Results 71 to 80 of about 81,989 (164)
Bayesian Nonparametric Hidden Semi-Markov Models
, 2012 There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data.
Johnson, Matthew J., Willsky, Alan S.
core
IEEE AccessThe charging scheduling problem of Electric Buses (EBs) is investigated based on Deep Reinforcement Learning (DRL). A Markov Decision Process (MDP) is conceived, where the time horizon includes multiple charging and operating periods in a day, while each
Jiaju Qi +3 more
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Future InternetFederated learning has attracted widespread attention due to its strong capabilities of privacy protection, making it a powerful supporting technology for addressing data silos in the future.
Peng Liu, Lili Jia, Yang Xiao
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Long-Term Energy Consumption Minimization Based on UAV Joint Content Fetching and Trajectory Design
SensorsCaching the contents of unmanned aerial vehicles (UAVs) could significantly improve the content fetching performance of request users (RUs). In this paper, we study UAV trajectory design, content fetching, power allocation, and content placement problems
Elhadj Moustapha Diallo +5 more
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RoboticsMulti-robot ensembles comprising several manipulators are commonly used in industrial settings to process non-deterministic flows of items loaded by an upstream source onto a shared transportation system.
Paolo Righettini, Filippo Cortinovis
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Semi-Markov Decision Process--Project Pictures
, 2005 1 image Provider Notes:This is a temporary page to hold project related pictures extracted from the 2004 EAC presentation. Related Documents:WSR23a, WSR23b, WSR23c, WSR23d, WSR23e, WSR23g, WSR23h, WSR23i, WSR23j, WSR23k, WSR23l, WSR23m, WSR23n, WSR23o, WSR23p, WSR23q ...
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Cost Rate Heuristics for Semi-Markov Decision Processes
, 1991 In response to the computational complexity of the dynamic programming/backwards induction approach to the development of optimal policies for semi-Markov decision processes, we propose a class of heuristics which result from an inductive process which proceeds forwards in time.
Glazebrook, K.D. +2 more
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SEMI-MARKOV DECISION PROCESSES WITH INCOMPLETE STATE OBSERVATION : DISCOUNTED COST CRITERION
Journal of the Operations Research Society of Japan, 1982 In this paper we study the infinite planning horizon, countable state, semi·Markov decision processes (SMDP's) with the incomplete state observation under the average cost criterion. We show that this model can be trans formed to ordinary SMDP's, i.e., SMDP's with the complete state observation.
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Uncertainty assessment of aquifer hydraulic parameters from pumping test data
Applied Water ScienceData from pumping tests is a noisy process, and therefore, performing the pumping test numerous times will not get the same drawdown values. As a consequence, various pumping experiments lead to different values for aquifer parameter estimates.
Azza M. Bashandy +2 more
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Optimizing warfarin dosing for patients with atrial fibrillation using machine learning
Scientific ReportsWhile novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world.
Jeremy Petch +14 more
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