基于GRA-ISM-HMM的广州市肉及肉制品安全风险评估

该研究旨在利用广州食品安全风险监测2015年至2020年针对肉及肉制品样本的检测数据,构建肉及肉制品的安全风险评估模型,从而了解广州市该段时间内肉及肉制品的食品安全风险及其时变特点。该研究采取灰色关联分析方法和解释结构模型建立风险指数,并基于该指标值作为隐马尔可夫模型的观测值探讨观测背后的隐含风险状态。分析结果显示,2015~2020年所有样本综合风险指数结果都在[0,0.45]之间,总体风险都较小,其中2019年风险最高;将风险指数进行等级划分,显示2015~2020年风险等级为[1,2,2,2,3,1];通过HMM分析得到这六年的隐藏风险等级为[0,1,1,1,2,0],与观测风险结果一致,且HMM预测2021年风险等级为1,即表明广州肉及肉制品风险往良好态势发展。最后,进行风险差异原因分析,发现各肉制品分类之间有差异,其中腊肠、鸡肉和腊肉的风险指数较高于其他种类,而2019年增加腊肠和腊肉的检测是风险增加的一个原因。总体来说,广州肉及肉制品风险较小,但依旧需要监督改善。

基于GWO-HMM的空中交通网络流系统态势预测研究

针对空中交通流量管理部门如何更高效地实施流量管理的问题,本文将态势感知理论应用于空中交通网络流系统(ATNFS,air traffic network flow system),建立空中交通网络流系统的运行态势预测模型。首先,给出了空中交通网络流系统的态势感知过程,从节点和航线的角度筛选出航线饱和度、不正常航班率、节点饱和度、节点延误架次比、节点航班取消率5个态势要素,使用态势值作为态势理解的指标;其次,分析隐马尔可夫模型(HMM,hidden Markov model)的优势与不足,建立了基于灰狼优化(GWO,grey wolf optimization)算法和改进隐马尔可夫模型的态势预测模型;最后,使用某空中交通网络流系统的实际运行数据进行算例验证。结果表明,改进后的预测模型相较于原本的隐马尔可夫预测模型精度更高,预测结果更准确。

基于改进HMM的车辆轨迹匹配方法研究

针对传统基于隐马尔可夫模型(HMM)的车辆轨迹匹配算法在复杂特殊路段无法兼备准确率与时间效率的问题,提出了基于改进HMM建模的车辆轨迹匹配方法。在候选路段的确定上通过生成网格索引提高整体检索效率,改进的HMM模型在发射概率的计算上综合航向角因素,考虑车辆速度对航向角造成的偏差影响,并设置经验因子进行调节。同时,考虑前后观测值误差过大及曲线路段等影响因素,采用单位采样间隔内的车辆实际行驶距离代替观测距离值,以保证传递概率计算的准确性。最后,利用实测数据进行试验,验证算法的性能。实验结果表明,所提方法匹配准确率约94.0%,相较于传统HMM轨迹匹配方法提高了2.8%,在提高时间效率及复杂路段的匹配准确度上也具有一定优势,单点匹配时间减少约0.9 ms,适用于交叉路口、立交桥、平行路段等复杂路况下的匹配。

Robust Speech Recognition System Using Conventional and Hybrid Features of MFCC,LPCC,PLP,RASTA-PLP and Hidden Markov Model Classifier in Noisy Conditions

In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate.

2D-HIDDEN MARKOV MODEL FEATURE EXTRACTION STRATEGY OF ROTATING MACHINERY FAULT DIAGNOSIS

A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.

Brief Introduction to Self-Adapting Hidden Markov Model Program for Multiple Sequences Alignment

In this letter, we briefly describe a program of self adapting hidden Markov model (SA HMM) and its application in multiple sequences alignment. Program consists of two stage optimisation algorithm.

Fault Pattern Recognition Based on Hidden Markov Model

Because performance parameters of gear have degradation,a method is proposed to recognize and analyze its faults using the hidden Markov model( HMM). In this method,firstly,the delayed correlation-envelope method is used to extract features from vibration signals. Then,HMMs are trained respectively using data under normal condition,gear root crack condition and gear root breaking condition. Further,the trained HMMs are used in pattern recognition and model assessment. Finally,the results from standard HMM and the proposed method are compared, which shows that the proposed methodology is feasible and effective.

Video object's behavior analyzing based on motion history image and hidden markov model

A novel method was proposed, which extracted video object＇ s track and analyzed video object＇ s be- havior. Firstly, this method tracked the video object based on motion history image, and obtained the co- ordinate-based track sequence and orientation-based track sequence of the video object. Then the pro- posed hidden markov model （HMM） based algorithm was used to analyze the behavior of video object with the track sequence as input. Experimental results on traffic object show that this method can achieve the statistics of a mass of traffic objects＇ behavior efficiently, can acquire the reasonable velocity behavior curve of traffic object, and can recognize traffic object＇ s various behaviors accurately. It provides a base for further research on video object behavior.

Hidden Markov Models for Automatic Speech Recognition

In this paper the authors look into the problem of Hidden Markov Models （HMM）： the evaluation, the decoding and the learning problem. The authors have explored an approach to increase the effectiveness of HMM in the speech recognition field. Although hidden Markov modeling has significantly improved the performance of current speech-recognition systems, the general problem of completely fluent speaker-independent speech recognition is still far from being solved. For example, there is no system which is capable of reliably recognizing unconstrained conversational speech. Also, there does not exist a good way to infer the language structure from a limited corpus of spoken sentences statistically. Therefore, the authors want to provide an overview of the theory of HMM, discuss the role of statistical methods, and point out a range of theoretical and practical issues that deserve attention and are necessary to understand so as to further advance research in the field of speech recognition.

The on-line direct fitting of low signal-noise ratio single ion channel recordings based on hidden Markov models

Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct fitting of the patch clamp recording, not of the histogram coming from the recordings, is a desirable way to estimate the transition probabilities. Iterative batch EM algorithm based on hidden markov model has been used in this field but which has the "curse of dimensionality" and besides cant keep tracking the varying of the parameters. A new on line sequential iterative one is proposed here, which needs fewer computational efforts and can adaptively keep tracking the varying of parameters. Simulations suggest its robust, effective and convenient.

Comparison of Khasi Speech Representations with Different Spectral Features and Hidden Markov States

In this paper,we present a comparison of Khasi speech representations with four different spectral features and novel extension towards the development of Khasi speech corpora.These four features include linear predictive coding(LPC),linear prediction cepstrum coefficient(LPCC),perceptual linear prediction(PLP),and Mel frequency cepstral coefficient(MFCC).The 10-hour speech data were used for training and 3-hour data for testing.For each spectral feature,different hidden Markov model(HMM)based recognizers with variations in HMM states and different Gaussian mixture models(GMMs)were built.The performance was evaluated by using the word error rate(WER).The experimental results show that MFCC provides a better representation for Khasi speech compared with the other three spectral features.

基于HMM的低空目标航迹威胁识别

稳健的低空目标威胁识别是低空域安全防护的重要任务。传统的多属性决策方法对目标运动参数的量测精度要求较高,忽略了目标运动的时序关联信息,在实际应用中缺乏噪声稳健性和动态分析能力。因此,在多属性决策方法的基础上引入了隐马尔可夫模型,提出了一种动态稳健的低空目标威胁等级识别方法。通过建立隐状态与威胁等级、威胁数值之间的内在联系,将威胁识别问题转化为隐马尔可夫模型的状态解码问题。相比于常规算法,所提方法能够有效地抑制量测噪声干扰并具有一定的威胁预测能力。仿真实验验证了所提方法的有效性和稳健性。

多重防护机制下LNG动力船风险态势分析方法

为探讨多重防护机制下的液化天然气(Liquefied Natural Gas,LNG)动力船营运风险特征,提出了一种基于复杂性控制系统作用模式的风险态势分析方法。从系统控制过程视角,采用系统控制过程分析方法(System-Theoretic Process Analysis,STPA)确立LNG动力船系统安全控制的风险致因,揭示基于LNG泄漏三重防护机制的风险因子体系和风险形成机制;引入隐马尔可夫模型(Hidden Markov Model,HMM),进行无监督学习并确立模型参数,推理多重防护机制下的LNG动力船风险转移特征;结合LNG动力船营运的具体场景,对三重防护机制分别进行仿真并统计风险态势。仿真结果表明,三重防护机制在降低LNG动力船营运风险态势方面具有因子诱导的针对性,在防漏因子、止漏因子和治漏因子诱导下LNG动力船分别趋向一般风险状态、较高风险状态和高风险状态。基于复杂性控制系统作用模式的风险态势分析可为LNG动力船营运的安全控制和风险管理提供管控模式和手段。

基于乘积HMM的双模态语音识别方法

针对噪声环境中的语音识别,提出一种用于双模态语音识别的乘积隐马尔可夫模型(HMM)。在独立训练音频HMM和视频HMM的基础上,建立二维训练模型,表征音频流和视频流之间的异步特性。引入权重系数,根据不同噪声环境自适应调整音频流与视频流的权重。实验结果证明,与其他双模态语音识别方法相比,该方法的识别性能更高。

SVM+BiHMM:基于统计方法的元数据抽取混合模型

提出了一种SVM+BiHMM的混合元数据自动抽取方法.该方法基于SVM(support vector machine)和二元HMM(bigram HMM(hidden Markov model),简称BiHMM)理论.二元HMM模型BiHMM在保持模型结构不变的前提下,通过区分首发概率和状态内部发射概率,修改了HMM发射概率计算模型.在SVM+BiHMM复合模型中,首先根据规则把论文粗分为论文头、正文以及引文部分,然后建立SVM模型把文本块划分为元数据子类,接着采用Sigmoid双弯曲函数把SVM分类结果用于拟合调整BiHMM模型的单词发射概率,最后用复合模型进行元数据抽取.SVM方法有效考虑了块间联系,BiHMM模型充分考虑了单词在状态内部的位置信息,二者的元数据抽取结果得到了很好的互补和修正,实验评测结果表明,SVM+BiHMM算法的抽取效果优于其他方法.

基于HMM方法的银行票据自动识别

利用隐态马尔可夫模型 (HMMs) ,对银行票据中金额的大小写数据识别问题进行了研究 主要内容包括建立新颖的文字分割算法 ;设计HMM训练和识别算法 在HMM系统中 ,将使用频率比较高的手写体错别字和同音字作为不同的字符类来处理 ;同时在HMM的训练过程中 ,提出了平滑参数的新方法 实验结果表明 ,该方法在实践中是可行的 。

基于SVM-HMM混合模型的说话人确认

提出一个文本无关的说话人确认的算法 .该算法将支持向量机 (SVM)的输出通过 Sigm oid函数和高斯模型转化为概率 ,并作为隐式马尔可夫模型 (HMM)中各个隐状态的输出概率 .由于 HMM适于处理连续信号 ,SVM适于处理分类问题 ;同时 ,HMM更多地表达了类别内部的相似性 ,而 SVM则很大程度上反映了类别间的差异 ,因而根据两者不同的侧重点 ,使其组合获得了很好的效果 .

基于ICA与HMM的表情识别

独立分量分析(independent component analysis,ICA)是一种盲源分离的有效方法,为了进一步有效提取表情图像中隐藏的信息和提高表情识别率,可将它应用于人脸表情识别。由于脸部表情为人类情感、认知过程的研究提供了极为重要的测量依据,因此表情特征的提取和特征序列所代表的表情状态是表情识别过程中的重要步骤。为了更好地进行表情和情感的分类,提出了一种ICA结合隐马尔可夫模型(HMM)识别表情的情感分类系统,该系统首先利用ICA算法进行表情特征提取,为了加快特征提取的速度,这里采用了FastICA算法;然后通过7个训练好的HMM进行表情识别。实验结果显示,该系统使人脸表情识别的整体效果有了提高,取得了令人满意的效果,可以用来识别人脸表情。

基于小波域HMM模型的稳健多比特图像水印算法

稳健性是多比特图像水印的关键问题之一,提出了一种基于小波域隐马尔可夫模型(hiddenMarkovmodel,简称HMM)的多比特图像水印算法,该算法的主要特点为:(1)利用向量HMM模型精确描述图像小波系数间的统计特性,基于此统计模型的水印盲检测系统较之传统的相关检测器,在性能上有显著的提升;(2)结合视觉掩盖特性,自适应地调整水印嵌入强度,使之在一定的嵌入强度下,视觉主观失真较小;(3)提出了一种适合隐马尔可夫模型树型结构的多比特数据优化嵌入策略和最大似然检测.数值仿真结果表明,该算法可以较好地利用图像小波域的低频子带以实现较大容量图像水印的嵌入,并在抵抗Stirmark平台攻击,如JPEG压缩、加噪、中值滤波和线性滤波等方面具有很强的稳健性.

基于HMM的列车轨道占用自动识别算法研究

在列车运行控制系统中,及时准确地了解列车所在位置事关列车运行安全。在车站,列控系统需要准确了解列车所在股道,以控制两列列车在车站交会或越行。由于车站股道密集,单纯依靠卫星定位系统(GNSS)确定列车所在的股道有较大困难。隐马尔可夫模型(HMM)是广泛应用于语音处理的一种时间序列统计模型,本文将HMM应用到列车股道占用自动识别中,对列车运行轨迹建立HMM,解决了卫星定位系统用于列车定位时列车占用股道的识别问题。对于HMM状态个数、卫星定位输出频率与列车运行速度对识别的影响等做了进一步的研究,得出优化参数。