Results 91 to 100 of about 5,472,482 (287)

Foundation Model‐Enabled Pathology‐Informed MRI Model for Preoperative Prediction of Vessels Encapsulating Tumor Clusters in Resectable Hepatocellular Carcinoma: A Multicenter Retrospective Study

Advanced Science, EarlyView.
PIVOT uses pathology‐derived morphologic and vascular representations from postoperative H&E and CD34 whole‐slide images to guide model development, while clinical inference requires only preoperative multiparametric MRI. Across five centers, PIVOT enabled accurate preoperative assessment of vessels encapsulating tumor clusters, stratified patients by ...
Zhehan Shen, Wanmin Liu, Xiaoming Li, Wenjie Zou, Yiman Li, Jie Yuan, Yasen Yimit, Shihang Wang, Linhao Qu, Hui Dong, Wei Chen, Ningyang Jia, Peijun Wang   +12 more
wiley   +1 more source

Enhancing Specific Stiffness of Nano‐Architected Materials Through Generative AI and Active Learning

Advanced Science, EarlyView.
A structure‐centric generative framework searches voxelated design space directly, coupling a variational autoencoder with multi‐objective optimization and active learning to progressively expand the validated design domain. The discovered nano‐architectures, realized as pyrolytic carbon by two‐photon lithography, deliver 18–23% higher stiffness and 26–
Jinwook Yeo, Peter Serles, Donggeun Park, Julia R. Greer, Seunghwa Ryu   +4 more
wiley   +1 more source

On fuzzy probability theory

, 2002
Our main aim from this work is to see which theorems in classical probability theory are still valid in fuzzy probability theory. Following Gudder's approach [Demonestratio Mathematica 31(3), 1998, 235–254; Foundations of Physics, 30, 1663–1678] to fuzzy
Habil, Eissa D., Nasr, Taghreed Z
core   +1 more source

Hardware‐Native Gaussian Sampling With Probabilistic Ising Machines for Modern Communication Paradigms

Advanced Science, EarlyView.
Probabilistic Ising machines are introduced as hardware‐native Gaussian samplers for edge communication workloads. Tunable variance and sampling density enable accurate distributions for Falcon‐class post‐quantum signatures and diffusion‐based semantic communication.
Davi Rodrigues, Andrea Grimaldi, Deborah Volpe, Youwei Bao, Shuhan Yang, Eleonora Grassucci, Giulio Siracusano, Massimo Chiappini, Mario Carpentieri, Hyunsoo Yang, Giovanni Finocchio   +10 more
wiley   +1 more source

Data‐Driven and Physics‐Informed Modeling Perspectives for Soft Manipulators: From SINDy to Hybrid Intelligence

Advanced Science, EarlyView.
This Perspective charts the modeling landscape for soft manipulators, spanning sparse system identification, neural dynamics, physics‐informed models and hybrid intelligence. We examine how physics, data, sensing, adaptation and control can assume complementary roles within this landscape, and provide a framework for selecting modeling approaches ...
Wenbo Hui, Yunyi Wang, Teng Zhang, Yang Chen, Min Pan   +4 more
wiley   +1 more source

SOLVING STOCHASTIC TRANSPORTATION ELECTRICITY PROBLEM WITH FUZZY INFORMATION ON PROBABILITY DISTRIBUTION USING MATLAB PROGRAM

Science Journal of University of Zakho
This study focuses on MATLAB code programs of the entire stages of solving Stochastic Transportation Linear Programming Problems with Fuzzy Uncertainty Information on Probability Distribution Space (STLPPFI) with its algorithm outlines.
Halgurd N. Azeez, Abdulqader O. Ameen
doaj   +1 more source

ONLINE FUZZY CLUSTERING OF HIGH DIMENSION DATA STREAMS BASED ON NEURAL NETWORK ENSEMBLES

Сучасний стан наукових досліджень та технологій в промисловості, 2019
The subject matter of the article is fuzzy clustering of high-dimensional data based on the ensemble approach, provided that a number and shape of clusters are not known. The goal of the work is to create the neuro-fuzzy approach for clustering data when
Yevgeniy Bodyanskiy, Iryna Perova, Polina Zhernova   +2 more
doaj   +1 more source

Methodology for adapting the parameters of a fuzzy system using the extended Kalman filter [PDF]

, 2011
When we try to analyze and to control a system whose model was obtained only based on input/output data, accuracy is essential in the model. On the other hand, to make the procedure practical, the modeling stage must be computationally efficient. In this
Jose Manuel Andujar Marquez, Andujar Marquez, Jose Manuel, Aznar Torres, Mariano Jose, Al-Hadithi, Basil Mohammed, Barragán Piña, Antonio Javier, Basil M. Al-Hadithi, Antonio Javier Barragan Pina, Al-Hadithi, Basil M., Aznar Torres, Mariano J., Jiménez Avello, Agustín, Agustin Jimenez Avello, Mariano J. Aznar Torres   +11 more
core   +1 more source

From Artificial to Embodied and Physical Intelligence in Smart Manufacturing

Advanced Science, EarlyView.
Smart manufacturing is reframed through complementary artificial, embodied, and physical intelligence, spanning computational reasoning, perception‐action coupling, and material‐level adaptation. Their integration across design, fabrication, operation, and maintenance reveals how intelligence can be co‐designed across algorithms, embodied agents, and ...
Kang Wang, Xinyu Lu, Tong Liu, Jinjie Fang, Xin Li, Siqian Wu, Liuchao Jin, Wei‐Hsin Liao, Shuyou Zhang, Jianrong Tan   +9 more
wiley   +1 more source

On-line identification of MIMO evolving Takagi-Sugeno fuzzy models [PDF]

, 2004
Evolving Takagi-Sugeno (eTS) fuzzy models and the method for their on-line identification has been recently introduced as an effective tool for design of flexible system models with minimum a priori information.
Angelov, Plamen, Xydeas, C, Filev, D
core   +2 more sources