Results 41 to 50 of about 6,719 (190)

Introduction to Hidden Semi-Markov Models

, 2018
The purpose of this volume is to present the theory of Markov and semi-Markov processes in a discrete-time, finite-state framework. Given this background, hidden versions of these processes are introduced and related estimation and filtering results developed. The approach is similar to the earlier book, Elliott et al. (1995).
John van der Hoek, Robert J. Elliott
openaire   +3 more sources

Multi-branch Hidden semi-Markov modeling for RUL prognosis [PDF]

2015 Annual Reliability and Maintainability Symposium (RAMS), 2015
Deterioration modeling and remaining useful life (RUL) estimation of equipment are key enabling tasks for the implementation of a predictive maintenance (PM) policy, which plays nowadays an important role for maintaining engineering systems. Hidden Markov Models (HMM) have been used as an efficient tool for modeling the deterioration mechanisms as well
Le, Thanh Trung, Bérenguer, Christophe, Chatelain, Florent   +2 more
openaire   +1 more source

HVGH: Unsupervised Segmentation for High-Dimensional Time Series Using Deep Neural Compression and Statistical Generative Model

Frontiers in Robotics and AI, 2019
Humans perceive continuous high-dimensional information by dividing it into meaningful segments, such as words and units of motion. We believe that such unsupervised segmentation is also important for robots to learn topics such as language and motion ...
Masatoshi Nagano, Tomoaki Nakamura, Takayuki Nagai, Takayuki Nagai, Daichi Mochihashi, Ichiro Kobayashi, Wataru Takano   +6 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

Adaptive Autonomy in Microrobot Motion Control via Deep Reinforcement Learning and Path Planning Synergy

Advanced Intelligent Systems, EarlyView.
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi, Bairong Zhu, Haoyu Liu, Tingting Yu   +3 more
wiley   +1 more source

First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data

IEEE Access, 2020
Failure prognostics aims at predicting the object equipment's future degradation trend and derives the remaining useful life with a predefined failure threshold.
Jie Liu
doaj   +1 more source

Regional Shopping Objectives in British Grocery Retail Transactions Using Segmented Topic Models

Applied Stochastic Models in Business and Industry, EarlyView.
ABSTRACT Understanding the customer behaviours behind transactional data has high commercial value in the grocery retail industry. Customers generate millions of transactions every day, choosing and buying products to satisfy specific shopping needs.
Mariflor Vega Carrasco, Mirco Musolesi, Jason O'Sullivan, Rosie Prior, Ioanna Manolopoulou   +4 more
wiley   +1 more source

Bayesian Nonparametric Hidden Semi-Markov Models

, 2012
There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode
Johnson, Matthew James, Willsky, Alan S.
openaire   +2 more sources

A Comparison of Realized Measures of Integrated Volatility: Price Duration‐ vs. Return‐Based Approaches

Journal of Forecasting, EarlyView.
ABSTRACT We study the accuracy of a variety of parametric price duration‐based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return‐based realized variance estimators.
Björn Schulte‐Tillmann, Mawuli Segnon, Timo Wiedemann   +2 more
wiley   +1 more source

Ophidiomycosis Prevalence and Disease Ecology in a Natrix tessellata (Laurenti, 1768) Population From Northern Italy

Journal of Experimental Zoology Part A: Ecological and Integrative Physiology, EarlyView.
ABSTRACT Fungal pathogens pose a growing threat to vertebrate biodiversity. In snakes, Ophidiomyces ophidiicola (Oo) has garnered particular concern, although its impact in Europe remains poorly understood. We conducted a season‐long, standardized survey of dice snakes (Natrix tessellata) along the northern shore of Lake Como (Italy) to quantify Oo and
Matteo Riccardo Di Nicola, Luca Colla, Kevin P Mulder, Federico Storniolo, Elin Verbrugghe, Giuseppe Esposito, Donato A. Grasso, Frank Pasmans, An Martel   +8 more
wiley   +1 more source