一种基于隐Markov模型的异常检测技术

给出了一种建立隐Markov异常检测模型的算法,并从序列支持度分析、序列预测两个方面研究了该模型在异常检测中的应用,通过实验,分析了影响这一检测方法效果和效率的因素。实验表明,该方法能在不需要任何安全方面背景知识的情况下,有效地检测出入侵行为。     

Bayesian event detection for sport games with hidden Markov model

Event detection can be defined as the problem of detecting when a target event has occurred, from a given data sequence. Such an event detection problem can be found in many fields in science and engineering, such as signal processing, pattern recognition, and image processing. In recent years, many data sequences used in these fields, especially in video data analysis, tend to be high dimensional. In this paper, we propose a novel event detection method for high-dimensional data sequences in soccer video analysis. The proposed method assumes a Bayesian hidden Markov model with hyperparameter learning in addition to the parameter leaning. This is in an attempt to reduce undesired influences from ineffective components within the high-dimensional data. Implemention is performed by Markov Chain Monte Carlo. The proposed method was tested against an event detection problem with sequences of 40-dimensional feature values extracted from real professional soccer games. The algorithm appears functional.     

隐马尔可夫模型的多序列比对研究

研究一种关于隐马尔可夫模型的多序列比对,利用值和特征序列的保守性,通过增加频率因子,改进传统隐马尔可夫模型算法的不足。实验表明,新算法不但提高了模型的稳定性,而且应用于蛋白质家族识别,平均识别率比传统隐马尔可夫算法提高了3.3个百分点。     

基于隐马尔可夫模型的语音激活检测算法

针对现有基于隐马尔可夫模型（HMM）的语音激活检测（VAD）算法对噪声的跟踪性能不佳的问题,提出采用Baum-Welch算法对具有不同特性的噪声进行训练,并生成相应噪声模型,建立噪声库的方法。在语音激活检测时,根据待测语音背景噪声的不同,动态地匹配噪声库中的噪声模型;同时,为了适应语音信号的实时处理,降低了语音参数提取的复杂度,并对判决阈值提出改进,以保证语音信号帧间的相关性。在不同噪声环境下对改进算法进行性能测试并与自适应多速率编码（AMR）标准、国际电信联盟电信标准分局（ITU-T）的G.729B标准比较,测试结果表明,改进算法在实时语音信号处理中能够有效提高检测的准确率及噪声跟踪能力。     

基于隐马尔可夫模型的视频异常场景检测

视频技术的广泛应用带来海量的视频数据,仅依靠人力对监控视频中的异常进行检测是不太可能的。异常行为的自动化检测在公共安全等领域的地位极其重要。提出一种综合考虑目标特性和时空上下文的异常检测方法,该方法利用光流纹理图描述移动物体的刚性特征,建立基于隐马尔可夫模型HMM的时间上下文异常检测模型。在此基础上,提取异常目标的Radon特征,以支持向量机SVM的异常预分类结果为基础,通过HMM建立异常场景的空间上下文分类模型。该模型在公共数据集UCSD PED2上进行了实验验证,结果表明,本算法不仅在异常检测方面优于已有算法,而且还能给出异常分类。     

Hybrid fire detection using hidden Markov model and luminance map

Recently, fire detection is a hot research topic. Although many detection methods have been proposed, there exist high false alarms because of the interference of fire-colored moving object in the complex environments. In this paper, a hybrid method is proposed. First, we get the set of candidate fire regions. Then these candidate fire regions are analyzed to exclude the fire-colored moving object. Our contributions are using the hidden Markov model (HMM) based on spatio-temporal feature and the variance of luminance map motivated by visual attention, and combining both for fire detection. The wrong detection can be reduced greatly. Experiment results show our proposed method has a good performance and it is robust to be used in complex environment compared with previous algorithms.     

基于隐马尔可夫模型的内存碎片检测机制

当前VxWorks操作系统缺少内存碎片的检测机制。通过增加内存统计信息,基于隐马尔可夫模型的检测程序分析出系统中哪些任务可能是造成内存碎片的根源。软件开发人员根据分析结果对可能造成内存碎片的代码进行优化,且优化前后的分析数据表明内存碎片问题得到了有效的改善,可满足嵌入式设备减少内存碎片的需求。     

基于隐马尔科夫模型的J波自动识别检测

J波检测在临床上可以作为判定某些心脏病的一种非创性的标记手段。主要定义了5个精确反映J波特性的特征向量,包括3个时域特征向量和两个基于小波的特征向量,并使用主成分分析减少特征向量的维数,作为分类器的输入。利用这些特征向量训练隐马尔可夫模型作为分类器,输出最终的判定结果。结果表明,提出的方法提供了93.8%的平均准确度、94.2%的平均敏感性、93.3%的平均特异性和93.4%的平均阳性预测值,揭示了很高的评价标准,表明该方法有能力准确地检测识别J波,并且可以利用该方法检测心电图中的其他病变波形。     

Improved detection algorithm for copy number variations based on hidden Markov model

Multimedia Tools and Applications - Aiming at the problems of parameter optimization and insufficient utilization of split reads in the detection for copy number variation (CNV), a new definition...     

基于隐马尔可夫模型的内部威胁检测方法

提出了一种基于隐马尔可夫模型的内部威胁检测方法.针对隐马尔可夫模型评估问题的解法在实际应用中存在利用滑动窗口将观测事件序列经过放大处理导致误报率偏高的缺陷,在Windows平台上设计并实现了一个基于系统调用的内部威胁检测原型系统,利用截获Windows Native API的方法,通过程序行为的正常轮廓库来检测程序异常行为模式.实验结果表明,新方法以程序的内在运行状态作为处理对象,正常轮廓库较小,克服了传统评估方法因P(O|λ)值太小而无法有效区分正常与异常的问题,检测性能更好.     

基于动态内部主元分析和隐马尔科夫模型的动态过程故障检测与分类方法

随着工业生产过程的扩大,保证生产过程的安全平稳高效运行日益受到重视.因此,对工业过程进行及时有效的监测与故障诊断具有重要意义.一般而言,工业过程采集的数据具有较强的动态性,有效提取数据中的动态信息并进行分析极其重要.本文基于动态内部主元分析(DiPCA)进行动态性分析并结合隐马尔科夫模型(HMM),提出了一种新的故障诊断框架,实现了动态过程故障检测与故障分类.首先,利用DiPCA算法提取正常工况下数据的动态特征;然后,利用HMM能够有效处理时序数据的特点,对所提取的动态特征进行建模,构建了动态过程的故障检测框架;并利用HMM强大的模式分类能力,对故障数据进行建模,实现故障的分类;最后,将提出的方法用于田纳西–伊斯曼过程,验证了该方法的有效性与优越性.     

A modified hidden Markov model

This paper considers two discrete time, finite state processes X$X$ and Y$Y$. In the usual hidden Markov model X$X$ modulates the values of Y$Y$. However, the values of Y$Y$ are then i.i.d. given X$X$. In this paper a new model is considered where the Markov chain X$X$ modulates the transition probabilities of the second, observed chain Y$Y$. This more realistically can represent problems arising in DNA sequencing. Algorithms for all related filters, smoothers and parameter estimations are derived. Versions of the Viterbi algorithms are obtained.     

基于模糊滑窗隐马尔可夫模型的入侵检测研究

针对传统基于隐马尔可夫模型（HMM）入侵检测中普遍存在误报与漏报过高的问题,提出了一种基于模糊窗口隐马尔可夫模型（FWHMM）的入侵检测新方法。该方法通过运用状态转移依赖滑窗的设置提高了系统的检测精度,通过将状态的随机转移转变为模糊随机转移,提高了系统的鲁棒性和自适应性。实验结果表明,使用本文方法的检测效果要明显优于基于经典HMM的方法。     

Exploring the state sequence space for hidden Markov and semi-Markov chains

The knowledge of the state sequences that explain a given observed sequence for a known hidden Markovian model is the basis of various methods that may be divided into three categories: (i) enumeration of state sequences; (ii) summary of the possible state sequences in state profiles; (iii) computation of a global measure of the state sequence uncertainty. Concerning the first category, the generalized Viterbi algorithm for computing the top L most probable state sequences and the forward-backward algorithm for sampling state sequences are derived for hidden semi-Markov chains and hidden hybrid models combining Markovian and semi-Markovian states. Concerning the second category, a new type of state (and state change) profiles is proposed. The Viterbi forward-backward algorithm for computing these state profiles is derived for hidden semi-Markov chains and hidden hybrid models combining Markovian and semi-Markovian states. Concerning the third category, an algorithm for computing the entropy of the state sequence that explains an observed sequence is proposed. The complementarity and properties of these methods for exploring the state sequence space (including the classical state profiles computed by the forward-backward algorithm) are investigated and illustrated with examples.     

A probabilistic model for state sequence analysis in hidden Markov model for hand gesture recognition

The role of gesture recognition is significant in areas like human‐computer interaction, sign language, virtual reality, machine vision, etc. Among various gestures of the human body, hand gestures play a major role to communicate nonverbally with the computer. As the hand gesture is a continuous pattern with respect to time, the hidden Markov model (HMM) is found to be the most suitable pattern recognition tool, which can be modeled using the hand gesture parameters. The HMM considers the speeded up robust feature features of hand gesture and uses them to train and test the system. Conventionally, the Viterbi algorithm has been used for training process in HMM by discovering the shortest decoded path in the state diagram. The recursiveness of the Viterbi algorithm leads to computational complexity during the execution process. In order to reduce the complexity, the state sequence analysis approach is proposed for training the hand gesture model, which provides a better recognition rate and accuracy than that of the Viterbi algorithm. The performance of the proposed approach is explored in the context of pattern recognition with the Cambridge hand gesture data set.     

Sentiment analysis incorporating convolutional neural network into hidden Markov model

The analysis of sentiments and mining of opinions have become more and more important in years because of the development of social media technologies. The methods that utilize natural language processing and lexicon-based sentiment analysis techniques to analyze people's opinions in texts require the proper extraction of sentiment words to ensure accuracy. The current issue is tackled with a novel perspective in this paper by introducing a hybrid sentiment analysis technique. This technique brings together Convolutional Neural Network (CNN) and Hidden Markov Models (HMMs), to accurately categorize text data and pinpoint feelings. The proposed method involves 1D convolutional-layer CNN to extract hidden features from comments and applying HMMs on a feature-sentence matrix, allowing for the utilization of word sequences in extracting opinions. The method effectively captures diverse text patterns by extracting a range of features from texts using CNN. Text patterns are learned using text HMM by calculating the probabilities between sequences of feature vectors and clustering feature vectors. The paper's experimental evaluation employs benchmark datasets such as CR, MR, Subj, and SST2, demonstrating that the proposed method surpasses existing sentiment analysis techniques and traditional HMMs. One of its strengths is to analyze a range of text patterns and identify crucial features that recognize the emotion of different pieces of a sentence. Additionally, the research findings highlight the improved performance of sentiment analysis tasks through the strategic use of zero padding in conjunction with the masking technique.     

基于隐马尔可夫模型的股票价格预测组合模型

提出了一种用于股票价格预测的人工神经网络(ANN),隐马尔可夫模型(HMM)和粒子群优化算法(PSO)的组合模型-APHMM模型.在APHMM模型中,ANN算法将股票的每日开盘价、最高价、最低价与收盘价转换为相互独立的量并作为HMM的输入.然后,利用PSO算法对HMM的参数初始值进行优化,并用Baum-Welch算法进行参数训练.经过训练后的HMM在历史数据中找出一组与今天股票的上述4个指标模式最相似数据,加权平均计算每个数据与它后一天的收盘价格差,则今天的股票收盘价加上这个加权平均价格差便为预测的股票收盘价.实验结果表明,APHMM模型具有良好的预测性能.     

Profile hidden Markov models and metamorphic virus detection

Metamorphic computer viruses “mutate” by changing their internal structure and, consequently, different instances of the same virus may not exhibit a common signature. With the advent of construction kits, it is easy to generate metamorphic strains of a given virus. In contrast to standard hidden Markov models (HMMs), profile hidden Markov models (PHMMs) explicitly account for positional information. In principle, this positional information could yield stronger models for virus detection. However, there are many practical difficulties that arise when using PHMMs, as compared to standard HMMs. PHMMs are widely used in bioinformatics. For example, PHMMs are the most effective tool yet developed for finding family related DNA sequences. In this paper, we consider the utility of PHMMs for detecting metamorphic virus variants generated from virus construction kits. PHMMs are generated for each construction kit under consideration and the resulting models are used to score virus and non-virus files. Our results are encouraging, but several problems must be resolved for the technique to be truly practical.     

Zero knowledge hidden Markov model inference

Hidden Markov models (HMMs) are widely used in pattern recognition. HMM construction requires an initial model structure that is used as a starting point to estimate the model’s parameters. To construct a HMM without a priori knowledge of the structure, we use an approach developed by Crutchfield and Shalizi that requires only a sequence of observations and a maximum data window size. Values of the maximum data window size that are too small result in incorrect models being constructed. Values that are too large reduce the number of data samples that can be considered and exponentially increase the algorithm’s computational complexity. In this paper, we present a method for automatically inferring this parameter directly from training data as part of the model construction process. We present theoretical and experimental results that confirm the utility of the proposed extension.     

A reservoir-driven non-stationary hidden Markov model

In this work, we propose a novel approach towards sequential data modeling that leverages the strengths of hidden Markov models and echo-state networks (ESNs) in the context of non-parametric Bayesian inference approaches. We introduce a non-stationary hidden Markov model, the time-dependent state transition probabilities of which are driven by a high-dimensional signal that encodes the whole history of the modeled observations, namely the state vector of a postulated observations-driven ESN reservoir. We derive an efficient inference algorithm for our model under the variational Bayesian paradigm, and we examine the efficacy of our approach considering a number of sequential data modeling applications.