Results 151 to 160 of about 250,191 (314)

A Convolutional Neural Network Using Anterior Segment Photos for Infectious Keratitis Identification [PDF]

Vannarut Satitpitakul,1,2 Apiwit Puangsricharern,3 Surachet Yuktiratna,3 Yossapon Jaisarn,4 Keeratika Sangsao,4 Vilavun Puangsricharern,1,2 Ngamjit Kasetsuwan,1,2 Usanee Reinprayoon,1,2 Thanachaporn Kittipibul1,2 1Center of Excellence for Cornea and Stem
Satitpitakul V, Puangsricharern A, Sangsao K, Kasetsuwan N, Reinprayoon U, Jaisarn Y, Kittipibul T, Puangsricharern V, Yuktiratna S   +8 more
core   +1 more source

Improving the Robustness of Visual Teach‐and‐Repeat Navigation Using Drift Error Correction and Event‐Based Vision for Low‐Light Environments

Advanced Robotics Research, EarlyView.
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

Multimodal Human–Robot Interaction Using Human Pose Estimation and Local Large Language Models

Advanced Robotics Research, EarlyView.
A multimodal human–robot interaction framework integrates human pose estimation (HPE) and a large language model (LLM) for gesture‐ and voice‐based robot control. Speech‐to‐text (STT) enables voice command interpretation, while a safety‐aware arbitration mechanism prioritizes gesture input for rapid intervention.
Nasiru Aboki, Ilche Georgievski, Marco Aiello   +2 more
wiley   +1 more source

Frequency-Domain and Spatial-Domain MLMVN-Based Convolutional Neural Networks

Algorithms
This paper presents a detailed analysis of a convolutional neural network based on multi-valued neurons (CNNMVN) and a fully connected multilayer neural network based on multi-valued neurons (MLMVN), employed here as a convolutional neural network in the
Igor Aizenberg, Alexander Vasko
doaj   +1 more source

Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Advanced Robotics Research, EarlyView.
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, Ganesh Arivoli, Huzaifa Unjhawala, Luning Bakke, Radu Serban, Dan Negrut   +5 more
wiley   +1 more source

Predicting lysine methylation sites using a convolutional neural network [PDF]

Protein lysine methylation is a particular type of post translational modification that plays an important role in both histone and non-histone function regulation in proteins.
Spadaro, Austin, Dehzangi, Iman, Sharma, Alok   +2 more
core   +1 more source

Cross‐Scale Hierarchical Targeted Delivery System Based on Small‐Scale Magnetic Robots

Advanced Robotics Research, EarlyView.
This article reviews a cross‐scale hierarchical targeted delivery system that integrates magnetic continuum robots and magnetic microrobots. By combining rapid long‐range navigation with precise microscale targeting, the system overcomes key limitations of single‐scale approaches.
Junjian Zhou, Zhiqiang Chen, Yuanbiao Ma, Zhiqiang Tang, Qianqian Wang   +4 more
wiley   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

Morphological convolutional neural network architecture for digit recognition [PDF]

, 2019
Deep neural networks have proved promising results in many applications and fields, but they are still assimilated to a black box. Thus, it is very useful to introduce interpretability aspects to prevent the blind application of deep networks. This paper
Hamdani, Tarek M., Ayed, Mounir Ben, Sanchez-Medina, Javier J., Mellouli, Dorra, Alimi, Adel M.   +4 more
core   +1 more source

A comparison of deep convolutional neural networks for image-based detection of concrete surface cracks

Computer Assisted Methods in Engineering and Science, 2019
The aim of this paper is to compare the performance of four deep convolutional neural networks in the problem of image-based automated detection of concrete surface cracks in the case of a small dataset.
Marek Słoński
doaj   +1 more source