Showing papers on "Hidden Markov model published in 2021"

Robot Programming by Demonstration: A Probabilistic Approach

Abstract: ACKNOWLEDGMENT INTRODUCTION Contributions Organization of the book Review of Robot Programming by Demonstration (PBD) Current state of the art in PbD SYSTEM ARCHITECTURE Illustration of the proposed probabilistic approach Encoding of motion in a Gaussian Mixture Model (GMM) Encoding of motion in Hidden Markov Model (HMM) Reproduction through Gaussian Mixture Regression (GMR) Reproduction by considering multiple constraints Learning of model parameters Reduction of dimensionality and latent space projection Model selection and initialization Regularization of GMM parameters Use of prior information to speed up the learning process Extension to mixture models of varying density distributions Summary of the chapter COMPARISON AND OPTIMIZATION OF THE PARAMETERS Optimal reproduction of trajectories through HMM and GMM/GMR Optimal latent space of motion Optimal selection of the number of Gaussians Robustness evaluation of the incremental learning process HANDLING OF CONSTRAINTS IN JOINT SPACE AND TASK SPACE Inverse kinematics Handling of task constraints in joint spaceexperiment with industrial robot Handling of task constraints in latent spaceexperiment with humanoid robot EXTENSION TO DYNAMICAL SYSTEM AND HANDLING OF PERTURBATIONS Proposed dynamical system Influence of the dynamical system parameters Experimental setup Experimental results TRANSFERRING SKILLS THROUGH ACTIVE TEACHING METHODS Experimental setup Experimental results Roles of an active teaching scenario USING SOCIAL CUES TO SPEED UP THE LEARNING PROCESS Experimental setup Experimental results DISCUSSION, FUTURE WORK AND CONCLUSIONS Advantages of the proposed approach Failures and limitations of the proposed approach Further issues Final words REFERENCES INDEX

Asynchronous Fault Detection for Interval Type-2 Fuzzy Nonhomogeneous Higher-level Markov Jump Systems with Uncertain Transition Probabilities

Abstract: Based on the interval type-2 fuzzy (IT2F) approach, this paper investigates the fault detection filter design problem for a class of nonhomogeneous higher-level Markov jump systems with uncertain transition probabilities. Considering that the mode information of the system cannot be obtained synchronously by the filter, the hidden Markov model can be seen as a detector to handle this asynchronous problem, and the parameter uncertainty can be processed by the IT2F approach with the lower and upper membership functions. Then, the asynchronous IT2F filter is designed to deal with the fault detection problem. Furthermore, the Gaussian transition probability density function is introduced to describe the uncertainty transition probabilities of the system and the filter. Based on Lyapunov theory, the existence of the designed asynchronous IT2F filter and the dissipativity of the filter error system can be well ensured. The simulation study on a quarter-car suspension system verifies that the designed asynchronous IT2F filter can detect faults without error alarms.

Finite-Time L 2 -Gain Asynchronous Control for Continuous-Time Positive Hidden Markov Jump Systems via T–S Fuzzy Model Approach

Abstract: This article investigates the finite-time asynchronous control problem for continuous-time positive hidden Markov jump systems (HMJSs) by using the Takagi–Sugeno fuzzy model method. Different from the existing methods, the Markov jump systems under consideration are considered with the hidden Markov model in the continuous-time case, that is, the Markov model consists of the hidden state and the observed state. We aim to derive a suitable controller that depends on the observation mode which makes the closed-loop fuzzy HMJSs be stochastically finite-time bounded and positive, and fulfill the given $L_{2}$ performance index. Applying the stochastic Lyapunov–Krasovskii functional (SLKF) methods, we establish sufficient conditions to obtain the finite-time state-feedback controller. Finally, a Lotka–Volterra population model is used to show the feasibility and validity of the main results.

Fuzzy Fault Detection for Markov Jump Systems With Partly Accessible Hidden Information: An Event-Triggered Approach.

Abstract: This article addresses the design issue of fuzzy asynchronous fault detection filter (FAFDF) for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme. The ET scheme can be applied to cut down the transmission times from the system to FAFDF. It is assumed that the system modes cannot be obtained synchronously by the filter, and instead, there is a detector that can measure the estimated modes of the system. The asynchronous phenomenon between the system and the filter is characterized via a hidden Markov model with partly accessible mode detection probabilities. Applying the Lyapunov function methods, sufficient conditions for the presence of FAFDF are obtained. Finally, an application of a wheeled mobile manipulator with hybrid joints is employed to clarify that the devised FAFDF can detect the faults without any incorrect alarm.

A Systematic Review of Hidden Markov Models and Their Applications

Abstract: The hidden Markov models are statistical models used in many real-world applications and communities. The use of hidden Markov models has become predominant in the last decades, as evidenced by a large number of published papers. In this survey, 146 papers (101 from Journals and 45 from Conferences/Workshops) from 93 Journals and 44 Conferences/Workshops are considered. The authors evaluate the literature based on hidden Markov model variants that have been applied to various application fields. The paper represents a short but comprehensive description of research on hidden Markov model and its variants for various applications. The paper shows the significant trends in the research on hidden Markov model variants and their applications.

Semi-selfish mining based on hidden Markov decision process

Abstract: Selfish mining attacks sabotage the blockchain systems by utilizing the vulnerabilities of consensus mechanism. The attackers' main target is to obtain higher revenues compared with honest parties. More specifically, the essence of selfish mining is to waste the power of honest parties by generating a private chain. However, these attacks are not practical due to high forking rate. The honest parties may quit the blockchain system once they detect the abnormal forking rate, which impairs their revenues. While selfish mining attacks make no sense anymore with the honest parties' departure. Therefore, selfish miners need to restrain when launch selfish mining attacks such that the forking rate is not preposterously higher than normal level. The crux is how to illustrate the attacks toward the view of honest parties, who are blind to the private chain. Generally, previous works, especially those using Markov decision processes, stress on the increment of attackers' revenues, while overlooking the detection on forking rate. In this paper, we propose, to maintain the benefit from selfish mining, an improved selfish mining based on hidden Markov decision processes (SMHMDP). To reduce the forking rate, we also relax the behaviors of selfish miners (also known as semi‐selfish miners), who mine on the private chain, to mine on public chain with a small probability ρ . Simulation results show that SMHMDP can trade off between revenues and forking rate. Put differently, selfish miners benefit from attacking within an acceptable forking rate toward the view of honest parties, without leading selfish mining attacks to be an armchair strategist.

Observer-Based Sliding Mode Control for Markov Jump Systems With Actuator Failures and Asynchronous Modes

Abstract: The brief studies the asynchronous observer-based sliding mode control (SMC) for Markov jump systems (MJSs) with actuator failures. Considering the phenomena of unmeasurable states and the case that the controller/observer to be devised have different modes from the original systems, a hidden Markov model (HMM) is used to construct an asynchronous observer and the corresponding sliding surface is designed. Then, the asynchronous SMC strategy is developed to guarantee the reachability of the predetermined sliding surface in a limited time. A sufficient condition is established for the mean-square stability of the overall closed-loop systems and the desired controller is designed. Moreover, when the conditional probabilities describing the mode asynchronism are only partially known for the HMM in the systems, the related results are also given. Finally, simulation results show the usefulness of the developed techniques.

A Sleep Apnea Detection Method Based on Unsupervised Feature Learning and Single-Lead Electrocardiogram

Abstract: Sleep apnea (SA) is a harmful respiratory disorder that has caused widespread concern around the world. Considering that electrocardiogram (ECG)-based SA diagnostic methods were effective and human-friendly, many machine learning or deep learning methods based on ECG have been proposed by prior works. However, these methods are based on feature engineering or supervised and semisupervised learning techniques, and the feature sets are always incomplete, subjective, and highly dependent on labeled data. In addition, some related studies ignored the data imbalance problem which leads to poor performance of classifier on minority classes. In this study, an SA detection model based on frequential stacked sparse auto-encoder (FSSAE) and time-dependent cost-sensitive (TDCS) classification model was proposed. The FSSAE extracts feature set automatically with unsupervised learning technique, and the TDCS classification model is proposed by combining the hidden Markov model (HMM) and the MetaCost algorithm to improve the performance of the classifier by considering temporal dependence and the imbalance problem. In the test set, the result of per-segment classification achieved 85.1%, 86.2%, and 84.4% for accuracy, sensitivity, and specificity, respectively, proving that our method is helpful for SA detection.

Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems

Abstract: In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied in the system modes and the filter modes reasonably. Moreover, a suitable Lyapunov-Krasovskii function is utilized and linear matrix inequalities are applied to obtain adequate conditions. These techniques guarantee the finite-time boundedness and strict dissipativity of the filtering error dynamic system. Furthermore, the design problems of the passive filter and the H∞ filter are studied by adjusting the three parameters $${\cal U}$$ , $${\cal G}$$ and $${\cal V}$$ . Finally, the filter gains and the optimal index α* are obtained and the correctness and feasibility of the designed approach are verified by a simulation example.

Asynchronous Output Feedback Control for a Class of Conic-Type Nonlinear Hidden Markov Jump Systems Within a Finite-Time Interval

Abstract: This article focuses on the finite-time asynchronous output feedback control scheme for a class of Markov jump systems subject to external disturbances and nonlinearities. The conic-type nonlinearities hold a constraint condition which locates in a known hyper-sphere with an indefinite center. In addition, the asynchronization phenomenon occurs between the system and the controller, which can be represented by means of a hidden Markov model. A sufficient condition is derived not only to guarantee the finite-time boundedness of the acquired closed-loop systems but also to possess a desired $H_{\infty }$ performance on the basis of Lyapunov functional technique. Finally, the validity and feasibility of the proposed method are demonstrated with a dc-motor experiment.

A QoS-Aware Service Composition Mechanism in the Internet of Things Using a Hidden-Markov-Model-Based Optimization Algorithm

Abstract: Recently, a new technology topic has been known as the Internet of Things (IoT), where all devices like smartphones, smart TVs, medical and healthcare ones, and home appliances have been applied for data generating. Due to the variety of services, the numerous service composition problems, mostly related to the Quality-of-Service (QoS) parameters, are recognized in the IoT domain. Since this issue is an NP-hard obstacle, different metaheuristic approaches have been utilized up until now to solve it. Many varieties of services can be brought into the IoT, depending on users’ demands. In this research, we have proposed an effective way based on a hidden Markov model (HMM) and an ant colony optimization (ACO) to answer the service composition issue by enhancing the QoS. The HMM has been trained to predict QoS. The emission and transition matrices have been improved using the Viterbi algorithm. We have executed the QoS estimation using the ACO algorithm and found a suitable path. The outcomes have illustrated the efficacy of the introduced method regarding availability, response time, cost, reliability, and energy consumption compared to the previous methods.

Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview

Abstract: We present a structured overview of adaptation algorithms for neural network-based speech recognition, considering both hybrid hidden Markov model / neural network systems and end-to-end neural network systems, with a focus on speaker adaptation, domain adaptation, and accent adaptation. The overview characterizes adaptation algorithms as based on embeddings, model parameter adaptation, or data augmentation. We present a meta-analysis of the performance of speech recognition adaptation algorithms, based on relative error rate reductions as reported in the literature.

Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control

Abstract: This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human motion modeling. We first describe an efficient method for training an RNN model from prerecorded motion data. We implement RNNs with long short-term memory (LSTM) cells because they are capable of addressing the nonlinear dynamics and long term temporal dependencies present in human motions. Next, we train a refiner network using an adversarial loss, similar to generative adversarial networks (GANs), such that refined motion sequences are indistinguishable from real mocap data using a discriminative network. The resulting model is appealing for motion synthesis and control because it is compact, contact-aware, and can generate an infinite number of naturally looking motions with infinite lengths. Our experiments show that motions generated by our deep learning model are always highly realistic and comparable to high-quality motion capture data. We demonstrate the power and effectiveness of our models by exploring a variety of applications, ranging from random motion synthesis, online/offline motion control, and motion filtering. We show the superiority of our generative model by comparison against baseline models.

Genetic-Algorithm-Assisted Sliding-Mode Control for Networked State-Saturated Systems Over Hidden Markov Fading Channels

Abstract: The sliding-mode control (SMC) problem is studied in this article for state-saturated systems over a class of time-varying fading channels. The underlying fading channels, whose channel fading amplitudes (characterized by the expectation and variance) are allowed to be different, are modeled as a finite-state Markov process. A key feature of the problem addressed is to use a hidden Markov mode detector to estimate the actual network mode. The novel model of hidden Markov fading channels (HMFCs) is shown to be more general yet practical than the existing fading channel models. Based on a linear sliding surface, a switching-type SMC law is dedicatedly constructed by just using the estimated network mode. By exploiting the concept of stochastic Lyapunov stability and the approach of hidden Markov models, sufficient conditions are obtained for the resultant SMC systems that ensure both the mean-square stability and the reachability with a sliding region. With the aid of the Hadamard product, a binary genetic algorithm (GA) is developed to solve the proposed SMC design problem subject to some nonconvex constraints induced by the state saturations and the fading channels, where the proposed GA is based on the objective function for optimal reachability. Finally, a numerical example is employed to verify the proposed GA-assisted SMC scheme over the HMFCs.

Remaining Useful Life Prediction by Fusing Expert Knowledge and Condition Monitoring Information

Abstract: In this article, we develop a mixture of Gaussians-evidential hidden Markov model (MoG-EHMM) to fuse expert knowledge and condition monitoring information for remaining useful life (RUL) prediction under the belief function theory framework. The evidential expectation–maximization algorithm is implemented in the offline phase to train the MoG-EHMM based on historical data. In the online phase, the trained model is used to recursively update the health state and reliability of a particular individual system. The predicted RUL is, then, represented in the form of its probability mass function. A numerical metric is defined based on the Bhattacharyya distance to measure the RUL prediction accuracy of the developed methods. We applied the developed methods on a simulation experiment and a real-world dataset from a bearing degradation test. The results demonstrate that despite imprecisions in expert knowledge, the performance of RUL prediction can be substantially improved by fusing expert knowledge with condition monitoring information.

Adaptive Event-Triggered Finite-Time Dissipative Filtering for Interval Type-2 Fuzzy Markov Jump Systems With Asynchronous Modes.

Abstract: This article investigates the adaptive event-triggered finite-time dissipative filtering problems for the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy Markov jump systems (MJSs) with asynchronous modes. By designing a generalized performance index, the H∞, L₂-L∞, and dissipative fuzzy filtering problems with network transmission delay are addressed. The adaptive event-triggered scheme (ETS) is proposed to guarantee that the IT2 T-S fuzzy MJSs are finite-time boundedness (FTB) and, thus, lower the energy consumption of communication while ensuring the performance of the system with extended dissipativity. Different from the conventional triggering mechanism, in this article, the parameters of the triggering function are based on an adaptive law, which is obtained online rather than as a predefined constant. Besides, the asynchronous phenomenon between the plant and the filter is considered, which is described by a hidden Markov model (HMM). Finally, two examples are presented to show the availability of the proposed algorithms.

Hidden-Markov-Model-Based Asynchronous $H_{\infty }$ Tracking Control of Fuzzy Markov Jump Systems

Abstract: This article is concerned with the problem of imperfect premise matching asynchronous $H_{\infty }$ output tracking control for Takagi–Sugeno fuzzy Markov jump systems. A hidden Markov model is established due to the fact that the modes information of the system may not be accurately transmitted to the controller, which is used to depict the asynchronous phenomenon between the system modes and controller modes. The packet loss in the communication process is described by a stochastic variable subject to Bernoulli distribution. Then, based on a novel Lyapunov function, the mode-dependent and fuzzy-basis-dependent stability criteria are derived and the asynchronous control scheme is developed subject to an $H_{\infty }$ tracking performance. Finally, two examples are provided to demonstrate the effectiveness of the proposed approach.

Anomaly Detection for In-Vehicle Network Using CNN-LSTM With Attention Mechanism

Abstract: With the increasing connectivity between the Electronic Control Units (ECUs) and the outside world, safety and security have become stringent problems. The Controller Area Network (CAN) bus is the most commonly used in-vehicle network protocol, which lacks security mechanisms by design, so it is vulnerable to various attacks. In this paper, we propose a novel intrusion detection model called CNN-LSTM with Attention model (CLAM) for the in-vehicle network, especially CAN. The CLAM model uses one-dimensional convolution (Conv1D) to extract the abstract features of the signal values at each time step and feeds it into the Bidirectional Long Short Term Memory (Bi-LSTM) to extract the time dependence. By combining the attention mechanisms, we calculate the weight of each hidden state output by Bi-LSTM and perform a weighted summation, so that the model focuses only on locally important time steps, which can improve the convergence speed of the model and prediction accuracy. The proposed model uses the bit flip rate to extract continuous signal boundaries in the 64-bit CAN data, so it does not need to parse the CAN communication matrix and is suitable for different vehicles. The extensive evaluation results demonstrate that our proposed CLAM model can effectively detect CAN attacks, with an average F1-score of 0.951 and an error rate of 2.16%. Compared with the work in related research fields, the accuracy of attack detection is improved by 2.5%.

A Unified Framework for Multilingual Speech Recognition in Air Traffic Control Systems

Abstract: This work focuses on robust speech recognition in air traffic control (ATC) by designing a novel processing paradigm to integrate multilingual speech recognition into a single framework using three cascaded modules: an acoustic model (AM), a pronunciation model (PM), and a language model (LM). The AM converts ATC speech into phoneme-based text sequences that the PM then translates into a word-based sequence, which is the ultimate goal of this research. The LM corrects both phoneme- and word-based errors in the decoding results. The AM, including the convolutional neural network (CNN) and recurrent neural network (RNN), considers the spatial and temporal dependences of the speech features and is trained by the connectionist temporal classification loss. To cope with radio transmission noise and diversity among speakers, a multiscale CNN architecture is proposed to fit the diverse data distributions and improve the performance. Phoneme-to-word translation is addressed via a proposed machine translation PM with an encoder–decoder architecture. RNN-based LMs are trained to consider the code-switching specificity of the ATC speech by building dependences with common words. We validate the proposed approach using large amounts of real Chinese and English ATC recordings and achieve a 3.95% label error rate on Chinese characters and English words, outperforming other popular approaches. The decoding efficiency is also comparable to that of the end-to-end model, and its generalizability is validated on several open corpora, making it suitable for real-time approaches to further support ATC applications, such as ATC prediction and safety checking.

Efficient 3D medical image segmentation algorithm over a secured multimedia network

Abstract: Image segmentation has proved its importance and plays an important role in various domains such as health systems and satellite-oriented military applications. In this context, accuracy, image quality, and execution time deem to be the major issues to always consider. Although many techniques have been applied, and their experimental results have shown appealing achievements for 2D images in real-time environments, however, there is a lack of works about 3D image segmentation despite its importance in improving segmentation accuracy. Specifically, HMM was used in this domain. However, it suffers from the time complexity, which was updated using different accelerators. As it is important to have efficient 3D image segmentation, we propose in this paper a novel system for partitioning the 3D segmentation process across several distributed machines. The concepts behind distributed multimedia network segmentation were employed to accelerate the segmentation computational time of training Hidden Markov Model (HMMs). Furthermore, a secure transmission has been considered in this distributed environment and various bidirectional multimedia security algorithms have been applied. The contribution of this work lies in providing an efficient and secure algorithm for 3D image segmentation. Through a number of extensive experiments, it was proved that our proposed system is of comparable efficiency to the state of art methods in terms of segmentation accuracy, security and execution time.

Speech-Driven Expressive Talking Lips with Conditional Sequential Generative Adversarial Networks

Abstract: Articulation, emotion, and personality play strong roles in the orofacial movements. To improve the naturalness and expressiveness of virtual agents(VAs), it is important that we carefully model the complex interplay between these factors. This paper proposes a conditional generative adversarial network, called conditional sequential GAN(CSG), which learns the relationship between emotion, lexical content and lip movements in a principled manner. This model uses a set of spectral and emotional speech features directly extracted from the speech signal as conditioning inputs, generating realistic movements. A key feature of the approach is that it is a speech-driven framework that does not require transcripts. Our experiments show the superiority of this model over three state-of-the-art baselines in terms of objective and subjective evaluations. When the target emotion is known, we propose to create emotionally dependent models by either adapting the base model with the target emotional data (CSG-Emo-Adapted), or adding emotional conditions as the input of the model(CSG-Emo-Aware). Objective evaluations of these models show improvements for the CSG-Emo-Adapted compared with the CSG model, as the trajectory sequences are closer to the original sequences. Subjective evaluations show significantly better results for this model compared with the CSG model when the target emotion is happiness.

Autoencoder With Emotion Embedding for Speech Emotion Recognition

Abstract: An important part of the human-computer interaction process is speech emotion recognition (SER), which has been receiving more attention in recent years However, although a wide diversity of methods has been proposed in SER, these approaches still cannot improve the performance A key issue in the low performance of the SER system is how to effectively extract emotion-oriented features In this paper, we propose a novel algorithm, an autoencoder with emotion embedding, to extract deep emotion features Unlike many previous works, instance normalization, which is a common technique in the style transfer field, is introduced into our model rather than batch normalization Furthermore, the emotion embedding path in our method can lead the autoencoder to efficiently learn a priori knowledge from the label It can enable the model to distinguish which features are most related to human emotion We concatenate the latent representation learned by the autoencoder and acoustic features obtained by the openSMILE toolkit Finally, the concatenated feature vector is utilized for emotion classification To improve the generalization of our method, a simple data augmentation approach is applied Two publicly available and highly popular databases, IEMOCAP and EMODB, are chosen to evaluate our method Experimental results demonstrate that the proposed model achieves significant performance improvement compared to other speech emotion recognition systems

Generalized hidden Markov models for phylogenetic comparative datasets

Abstract: O_LIHidden Markov models (HMM) have emerged as an important tool for understanding the evolution of characters that take on discrete states. Their flexibility and biological sensibility make them appealing for many phylogenetic comparative applications. C_LIO_LIPreviously available packages placed unnecessary limits on the number of observed and hidden states that can be considered when estimating transition rates and inferring ancestral states on a phylogeny. C_LIO_LITo address these issues, we expanded the capabilities of the R package corHMM to handle n-state and n-character problems and provide users with a streamlined set of functions to create custom HMMs for any biological question of arbitrary complexity. C_LIO_LIWe show that increasing the number of observed states increases the accuracy of ancestral state reconstruction. We also explore the conditions for when an HMM is most effective, finding that an HMM outperforms a Markov model when the degree of rate heterogeneity is moderate to high. C_LIO_LIFinally, we demonstrate the importance of these generalizations by reconstructing the morphology of the ancestral angiosperm flower. Exactly opposite to previous results, we find the most likely state to be a spiral perianth, spiral androecium, whorled gynoecium. The difference between our analysis and previous studies was that our modeling allowed for the correlated evolution of several flower characters. C_LI