Results 101 to 110 of about 5,728,670 (281)

Multicrack Fatigue Life Prediction Based on Dynamic Bayesian Networks

Journal of Marine Science and Engineering
To address the challenge of fatigue life prediction caused by multiple-crack interactions in ship and offshore structures, this study proposes a dynamic Bayesian network (DBN)-based method for predicting the fatigue life of structures with multiple ...
Yitao Wang, Weidong Zhao, Zichen Xiao, Yifan Wang   +3 more
doaj   +1 more source

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

Advanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu, Bin Cui, Xumin Huang, Xiaoyu (Rayne) Zheng, T. Thang Vo‐Doan, Ruirui Qiao   +5 more
wiley   +1 more source

The Computational Power of Dynamic Bayesian Networks

CoRR, 2016
This paper considers the computational power of constant size, dynamic Bayesian networks. Although discrete dynamic Bayesian networks are no more powerful than hidden Markov models, dynamic Bayesian networks with continuous random variables and discrete children of continuous parents are capable of performing Turing-complete computation.
openaire   +4 more sources

Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox [PDF]

, 2012
The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better
Wilson, Graeme, Plumley, Charles Edward, Quail, Francis, Kenyon, Andrew, Zitrou, Athena   +4 more
core   +3 more sources

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

Characterization of Dynamic Bayesian Network

CoRR, 2012
In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a model that tries to incorporate temporal dimension with uncertainty. We start with basics of DBN where we especially focus in Inference and Learning concepts and algorithms.
Nabil Ghanmy, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara   +2 more
openaire   +2 more sources

Learning the structure of gene regulatory networks from time series gene expression data

BMC Genomics, 2011
Background Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on structure learning algorithm.
Li Haoni, Wang Nan, Gong Ping, Perkins Edward J, Zhang Chaoyang   +4 more
doaj   +1 more source

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Compliant Pneumatic Feet with Real‐Time Stiffness Adaptation for Humanoid Locomotion

Advanced Robotics Research, EarlyView.
A compliant pneumatic foot with real‐time variable stiffness enables humanoid robots to adapt to changing terrains. Using onboard vision and pressure control, the foot modulates stiffness within each gait cycle, reducing impact forces and improving balance. The design, cast in soft silicone with embedded air chambers and Kevlar wrapping, offers durable,
Irene Frizza, Hiroshi Kaminaga, Philippe Fraisse, Gentiane Venture   +3 more
wiley   +1 more source

Causality, Propensity and Bayesian Networks [PDF]

, 2002
The notion of a Bayesian propensity network is introduced, and the relations between such networks and causal networks is investigated. It is argued that causal networks cannot be identified with Bayesian propensity networks, but that causal networks can
Gillies, D, Gillies, DA
core