BookR: School of Health Core Facilities Booking
PubMed Publications| Keyword search (4,163 papers available) |  |
"reinforcement learning" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | Disentangling prediction error and value in a formal test of dopamine s role in reinforcement learning | Usypchuk AA; Maes EJP; Lozzi M; Avramidis DK; Schoenbaum G; Esber GR; Gardner MPH; Iordanova MD; | 40738112 CSBN |
| 2 | Comprehensive review of reinforcement learning for medical ultrasound imaging | Elmekki H; Islam S; Alagha A; Sami H; Spilkin A; Zakeri E; Zanuttini AM; Bentahar J; Kadem L; Xie WF; Pibarot P; Mizouni R; Otrok H; Singh S; Mourad A; | 40567264 ENCS |
| 3 | Machine learning innovations in CPR: a comprehensive survey on enhanced resuscitation techniques | Islam S; Rjoub G; Elmekki H; Bentahar J; Pedrycz W; Cohen R; | 40336660 ENCS |
| 4 | Computational neuroscience across the lifespan: Promises and pitfalls | van den Bos W; Bruckner R; Nassar MR; Mata R; Eppinger B; | 29066078 PSYCHOLOGY |
| 5 | Does phasic dopamine release cause policy updates? | Carter F; Cossette MP; Trujillo-Pisanty I; Pallikaras V; Breton YA; Conover K; Caplan J; Solis P; Voisard J; Yaksich A; Shizgal P; | 38039083 PSYCHOLOGY |
| 6 | Nonlinear dynamic modeling and model-based AI-driven control of a magnetoactive soft continuum robot in a fluidic environment | Moezi SA; Sedaghati R; Rakheja S; | 37932207 ENCS |
| 7 | Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate | Sartori I; Walnum HT; Skeie KS; Georges L; Knudsen MD; Bacher P; Candanedo J; Sigounis AM; Prakash AK; Pritoni M; Granderson J; Yang S; Wan MP; | 37153123 ENCS |
| 8 | Reinforcement learning for automatic quadrilateral mesh generation: A soft actor-critic approach | Pan J; Huang J; Cheng G; Zeng Y; | 36375347 ENCS |
| 9 | Trust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection | Rjoub G; Wahab OA; Bentahar J; Cohen R; Bataineh AS; | 35875592 ENCS |
| 10 | Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec. | Khalilpourazari S, Hashemi Doulabi H | 33424076 ENCS |
| 11 | Cue-Evoked Dopamine Neuron Activity Helps Maintain but Does Not Encode Expected Value. | Mendoza JA, Lafferty CK, Yang AK, Britt JP | 31693885 CSBN |
| 12 | Metacontrol of decision-making strategies in human aging. | Bolenz F, Kool W, Reiter AM, Eppinger B | 31397670 PERFORM |
| 13 | Developmental Changes in Learning: Computational Mechanisms and Social Influences. | Bolenz F, Reiter AMF, Eppinger B | 29250006 PERFORM |
| Title: | Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate | ||||
| Authors: | Sartori I, Walnum HT, Skeie KS, Georges L, Knudsen MD, Bacher P, Candanedo J, Sigounis AM, Prakash AK, Pritoni M, Granderson J, Yang S, Wan MP | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/37153123/ | ||||
| DOI: | 10.1016/j.dib.2023.109149 | ||||
| Publication: | Data in brief | ||||
| Keywords: | CSV files; Deep Reinforcement Learning (DRL); Heating; High resolution; Model Predictive Control (MPC); Model identification; Pseudo-Random Binary Sequence (PRBS); Ventilation and Air Conditioning (HVAC); | ||||
| PMID: | 37153123 | Category: | Date Added: | 2023-05-08 | |
| Dept Affiliation: | ENCS | ||||
Description: |
The data presented here were collected independently for 6 real buildings by researchers of different institutions and gathered in the context of the IEA EBC Annex 81 Data-driven Smart Buildings, as a joint effort to compile a diverse range of datasets suitable for advanced control applications of indoor climate and energy use in buildings. The data were acquired by energy meters, both consumption and PV generation, and sensors of technical installation and indoor climate variables, such as temperature, flow rate, relative humidity, CO2 level, illuminance. Weather variables were either acquired by local sensors or obtained from a close by meteorological station. The data were collected either during normal operation of the building, with observation periods between 2 weeks and 2 months, or during experiments designed to excite the thermal mass of the building, with observation periods of approximately one week. The data have a time resolution varying between 1 min and 15 min; in some case the highest resolution data are also averaged at larger intervals, up to 30 min. |
