Results 41 to 50 of about 15,837 (289)
Advanced Intelligent Systems, EarlyView.This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi +3 more
wiley +1 more source
International Journal of Mathematics and Mathematical Sciences, 1992 The authors study the queueing process in a single-server queueing system with state dependent service and with the input modulated by a semi-Markov process embedded in the queueing process. It is also assumed that the server capacity is r≥1 and that any
Jewgeni H. Dshalalow, Gary Russell
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MODELING STRENGTH PROPERTIES OF PRODUCTS OF ADDITIVE TECHNOLOGIES USING PARALLEL COMPUTING
Современная наука и инновации, 2022 In the modern world, the importance of scientific results is growing, ensuring the creation of fundamentally new technologies and products with new consumer properties.
A. N. Privalov, Y. I. Bogatyryova
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Advanced Intelligent Systems, EarlyView.This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy +2 more
wiley +1 more source
Advanced Intelligent Systems, EarlyView.Artificial intelligence (AI) is reshaping autonomous mobile robot navigation beyond classical pipelines. This review analyzes how AI techniques are integrated into core navigation tasks, including path planning and control, localization and mapping, perception, and context‐aware decision‐making. Learning‐based, probabilistic, and soft‐computing methods
Giovanna Guaragnella +5 more
wiley +1 more source
Inference, Prediction, & Entropy-Rate Estimation of Continuous-Time, Discrete-Event Processes
Entropy, 2022 Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time.
Sarah E. Marzen, James P. Crutchfield
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American Journal of Agricultural Economics, EarlyView.Abstract Large‐scale land reforms constitute a substantial redistribution of wealth and reallocation of agricultural land, which is a major form of asset and production input in developing countries. While land redistribution (from the rich to the poor) remains a highly controversial issue, extensive evidence on its effect is limited.
Devashish Mitra +3 more
wiley +1 more source
Critically Important Object Security System Element Model
Безопасность информационных технологий, 2012 A stochastic model of critically important object security system element has been developed. The model includes mathematical description of the security system element properties and external influences.
I. V. Khomyackov
doaj
Risk‐aware safe reinforcement learning for control of stochastic linear systems
Asian Journal of Control, EarlyView.Abstract This paper presents a risk‐aware safe reinforcement learning (RL) control design for stochastic discrete‐time linear systems. Rather than using a safety certifier to myopically intervene with the RL controller, a risk‐informed safe controller is also learned besides the RL controller, and the RL and safe controllers are combined together ...
Babak Esmaeili +2 more
wiley +1 more source
First-Order Uncertain Hidden Semi-Markov Process for Failure Prognostics With Scarce Data
IEEE Access, 2020 Failure prognostics aims at predicting the object equipment's future degradation trend and derives the remaining useful life with a predefined failure threshold.
Jie Liu
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