Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models

This study examines vishing, a form of social engineering scam using voice communication to deceive individuals into revealing sensitive information or losing money. With the rise of smartphone usage, people are more susceptible to vishing attacks. The proposed Emoti-Shing model analyzes potential victims’ emotions using Hidden Markov Models to track vishing scams by examining the emotional content of phone call audio conversations. This approach aims to detect vishing scams using biological features of humans, specifically emotions, which cannot be easily masked or spoofed. Experimental results on 30 generated emotions indicate the potential for increased vishing scam detection through this approach.

Morpho-Syntactic Tagging of Text in “Baoule” Language Based on Hidden Markov Models (HMM)

The label text is a very important tool for the automatic processing of language. It is used in several applications such as morphological and syntactic text analysis, index-ing, retrieval, finished networks deterministic (in which all combinations of words that are accepted by the grammar are listed) or by statistical grammars (e.g., an n-gram in which the probabilities of sequences of n words in a specific order are given), etc. In this article, we developed a morphosyntactic labeling system language “Baoule” using hidden Markov models. This will allow us to build a tagged reference corpus and rep-resent major grammatical rules faced “Baoule” language in general. To estimate the parameters of this model, we used a training corpus manually labeled using a set of morpho-syntactic labels. We then proceed to an improvement of the system through the re-estimation procedure parameters of this model.

Ontology mapping based on hidden Markov model

The existing ontology mapping methods mainly consider the structure of the ontology and the mapping precision is lower to some extent. According to statistical theory, a method which is based on the hidden Markov model is presented to establish ontology mapping. This method considers concepts as models, and attributes, relations, hierarchies, siblings and rules of the concepts as the states of the HMM, respectively. The models corresponding to the concepts are built by virtue of learning many training instances. On the basis of the best state sequence that is decided by the Viterbi algorithm and corresponding to the instance, mapping between the concepts can be established by maximum likelihood estimation. Experimental results show that this method can improve the precision of heterogeneous ontology mapping effectively.

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。

Optimal state and branch sequence based parameter estimation of continuous hidden Markov model

A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.

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DHMM-ESUM的露天矿运输系统车铲比优化研究

在露天矿生产中,如何利用现有的资源条件对电铲与卡车进行合理配比,充分发挥设备的效率,是提高矿山企业生产效益的关键。以安家岭露天矿生产系统收集的数据为基础,首先运用排队理论构建以电铲为采掘中心,卡车为运输工具的闭合排队网络模型,将运输系统分为4个排队子系统,分析各项服务时间的概率分布;基于离散隐马尔可夫模型(discrete hidden Markov model,DHMM)与拓展求和(extended summation,ESUM)算法分析系统作业性能,获取电铲设备的运行状态和班次生产能力。然后,应用蒙特卡洛法建立随机动态规划模型,以产量期望值最大化为目标对露天矿的车铲比进行优化。最后,对生产系统进行仿真建模分析,结果验证了基于DHMM-ESUM对车铲比进行优化的有效性和准确性。

基于HMM-MLP的泵站监测健康诊断系统研究

为实现泵站工程在生产运行过程中有效预测设备潜在故障风险,提升泵站设备运行效率,在数字孪生水利工程数据底板基础上,基于现有硬件设备,以结构故障机理为导向,提出了一种HMM-MLP的泵站设备故障预测方法。先由连续小波包变换处理设备的运行信号,然后通过HMM模型生成设备运行状态序列作为MLP网络的输入预测设备故障,最后通过仿真实验表明,HMM-MLP模型可有效提高泵站设备故障的预测准确率。同时,依托在线监测数据和离线检查与试验数据,建立了设备健康评价指标体系,并开发了泵站监测健康诊断系统,协助运行管理人员充分了解和掌握机组设备的“健康”状态,提升设备管理的信息化水平。结果表明:该研究可为泵站健康系统建设提供实际案例指导与经验启示。

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。

基于简化HMM和时间分段的非侵入式负荷分解算法

针对现有非侵入式负荷分解算法需要以过去时刻的分解结果为依据,从而造成误差累积的问题,提出一种基于简化的隐马尔可夫模型和时间分段的非侵入式负荷分解算法,以实现居民家庭的负荷分解。对负荷的低频功率信号进行分层抽样和聚类分析,构建负荷功率模板并利用独热码对超状态进行编码表示。基于简化的隐马尔可夫模型和普遍生活规律对家庭用电时间段进行划分,在每个时间段内单独训练参数。结合总线数据和各时间段参数实现对各时刻负荷功率的独立求解。基于2种国外公开数据集的测试结果验证了所提算法的准确性和实时性。

On-line Fault Diagnosis in Industrial Processes Using Variable Moving Window and Hidden Markov Model

An integrated framework is presented to represent and classify process data for on-line identifying abnormal operating conditions. It is based on pattern recognition principles and consists of a feature extraction step, by which wavelet transform and principal component analysis are used to capture the inherent characteristics from process measurements, followed by a similarity assessment step using hidden Markov model (HMM) for pattern comparison. In most previous cases, a fixed-length moving window was employed to track dynamic data, and often failed to capture enough information for each fault and sometimes even deteriorated the diagnostic performance. A variable moving window, the length of which is modified with time, is introduced in this paper and case studies on the Tennessee Eastman process illustrate the potential of the proposed method.

Frame erasure concealment in wideband speech coding based on large hidden Markov model

Frame erasure concealment is studied to solve the problem of rapid speech quality reduction due to the loss of speech parameters during speech transmission. A large hidden Markov model is applied to model the immittance spectral frequency （ISF） parameters in AMR-WB codec to optimally estimate the lost ISFs based on the minimum mean square error （MMSE） rule. The estimated ISFs are weighted with the ones of their previous neighbors to smooth the speech, resulting in the actual concealed ISF vectors. They are used instead of the lost ISFs in the speech synthesis on the receiver. Comparison is made between the speech concealed by this algorithm and by Annex I of G. 722. 2 specification, and simulation shows that the proposed concealment algorithm can lead to better performance in terms of frequency-weighted spectral distortion and signal-to-noise ratio compared to the baseline method, with an increase of 2.41 dB in signal-to-noise ratio （SNR） and a reduction of 0. 885 dB in frequency-weighted spectral distortion.

Improved hidden Markov model for speech recognition and POS tagging

In order to overcome defects of the classical hidden Markov model (HMM), Markov family model (MFM), a new statistical model was proposed. Markov family model was applied to speech recognition and natural language processing. The speaker independently continuous speech recognition experiments and the part-of-speech tagging experiments show that Markov family model has higher performance than hidden Markov model. The precision is enhanced from 94.642% to 96.214% in the part-of-speech tagging experiments, and the work rate is reduced by 11.9% in the speech recognition experiments with respect to HMM baseline system.

An Intrusion Detection Method Based on Hierarchical Hidden Markov Models

This paper presents an anomaly detection approach to detect intrusions into computer systems. In this approach, a hierarchical hidden Markov model （HHMM） is used to represent a temporal profile of normal behavior in a computer system. The HHMM of the norm profile is learned from historic data of the system＇s normal behavior. The observed behavior of the system is analyzed to infer the probability that the HHMM of the norm profile supports the observed behavior. A low probability of support indicates an anomalous behavior that may result from intrusive activities. The model was implemented and tested on the UNIX system call sequences collected by the University of New Mexico group. The testing results showed that the model can clearly identify the anomaly activities and has a better performance than hidden Markov model.

Hidden Markov model based epileptic seizure detection using tunable Q wavelet transform

Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide.The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroencephalogram(EEG) as a noninvasive procedure to record neuronal activities in the brain.EEG signals' underlying dynamics are extracted to differentiate healthy and seizure EEG signals.Shannon entropy,collision entropy,transfer entropy,conditional probability,and Hjorth parameter features are extracted from subbands of tunable Q wavelet transform.Efficient decomposition level for different feature vector is selected using the Kruskal-Wallis test to achieve good classification.Different features are combined using the discriminant correlation analysis fusion technique to form a single fused feature vector.The accuracy of the proposed approach is higher for Q=2 and J=10.Transfer entropy is observed to be significant for different class combinations.Proposed approach achieved 100% accuracy in classifying healthy-seizure EEG signal using simple and robust features and hidden Markov model with less computation time.The proposed approach efficiency is evaluated in classifying seizure and non-seizure surface EEG signals.The system has achieved 96.87% accuracy in classifying surface seizure and nonseizure EEG segments using efficient features extracted from different J level.

FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

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.

Statistical Modeling with a Hidden Markov Tree and High-resolution Interpolation for Spaceborne Radar Reflectivity in the Wavelet Domain

With the increasing availability of precipitation radar data from space,enhancement of the resolution of spaceborne precipitation observations is important,particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients.In this paper,the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree(HMT)in the wavelet domain.Then,a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information.Owing to the small and transient storm elements embedded in the larger and slowly varying elements,the radar precipitation data exhibit distinct multiscale statistical properties,including a non-Gaussian structure and scale-to-scale dependency.An HMT model can capture well the statistical properties of radar precipitation,where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model(GMM),and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process.The state probabilities of the GMM are determined using the expectation maximization method,and other parameters,for instance,the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images.Using the prior model,the wavelet coefficients at finer scales are estimated using local Wiener filtering.The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite,and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.

Improving Language Translation Using the Hidden Markov Model

Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.

基于HMM的逆雷达辐射源状态识别推理方法

雷达对抗场景中雷达方和干扰方相互感知、识别以及博弈对抗.针对雷达方对干扰方系统内部状态的非合作识别推理的问题,提出了一种对干扰系统中雷达辐射源状态识别模块处理结果进行逆向估计的方法.建立了逆状态识别任务模型,任务中的干扰系统根据雷达工作状态识别结果对其干扰动作进行优化,雷达方则基于对干扰方干扰样式序列的观测,估计干扰方对雷达工作状态的识别结果;设计了基于隐马尔可夫模型(HMM)的逆状态识别任务求解方法,具体包括通过自适应粒子群算法进行模型参数初始化,采取多观测序列的鲍姆-韦尔奇算法进行模型参数估计,采用对数维特比算法估计干扰方的雷达状态识别结果;通过典型雷达对抗场景设定下的数字仿真验证了所给逆状态识别方法的可行性和有效性.