Pub Date : 1900-01-01DOI: 10.46354/i3m.2021.emss.020
{"title":"\"Time-to-idle Control Variate Performance in the Single Queue Case\"","authors":"","doi":"10.46354/i3m.2021.emss.020","DOIUrl":"https://doi.org/10.46354/i3m.2021.emss.020","url":null,"abstract":"","PeriodicalId":322169,"journal":{"name":"Proceedings of the 33rd European Modeling & Simulation Symposium","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134389462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.46354/i3m.2021.emss.054
G. Di Bona
{"title":"A Hybrid Multi-Criteria Decision Model (HMCDM) based on AHP and TOPSIS analysis to evaluate Maintenance Strategy","authors":"G. Di Bona","doi":"10.46354/i3m.2021.emss.054","DOIUrl":"https://doi.org/10.46354/i3m.2021.emss.054","url":null,"abstract":"","PeriodicalId":322169,"journal":{"name":"Proceedings of the 33rd European Modeling & Simulation Symposium","volume":"14 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129395465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.46354/i3m.2021.emss.007
O. Chernoyarov
{"title":"Digital simulators of the random processes","authors":"O. Chernoyarov","doi":"10.46354/i3m.2021.emss.007","DOIUrl":"https://doi.org/10.46354/i3m.2021.emss.007","url":null,"abstract":"","PeriodicalId":322169,"journal":{"name":"Proceedings of the 33rd European Modeling & Simulation Symposium","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125258420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.46354/i3m.2021.emss.040
E. Tarasova
{"title":"The Physical Topological Modeling Of Single Radiation Effects In Submicron Ultrahigh-Frequency Semiconductor Diode Structures With Taking In Account The Heating Of An Electron-Hole Gas In The Charged Particle Track","authors":"E. Tarasova","doi":"10.46354/i3m.2021.emss.040","DOIUrl":"https://doi.org/10.46354/i3m.2021.emss.040","url":null,"abstract":"","PeriodicalId":322169,"journal":{"name":"Proceedings of the 33rd European Modeling & Simulation Symposium","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124488839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.46354/i3m.2021.emss.035
L. Lioce
{"title":"Development of a Needle Injection Pad Trainer for Simulating Intradermal, Subcutaneous and Intramuscular Injections: Used in Student Nurse Training","authors":"L. Lioce","doi":"10.46354/i3m.2021.emss.035","DOIUrl":"https://doi.org/10.46354/i3m.2021.emss.035","url":null,"abstract":"","PeriodicalId":322169,"journal":{"name":"Proceedings of the 33rd European Modeling & Simulation Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127929214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.46354/i3m.2021.emss.015
Troyle Thomas, Armando Fandango, D. Reed, C. Hoayun, J. Hurter, Alexander Gutierrez, K. Brawner
By testing various physics levels for training autonomous-vehicle navigation using a deep deterministic policy gradient algorithm, the present study fills a lack of research on the impact of physics levels for vehicle behaviour, specifically for reinforcement-learning algorithms. Measures from a PointGoal Navigation task were investigated: simulator run-time, training steps, and agent effectiveness through the Success weighted by (normalised inverse) Path Length (SPL) measure. Training and testing occurred in the novel simulator ARORA, or A Realistic Open environment for Rapid Agent training. The goal of ARORA is to provide a high-fidelity, open-source platform for simulation, using physics-based movement, vehicle modelling, and a continuous action space within a large-scale geospecific city environment. Using four physics levels, or models, to create four different curriculum conditions for training, the SPL was highest for the condition using all physics levels defined for the experiment, with two conditions returning zero values. Future researchers should consider providing adequate support when training complex-physics vehicle models. The run-time results revealed a benefit for experimental machines with a better CPU, at least for the vector-only observations we employed.
{"title":"Comparing Physics Effects through Reinforcement Learning in the ARORA Simulator","authors":"Troyle Thomas, Armando Fandango, D. Reed, C. Hoayun, J. Hurter, Alexander Gutierrez, K. Brawner","doi":"10.46354/i3m.2021.emss.015","DOIUrl":"https://doi.org/10.46354/i3m.2021.emss.015","url":null,"abstract":"By testing various physics levels for training autonomous-vehicle navigation using a deep deterministic policy gradient algorithm, the present study fills a lack of research on the impact of physics levels for vehicle behaviour, specifically for reinforcement-learning algorithms. Measures from a PointGoal Navigation task were investigated: simulator run-time, training steps, and agent effectiveness through the Success weighted by (normalised inverse) Path Length (SPL) measure. Training and testing occurred in the novel simulator ARORA, or A Realistic Open environment for Rapid Agent training. The goal of ARORA is to provide a high-fidelity, open-source platform for simulation, using physics-based movement, vehicle modelling, and a continuous action space within a large-scale geospecific city environment. Using four physics levels, or models, to create four different curriculum conditions for training, the SPL was highest for the condition using all physics levels defined for the experiment, with two conditions returning zero values. Future researchers should consider providing adequate support when training complex-physics vehicle models. The run-time results revealed a benefit for experimental machines with a better CPU, at least for the vector-only observations we employed.","PeriodicalId":322169,"journal":{"name":"Proceedings of the 33rd European Modeling & Simulation Symposium","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122436149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.46354/i3m.2021.emss.057
A. Bruzzone, K. Sinelshchikov
{"title":"Autonomous Systems for Industrial Plants and Iron & Steel Facilities","authors":"A. Bruzzone, K. Sinelshchikov","doi":"10.46354/i3m.2021.emss.057","DOIUrl":"https://doi.org/10.46354/i3m.2021.emss.057","url":null,"abstract":"","PeriodicalId":322169,"journal":{"name":"Proceedings of the 33rd European Modeling & Simulation Symposium","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126660582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.46354/i3m.2021.emss.021
Zheng Li
{"title":"A Multi-level Heterogeneous Model data Framework for Intelligent Factory Digital-Twin Systems","authors":"Zheng Li","doi":"10.46354/i3m.2021.emss.021","DOIUrl":"https://doi.org/10.46354/i3m.2021.emss.021","url":null,"abstract":"","PeriodicalId":322169,"journal":{"name":"Proceedings of the 33rd European Modeling & Simulation Symposium","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124590424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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