Results 51 to 60 of about 67,782 (253)

A Spectral Algorithm for Inference in Hidden Semi-Markov Models

CoRR, 2014
Hidden semi-Markov models (HSMMs) are latent variable models which allow latent state persistence and can be viewed as a generalization of the popular hidden Markov models (HMMs). In this paper, we introduce a novel spectral algorithm to perform inference in HSMMs.
Igor Melnyk, Arindam Banerjee 0001
openaire   +4 more sources

Health-state estimation and prognostics in machining processes [PDF]

, 2010
Failure mechanisms of electromechanical systems usually involve several degraded health-states. Tracking and forecasting the evolution of health-states and impending failures, in the form of remaining-useful-life (RUL), is a critical challenge and ...
Fatih Camci, Chinnam, R. B., Camci, Fatih, Ratna Babu Chinnam, Chinnam, Ratna Babu   +4 more
core   +1 more source

Estimating hidden semi-Markov chains from discrete sequences. [PDF]

, 2003
International audienceThis article addresses the estimation of hidden semi-Markov chains from nonstationary discrete sequences. Hidden semi-Markov chains are particularly useful to model the succession of homogeneous zones or segments along sequences.
Guédon, Yann
core   +1 more source

Deep Hidden Semi-Markov Model-Based Speech Synthesis

IEEE Access
This paper proposes a speech synthesis technique based on a neural sequence-to-sequence (Seq2Seq) model that incorporates the structure of hidden semi-Markov models (HSMMs).
Yoshihiko Nankaku, Takato Fujimoto, Takenori Yoshimura, Shinji Takaki, Kei Hashimoto, Keiichiro Oura, Keiichi Tokuda   +6 more
doaj   +1 more source

Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

Complexity, 2018
We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography.
Nebojša Malešević, Dimitrije Marković, Gunter Kanitz, Marco Controzzi, Christian Cipriani, Christian Antfolk   +5 more
doaj   +1 more source

Quantifying the uncertainty in change points [PDF]

, 2011
Quantifying the uncertainty in the location and nature of change points in time series is important in a variety of applications. Many existing methods for estimation of the number and location of change points fail to capture fully or explicitly the ...
Aston, John A. D., Christopher F. H. Nam, John A. D. Aston, Johansen, Adam M., Nam, Christopher F. H., Adam M. Johansen   +5 more
core   +1 more source

Hidden semi-Markov-switching quantile regression for time series [PDF]

, 2021
A hidden semi-Markov-switching quantile regression model is introduced as an extension of the hidden Markov-switching one. The proposed model allows for arbitrary sojourn-time distributions in the states of the Markov-switching chain.
Lea Petrella, Maruotti, Antonello, Antonello Maruotti, Luca Sposito, Petrella, Lea, Sposito, Luca   +5 more
core   +1 more source

Proper account of auto-correlations improves decoding performances of state-space (semi) Markov models

Peer Community Journal
State-space models are widely used in ecology to infer hidden behaviors. This study develops an extensive numerical simulation-estimation experiment to evaluate the state decoding accuracy of four simple state-space models.
Bez, Nicolas, Gloaguen, Pierre, Etienne, Marie-Pierre, Lanco, Sophie, Joo, Rocío, Rivot, Etienne, Walker, Emily, Woillez, Mathieu, Mahévas, Stéphanie   +8 more
doaj   +1 more source

Efficient Estimation of Time-Dependent Brain Functional Connectivity Using Anatomical Connectivity Constraints

IEEE Access, 2023
There is ongoing interest in the dynamics of resting state brain networks (RSNs) as potential predictors of cognitive and behavioural states. Multivariate Autoregressors (MAR) are used to model regional brain activity as a linear combination of past ...
Hernan Hernandez Larzabal, David Araya, Lazara Liset Gonzalez Rodriguez, Claudio Roman, Nelson Trujillo-Barreto, Pamela Guevara, Wael El-Deredy   +6 more
doaj   +1 more source

AI‐Assisted Digital Single‐Molecule Activity Tracker for Decoupling Intrinsic Heterogeneity from Photo‐Oxidative Damage in High‐Photon‐Flux Enzymology

Advanced Science, EarlyView.
Employing a digital single‐molecule activity tracker (dSMAT), this research demonstrates that high‐photon‐flux irradiation drives progressive oxidative scarring in polymerases. Unlike simple thermal denaturation, real‐time kinetic tracking dynamically visualizes enzymes degrading into multiple impaired subpopulations.
Anran Zheng, Qi Yang, Jinze Li, Fuqiang Ma, Xuefeng Wang, Zhen Guo, Chuanyu Li, Dongshu Li, Jia Yao, Zhiqi Zhang, Wei Zhang, Lianqun Zhou   +11 more
wiley   +1 more source