Results 31 to 40 of about 53,114 (263)

Scoring hidden Markov models [PDF]

Bioinformatics, 1997
Statistical sequence comparison techniques, such as hidden Markov models and generalized profiles, calculate the probability that a sequence was generated by a given model. Log-odds scoring is a means of evaluating this probability by comparing it to a null hypothesis, usually a simpler statistical model intended to represent the universe of sequences ...
Christian Barrett, Richard Hughey, Kevin Karplus   +2 more
openaire   +2 more sources

Analysis of multimodal Bayesian nonparametric autoregressive hidden Markov models for process monitoring in robotic contact tasks

International Journal of Advanced Robotic Systems, 2019
Robot introspection aids robots to understand what they do and how they do it. Previous robot introspection techniques have often used parametric hidden Markov models or supervised learning techniques, implying that the number of hidden states or classes
Hongmin Wu, Yisheng Guan, Juan Rojas
doaj   +1 more source

Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

Mathematics, 2022
This paper presents a context-aware adaptive assembly assistance system meant to support factory workers by embedding predictive capabilities. The research is focused on the predictor which suggests the next assembly step.
Arpad Gellert, Stefan-Alexandru Precup, Alexandru Matei, Bogdan-Constantin Pirvu, Constantin-Bala Zamfirescu   +4 more
doaj   +1 more source

Recent Advances of Slip Sensors for Smart Robotics

Advanced Materials Technologies, EarlyView.
This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang, Liao Wu, Shuai He, Zhao Sha, Yuwan Huang, Shuying Wu, Dewei Chu, Chun H. Wang, Shuhua Peng   +8 more
wiley   +1 more source

Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

Advanced Robotics Research, EarlyView.
A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo, Jiahui Zhang, Yimin Han, Ji Ma, Minghao Lu, Ioannis Havoutis, Peng Lu   +6 more
wiley   +1 more source

Feedforward Factorial Hidden Markov Model

Mathematics
This paper introduces a novel kind of factorial hidden Markov model (FHMM), specifically the feedforward FHMM (FFHMM). In contrast to traditional FHMMs, the FFHMM is capable of directly utilizing supplementary information from observations through ...
Zhongxing Peng, Wei Huang, Yinghui Zhu
doaj   +1 more source

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

Advanced Robotics Research, EarlyView.
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek, Herman van der Kooij, Douwe Dresscher, Mark Vlutters   +3 more
wiley   +1 more source

Hidden Markov Model for Stock Selection

Risks, 2015
The hidden Markov model (HMM) is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market ...
Nguyet Nguyen, Dung Nguyen
doaj   +1 more source

Hidden Markov Model Cryptanalysis [PDF]

, 2003
We present HMM attacks, a new type of cryptanalysis based on modeling randomized side channel countermeasures as Hidden Markov Models (HMM’s). We also introduce Input Driven Hidden Markov Models (IDHMM’s), a generalization of HMM’s that provides a powerful and unified cryptanalytic framework for analyzing countermeasures whose operational behavior can ...
Chris Karlof, David A. Wagner 0001
openaire   +1 more source

Duration modeling with hidden Markov models [PDF]

The Journal of the Acoustical Society of America, 1993
In hidden Markov modeling (HMM) of speech signals, the statistics of speech characteristics are represented by HMM parameters after the HMM training. This procedure is purely statistical. This study concerns the incorporation of explicit knowledge into the HMM training. Therefore one specific parameter, i.e., segment duration, was selected. In order to
ten Bosch, L.F.M., Wang, X., Pols, L.C.W.   +2 more
openaire   +2 more sources