基于非负稀疏协作模型的目标跟踪算法

目标跟踪是计算机视觉研究领域中一个最基本的问题.为解决在复杂场景下目标跟踪效果不佳的问题,作者搭建了一个基于非负稀疏的协作模型,该模型将非负稀疏表示的产生式模型与全局模板判别式模型相结合,并提出了基于非负稀疏协作模型的目标跟踪算法.首先对每一帧图像使用粒子滤波得到若干个候选框,然后再利用非负稀疏协作模型对每一个候选跟踪框进行评分,根据得分最高判为是跟踪目标的候选框.在多个视频序列上的实验结果表明,所提出的方法可以有效地提高目标跟踪的性能.     

三维机织几何结构的数值表征

推荐一种利用一组数值型参数来定量表征三维机织几何结构的方法。在给定的几何约束和加工约束条件下，由4种纱线系统组成的三雏机织几何结构可用9个结构参数予以数值表征。根据贯穿或分层的角联接结和正交接结的几何特征，分别建立了与之对应的结构参数以及参数间的联系。进一步的研究表明，对于角联接结的三维机织几何结构，9个结构参数中仅有4个是独立的；而对于正交接结的三维机织几何结构，独立的结构参数为5个。一旦确定了独立的结构参数，三维机织几何结构就能被确定；反之，只要织物几何结构已知，则与之相对应的结构参数也就确定了。这里所推荐的数值表征方法可用于三维机织几何结构的设计，如根据三维机织复合材料的性能要求确定各取向纤维的分布等，从而为建立一种具有普适意义的三维机织结构几何模型提供了前提条件，同时也为三雏机织复合材料力学性能的设计提供了统一的设计平台。     

红-黑小波变换

介绍一种新的第二代小波变换——红-黑小波变换，它是把矩形栅格的数字图像分成红块和黑块，利用二雏提升格式进行构造。并从理论上与张量积小波变换进行了比较。实验表明红．黑小波变换可以更有效地去除图像的相关性并得到更稀疏的图像表示，在图像压缩中红．黑小波变换比传统小波变换更有效。     

多项式恒等式d_M~H(AX)=d_M~H(XA)成立的条件

讨论Ａ∈ＧＬ（ｎ,ｃ）时,　 Ｘ∈Ｍｎ（Ｃ）,多项式恒等式ｄ_Ｍ~Ｈ（ＡＸ）＝ｄ_Ｍ~Ｈ（ＸＡ）成立的充要条件,这里Ｈ是ｎ次对称群Ｓ 的子群,而ｄ_Ｍ~Ｈ表示由群Ｈ的酉表示Ｍ诱导的矩阵函数．     

基于DDR的字典学习在光照变化人脸识别中的应用

针对光照变化人脸识别中大多数现有的人脸识别算法只能单独实施降维或者字典学习而不能完全利用训练样本判别信息的问题,提出了基于判别性降维的字典学习算法。首先,利用经典的特征提取算法PCA初始化降维投影矩阵;然后,计算字典和系数,通过联合降维与字典学习使得投影矩阵和字典更好地相互拟合;最后,利用迭代算法输出字典和投影矩阵,并利用经l2-范数正则化的分类器完成人脸的识别。在PIE及扩展的YaleB两大人脸数据库上得到了验证了所提算法的有效性及可靠性,实验结果表明,相比几种较为先进的线性表示算法,所提算法在处理光照变化人脸识别时取得了更高的识别率。     

Semi-Supervised Cross-Subject Emotion Recognition Based on Stacked Denoising Autoencoder Architecture Using a Fusion of Multi-Modal Physiological Signals

In recent decades, emotion recognition has received considerable attention. As more enthusiasm has shifted to the physiological pattern, a wide range of elaborate physiological emotion data features come up and are combined with various classifying models to detect one’s emotional states. To circumvent the labor of artificially designing features, we propose to acquire affective and robust representations automatically through the Stacked Denoising Autoencoder (SDA) architecture with unsupervised pre-training, followed by supervised fine-tuning. In this paper, we compare the performances of different features and models through three binary classification tasks based on the Valence-Arousal-Dominance (VAD) affection model. Decision fusion and feature fusion of electroencephalogram (EEG) and peripheral signals are performed on hand-engineered features; data-level fusion is performed on deep-learning methods. It turns out that the fusion data perform better than the two modalities. To take advantage of deep-learning algorithms, we augment the original data and feed it directly into our training model. We use two deep architectures and another generative stacked semi-supervised architecture as references for comparison to test the method’s practical effects. The results reveal that our scheme slightly outperforms the other three deep feature extractors and surpasses the state-of-the-art of hand-engineered features.     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

伪辛空间的生成及其表现形式

通过对辛空间的深入分析和研究,从空间中的度量与向量的迷向性问题入手并展开较为详细的讨论,定义"伪辛"的概念,阐述"辛"概念的演变、扩充与伪辛空间生成的事实,进而揭示伪辛空间的本质属性及其与辛空间的内在联系与区别.论述过程同时给出了伪辛空间关于辛空间的内蕴和外延表示式.     

向量随机场模拟及其在岩土工程中的应用

对于岩土工程中多个土性随机参数向量随机场,将随机场谱表示模拟方法与Monte-Carlo法相结合,得到了考虑土性参数自相关性与互相关性的Monte-Carlo随机有限元法,并应用于堤坡失效概率的计算。计算结果表明:谱表示模拟方法比普通协方差分解方法收敛速度快,采用该模拟方法的Monte-Carlo随机有限元法是计算岩土工程可靠度的有效方法;在土性参数随机场的模拟中应考虑参数间的互相关性对于岩土工程可靠度的显著影响。     

高光谱图像半监督局部稀疏嵌入降维算法

针对目前高光谱图像半监督降维算法中基于流形学习的开放性选择近邻参数问题,以及利用传统算法不能有效地获取标签数据的局部信息,提出了一种无需考虑近邻参数的半监督局部稀疏嵌入（SELSE）算法.该算法基于稀疏表示理论,通过求解范数优化问题构建稀疏系数图,并且利用有限的标签数据最大化类间信息,提取高光谱图像的特征.在AVIRIS高光谱遥感图像的Indian Pine数据集上进行仿真实验,结果表明所提出算法在分类精度和计算效率上都有所提高.