High reliable real-time bandwidth scheduling for virtual machines with hidden Markov predicting in telehealth platform

Reliable and high-performance resource scheduling for Virtual Machines (VMs) in cloud can guarantee the efficiency of remote rescue with telehealth system. When a local disaster, e.g. earthquake and tsunami, happens in a densely populated area, the surging health care demand leads to the increasing workload in Data Centers (DCs) by storing and delivering a mass of patients’ information and real-time physiology signals. However, the current self-adaptive scheduling methods cannot provide a high-accuracy recognizing of the two conditions: urgency or normal, which would procrastinate the system into a high-performance status, while the best rescue time is lost. In this paper, we propose a Primary Node-based architecture for typical telehealth service on cloud, which takes into account both storage and delivery efficiency. We also design a novel algorithm to predicting and allocating the future bandwidth of all VMs in the telehealth service context. This method is able to dynamically adjust each parameter of a Hidden Markov Model (HMM) through collecting the historical information of the bandwidth workload. After we predict the future bandwidth consumption of VMs, a high-performance scheduling method is used to adjust the bandwidth to each VM for health care service. The simulation results prove that this algorithm provides a high-accurate prediction, which guides the allocating module to make decision before the request burst comes. Nevertheless, our algorithm improves the reliability of telehealth services for storing and delivering patients’ information among DCs.     

MATLAB下的基于HMM模型的语音识别技术的实现

HMM模型(隐含马尔科夫模型)由于对时间序列结构具有较强的建模能力.而逐步成为主流的语音识别技术.该文首先深入浅出地概述了基于HMM的语音识别技术,然后介绍了三个基本问题,最后在MATLAB下实现了孤立词语音识别系统.     

轴承故障诊断与故障预测方法

分析了利用HMM进行故障诊断和HHMM进行故障预测的框架,针对传统HHMM推理算法复杂,推理时间长的问题,将HHMM转化为DBN,并应用交叉树推理算法,缩短了推理时间。最后将HMM和HHMM应用于轴承故障诊断和故障预测或剩余寿命预测(RUL),通过试验结果验证了这种方法的有效性。     

EEMD与HMM在齿轮故障诊断方法中的研究

为了解决EMD方法存在的模态混叠的问题,更加精确有效的利用振动信号进行齿轮的故障识别和诊断,提出一种将总体平均经验模态分解(EEMD)和隐马尔科夫模型(HMM)结合的齿轮故障诊断方法。首先对采集到的原始齿轮振动信号进行EEMD处理,获得包含主要故障信息的各阶固有模态函数(IMF)分量,以能量为元素,提取并构造特征向量,对特征向量进行HMM模型训练和诊断测试,来识别齿轮的工作状态和故障类型,实验结果表明,该方法可以有效提高齿轮的故障诊断准确率和精度。     

基于垃圾模型的维吾尔语关键词识别

关键词识别是语音识别中的一个重要研究方向，而维吾尔语的关键词识别研究刚刚开始．结合维吾尔语音节特点和考虑影响关键词识别因素，提出在HMM模型的基础上对非关键词建立垃圾模型的方法，来提高关键词的识别效率。     

Towards subject independent continuous sign language recognition: A segment and merge approach

This paper presents a segment-based probabilistic approach to robustly recognize continuous sign language sentences. The recognition strategy is based on a two-layer conditional random field (CRF) model, where the lower layer processes the component channels and provides outputs to the upper layer for sign recognition. The continuously signed sentences are first segmented, and the sub-segments are labeled SIGN or ME (movement epenthesis) by a Bayesian network (BN) which fuses the outputs of independent CRF and support vector machine (SVM) classifiers. The sub-segments labeled as ME are discarded and the remaining SIGN sub-segments are merged and recognized by the two-layer CRF classifier; for this we have proposed a new algorithm based on the semi-Markov CRF decoding scheme. With eight signers, we obtained a recall rate of 95.7% and a precision of 96.6% for unseen samples from seen signers, and a recall rate of 86.6% and a precision of 89.9% for unseen signers.     

基于隐马尔可夫模型的电子商务网站结构优化*

针对电子商务网站中部分商品页面不处于用户预期位置,导致用户访问代价较大的情况,提出一种使用隐马尔可夫模型对网站结构进行优化建模的方法。首先建立一个隐马尔可夫模型用于模拟用户带有目的(表现为某种目标商品)的访问过程,再使用该模型挖掘出用户隐藏在访问序列中的目的,最后通过新增超链接实现网站结构优化。实验证明用该方法解决网站结构优化问题具备一定的可行性和有效性。     

基于人脸表情特征的情感交互系统*

设计了一套基于人脸表情特征的情感交互系统(情感虚拟人),关键技术分别为情感识别、情感计算、情感合成与输出三个方面。情感识别部分首先采用特征块的方法对面部静态表情图形进行预处理,然后利用二维主元分析(2DPCA)提取特征,最后利用多级量子神经网络分类器实现七类表情识别分类;在情感计算部分建立了隐马尔可夫情感模型(HMM),并且用改进的遗传算法估计模型中的参数;在情感合成与输出阶段,首先采用NURBS曲面和面片相结合的算法,建立人脸三维网格模型,然后采用关键帧技术,实现了符合人类行为规律的连续表情动画。最后完成了基于人脸表情特征的情感交互系统的设计。     

基于隐马尔可夫模型的3D手写识别方法

为实现便捷高效的人机交互,提高交互能力,在基于隐马尔可夫模型(HMM)的基础上,提出了一种新的3D手写识别方法。该方法使用带有三轴加速度传感器的手持设备去采集各种手写数据;使用插值及快速傅里叶变换(FFT)滤波等方法对采集的数据进行预处理;使用隐马尔可夫模型对每个手写动作进行模型训练;使用训练过的手写模型对采集的数据进行手写识别。数据测试结果表明,该方法在手持移动设备上数据分类的准确性可达到84.5%。     

基于Kinect深度图像信息的手势轨迹识别及应用

为实现基于Kinect深度图像信息的手势轨迹识别,提出了一种基于隐马尔可夫模型(HMM)的手势轨迹识别的方法。首先采用新型Kinect传感器获取图像深度信息;然后通过OpenNI的手部分析模块获得手心的位置,提取轨迹特征;最后利用隐马尔可夫模型训练有效的轨迹样本并实现轨迹的识别。实验结果证明,该方法能有效地识别手势轨迹,并可用于控制智能轮椅的运动。