Results 131 to 140 of about 2,876,440 (332)
Advances in Fuzzy Systems, 2011 In many practical applications, it turns out to be useful to use the notion of fuzzy transform: once we have functions A1(x)≥0,...,An≥0, with ∑i=1nAi(x)=1, we can then represent each function f(x) by the coefficients Fi=(∫f(x)⋅Ai(x)dx)/(∫Ai(x)dx).
Irina Perfilieva, Vladik Kreinovich
doaj +1 more source
Energy Consumption Optimization in Trajectory Planning for Fuel Cell Hybrid Uavs Based On HMPC
Advanced Robotics Research, EarlyView.The endurance limitation of multirotor drones is a critical challenge. This study adopts a hybrid power system of fuel cells and lithium‐ion batteries. Using Nondominated Sorting Genetic Algorithm II, it integrates trajectory planning with energy management optimization.
Xindi Wang +7 more
wiley +1 more source
Solving nth order fuzzy differential equation by fuzzy Laplace transform
, 2014 In this paper, we generalize the fuzzy Laplace transformation (FLT) for the nth derivative of a fuzzy-valued function named as nth derivative theorem and under the strongly generalized differentiability concept, we use it in an analytical solution method for the solution of an nth order fuzzy initial value problem (FIVP).
Ahmad, Latif +2 more
openaire +3 more sources
An AI‐Enabled All‐In‐One Visual, Proximity, and Tactile Perception Multimodal Sensor
Advanced Robotics Research, EarlyView.Targeting integrated multimodal perception of robots, an AI‐enabled all‐in‐one multimodal sensor is proposed. This sensor is capable of perceiving three types of modalities, including vision, proximity, and tactility. By toggling an ultraviolet light and adjusting the camera focus, it switches smoothly between multiple perceptual modalities, enabling ...
Menghao Pu +7 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
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 Science, EarlyView.This study uncovers a new allosteric site in the Josephin domain of ataxin‐3 targeted by the molecular tweezer CLR01, which modulates protein aggregation, improves synaptic function in neuronal cells, and delays motor dysfunction in animal models.
Alexandra Silva +28 more
wiley +1 more source
Nanoscale Mapping of the Subcellular Glycosylation Landscape
Advanced Science, EarlyView.Using multiplexed super‐resolution imaging with fluorophore‐labeled lectins, this study reports intracellular glycosylation at the nanoscale across organelles and synaptic specializations. Extending glycan analysis beyond the cell surface, Glyco‐STORM reveals distinct glycosylation nanodomains in the ER, Golgi, lysosomes, and synaptic sites.
Helene Gregoria Schroeter +4 more
wiley +1 more source
Advanced Science, EarlyView.A predictive model for 3D printability is developed by integrating rheological analysis, including the Large Amplitude Oscillatory Shear (LAOS) test, with machine learning. With prediction errors under 10%, the model shows that post‐extrusion recovery controls horizontal printability, while high‐strain‐rate nozzle flow dictates vertical printability ...
Eun Hui Jeong +7 more
wiley +1 more source
Advanced Science, EarlyView.CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.
Guihai Feng +27 more
wiley +1 more source