无端点检测汉语识别算法的实现及改进——动态时间规整和隐马尔可夫统一模型的应用

语音识别算法中，动态时间规整（ＤＴＷ）和隐马尔可夫模型（ＨＭＭ）是最有效的识别算法，并且两者之间有着本质的联系和内在的统一［１］，据此前期工作中，已经建立了ＤＴＷ和ＨＭＭ的统一模型（ＤＨＵＭ）［２、３］。本文对ＤＨＵＭ进行了改进，在ＤＨＵＭ中引进寂静段自环，并根据汉语语音的特点，提出了一种无端点检测的语音识别算法。在识别过程中，该算法无需确定语音信号起止点位置，而是从寂静段开始，直接按帧提取特征（帧长２０ｍｓ，帧间重叠５０％），特征向量由１５阶倒谱系数和帧平均能量组成。实验中，用ＤＨＵＭ实现了该算法，对９９个相似汉语单字的识别测试结果表明：无端点检测的识别正识率为９４．９５％，正识率下降很少，但不作端点检测却降低了算法的复杂程度。为进一步改善识别性能，特征向量采用一种听觉模型特征，识别器具有更好的鲁棒性，识别率会略有提高。     

Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model

We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM represents driving intention in a combined working case, constructed according to the driving behaviours in certain single working cases in the lower-layer multi-dimensional Gaussian HMM (MGHMM). The driving behaviours are recognised by manoeuvring the signals of the driver and vehicle state information, and the recognised results are sent to the upper-layer HMM to recognise driving intentions. Also, driving behaviours in the near future are predicted using the likelihood-maximum method. A real-time driving simulator test on the combined working cases showed that the double-layer HMM can recognise driving intention and predict driving behaviour accurately and efficiently. As a result, the model provides the basis for pre-warning and intervention of danger and improving comfort performance.     

如何解决乙炔发生系统蝶阀泄漏乙炔气的问题

蝶阀装置在乙炔生产中具有安全密封作用,但加料操作过程易对其造成损伤,不仅影响生产,而且是造成乙炔气大量泄漏的主要原因。根据长期生产实践,分别从材质到操作等细节问题进行了分析,为避免蝶阀泄漏找到了比较妥善的解决办法,降低了蝶阀装置的维修率。     

HMM-based state classification of a user with a walking support system using visual PCA features

The improvement of safety and dependability in systems that physically interact with humans requires investigation with respect to the possible states of the user’s motion and an attempt to recognize these states. In this study, we propose a method for real-time visual state classification of a user with a walking support system. The visual features are extracted using principal component analysis and classification is performed by hidden Markov models, both for real-time fall detection (one-class classification) and real-time state recognition (multi-class classification). The algorithms are used in experiments with a passive-type walker robot called “RT Walker” equipped with servo brakes and a depth sensor (Microsoft Kinect). The experiments are performed with 10 subjects, including an experienced physiotherapist who can imitate the walking pattern of the elderly and people with disabilities. The results of the state classification can be used to improve fall-prevention control algorithms for walking support systems. The proposed method can also be used for other vision-based classification applications, which require real-time abnormality detection or state recognition.     

基于虚拟磁盘的携带型操作系统加密方案

针对移动硬盘的加密系统分区无法被正确辨识而导致误格式化的问题,应用文件系统过滤驱动技术,结合硬盘主引导程序及分区表机理,对现有的基于虚拟磁盘的移动硬盘加解密方案进行了改进,在加密分区包含可运行操作系统的情况下,增加了盘符自动隐藏及恢复的功能。以开源加密软件Truecrypt为例,利用安装了视窗操作系统的固态移动硬盘进行测试。结果表明,在不影响加密操作系统正常运行的同时,盘符自动隐藏及恢复功能避免了用户的误格式化操作,从而提高了移动硬盘加密系统分区的安全性和隐蔽性。     

Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching

Abstract. This paper is concerned with the problem of joint determination of the state dimension and autoregressive order of models with Markov‐switching parameters. A model selection procedure is proposed which is based on optimization of complexity‐penalized likelihood criteria. The efficacy of the procedure is evaluated by means of Monte Carlo experiments.     

基于贝叶斯网络的新媒体事件分类模型

为了对新媒体事件进行准确有效分类,提出一种将K-means算法和贝叶斯网络相结合的混合算法。该算法首先运用K-means算法将训练样本聚类,再根据聚类的结果,运用改进的贝叶斯网络对新媒体事件进行分类。其中,层次贝叶斯网络模型的构建,避免了贝叶斯网络在参数学习时陷入局部寻优;同时引入隐藏节点,更大程度满足了贝叶斯网络的条件独立假设。实验结果表明该算法效果明显。     

RBM学习方法对比

随着深度学习在模型、算法与理论上的突破性进展,以玻尔兹曼机为基础的各类深度模型近年来在目标识别与自然语言处理等诸多领域得到广泛应用。概述了玻尔兹曼机的相关概念,分析了受限玻尔兹曼机模型所具有的优势。对RBM中的学习方法进行了详细的描述,对应用最为广泛的受限玻尔兹曼机的几种典型学习算法进行了对比,并指出学习算法的研究在未来仍将是深度学习中的一项核心问题。     

城市环境中交通信号灯准确识别与状态估计

根据交通信号灯灯板颜色和形状定位图像中的灯板位置. 将灯板区域的彩色图像转换到YCbCr 空间, 分割灯板区域中的红、黄、绿三色区域, 利用交通信号灯的形态特性定位交通灯位置. 用Gabor 小波和2 维独立分量分析提取感兴趣区域的特征, 送入最近邻分类器分类信号灯的类型. 用代表性的观测序列建立隐马尔科夫模型, 并结合识别和跟踪结果估计信号灯状态. 实验结果表明, 该算法能可靠、准确地识别出信号灯, 并有效地估计出信号灯的状态.     

802.11 DCF协议引入信道预约机制的必要性分析

论文对802.11 DCF协议引入信道预约机制的必要性问题进行了探讨.通过分析信道预约机制与隐藏/暴露终端问题之间的关系,提出一种比较隐藏终端和暴露终端出现概率大小的等效方法,并给出了相应的数值估算结果.理论分析结果验证了802.11 DCF协议引入信道预约机制的有效性.