为强调全局最优粒子的指导作用, 提出了基于自身最优粒子加权平均和动态随机变量的两种设计策略. 实验结果表明, 三种势阱粒子群优化算法性能比较接近, 都优于原算法, 且Delta势阱模型略优于其他两种.     

基于蝙蝠优化算法的无线传感器网络节点定位研究

节点定位是无线传感器网络应用中的关键技术,Dv-Hop算法的定位精度不尽人意,因此将三方面改进的蝙蝠算法应用于Dv-Hop平均跳距的计算过程中,在Dv-Hop的第三阶段引入改进后的蝙蝠算法代替最小二乘法来计算未知节点的坐标,大大降低平均跳距导致的定位误差,提高定位精度;仿真结果表明,改进的BA算法优化的DV-hop定位算法在不同锚节点密度、不同通信半径、不同节点数量以及定位精确度等方面表现出良好的性能。     

基于改进的分块压缩感知红外图像重建

针对基于压缩感知理论的红外图像重建问题,提出一种基于改进的分块压缩感知红外图像重建方法。该方法首先对原始红外图像进行分块,并对每个子块用相同的观测矩阵进行随机观测,获得少量的观测数据;然后利用谱图小波变换优异的稀疏特性,将其引入平滑投影Landweber算法进行迭代优化重建,同时采用混合中值滤波进行处理以增加图像的平滑度和减少块伪影,最后输出满足要求的高质量红外图像。实验结果表明,在相同采样率下,该方法对于不同类型红外图像的重建性能均优于目前广为采用的一些小波压缩感知方法,可获得更高质量的红外图像。     

An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). However, choosing an appropriate filter feature selection (FFS) method that will generate and guarantee optimal features in SDP is an open research issue, known as the filter rank selection problem. As a solution, the combination of multiple filter methods can alleviate the filter rank selection problem. In this study, a novel adaptive rank aggregation-based ensemble multi-filter feature selection (AREMFFS) method is proposed to resolve high dimensionality and filter rank selection problems in SDP. Specifically, the proposed AREMFFS method is based on assessing and combining the strengths of individual FFS methods by aggregating multiple rank lists in the generation and subsequent selection of top-ranked features to be used in the SDP process. The efficacy of the proposed AREMFFS method is evaluated with decision tree (DT) and naïve Bayes (NB) models on defect datasets from different repositories with diverse defect granularities. Findings from the experimental results indicated the superiority of AREMFFS over other baseline FFS methods that were evaluated, existing rank aggregation based multi-filter FS methods, and variants of AREMFFS as developed in this study. That is, the proposed AREMFFS method not only had a superior effect on prediction performances of SDP models but also outperformed baseline FS methods and existing rank aggregation based multi-filter FS methods. Therefore, this study recommends the combination of multiple FFS methods to utilize the strength of respective FFS methods and take advantage of filter–filter relationships in selecting optimal features for SDP processes.     

Fitting Chinese syllable-to-character mapping spectrum by the beta rank function

We define the syllable-to-character mapping spectrum in Chinese as the normalized number of characters per syllable ranked from high to low. This spectrum provides a statistical characterization of the relationship between spoken and written Chinese. We have shown that two functions, the logarithmic function and the beta rank function, fit the syllable-to-character mapping spectrum well. The beta rank function is even better than the logarithmic function judged by two measures of data-fitting performance: the sum of square errors, and Akaike information criterion. We comment on why the beta rank function is a good fitting function for many range-limited ranking data, whereas for range-open data it may be out-performed by other functions, such as a power-law function in the case of Zipf’s law.     

Model for folding and aggregation in RNA secondary structures

We study the statistical mechanics of RNA secondary structures designed to have an attraction between two different types of structures as a model system for heteropolymer aggregation. The competition between the branching entropy of the secondary structure and the energy gained by pairing drives the RNA to undergo a "temperature independent" second order phase transition from a molten to an aggregated phase. The aggregated phase thus obtained has a macroscopically large number of contacts between different RNAs. The partition function scaling exponent for this phase is theta approximately 1/2 and the crossover exponent of the phase transition is nu approximately 5/3. The relevance of these calculations to the aggregation of biological molecules is discussed.     

Drogue detection for vision-based autonomous aerial refueling via low rank and sparse decomposition with multiple features

The technology of vision-based probe-and-drogue autonomous aerial refueling is an amazing task in modern aviation for both manned and unmanned aircraft. A key issue is to determine the relative orientation and position of the drogue and the probe accurately for relative navigation system during the approach phase, which requires locating the drogue precisely. Drogue detection is a challenging task due to disorderly motion of drogue caused by both the tanker wake vortex and atmospheric turbulence. In this paper, the problem of drogue detection is considered as a problem of moving object detection. A drogue detection algorithm based on low rank and sparse decomposition with local multiple features is proposed. The global and local information of drogue is introduced into the detection model in a unified way. The experimental results on real autonomous aerial refueling videos show that the proposed drogue detection algorithm is effective.     

Normalized entropy of rank distribution： a novel measure of heterogeneity of complex networks

Many unique properties of complex networks result from heterogeneity. The measure and analysis of heterogeneity are important and desirable to the research of the properties and functions of complex networks. In this paper, the rank distribution is proposed as a new statistic feature of complex networks. Based on the rank distribution, a novel measure of the heterogeneity called a normalized entropy of rank distribution (NERD) is proposed. The NERD accords with the normal meaning of heterogeneity within the context of complex networks compared with conventional measures. The heterogeneity of scale-free networks is studied using the NERD. It is shown that scale-free networks become more heterogeneous as the scaling exponent decreases and the NERD of scale-free networks is independent of the number of vertices, which indicates that the NERD is a suitable and effective measure of heterogeneity for networks with different sizes.     

Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising

Traditional image denoising algorithms obtain prior information from noisy images that are directly based on low rank matrix restoration, which pays little attention to the nonlocal self-similarity errors between clear images and noisy images. This paper proposes a new image denoising algorithm based on low rank matrix restoration in order to solve this problem. The proposed algorithm introduces the non-local self-similarity error between the clear image and noisy image into the weighted Schatten p-norm minimization model using the non-local self-similarity of the image. In addition, the low rank error is constrained by using Schatten p-norm to obtain a better low rank matrix in order to improve the performance of the image denoising algorithm. The results demonstrate that, on the classic data set, when comparing with block matching 3D filtering (BM3D), weighted nuclear norm minimization (WNNM), weighted Schatten p-norm minimization (WSNM), and FFDNet, the proposed algorithm achieves a higher peak signal-to-noise ratio, better denoising effect, and visual effects with improved robustness and generalization.     

基于Kendall改进的同步算法癫痫脑网络分析

提出了一种基于Kendall等级相关改进的同步算法IRC(inverse rank correlation).Kendall等级相关是非线性动力学分析的一般化算法,可有效地度量变量间的非线性相关性.复杂网络的研究已逐渐深入到社会科学的各个领域,脑网络的研究已经成为当今脑功能研究的热点.利用改进的IRC算法,基于脑电EEG(electroencephalogram)数据来构建大脑功能性网络.对构建的脑功能网络的度指标进行了分析,以调查癫痫脑功能网络是否异于正常人.结果显示:使用该改进的算法能够对癫痫和正常脑功能网络显著区分,且只需要记录很短的脑电数据.实验结果数据表明,该方法适用于区分癫痫和正常脑组织网络度指标,它可有助于进一步地加深对大脑的神经动力学行为的研究,并为临床诊断提供有效工具.     

Power-law and exponential rank distributions: A panoramic Gibbsian perspective

Rank distributions are collections of positive sizes ordered either increasingly or decreasingly. Many decreasing rank distributions, formed by the collective collaboration of human actions, follow an inverse power-law relation between ranks and sizes. This remarkable empirical fact is termed Zipf’s law, and one of its quintessential manifestations is the demography of human settlements — which exhibits a harmonic relation between ranks and sizes. In this paper we present a comprehensive statistical-physics analysis of rank distributions, establish that power-law and exponential rank distributions stand out as optimal in various entropy-based senses, and unveil the special role of the harmonic relation between ranks and sizes. Our results extend the contemporary entropy-maximization view of Zipf’s law to a broader, panoramic, Gibbsian perspective of increasing and decreasing power-law and exponential rank distributions — of which Zipf’s law is one out of four pillars.     

Performance of blind source separation algorithms for fMRI analysis using a group ICA method

Independent component analysis (ICA) is a popular blind source separation technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist; however, the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely, information maximization, maximization of non-Gaussianity, joint diagonalization of cross-cumulant matrices and second-order correlation-based methods, when they are applied to fMRI data from subjects performing a visuo-motor task. We use a group ICA method to study variability among different ICA algorithms, and we propose several analysis techniques to evaluate their performance. We compare how different ICA algorithms estimate activations in expected neuronal areas. The results demonstrate that the ICA algorithms using higher-order statistical information prove to be quite consistent for fMRI data analysis. Infomax, FastICA and joint approximate diagonalization of eigenmatrices (JADE) all yield reliable results, with each having its strengths in specific areas. Eigenvalue decomposition (EVD), an algorithm using second-order statistics, does not perform reliably for fMRI data. Additionally, for iterative ICA algorithms, it is important to investigate the variability of estimates from different runs. We test the consistency of the iterative algorithms Infomax and FastICA by running the algorithm a number of times with different initializations, and we note that they yield consistent results over these multiple runs. Our results greatly improve our confidence in the consistency of ICA for fMRI data analysis.     

Time scaling regimes in aggregation of magnetic dipolar particles: scattering dichroism results

We report experimental results on the aggregation kinetics in magnetorheological fluids subject to a constant uniaxial magnetic field using the technique of scattering dichroism. We show that the number of aggregated particles displays a long-time power-law dependence with exponents that correspond to two different aggregation regimes. These regimes coincide with 3D and 1D-like aggregation. We also derive the values of both time exponents for the number of aggregated particles.