Results 41 to 50 of about 186,614 (271)

State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing

Entropy, 2021
In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object.
Wasiq Ali, Yaan Li, Muhammad Asif Zahoor Raja, Wasim Ullah Khan, Yigang He   +4 more
doaj   +1 more source

The power of averaging at two consecutive time steps: Proof of a mixing conjecture by Aldous and Fill [PDF]

, 2016
Let $(X_t)_{t = 0 }^{\infty}$ be an irreducible reversible discrete time Markov chain on a finite state space $\Omega $. Denote its transition matrix by $P$.
Hermon, Jonathan, Peres, Yuval
core   +2 more sources

HiST: Histological Images Reconstruct Tumor Spatial Transcriptomics via MultiScale Fusion Deep Learning

Advanced Science, EarlyView.
HiST, a multiscale deep learning framework, reconstructs spatially resolved gene expression profiles directly from histological images. It accurately identifies tumor regions, captures intratumoral heterogeneity, and predicts patient prognosis and immunotherapy response.
Wei Li, Dong Zhang, Eryu Peng, Shijun Shen, Hamid Alinejad‐Rokny, Yao Liu, Junke Zheng, Cizhong Jiang, Youqiong Ye   +8 more
wiley   +1 more source

Computing Inferences for Large-Scale Continuous-Time Markov Chains by Combining Lumping with Imprecision [PDF]

, 2018
If the state space of a homogeneous continuous-time Markov chain is too large, making inferences - here limited to determining marginal or limit expectations - becomes computationally infeasible.
A Ganguly, C Moler, CJ Burke, D Škulj, F Ball, G Franceschinis, J Bock De, JR Norris, P Buchholz, T Krak   +9 more
core   +2 more sources

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Performance enhancement of steady-state Markov analysis for cognitive radio networks via channel reservation

Alexandria Engineering Journal, 2017
Cognitive radio wireless networks CRNs have been considered as an efficient communication paradigm to the utilization of scarce spectrum. The main purpose of channel reservation of dynamic spectrum access (DSA) is to access these idle channels ...
Nehal M. El Azaly, Ehab F. Badran, M.R.M. Rizk, M. Amr Mokhtar   +3 more
doaj   +1 more source

Ergodicity of inhomogeneous Markov chains through asymptotic pseudotrajectories [PDF]

, 2016
In this work, we consider an inhomogeneous (discrete time) Markov chain and are interested in its long time behavior. We provide sufficient conditions to ensure that some of its asymptotic properties can be related to the ones of a homogeneous ...
Benaïm, Michel, Bouguet, Florian, Cloez, Bertrand   +2 more
core   +7 more sources

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Flow Faster: Efficient Decision Algorithms for Probabilistic Simulations [PDF]

Logical Methods in Computer Science, 2008
Strong and weak simulation relations have been proposed for Markov chains, while strong simulation and strong probabilistic simulation relations have been proposed for probabilistic automata.
Lijun Zhang, Holger Hermanns, Friedrich Eisenbrand, David N. Jansen   +3 more
doaj   +1 more source

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence‐Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study

Advanced Intelligent Systems, EarlyView.
The polymerase chain reaction (PCR).Perturbation Theory and Machine Learning framework integrates perturbation theory and machine learning to classify genetic sequences, distinguishing ancient DNA from modern controls and predicting tree health from soil metagenomic data.
Jose L. Rodriguez, Estefania Ascencio, Jose M. Ageitos, Lucia Feijoo, Shan He, Amirreza Daghighi, Gerardo M. Casanola‐Martin, Cristian R. Munteanu, Santiago Rodriguez Yañez, Alejandro Pazos, Harbil Bediaga, Brenda Durand Ordiales, Raquel Esteban, Ana‐Maria Heres, Jorge Curiel Yuste, Lur Epelde, Bakhtiyor Rasulev, Tomas Gonzalez, Sonia Arrasate, Humberto González‐Diaz   +19 more
wiley   +1 more source