相空间重构在语音情感识别中的研究

为了更为全面地表征语音情感状态,弥补线性情感特征参数在刻画不同情感类型上的不足,将相空间重构理论引入语音情感识别中来,通过分析不同情感状态下的混沌特征,提取Kolmogorov熵和关联维作为新的情感特征参数,并结合传统语音特征使用支持向量机（SVM）进行语音情感识别。实验结果表明,通过引入混沌参数,与传统物理特征进行识别的方案相比,准确率有了一定的提高,为语音情感的识别提供了一个新的研究途径。     

基于类属属性的多标记学习算法

在多标记学习框架中,每个对象由一个示例(属性向量)描述,却同时具有多个类别标记.在已有的多标记学习算法中,一种常用的策略是将相同的属性集合应用于所有类别标记的预测中.然而,该策略并不一定是最优选择,原因在于每个标记可能具有其自身独有的特征.基于这个假设,目前已经出现了基于标记的类属属性进行建模的多标记学习算法LIFT.LIFT包含两个步骤:属属性构建与分类模型训练.LIFT首先通过在标记的正类与负类示例上进行聚类分析,构建该标记的类属属性;然后,使用每个标记的类属属性训练对应的二类分类模型.在保留LIFT分类模型训练方法的同时,考察了另外3种多标记类属属性构造机制,从而实现LIFT算法的3种变体——LIFT-MDDM,LIFT-INSDIF以及LIFT-MLF.在12个数据集上进行了两组实验,验证了类属属性对多标记学习系统性能的影响以及LIFT采用的类属属性构造方法的有效性.     

多传感器数据采集系统中的数据融合研究

针对多传感器数据采集系统的准确性与可靠性的要求,提出了单传感器分批估计融合与模糊理论中的相关性函数与加权自适应算法相结合的数据融合方法。首先,对每一只传感器进行分批估计融合,得到了较为可靠的局部决策值。然后,利用模糊理论中的相关性函数,计算出每只传感器与其它传感器的相关程度,并剔除掉那些相关程度较低的传感器的局部决策值。最后,在权的最优分配原则下,利用加权自适应算法对剩下的局部决策值进行数据融合。该方法能在数据先验知识未知的情况下,客观地反映各个传感器的可靠程度。数据分析结果表明:与同类融合方法相比,该方法具有更高的精度。     

煤矸石混合材对标准稠度影响的灰色关联分析

利用灰色关联分析的数学方法,对利用煤矸石作水泥混合材所产生的标准稠度用水量改变的影响,做了分析。论述了不同细度水泥条件下,煤矸石粒径分布范围对水泥需水量的影响。发现了在水泥较细时,在粒径范围<5 μm下的煤矸石颗粒,对标准稠度用水量影响最大;在水泥较粗时,粒径范围在5～10 μm下的煤矸石颗粒,对标准稠度用水量影响最大。     

基于变权灰色关联法的长江荆南三口地区水资源短缺程度分析

对荆南三口地区水资源短缺程度进行分析研究,为实现区域水资源可持续合理利用提供科学依据。在水资源短缺评价研究中,前期研究成果大多采用常权法确定评价指标,致使评价指标产生状态失衡。将变权理论引入到水资源短缺程度分析中,从而确定评价指标变权重,并结合灰色关联法,构建水资源短缺程度分析的变权灰色关联法模型并加以应用。最终得出荆南三口地区三县的水资源短缺程度均为轻度缺水,其缺水程度排序为:华容县南县安乡县。利用变权灰色关联法计算水资源短缺程度,方法可行,结果合理,满足了评价指标均衡性要求。     

水源热泵影响下地下水温度主控因素关联度分析

为研究地下水源热泵长期影响下,地下水温度主控因素的变化特征,选择安阳市第五人民医院为研究区,将地下水温度作为典型的灰色系统进行研究,采用灰色关联度的计算方法进行地下水温度、地下水位埋深、气温、降水量以及回水井水温之间的关联度分析,获取地下水温变化的主控因素。研究结果表明:在不受水源热泵影响时,地下水位埋深是地下水温度的主控因素。在水源热泵影响下,地下水温受气温、降水量、地下水位埋深和回水井水温的共同影响:在垂向上,回水井水温对监测井水温的影响程度随着深度的增大而逐渐降低,地下水温度的主控因素由回水井水温转变为地下水位埋深,在水平方向上,回水井水温对监测井水温的影响程度随着距离的增大而逐渐减小,地下水温度的主控因素由回水井水温转变为气温和地下水位埋深。     

勉县地质灾害分布与影响因素的相关性分析

受水文地质、工程地质条件及人类工程活动等影响因素的影响,勉县是陕南地区地质灾害的多发区。为了研究勉县地质灾害分布与影响因素之间的相关性,分别绘制了各影响因素与地质灾害分布的叠加图,并对其进行统计分析。得出结论:勉县地质灾害集中发育于汉江盆地年平均降雨量为850mm~900 mm的区域、中低山区和丘陵区以及志留系-奥陶系下统和侏罗系地层岩性区。地质灾害沿主要水系、断裂、公路等两侧一定范围内呈"带状"分布。汉江两侧1 km范围内灾害点最多,灾害点密度最大;勉县—略阳构造带两侧1 km范围内灾害点最多,灾害点密度最大;十天高速两侧1 km范围内灾害点最多,灾害点密度最大。分析结论为勉县移民搬迁项目和基础设施建设提供可靠的地质灾害防治依据。     

Graph regularized multiset canonical correlations with applications to joint feature extraction

Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.     

Multi-resolution feature fusion for face recognition

For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ “Gabor-feature hallucination”, which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation/super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods.     

Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition

Due to the noise disturbance and limited number of training samples, within-set and between-set sample covariance matrices in canonical correlation analysis (CCA) usually deviate from the true ones. In this paper, we re-estimate within-set and between-set covariance matrices to reduce the negative effect of this deviation. Specifically, we use the idea of fractional order to respectively correct the eigenvalues and singular values in the corresponding sample covariance matrices, and then construct fractional-order within-set and between-set scatter matrices which can obviously alleviate the problem of the deviation. On this basis, a new approach is proposed to reduce the dimensionality of multi-view data for classification tasks, called fractional-order embedding canonical correlation analysis (FECCA). The proposed method is evaluated on various handwritten numeral, face and object recognition problems. Extensive experimental results on the CENPARMI, UCI, AT&T, AR, and COIL-20 databases show that FECCA is very effective and obviously outperforms the existing joint dimensionality reduction or feature extraction methods in terms of classification accuracy. Moreover, its improvements for recognition rates are statistically significant on most cases below the significance level 0.05.