ObjectiveTo evaluate the decoding accuracy and model performance of a graph spatio-temporal convolutional neural network (G-STCNN) in motor intention recognition of stroke patients.MethodsWe developed a novel G-STCNN model by integrating graph ...
XU Hui +5 more
doaj
3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends
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
A Review on Sensor Technologies, Control Approaches, and Emerging Challenges in Soft Robotics
This review provides an introspective of sensors and controllers in soft robotics. Initially describing the current sensing methods, then moving on to the control methods utilized, and finally ending with challenges and future directions in soft robotics focusing on the material innovations, sensor fusion, and embedded intelligence for sensors and ...
Ean Lovett +5 more
wiley +1 more source
Visual teach‐and‐repeat (VTR) navigation allows robots to learn and follow routes without building a full metric map. We show that navigation accuracy for VTR can be improved by integrating a topological map with error‐drift correction based on stereo vision.
Fuhai Ling, Ze Huang, Tony J. Prescott
wiley +1 more source
Convolutional Neural Network-Based Bidirectional Gated Recurrent Unit–Additive Attention Mechanism Hybrid Deep Neural Networks for Short-Term Traffic Flow Prediction [PDF]
Song Liu +5 more
openalex +1 more source
Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentation [PDF]
Guillermo Jiménez-Pérez +3 more
openalex +1 more source
Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang +5 more
wiley +1 more source
This study highlights the potential of deep learning, particularly Convolutional Neural Networks (CNNs), for predicting the photovoltaic performance of organic solar cells. By leveraging 2D images representing donor/acceptor molecular pairs, the model accurately estimates key performance indicators proving that this image‐based approach offers a fast ...
Khoukha Khoussa +2 more
wiley +1 more source
Characterizing Lossy Dielectric Materials in Shock Physics by Millimeter-Wave Interferometry Using One-Dimensional Convolutional Neural Networks and Nonlinear Optimization [PDF]
Ngoc Tuan Pham +2 more
openalex +1 more source
A Survey on 3D Hand Skeleton and Pose Estimation by Convolutional Neural Network
Van-Hung Le, Nguyen Hung-Cuong
openalex +2 more sources

