Failure Event Prediction Using Hidden Markov Model Approaches

Hidden Markov Model(HMM) has a doubly embedded stochastic process with an underlying stochastic process that can be observed through another set of stochastic processes. This structure of HMM is useful for modeling vector sequence that doesn't look like a stochastic process but has a hidden stochastic process. So, HMM approach has become popular in various areas in last decade. The increasing popularity of HMM is based on two facts : rich mathematical structure and proven accuracy on critical application. In this paper, we applied continuous HMM (CHMM) approach with AR coefficient to detect and predict the chatter of lathe bite and to diagnose the wear of oil Journal bearing using rotor shaft displacement. Our examples show that CHMM approach is very efficient method for machine health monitoring and prediction.

We present a path planning framework that takes into account the human's safety perception in the presence of a flying robot. The framework addresses two objectives: (i) estimation of the uncertain parameters of the proposed safety perception model based on test data collected using Virtual Reality (VR) testbed, and (ii) offline optimal control computation using the estimated safety perception model. Due to the unknown factors in the human tests data, it is not suitable to use standard regression techniques that minimize the mean squared error (MSE). We propose to use a Hidden Markov model (HMM) approach where human's attention is considered as a hidden state to infer whether the data samples are relevant to learn the safety perception model. The HMM approach improved log-likelihood over the standard least squares solution. For path planning, we use Bernstein polynomials for discretization, as the resulting path remains within the convex hull of the control points, providing guarantees for deconfliction with obstacles at low computational cost. An example of optimal trajectory generation using the learned human model is presented. The optimal trajectory generated using the proposed model results in reasonable safety distance from the human. In contrast, the paths generated using the standard regression model have undesirable shapes due to overfitting. The example demonstrates that the HMM approach has robustness to the unknown factors compared to the standard MSE model.

This paper introduces two unsupervised learning methods for analyzing functional magnetic resonance imaging (fMRI) data based on hidden Markov model (HMM). HMM approach is focused on capturing the first-order statistical evolution among the samples of a voxel time series, and it can provide a complimentary perspective of the BOLD signals. Two-state HMM is created for each voxel, and the model parameters are estimated from the voxel time series and the stimulus paradigm. Two different activation detection methods are presented in this paper. The first method is based on the likelihood and likelihood-ratio test, in which an additional Gaussian model is used to enhance the contrast of the HMM likelihood map. The second method is based on certain distance measures between the two state distributions, in which the most likely HMM state sequence is estimated through the Viterbi algorithm. The distance between the on-state and off-state distributions is measured either through a t-test, or using the Kullback-Leibler distance (KLD). Experimental results on both normal subject and brain tumor subject are presented. HMM approach appears to be more robust in detecting the supplemental active voxels comparing with SPM, especially for brain tumor subject.

Masquerade detection is the ability to detect attackers known as masqueraders that intrude on another user’s system and pose as legitimate users. Once a masquerader obtains access to a user’s system, the masquerader has free reign over whatever data is on that system. In this research, we focus on masquerade detection and user classi cation using the following two di erent approaches: the heavy hitter approach and 2 di erent approaches based on hidden Markov models (HMMs), the dueling-HMM and threshold-HMM strategies. The heavy hitter approach computes the frequent elements seen in the training data sequence and test data sequence and computes the distance to see whether the test data sequence is masqueraded or not. The results show very misleading classi cations, suggesting that the approach is not viable for masquerade detection. A hidden Markov model is a tool for representing probability distributions over sequences of observations [9]. Previous research has shown that using a threshold-based hidden Markov model (HMM) approach is successful in a variety of categories: malware detection, intrusion detection, pattern recognition, etc. We have veri ed that using a threshold-based HMM approach produces high accuracy with low amounts of a false positives. Using the dueling- HMM approach, which utilizes multiple training HMMs, we obtain an overall accuracy of 81.96%. With the introduction of the bias in the dueling-HMM approach, we produce similar results to the results obtained in the threshold-based HMM approach, where we see many non-masqueraded data detected, while many masqueraded data avoid detection, yet still result in an high overall accuracy.

Sleep quantity affects an individual's personal health. The gold standard of measuring sleep and diagnosing sleep disorders is Polysomnography (PSG). Although PSG is accurate, it is expensive and it lacks portability. A number of wearable devices with embedded sensors have emerged in the recent past as an alternative to PSG for regular sleep monitoring directly by the user. These devices are intrusive and cause discomfort besides being expensive. In this work, we present an algorithm to detect sleep using a smartphone with the help of its inbuilt accelerometer sensor. We present three different approaches to classify raw acceleration data into two states - Sleep and Wake. In the first approach, we take an equation from Kushida's algorithm to process accelerometer data. Henceforth, we call it Kushida's equation. While the second is based on statistical functions, the third is based on Hidden Markov Model (HMM) training. Although all the three approaches are suitable for a phone's resources, each approach demands different amount of resources. While Kushida's equation-based approach demands the least, the HMM training-based approach demands the maximum. We collected data from mobile phone's accelerometer for four subjects for twelve days each. We compare accuracy of sleep detection using each of the three approaches with that of Zeo sensor, which is based on Electroencephalogram (EEG) sensor to detect sleep. EEG is an important modality in PSG. We find that HMM training-based approach is as much as 84% accurate. It is 15% more accurate as compared to Kushida's equation-based approach and 10% more accurate as compared to statistical method-based approach. In order to concisely represent the sleep quality of people, we model their sleep data using HMM. We present an analysis to find out a tradeoff between the amount of training data and the accuracy provided in the modeling of sleep. We find that six days of sleep data is sufficient for accurate modeling. We compare accuracy of our HMM training-based algorithm with a representative third party app SleepTime available from Google Play Store for Android. We find that the detection done using HMM approach is closer to that done by Zeo by 13% as compared to the third party Android application SleepTime. We show that our HMM training-based approach is efficient as it takes less than ten seconds to get executed on Moto G Android phone.

This paper describes an automatic text‐ and speaker‐independent language identification method based on hidden Markov models (HMMs) for acoustic features. The hidden Markov modeling is used to represent the phonotactics for each language. Each language has its proper phonotactics.The HMM topology here is a fully structured (ergodic) model wherein any state could transit to all states. Two kinds of HMMs are used: the discrete HMM (DHMM) with the codebook and the continuous density HMM (CHMM). The HMM was trained using both the Baum‐Welch (forward‐backward) algorithm and the Viterbi algorithm. The latter was used for emphasizing the state transition probability.For comparison, experiments also were conducted on the identification using a mixtured Gaussian distribution model with one state. This single‐state Gaussian distribution model gave the same performance as the HMM trained with the Baum‐Welch algorithm. This is because the transition between states which reflects the characteristics of each language does not affect the likelihood scores very much. This problem was addressed by emphasizing the transition probabilities and using the Viterbi algorithm, which resulted in an improvement in the recognition rates. The trigram for optimal state sequence is introduced. Combining it with the HMM produced the best results.

Systems and methods for automatically segmenting speech inventories. A set of Hidden Markov Models (HMMs) are initialized using bootstrap data. The HMMs are next re-estimated and aligned to produce phone labels. The phone boundaries of the phone labels are then corrected using spectral boundary correction. Optionally, this process of using the spectral-boundary-corrected phone labels as input instead of the bootstrap data is performed iteratively in order to further reduce mismatches between manual labels and phone labels assigned by the HMM approach.

In this chapter we introduce an original approach to predict a day-ahead energy demand, based on machine learning and pattern recognition. Our Hidden Markov Model (HMM) is a simple and explainable model that uses integer sequences to define emission probability distributions attached to states. We develop a Mathematica code which relies on the maximum likelihood principle in HMM environment. Based on these exploratory results, we conclude that the HMM approach is an efficient way in modeling short-term/day-ahead energy demand prediction, especially during peak period(s) and in accounting for the inherent stochastic nature of demand conditions. The model can be easily extended to predict energy demand values for more than one day in the future. However, the accuracy of such predictions would decrease as we expect.

An experiment is described to investigate the application of hidden Markov modeling (HMM) to QRS detection. Previously reported detection algorithms employ decision rule stages which require specification and adjustment of numerous parameters for adequate detection accuracy. The hidden Markov modeling approach provides automatic estimation of all parameters in the decision rule stage from each ECG file undergoing analysis. Preliminary results using a simple HMM detector show performance comparable to that of a multiparameter threshold based decision rule scheme.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A Hidden Markov Model to Detect On-Shelf Out-of-Stocks Using Point of Sales Data

Virus writers and anti-virus researches generally agree that metamorphism is the way to generate undetectable viruses. Several virus writers have released virus creation kits and claimed that they possess the ability to automatically produce morphed virus variants that look substantially different from one another. To see how effective these code morphing engines are, and how much difference exists between variants of a same virus, we measured the similarity between virus variants generated by four virus generators downloaded from the Internet. Our result shows that the effectiveness of these generators varies widely. While the best generator, NGVCK, is able to create viruses that share only a few percent of similarities, the other generators produce viruses that are over 60% similar, on average. In addition, our similarity graphs show that some of these variant pairs have long segments of identical assembly opcodes at identical positions of the virus files. Compared to random utility files which have a similarity of about 35%, we see that some of the virus creation kits are not very effective. To detect metamorphic virus variants, we experimented with the use of hidden Markov models (HMMs) to capture the statistical properties of viruses in the same family. We generated 200 NGVCK viruses, trained 25 models and used the trained models to classify 65 programs including both NGVCK viruses and other random non-viral programs. For seven of our models we were able to perfectly distinguish the two types of files by their scores. The other cases produced different number of false positives and false negatives, depending on the threshold used in the classifying process. In most cases, our models were able to have a detection rate of over 90% and a false positive rate of less than 10%. The number of states N of a model does not seem to have much impact on the performance of the HMM. We saw only small differences in the performance measures for models with N from 3 to 6. If the variants of a metamorphic virus are sufficiently different that signature-based scanning cannot detect a newly morphed variant, the HMM approach provides a feasible solution. As with any statistical detection method, false predictions are possible. We showed the tradeoff between the detection rate and false positive rate.

A continuous-parameter hidden Markov model (HMM) is proposed and compared with a discrete-parameter HMM, a time delay neural network (TDNN), and LVQ2 by using the same training and testing database. It is found that the proposed model's performance is comparable to that of TDNN or LVQ2. Higher performance (96 approximately 97%) is obtained for all Japanese phonemes in isolated words. The HMM approach is superior to others for the recognition of time-sequential patterns like continuous speech.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A hybrid approach of NN and HMM for facial emotion classification

Using IDE usage data to analyze the behavior of software developers in the field, during the course of their daily work, can lend support to (or dispute) laboratory studies of devel- opers. This paper describes a technique that leverages Hidden Markov Models (HMMs) as a means of mining high-level developer behavior from low-level IDE interaction traces of many developers in the field. HMMs use dual stochastic processes to model higher-level hidden behavior using observable input sequences of events. We propose an interactive approach of mining interpretable HMMs, based on guiding a human expert in building a high quality HMM in an iterative, one state at a time, manner. The final result is a model that is both representative of the field data and captures the field phenomena of interest. We apply our HMM construction approach to study debugging behavior, using a large IDE interaction dataset collected from nearly 200 developers at ABB, Inc. Our results highlight the different modes and constituent actions in debugging, exhibited by the developers in our dataset.

BackgroundMigraine is a common neurological disorder involving network-level dysfunction. Increasing evidence suggests that migraine involves network-level dysfunction and is associated with altered resting-state functional connectivity. Traditional static functional connectivity analyses are limited in capturing the temporal dynamics of large-scale brain networks. The Hidden Markov Model (HMM) provides an advanced analytical framework to identify discrete, recurrent brain states and characterize their temporal properties without the constraints of arbitrary windowing assumptions.ObjectiveTo characterize dynamic functional connectivity alterations in young patients with migraine without aura (MWoA) using HMM and examine associations between dynamic state metrics and clinical disability.MethodsResting-state fMRI data were obtained from 200 participants (100 young MWoA patients and 100 matched healthy controls). Using the Dosenbach 160 ROI template (cerebellum excluded; N = 142), HMM identified recurring brain states. Group differences in fractional occupancy (FO), mean dwell time (MDT), and state transition probabilities were assessed. State-specific functional connectivity patterns were compared, and correlations with clinical indices (MIDAS, VAS, HIT-6) were evaluated.ResultsEleven robust dynamic brain states were identified. Compared with controls, migraine patients demonstrated increased FO and MDT in States 7 (dorsal attention network–dominant) and 9 (multisensory integration), alongside reduced values in sensorimotor states (States 3, 4, 8, 11). State 9 exhibited significant abnormalities in DMN–SC and DMN–VAN connectivity (FDR-corrected q < 0.05). Transition analyses revealed enhanced self-transitions and increased incoming transitions to States 7 and 9, whereas transitions among sensorimotor states were diminished. MDT in State 9 was positively correlated with MIDAS scores (r = 0.38, p < 0.05), indicating its association with functional disability.ConclusionsYoung MWoA patients exhibit a dual-mode dysfunction in brain dynamics: heightened external vigilance (State 7) and impaired segregation of internal–external processing (State 9), accompanied by instability in baseline sensorimotor configurations. Prolonged dwelling in State 9 and its correlation with disability highlight this multisensory integration state as a potential biomarker of migraine-related functional impairment. These findings provide new insights into neurobiological mechanisms and support dynamic network–based therapeutic strategies.