基于增量学习SVM的人脸识别

为了提高人脸识别率,本文提出了一种增量学习支持矢量机(SVM)人脸识别方法,有效地对SVM的参数进行更新。提出的方法采用高斯概率模型描述SVM的参数统计特征,在无需额外存储训练数据的前提下,采用增量学习SVM的方式实现参数的更新;并通过最小化分类误差准则最大化SVM两类输出值概率分布间的距离。详细的实验以及与现有方法的比较结果表明,提出的识别方法具有更好的识别性能。     

便携式数据采集控制系统的实现及应用

本文提出了一种基于USB的便携式数据采集控制系统,对控制系统的硬件结构、USB控制器与单片机硬件连接、USB数据传输系统的软件设计等方面进行了详细介绍。并应用于试验机测量钢绞线的力学性能,测试结果表明该系统数据传输快,实时性强,可靠性好,测试精度高。     

一种DDOS在线检测法

由DDoS攻击时流量样本与Hurst指数的关系,得到检测DDoS的方法.以提升小波估计Hurst指数法为基础,设计出小波算法的能谱在线计算方法.与传统检测法相比,本法可在线观察业务流特征,能及时发现DDOS入侵。     

基于谱直方图与支持向量机的人脸检测

提出了一种基于谱直方图和支持向量机的人脸检测算法。首先使用梯度滤波器、LoG滤波器和LBP算子计算图像的谱直方图,然后使用支持向量机进行分类。经实验表明,算法可以克服光照、姿势、表情、眼镜等干扰,并允许有局部遮挡,具有较强的鲁棒性。     

基于ARM9-Linux的注塑机伺服控制系统

全电动注塑机的控制系统任务繁多且要求实时性强。结合全电动注塑机的工艺特点,提出了以ARM9处理器为核心的全电动注塑机硬件系统,并利用嵌入式操作系统Linux提供的多进程多线程通信机制进行软件设计,从而满足全电动注塑机的各种伺服控制要求。     

基于模糊控制的纸机蒸汽冷凝水控制系统

针对造纸干燥部的工况多变、选用传统控制方式不易控制的情况,设计了一种基于模糊PID算法设计的S7-300控制系统,介绍了理论上和实际组态、编程的实现方法。该系统鲁棒性强,可适应复杂工况,实际应用结果表明该系统算法合理、易实现,控制效果好。     

Adaptive signal processing of asset price dynamics with predictability analysis

In this paper we illustrate the optimal filtering of log returns of commodity prices in which both the mean and volatility are modulated by a hidden Markov chain with finite state space. The optimal estimate of the Markov chain and the parameters of the price model are given in terms of discrete-time recursive filters. We provide an application on a set of high frequency gold price data for the period 1973-2006 and analyse the h-step ahead price predictions against the Diebold-Kilian metric. Within the modelling framework where the mean and volatility are switching regimes, our findings suggest that a two-state hidden Markov model is sufficient to describe the dynamics of the data and the gold price is predictable up to a certain extent in the short term but almost impossible to predict in the long term. The proposed model is also benchmarked with ARCH and GARCH models with respect to price predictability and forecasting errors.     

A rough margin based support vector machine

By introducing the rough set theory into the support vector machine (SVM), a rough margin based SVM (RMSVM) is proposed to deal with the overfitting problem due to outliers. Similar to the classical SVM, the RMSVM searches for the separating hyper-plane that maximizes the rough margin, defined by the lower and upper margin. In this way, more data points are adaptively considered rather than the few extreme value points used in the classical SVM. In addition, different support vectors may have different effects on the learning of the separating hyper-plane depending on their positions in the rough margin. Points in the lower margin have more effects than those in the boundary of the rough margin. From experimental results on six benchmark datasets, the classification accuracy of this algorithm is improved without additional computational expense compared with the classical ν-SVM.     

Controllability of second-order infinite-dimensional systems

In the paper, the approximate controllability of linear abstract second-order infinite-dimensional dynamical systems is considered. It is proved using the frequency-domain method, that approximate controllability of second-order system can be verified by the approximate controllability conditions for the corresponding simplified first-order system. General results are then applied for approximate controllability investigation of a vibratory dynamical system modeling flexible mechanical structure. Some special cases are also considered. Moreover, remarks and comments on the relationships between different concepts of controllability are given. The paper extends earlier results on approximate controllability of second-order abstract dynamical systems.     

The scaling problem in the pattern recognition approach to machine translation

Statistical machine translation (SMT) has proven to be an interesting pattern recognition framework for automatically building machine translations systems from available parallel corpora. In the last few years, research in SMT has been characterized by two significant advances. First, the popularization of the so called phrase-based statistical translation models, which allows to incorporate local contextual information to the translation models. Second, the availability of larger and larger parallel corpora, which are composed of millions of sentence pairs, and tens of millions of running words. Since phrase-based models basically consists in statistical dictionaries of phrase pairs, their estimation from very large corpora is a very costly task that yields a huge number of parameters which are to be stored in memory. The handling of millions of model parameters and a similar number of training samples have become a bottleneck in the field of SMT, as well as in other well-known pattern recognition tasks such as speech recognition or handwritten recognition, just to name a few. In this paper, we propose a general framework that deals with the scaling problem in SMT without introducing significant time overhead by means of the combination of different scaling techniques. This new framework is based on the use of counts instead of probabilities, and on the concept of cache memory.