一种改进的融合关联词典的微博倾向性分析方法

大多数研究者对微博倾向性分析过多关注的是情感词、形容词和否定词,忽略了 关联词对其情感倾向的影响。为了提高微博情感倾向性分析的准确率,提出了融合关联词的微博倾向性分析方法,考虑微博文本中形容词、程度副词以及关联词之间的组合关系。 本文充分考虑了关联词的结构特点并在已有词典的基础上构建专门用于微博倾向性分析的微博词典、否定词词典和关联词词典,同时考虑到网络新词对微博倾向性的影响,还构建 了一个全新的网络新词词典。借助支持向量机（Support vector machine,SVM）将微博文本分为负向、正向和中性3 类,通过结合情感词典和SVM的方法提高微博文本倾向性分析的准确率。通过对COASE 2014 数据实验可以表明,本文方法对微博倾向性分析取得了较好的效果。     

Finite‐time H ∞ control for discrete‐time switched nonlinear systems with time delay

In this paper, the problem of finite‐time H _∞ control is addressed for a class of discrete‐time switched nonlinear systems with time delay. The concept of H _∞ finite‐time boundedness is first introduced for discrete‐time switched delay systems. Next, a set of switching signals are designed by using the average dwell time approach, under which some delay‐dependent sufficient conditions are derived to guarantee the H _∞ finite‐time boundedness of the closed‐loop system. Then, a finite‐time H _∞ state feedback controller is also designed by solving such conditions. Furthermore, the problem of uniform finite‐time H _∞ stabilization is also resolved. All the conditions are cast into linear matrix inequalities, which can be easily checked by using recently developed algorithms for solving linear matrix inequalities. A numerical example and a water‐quality control system are provided to demonstrate the effectiveness of the main results. Copyright © 2013 John Wiley & Sons, Ltd.     

Automatic recognition of object size and shape via user-dependent measurements of the grasping hand

An investigation is conducted on the feasibility of using the posture of the hand during prehension in order to identify geometric properties of grasped objects such as size and shape. A recent study of Paulson et al. (2011) already demonstrated the successful use of hand posture for discriminating between several actions in an office setting. Inspired by their approach and following closely the results in motor planning and control from psychology (Makenzie and Iberall, 1994), we adopt a more cautious and punctilious approach in order to understand the opportunities that hand posture brings for recognizing properties of target objects. We present results from an experiment designed in order to investigate recognition of object properties during grasping in two different conditions: object translation (involving firm grasps) and object exploration (which includes a large variety of different hand and finger configurations). We show that object size and shape can be recognized with up to 98% accuracy during translation and up to 95% and 91% accuracies during exploration by employing user-dependent training. In contrast, experiments show less accuracy (up to 60%) for user-independent training for all tested classification techniques. We also point out the variability of individual grasping postures resulted during object exploration and the need for using classifiers trained with a large set of examples. The results of this work can benefit psychologists and researchers interested in human studies and motor control by providing more insights on grasping measurements, pattern recognition practitioners by reporting recognition results of new algorithms, as well as designers of interactive systems that work on gesture-based interfaces by providing them with design guidelines issued from our experiment.     

Reducing cache and TLB power by exploiting memory region and privilege level semantics

The L1 cache in today’s high-performance processors accesses all ways of a selected set in parallel. This constitutes a major source of energy inefficiency: at most one of the N fetched blocks can be useful in an N-way set-associative cache. The other N-1 cachelines will all be tag mismatches and subsequently discarded.We propose to eliminate unnecessary associative fetches by exploiting certain software semantics in cache design, thus reducing dynamic power consumption. Specifically, we use memory region information to eliminate unnecessary fetches in the data cache, and ring level information to optimize fetches in the instruction cache. We present a design that is performance-neutral, transparent to applications, and incurs a space overhead of mere 0.41% of the L1 cache.We show significantly reduced cache lookups with benchmarks including SPEC CPU, SPECjbb, SPECjAppServer, PARSEC, and Apache. For example, for SPEC CPU 2006, the proposed mechanism helps to reduce cache block fetches from the data and instruction caches by an average of 29% and 53% respectively, resulting in power savings of 17% and 35% in the caches, compared to the aggressively clock-gated baselines.     

Interest point detection using imbalance oriented selection

Interest point detection has a wide range of applications, such as image retrieval and object recognition. Given an image, many previous interest point detectors first assign interest strength to each image point using a certain filtering technique, and then apply non-maximum suppression scheme to select a set of interest point candidates. However, we observe that non-maximum suppression tends to over-suppress good candidates for a weakly textured image such as a face image. We propose a new candidate selection scheme that chooses image points whose zero-/first-order intensities can be clustered into two imbalanced classes (in size), as candidates. Our tests of repeatability across image rotations and lighting conditions show the advantage of imbalance oriented selection. We further present a new face recognition application—facial identity representability evaluation—to show the value of imbalance oriented selection.     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.     

基于S变换的全桥整流电路故障诊断研究

针对应用广泛的全桥整流电路故障分析,根据整流电压波形的畸变规律,分析了2个以内的二极管故障分为7类共计22种故障元。运用S变换分析,提取了故障特征值,并以此构造故障模式向量作为RPORP神经网络的输入。实现了电路的故障诊断与定位。为双凸极发电机整流电流故障的进一步研究打下了基础。     

基于CBERS-02B和SPOT-5全色波段的图像融合纹理信息评价研究

CBERS-02B是我国第一代传输型陆地资源遥感卫星,搭载的传感器可以获得2.36 m分辨率的全色波段数据。通过遥感影像融合技术,将CBERS-02B全色数据和SPOT-5全色数据与SPOT-5多光谱数据10 m分辨率的图像进行了多方法的融合处理,通过对融合后图像的空间纹理信息进行比较和评价,获得了纹理信息的特征参数值。通过目视评价和定量分析,认为用CBERS-02B全色数据融合的影像在空间纹理上比SOPT-5融合的影像有优势。因此,CBERS-02B的全色波段是一种较高质量的高分辨率数据,应用前景广阔。     

随机非线性系统的输出反馈控制

针对满足线性增长条件的一类随机非线性系统, 本文研究了输出反馈镇定问题. 然而不同于现有的所有文献, 由于线性增长条件中含有不可量测的状态, 引入了一个待定的高增益观测器. 利用反推设计技术, 构造性地给出了一个输出反馈控制器的设计, 通过适当地选取高增益参数, 保证了闭环系统的零解是概率意义下全局渐近稳定的, 输出几乎处处调节于零.