Results 131 to 140 of about 7,255,480 (346)
Neural identification of compaction characteristics for granular soils
Computer Assisted Methods in Engineering and Science, 2017 The paper is a continuation of [9], where new experimental data were analysed. The Multi-Layered Perceptron and Semi-Bayesian Neural Networks were used.
Marzena Kłos +2 more
doaj
Hard‐Magnetic Soft Millirobots in Underactuated Systems
Advanced Robotics Research, EarlyView.This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang +4 more
wiley +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 +5 more
wiley +1 more source
Bayesian Networks and the Problem of Unreliable Instruments [PDF]
, 2000 We appeal to the theory of Bayesian Networks to model different strategies for obtaining confirmation for a hypothesis from experimental test results provided by less than fully reliable instruments.
Bovens, Luc, Hartmann, Stephan
core
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 +3 more
wiley +1 more source
Model assisted approaches to complex survey sampling from finite populations using Bayesian Networks [PDF]
A class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. The dependency structure is the one described by the Bayesian networks. This class allows ratio type estimators as a subclass
Paola Vicard, Marco Ballin, Mauro Scanu
core
Journal of the Brazilian Computer Society, 1997 Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a solution to the problem of ...
openaire +3 more sources
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
Bayesian MAP model selection of chain event graphs [PDF]
, 2009 The class of chain event graph models is a generalisation of the class of discrete Bayesian networks, retaining most of the structural advantages of the Bayesian network for model interrogation, propagation and learning, while more naturally encoding ...
Freeman, Guy, Smith, J. Q.
core
Learning Large-Scale Bayesian Networks with the sparsebn Package
Journal of Statistical Software, 2019 Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands - sometimes tens or hundreds of thousands - of variables and far fewer
Bryon Aragam, Jiaying Gu, Qing Zhou
doaj +1 more source