Results 241 to 250 of about 1,900 (308)

Hybrid reinforcement learning optimization of aging aware energy management and powertrain sizing in fuel cell hybrid electric vehicles. [PDF]

Sci Rep
Mostashiri A, Montazeri-Gh M.
europepmc   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

Advanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua   +4 more
wiley   +1 more source

Multi-objective inventory optimization using reinforcement learning: a comparative study on profitability and carbon emissions. [PDF]

Sci Rep
Sorour A, Sadek Y, Elshalakani M, Elshalakani M.   +3 more
europepmc   +1 more source

Cteno‐Bot: An Untethered Metachronally Swimming Robot With Magnetoactive Propulsors

Advanced Intelligent Systems, EarlyView.
We present Cteno‐bot, an untethered ctenophore‐inspired robot which swims using metachronally coordinated appendages. A single mechanism controls up to 216 magnetoactive propulsors via a dynamically varying magnetic field. We show that the swimming speed of the robot can be increased without a corresponding increase in power requirement, simply by ...
David J. Peterman, Margaret L. Byron
wiley   +1 more source

A two stage optimization model for sustainable location routing problem with capacity and time window constraints in smart parcel lockers. [PDF]

Sci Rep
Ghadirpour SM, Chaharsooghi SK, Hajiaghaei-Keshteli M.   +2 more
europepmc   +1 more source

A Self‐Driving Lab for Solution‐Processed Electrochromic Thin Films

Advanced Intelligent Systems, EarlyView.
A self‐driving laboratory accelerates the development of solution‐processed electrochromic thin films. By coupling machine learning with robotic fabrication and characterization, this closed‐loop platform systematically navigates complex processing parameters.
Selma Dahms, Luca Torresi, Shahbaz Tareq Bandesha, Jan Hansmann, Holger Röhm, Alexander Colsmann, Marco Schott, Pascal Friederich   +7 more
wiley   +1 more source

Expert experience-guided virtual datasets for adaptive automatic driving in metro trains. [PDF]

Sci Rep
Huang Y, Zhao W, Chen D, Lin G.
europepmc   +1 more source

Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning

Advanced Intelligent Systems, EarlyView.
A subjective risk‐driven inverse reinforcement learning framework is proposed to model driver decision‐making. It infers drivers' risk perception and risk tolerance from driving data. A learnable risk threshold is used to regulate decisions, enabling interpretable and human‐like driving behavior decisions.
Yang Liang, Zichao Wang, Zhiwei He, Qingquan Wei, Yong Zuo, Linzhen Nie, Zhishuai Yin   +6 more
wiley   +1 more source

Generative Multiobjective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design. [PDF]

Ind Eng Chem Res
Muthyala MR, Sorourifar F, Tan T, Peng Y, Paulson JA.   +4 more
europepmc   +1 more source

Time‐Delayed Spiking Reservoir Computing Enables Efficient Time Series Prediction

Advanced Intelligent Systems, EarlyView.
This study proposes time‐delayed spiking reservoir computing (TDSRC) for efficient time series prediction. By concatenating time‐lagged states, TDSRC constructs an expanded readout feature vector without altering internal reservoir dynamics. This approach enables highly accurate forecasting with significantly fewer neurons, providing a resource ...
Pin Jin, Ren Cao, Zhongxue Gan, Siyang Leng   +3 more
wiley   +1 more source