Hidden Markov Model Parameters Research Articles

Non-Intrusive Power Load Identification for High-Penetration Renewable Energy Grids

With the popularity of new energy grids with high penetration rate, classic non-intrusive power load identification algorithms such as hidden Markov model (HMM) need to face the uncertainty caused by new energy generation. It will cause the load state like active power to continue to change, and new state transitions appear during operation, resulting in the lack of robustness of state identification and power decomposition. Aiming to solve this problem, this study proposes and constructs a Gaussian mixture model–binary parameter hidden Markov model (GMM-BPHMM) which takes into account the randomness of new energy power supply, clusters the load status based on active power and steady-state current to reduce the possibility of volatile clustering results from the new energy grid under a high penetration rate, improves the Viterbi algorithm to take into account the updating HMM parameters to achieve the optimal prediction of the load state, considers the random volatility of load power brought by new energy grids, constructs a power calculation optimization model, and realizes the power decomposition of the load based on the principle of maximum likelihood estimation. Finally, on the basis of the public data set AMPds2, the study generated simulation data based on the new energy generation model and verified the method, and the test case verified the effectiveness of the method.

An Improved Hybrid Algorithm for Optimizing the Parameters of Hidden Markov Models

Hidden Markov Models (HMMs) have become increasingly popular in the last several years due to the fact that, the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Various algorithms have been proposed in literature for optimizing the parameters of these models to make them applicable in real-life. However, the performance of these algorithms has remained computationally challenging largely due to slow/premature convergence and their sensitivity to preliminary estimates. In this paper, a hybrid algorithm comprising the Particle Swarm Optimization (PSO), Baum-Welch (BW), and Genetic Algorithms (GA) is proposed and implemented for optimizing the parameters of HMMs. The algorithm not only overcomes the shortcomings of the slow convergence speed of the PSO but also helps the BW escape from local optimal solution whilst improving the performance of GA despite the increase in the search space. Detailed experimental results demonstrates the effectiveness of our proposed approach when compared to other techniques available in literature.

Hidden Markov Model energy conservation approach for continuous monitoring of vital signs in geriatric care applications

In the recent healthcare crisis engendered by the Covid19 pandemic, wireless body area networking devices have started to play a significant role in mitigating the health problems of the elderly. The energy conservation of the device during the temporary disconnection of the sensor node can play a vital role in the broader acceptance of this technology. Here, a probabilistic hidden Markov model (HMM) is used for energy conservation, a relatively less explored area of energy conservation approach within the field of wireless sensor networks. Since the vital signs of heart rate and blood pressure are highly correlated, the heart rate and blood pressure readings are taken for model development. The classification of normal and critical data is based on the probability of observation sequences in the particular model. The hidden states are estimated using the observation sequence and the HMM parameters during disconnection. Accuracy between 0.9 and 1.0 is obtained for different series. Dynamic threshold limits are included for more adaptability of the model for varying physiological conditions of the patients. The energy conservation possible using the model is discussed. This model presents a novel approach to energy conservation using HMM, which will help continuous home monitoring of vital parameters in geriatrics.

Human hand movement recognition using infinite hidden Markov model based sEMG classification

Hand movement recognition based on surface electromyography (sEMG) is challenging because sEMG signals are stochastic, noisy, and difficult to model and have limited datasets. This work improves the recognition accuracy using a small number of sEMG sequences and achieves the negative lag recognition for online applications. To handle the sEMG stochasticity, we applied the hidden Markov model (HMM) to decode the action primitives and characterise the transitions between them. On the challenging issue of selecting the number of hidden states, the parametric HMM was generalised to an infinite model by the hierarchical Dirichlet process, and a Gibbs sampling framework was implemented with the No-U-Turn sampler (NUTS) embedded to learn the parameters of the infinite HMM. For the recognition with negative lag, a generative classifier was built by all hand movement models and then was used with the online probability update. In the experiments of classifying 17 different hand movements, the proposed infinite hidden Markov model learned the number of states from data automatically and the generative classifier achieved an accuracy of 98.85%, 1.5% higher than that by parametric HMMs. The proposed recognition method can be applied to develop more reliable and efficient interfaces for prosthetics, rehabilitation, and robot teleoperation.

Modeling the received signal strength intensity of Wi-Fi signal using Hidden Markov Models

Wi-Fi fingerprinting is one of the methods that are widely used to provide Location Based Services (LBS). Gaussian, or a mixture of Gaussians, is the preferred model used by Wi-Fi fingerprinting for LBS. Nevertheless, Received Signal Strength Intensity (RSSI) Wi-Fi histograms are skewed, and a Gaussian model is not well suited for modeling data when their histogram is skewed. In addition, another important characteristic present in the RSSI Wi-Fi temporal series is autocorrelation, which cannot be modeled using a Gaussian model. In this paper, we explore the feasibility of using Hidden Markov Models (HMM) to model RSSI Wi-Fi signals. The mathematical derivation of formulas to calculate autocorrelation based on the HMM parameters is presented. Exhaustive experimentation, using data sampled in a real scenario, was performed to test the dependency of the autocorrelation coefficients on the number of hidden states, and the number of iterations used when creating the HMM. The results are compared with autocorrelation coefficients calculated using the real data. Kullback–Leibler (KL) divergence was used to compare the similarity of the real histograms and those provided by a mixture of Gaussians and by an HMM. HMM models reported more accurate results than a mixture of Gaussians model in both cases.

Genetic Algorithm Approach to Find the Estimated Value of HMM Parameters for NS5  Methyltransferase Protein

Dengue is the pandemic disease caused by Dengue virus (DENV), a mosquito-borne flavivirus. In recent years dengue has emerged as a foremost cause of severe illness and deaths in developing countries. About 400 million dengue infections occur worldwide each year. In general, dengue infections create only mild illness but infrequently expand into a lethal illness termed as severe dengue for which no specific treatment. The machine learning approach plays a significant role in bioinformatics and other fields of computer science. It exploits approaches like Hidden Markov Model (HMM), Genetic Algorithm (GA), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The GA is a randomized search algorithm for solving the problem based on natural selection phenomena. Many machine learning techniques are based on HMM have been positively applied. In this work, We firstly used HMM parameters on the biological sequence, and after that, we catch the probability of the observation sequence of a mutated gene sequence. This study compares both methods, G.A. and HMM, to get the highest estimated value of the observation sequence. In this paper, we also discuss the applications of GA in the bioinformatics field. In a further study, we will apply the other machine learning approaches to find the best result of protein studies. Funding Information: This work did not provide any source of financial support. Declaration of Interests: All the authors declare that there are no conflicts of interest regarding the publication of this research paper.

Isolated Word Recognition by Recursive HMM Parameter Estimation Algorithm

Automatic speech recognition (ASR) technologies enable humans to communicate with computers. Isolated word recognition (IWR) is an important part of many known ASR systems. Minimizing the word error rate in cases of incremental learning is a unique challenge for developing an on-line ASR system. This paper focuses on on-line IWR using a recursive hidden Markov model (HMM) multivariate parameter estimation algorithm. The maximum likelihood method was used to estimate the unknown parameters of the model, and an algorithm for the adapted recursive EM algorithm for HMMs parameter estimation was derived. The resulting recursive EM algorithm is unique among its counterparts because of state transition probabilities calculation. It obtains more accurate parameter estimates compared to other algorithms of this type. In our experiment, the algorithm was implemented and adapted to several datasets for IWR. Thus, the recognition rate and algorithm convergence results are discussed in this work.

MAP segmentation in Bayesian hidden Markov models: a case study

We consider the problem of estimating the maximum posterior probability (MAP) state sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the Bayesian setup, where both emission and transition matrices have Dirichlet priors. We study a training set consisting of thousands of protein alignment pairs. The training data is used to set the prior hyperparameters for Bayesian MAP segmentation. Since the Viterbi algorithm is not applicable any more, there is no simple procedure to find the MAP path, and several iterative algorithms are considered and compared. The main goal of the paper is to test the Bayesian setup against the frequentist one, where the parameters of HMM are estimated using the training data.

Disrupted brain dynamics across the Alzheimer’s disease spectrum is related to tau accumulation

AbstractBackgroundRecent evidence has implicated brain networks in the pathophysiology of Alzheimer’s disease (AD). However, the phenotypic variability in AD, inter‐individual differences in tau PET uptake patterns, and dynamic nature of functional connectivity have been relatively neglected. In this talk, I will discuss our recent work linking measures of dynamic brain organization to spatially independent tau PET patterns defined in a phenotypically diverse cohort.MethodsWe identified all Mayo Clinic participants with concurrent amyloid PET (PiB), tau PET (AV1451) and task‐free fMRI (impaired participants were all Aβ+). We used independent component analysis (ICA) to identify spatially independent patterns of tau PET uptake. For the fMRI data, we used a Hidden‐Markov Model (HMM) design to identify distinct patterns of activity/connectivity (‘states’). The ICA and HMM parameters were used to project/segment an independent cohort (ADNI) and subsequent longitudinal timepoints for the Mayo cohort. Multilevel Bayesian regression models were fit in Stan to explore the association between phenotype, component load and brain state.ResultsThe optimal HMM model identified 10 brain states (Figure 1). The optimal ICA decomposition identified 42 tau PET components, of which 13 were potentially related to AD pathobiology (Figure 1). Component loads and brain state dwell time differed between diagnostic groups in both Mayo and ADNI cohorts. One state was much more likely to occur in AD in both cohorts (State 8). Cross‐sectionally, State 8 dwell time was associated with higher tau loads, while higher baseline dwell‐time was predictive of ∆tau in participants with at least 2 timepoints (Figure 2).ConclusionWe found links between dynamics of functional connectivity, tau deposition, and cognitive impairment across the AD spectrum using two different cohorts and longitudinal data. Specifically: 1) our method for characterizing tau PET uptake was able to capture the magnitude and spatial pattern of binding across diverse phenotypes; 2) Our data supports a role for dynamic brain connectivity in cognitive impairment due to Alzheimer’s disease; 3) Dynamic connectivity appears to be directly related to AD pathobiology as measured by tau PET. Incorporating the time‐varying nature of connectivity will be important in future studies of connectome‐based disease progression in AD.

Computational phonogram archiving

A general approach to analyzing audio files makes it possible to re-create the sound of ancient instruments, identify cross-cultural musical properties, and more.

An evolutionary multi-hidden Markov model for intelligent threat sensing in industrial internet of things

Threat problem has become more complex in the industrial environment due to the need to secure a large number of devices from attack while maintaining system reliability and real-time response to threats. In such scenario detection of threat in Industrial Internet of things (IIoT) devices becomes an important factor to avoid injection by malicious IIoT devices. The techniques based on the Hidden Markov Models (HMM) are probably the most popular in detecting threat of detection. However, HMM requires extensive training of the models and computational resources. Also, HMM has the drawback of convergence to a local optimum while using Baum–Welch algorithm for parameter estimation. In order to optimize the HMM parameters, global search techniques can be used. This work proposes Genetic algorithms (GA) for optimizing HMM parameters. The other difficulty in threat detection is the dynamic nature of the attack. Several new threats are emerging with many variants which are created from existing attacks, making threat modeling an arduous task. As a result, good features are critical to model traffic and provide an efficient way to detect known and possibly unknown attacks to detect. To achieve a better feature extraction from the network traffic, we propose a dynamic sliding window $$W$$ which has a width of $$w$$ . The proposed multiple-HMM performs well to detect threats. The simulation results are compared to the results obtained by the Baum–Welch algorithm based approach showing higher accuracy and convergences.

Optimization learning of hidden Markov model using the bacterial foraging optimization algorithm for speech recognition

Nowadays, the speech recognition applications can be found in several activities, and their existence as a field of study and research lasts for a long time. Although, many studies deal with different problems, in security-related areas, biometric identification, access to the Smartphone… Etc. In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have widely used for modeling the temporal speech signal. In order to optimize HMM parameters (i.e., observation and transition probabilities), iterative algorithms commonly used such as Forward-Backward or Baum-Welch. In this article, we propose to use the bacterial foraging optimization algorithm (BFOA) to enhance HMM with Gaussian mixture densities. As a global optimization algorithm of current interest, BFOA has proven itself for distributed optimization and control. Our experimental results show that the proposed approach yields a significant improvement of the transcription accuracy at signal/noise ratios greater than 15 dB.

A space-frequency joint detection and tracking method for line-spectrum components of underwater acoustic signals

For the passive detection of azimuth and frequency of azimuths and frequencies of line-spectrum signals radiated from underwater weak targets, a space-frequency joint detection and tracking method based on track-before-detect (TBD) technology is proposed. This method works directly with the time sequence of frequency-azimuth (FRAZ) spectra to estimate the frequency and azimuth of line-spectrum signals. And the change of the frequency and azimuth states of a line-spectrum signal is modeled as a first-order hidden Markov process. The distribution of the FRAZ spectra of linear array received signal are derived in this paper to set the parameters of Hidden Markov Models (HMM). The FRAZ spectra at different times is joint process using Viterbi algorithm to obtain the maximum a posteriori estimation of the azimuth and frequency of line-spectrum signal. A dynamic searching method based on region segmentation and merging similar line-spectrum is designed to solve the problem of large computation of Viterbi algorithm in the detection of line-spectrum. The proposed method can make full use of the correlation between the target's information in the multi-frame FRAZ spectra to distinguish the target from the background noise. The processing results of simulation data and actual data show that the method has excellent line-spectrum detection performance at low SNR and can adapt to the complex situation such as there are multiple targets and multiple line-spectrum signals.

Unsupervised anomaly detection in peripheral venous pressure signals with hidden Markov models

This paper proposes an automatic anomaly detection and removal algorithm for peripheral venous pressure (PVP) signals, which can be used to predict intravascular volume loss in humans. PVP signal collection is a minimally invasive procedure that can be performed by using a standard peripheral intravenous (PIV) catheter and a commercial pressure-monitoring transducer. PVP signals are highly susceptible to motion and noise artifacts such as patient movements or unintended manipulation of PIV lines. Anomalies in PVP signals can corrupt useful information and seriously affect the integrity of PVP signal analysis. We propose to detect and remove such anomalies by exploiting the properties of PVP signals. Specifically, a dynamic linear model (DLM) with a Kalman filter is used to track and predict the time-domain evolution of PVP signals. The prediction residuals of the Kalman filter are then modeled with a hidden Markov model (HMM), with the normal and anomalous status of the signal modeled by using binary states of a hidden Markov chain. The HMM parameters along with the hidden states are iteratively estimated by using an unsupervised learning algorithm with a modified Baum–Welch method. The anomaly detection algorithm is applied to clinical data from a cohort of 24 pediatric patients with hypertrophic pyloric stenosis. Experimental results demonstrate that the proposed unsupervised anomaly detection algorithm can efficiently remove anomalies in PVP signals without the need of a training phase. The algorithm can also be applied to other time series signals, such as Electrocardiography (ECG) and Photoplethysmogram (PPG) signals.

Wearable Device Heart Rate and Activity Data in an Unsupervised Approach to Personalized Sleep Monitoring: Algorithm Validation.

BackgroundThe proliferation of wearable devices that collect activity and heart rate data has facilitated new ways to measure sleeping and waking durations unobtrusively and longitudinally. Most existing sleep/wake identification algorithms are based on activity only and are trained on expensive and laboriously annotated polysomnography (PSG). Heart rate can also be reflective of sleep/wake transitions, which has motivated its investigation herein in an unsupervised algorithm. Moreover, it is necessary to develop a personalized approach to deal with interindividual variance in sleep/wake patterns.ObjectiveWe aimed to develop an unsupervised personalized sleep/wake identification algorithm using multifaceted data to explore the benefits of incorporating both heart rate and activity level in these types of algorithms and to compare this approach’s output with that of an existing commercial wearable device’s algorithms.MethodsIn this study, a total of 14 community-dwelling older adults wore wearable devices (Fitbit Alta; Fitbit Inc) 24 hours a day and 7 days a week over period of 3 months during which their heart rate and activity data were collected. After preprocessing the data, a model was developed to distinguish sleep/wake states based on each individual’s data. We proposed the use of hidden Markov models and compared different modeling schemes. With the best model selected, sleep/wake patterns were characterized by estimated parameters in hidden Markov models, and sleep/wake states were identified.ResultsWhen applying our proposed algorithm on a daily basis, we found there were significant differences in estimated parameters between weekday models and weekend models for some participants.ConclusionsOur unsupervised approach can be effectively implemented based on an individual’s multifaceted sleep-related data from a commercial wearable device. A personalized model is shown to be necessary given the interindividual variability in estimated parameters.

An improved feature extraction algorithm for insider threat using hidden Markov model on user behavior detection

PurposeBy using a new feature extraction method on the Cert data set and using a hidden Markov model (HMM) to model and analyze the behavior of users to distinguish whether the behavior is normal within a continuous period.Design/methodology/approachFeature extraction of five parts of the time series by rules and sorting in chronological order. Use the obtained features to calculate the probability parameters required by the HMM model and establish a behavior model for each user. When the user has abnormal behavior, the model will return a very low probability value to distinguish between normal and abnormal information.FindingsGenerally, HMM parameters are obtained by supervised learning and unsupervised learning, but the hidden state cannot be clearly defined. When the hidden state is determined according to the data set, the accuracy of the model will be improved.Originality/valueThis paper proposes a new feature extraction method and analysis mode, which determines the shape of the hidden state according to the situation of the data set, making subsequent HMM modeling simple and efficient and in turn improving the accuracy of user behavior detection.

Vehicle’s Density Prediction Based on History Data of e-Toll in PT Jasamarga Pandaan Tol Using Hidden Markov Model

The Gempol-Pandaan toll is a strategic center for regional development. In 2017, the number of vehicles passing through this toll has increased. This condition could impact on traffic jam and road damage because of passes by many cars, especially trucks with loads that exceed their capacity. Therefore, it is necessary to do a vehicle’s density prediction not only from the Gempol toll gate but also from Kejapanan, Bangil and Rembang toll gates that will exit through Pandaan toll gate. In this research, vehicle’s density is viewed from the probability and number of each vehicles category I, II, III, IV, V. The origin gate and vehicle category can not be observed directly, but the car must pass the toll gate to do tapping the e-toll card, so the method used in this research is the Hidden Markov Model (HMM). Two estimate methods for HMM parameters are employed in this research, the Expectation-Maximization (EM) algorithm and Bayesian approach. The result shows that HMM parameters estimation using Bayesian is better than the EM algorithm. The Bayesian estimated parameter values are closer to the input parameters so that the model is more representative to explain the vehicle’s density prediction.

Key Algorithm for Human Motion Recognition in Virtual Reality Video Sequences Based on Hidden Markov Model

This paper provides an in-depth discussion of human motion recognition in Virtual Reality (VR) video sequences through hidden Markov models, which are four steps from VR video acquisition and pre-processing, foreground detection, extraction of human feature parameters, and hidden Markov model human motion recognition. A hybrid Gaussian model was used to build a background model in real-time based on changes in VR video information, and the image was subtracted by the background differential method. The optical flow method was used for foreground detection of the target, and the effects of sparse and dense optical flow were compared to obtain the motion characteristics and optical flow information of the target human body, respectively. Features were extracted for human motion, in terms of common geometric features of the body and optical flow information, respectively. In terms of common geometric information, the width-to-height ratio, perimeter-to-area ratio, center of mass, eccentricity, and feature angle were extracted, respectively. For the optical flow information, optical flow descriptors were constructed using a grid-based approach. And feature fusion was performed for the above two parameters by the k-means method to construct the word pocket model. The hidden Markov model parameters were obtained by using the hidden Markov model for the recognition of human motion and training the human feature parameters for each of the four motions. The recognition of the four common human body movements was realized by the forward-backward algorithm. The test results show that the motion recognition method in this paper has high recognition performance and good anti-interference performance. The time-sequence pooling is used to sort the effective video frame feature sequences to obtain the feature vectors that can represent the dynamic changes of video time sequence; finally, the time-sequence feature vectors are used to train the support vector machine for classification recognition. The recognition accuracy is 65.2% and 89.4% for the HMDB51 and UCF101 datasets, respectively.

Automatic Analysis of Large-scale Nanopore Data Using Hidden Markov Models

In this paper we developed a modified Hidden Markov Model (HMM) to analyze the raw nanopore experimental data. Traditionally, prior to further analysis the measured nanopore data must be pre-filtered, but the filtering usually distorts the waveform of the blockage current, especially for rapid translocations and bumping blockages. The HMM is known to be robust with respect to strong noise and thus suitable for processing the raw nanopore data, but its performance is susceptible to the setting of initial parameters. To overcome this problem, we use the Fuzzy c-Means (FCM) algorithm to initialize the HMM parameters in this work. Then we use the Viterbi training algorithm to optimize the HMM. Finally, both the simulated and experimental data analysis results are presented to show the effectiveness of the proposed method for detection of the nanopore current blockage events in analytical chemistry.

Pengenalan Gangguan Ginjal Melalui Iridologi Menggunakan Hidden Markov Model (HMM)

ABSTRACTIridology has not been widely applied for the recognition of kidney disorders. identification of kidney disorders through iris image using iridology chart, can make it easier to make diagnosis to find out about kidney disorders. The method used in the process of recognition of kidney disorders through iridology is the Hidden Markov Model (HMM) method, with a HMM parameter determination system using the calculation of the koefisien Singular Value Decomposition (SVD) coefficient. The size of the codebook used is 7, i.e. 16, 32, 64, 128, 256, 512 and 1024. Different sizes of codebooks will result in different recognition times. The time needed will be longer when the size of the codebook is getting bigger. The accuracy of the process of recognition of kidney disorders through iridology using the HMM method in this study is 68.75% for codebook 16, 87.5% for codebook 32, 100% for codebook 128 and 100% for codebook 512. Keywords : iridology, codebook, image processing, singular value decomposition (SVD), Hidden Markov Model (HMM).