Results 161 to 170 of about 46,171 (307)
RegCGAN: Resampling with Regularized CGAN for Imbalanced Big Data Problem
AxiomsWe consider the imbalanced data problem involving a new class of resampling-based models for classification. These models are variants of the conditional generative adversarial networks.
Liwen Xu, Ximeng Wang
doaj +1 more source
TacScope: A Miniaturized Vision‐Based Tactile Sensor for Surgical Applications
Advanced Robotics Research, EarlyView.TacScope is a compact, vision‐based tactile sensor designed for robot‐assisted surgery. By leveraging a curved elastomer surface with pressure‐sensitive particle redistribution, it captures high‐resolution 3D tactile feedback. TacScope enables accurate tumor detection and shape classification beneath soft tissue phantoms, offering a scalable, low‐cost ...
Md Rakibul Islam Prince +3 more
wiley +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
Data Augmentation with Variational Autoencoder for Imbalanced Dataset
Learning from an imbalanced distribution presents a major challenge in predictive modeling, as it generally leads to a reduction in the performance of standard algorithms. Various approaches exist to address this issue, but many of them concern classification problems, with a limited focus on regression. In this paper, we introduce a novel method aimed
Samuel Stocksieker +2 more
openaire +2 more sources
Modular, Textile‐Based Soft Robotic Grippers for Agricultural Produce Handling
Advanced Robotics Research, EarlyView.This article introduces textile‐based pneumatic grippers that transform simple textiles into robust bending actuators. Detailed experiments uncover how cut geometry and fabric selection shape performance. Successful handling of fragile agricultural items showcases the potential of textile robotics for safe, scalable automation in food processing and ...
Zeyu Hou +4 more
wiley +1 more source
Fault diagnosis methods for imbalanced samples of hydraulic pumps based on DA-DCGAN
Scientific ReportsStatus monitoring and fault diagnosis of mechanical equipment are vital for ensuring operational safety. However, real-world diagnostic scenarios often suffer from limited and imbalanced fault data, affecting model accuracy and reliability.
Yang Zhao +4 more
doaj +1 more source
From Lab to Landscape: Environmental Biohybrid Robotics for Ecological Futures
Advanced Robotics Research, EarlyView.This Perspective explores environmental biohybrid robotics, integrating living tissues, microorganisms, and insects for operation in real‐world ecosystems. It traces the leap from laboratory experiments to forests, wetlands, and urban environments and discusses key challenges, development pathways, and opportunities for ecological monitoring and ...
Miriam Filippi
wiley +1 more source
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 +6 more
wiley +1 more source
Imbalanced data training approaches in neural network
, 2023 This thesis deals with the research and implementation of methods that eliminate the influence of an imbalanced dataset on the learning of neural networks. Individual methods are compared with each other for different levels of imbalance. The experiments
Vicianová, Veronika
core
Chromosomal Instability Drives Glioblastoma Heterogeneity and Therapeutic Opportunities
Advanced Science, EarlyView.ABSTRACT Glioblastoma, the most aggressive and lethal form of brain cancer, is defined by profound genomic instability, with Chromosomal Instability (CIN) playing a central role in driving tumor progression, therapy resistance, and poor prognosis. CIN is characterized by numerical and structural alterations, is driven by mechanisms such as mitotic ...
Amarnath Pal +3 more
wiley +1 more source