Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552115
Román Cárdenas, Cristina Ruiz Martin, Gabriel A. Wainer, P. Dobias, Mark Rempel
Modeling and Simulation (M&S) techniques have been proven to be effective to understand how diseases spread and assess the effectiveness of decisions aimed to control them (e.g., mobility restrictions). Recently, governments used this approach to determine the evolution of the COVID-19 pandemic. In this context, M&S tools that consider geographical information can improve the quality of the simulations. This research presents a methodology that allows modelers to prototype disease spread models that include geographical information. The model can be easily parameterized for other geographical regions and diseases. We present a case study of a disease spread model to show how this methodology works.
{"title":"Studying the Spread of Diseases Using Geographical Data and Irregular Topologies with Cell-DEVS","authors":"Román Cárdenas, Cristina Ruiz Martin, Gabriel A. Wainer, P. Dobias, Mark Rempel","doi":"10.23919/ANNSIM52504.2021.9552115","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552115","url":null,"abstract":"Modeling and Simulation (M&S) techniques have been proven to be effective to understand how diseases spread and assess the effectiveness of decisions aimed to control them (e.g., mobility restrictions). Recently, governments used this approach to determine the evolution of the COVID-19 pandemic. In this context, M&S tools that consider geographical information can improve the quality of the simulations. This research presents a methodology that allows modelers to prototype disease spread models that include geographical information. The model can be easily parameterized for other geographical regions and diseases. We present a case study of a disease spread model to show how this methodology works.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"27 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83160163","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 : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552174
Xiaolin Hu, Mu Ge
Prescribed fire ignition techniques have significant impact on prescribed fires' growth behavior. This paper presents a systematic way of modeling and simulating prescribed fire ignition techniques. An ignition plan specification is developed to formally specify the ignition activities and schedules of prescribed burning events. The ignition plan specification is used by ignition agents, which transform an ignition plan into detailed tasks and carry out the ignition tasks while coupling with a fire spread simulation model. Simulation results of six ignition scenarios corresponding to six basic ignition techniques are provided. The simulation results demonstrate the effectiveness of the developed modeling approach and show that different ignition techniques can result in different fire growth patterns for prescribed fires.
{"title":"Modeling and Simulating Prescribed Fire Ignition Techniques","authors":"Xiaolin Hu, Mu Ge","doi":"10.23919/ANNSIM52504.2021.9552174","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552174","url":null,"abstract":"Prescribed fire ignition techniques have significant impact on prescribed fires' growth behavior. This paper presents a systematic way of modeling and simulating prescribed fire ignition techniques. An ignition plan specification is developed to formally specify the ignition activities and schedules of prescribed burning events. The ignition plan specification is used by ignition agents, which transform an ignition plan into detailed tasks and carry out the ignition tasks while coupling with a fire spread simulation model. Simulation results of six ignition scenarios corresponding to six basic ignition techniques are provided. The simulation results demonstrate the effectiveness of the developed modeling approach and show that different ignition techniques can result in different fire growth patterns for prescribed fires.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"287 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75335042","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 : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552072
Ben Earle, A. Al-Habashna, Gabriel A. Wainer, Xingliang Li, Guoqiang Xue
Next generation wireless communication systems use massive Multi Input Multi Output (m-MIMO) antenna arrays for their enhanced beamforming capabilities. Providing accurate Channel State Information (CSI) is vital for optimizing m-MIMO communication systems. The complexity of channel reconstruction grows exponentially with the number of antennas, causing traditional methods to become increasingly complicated. Machine-learning techniques can be a useful alternative for channel reconstruction using partial CSI feedback. This paper presents the results of a simulation study built using the MATLAB 5G Toolbox and a neural network trained using the simulated data. The simulator emulates a 5G channel to generate its path delays and gains, and the realistic CSI feedback. This data was used to train and test a neural network to estimate the dominant path gains and delays. The models showed promising results while operating on limited CSI data.
{"title":"Prediction of 5G New Radio Wireless Channel Path Gains and Delays Using Machine Learning and CSI Feedback","authors":"Ben Earle, A. Al-Habashna, Gabriel A. Wainer, Xingliang Li, Guoqiang Xue","doi":"10.23919/ANNSIM52504.2021.9552072","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552072","url":null,"abstract":"Next generation wireless communication systems use massive Multi Input Multi Output (m-MIMO) antenna arrays for their enhanced beamforming capabilities. Providing accurate Channel State Information (CSI) is vital for optimizing m-MIMO communication systems. The complexity of channel reconstruction grows exponentially with the number of antennas, causing traditional methods to become increasingly complicated. Machine-learning techniques can be a useful alternative for channel reconstruction using partial CSI feedback. This paper presents the results of a simulation study built using the MATLAB 5G Toolbox and a neural network trained using the simulated data. The simulator emulates a 5G channel to generate its path delays and gains, and the realistic CSI feedback. This data was used to train and test a neural network to estimate the dominant path gains and delays. The models showed promising results while operating on limited CSI data.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"8 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75588848","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 : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552061
Nahom Kidane, Yuzhong Shen, R. Kelly
A non-invasive and objective method of capturing upper body surface anatomy, such as 3D optical scanning, would be advantageous for evaluating chest wall deformity. By potentially eliminating the need for computed tomography scanning and superseding manual measurements subject to errors, a system that utilizes optical scanning presents great value to patients and practitioners. This work aimed to quantify the accuracy of two current generation 3D surface scanners, Azure Kinect & StructureIO Mark-II, in assessing the severity of chest wall deformities. 3D surface deviation analysis was conducted on the models created by each scanner, and the findings are reported.
{"title":"Evaluating Azure Kinect and Structure Mark-II 3D Surface Scanners for Clinical Chest Wall Deformity Assessment","authors":"Nahom Kidane, Yuzhong Shen, R. Kelly","doi":"10.23919/ANNSIM52504.2021.9552061","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552061","url":null,"abstract":"A non-invasive and objective method of capturing upper body surface anatomy, such as 3D optical scanning, would be advantageous for evaluating chest wall deformity. By potentially eliminating the need for computed tomography scanning and superseding manual measurements subject to errors, a system that utilizes optical scanning presents great value to patients and practitioners. This work aimed to quantify the accuracy of two current generation 3D surface scanners, Azure Kinect & StructureIO Mark-II, in assessing the severity of chest wall deformities. 3D surface deviation analysis was conducted on the models created by each scanner, and the findings are reported.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"99 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73732221","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 : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552035
A. Bruzzone, M. Massei, K. Sinelshchikov, A. Giovannetti, Bharath Kumar Gadupuri
The paper proposes an example of Strategic Engineering approach applied to a complex system related to Marine Environment with special attention to traffic control. This case represents an application of innovative discipline in terms of Strategic Management based on Artificial Intelligence, Modeling and Simulation to support decision makers while operating into a dynamic environment. The authors propose this methodological approach using data and extra information based on the strong combination of Simulation with other techniques. In facts the adoption of Strategic Engineering improves Strategic Management capabilities within Organizations or Institutions and the proposed case study is based on a realistic scenario and developed through different elements, models and simulators.
{"title":"Strategic Engineering Applied to Complex Systems within Marine Environment","authors":"A. Bruzzone, M. Massei, K. Sinelshchikov, A. Giovannetti, Bharath Kumar Gadupuri","doi":"10.23919/ANNSIM52504.2021.9552035","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552035","url":null,"abstract":"The paper proposes an example of Strategic Engineering approach applied to a complex system related to Marine Environment with special attention to traffic control. This case represents an application of innovative discipline in terms of Strategic Management based on Artificial Intelligence, Modeling and Simulation to support decision makers while operating into a dynamic environment. The authors propose this methodological approach using data and extra information based on the strong combination of Simulation with other techniques. In facts the adoption of Strategic Engineering improves Strategic Management capabilities within Organizations or Institutions and the proposed case study is based on a realistic scenario and developed through different elements, models and simulators.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"117 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79494251","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 : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552108
Li Huang, D. Cronan, A. Kliskey
The modeling of landscape change and socio-ecological systems (SES) tends to ignore the interactions across distance and boundaries. To fill the gap, this research analyzes landscape change by considering the tele-coupling effects at the local scale between Owyhee county and Treasure Valley in Idaho, United States. The spatial distribution of recreational activities in Owyhee county are modeled by Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST). Land use and cover change (LUCC) are simulated using Multi-Layer Perceptron Neural Network (MLPNN). Results show that the tele-coupling effects have significant impacts on the nature-based recreation in Owyhee county. With the tele-coupling effects, MLPNN has achieved a high overall accuracy and kappa coefficient in LUCC. The findings suggest that the tele-coupling effects should be incorporated into the modeling of landscape change and SES. This study also provides policy implications for land management and stakeholder involvement in accommodating landscape change.
{"title":"Modeling of Landscape Change and Tele-Coupling in Local Socio-Ecological Systems: A Simulation of Land Use Change and Recreational Activities in Southern Idaho, United States","authors":"Li Huang, D. Cronan, A. Kliskey","doi":"10.23919/ANNSIM52504.2021.9552108","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552108","url":null,"abstract":"The modeling of landscape change and socio-ecological systems (SES) tends to ignore the interactions across distance and boundaries. To fill the gap, this research analyzes landscape change by considering the tele-coupling effects at the local scale between Owyhee county and Treasure Valley in Idaho, United States. The spatial distribution of recreational activities in Owyhee county are modeled by Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST). Land use and cover change (LUCC) are simulated using Multi-Layer Perceptron Neural Network (MLPNN). Results show that the tele-coupling effects have significant impacts on the nature-based recreation in Owyhee county. With the tele-coupling effects, MLPNN has achieved a high overall accuracy and kappa coefficient in LUCC. The findings suggest that the tele-coupling effects should be incorporated into the modeling of landscape change and SES. This study also provides policy implications for land management and stakeholder involvement in accommodating landscape change.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"10 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80277048","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 : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552156
Kadir Amasyali, M. Olama
Demand response (DR) is one of the most effective ways to maintain the reliability and improve the flexibility of power systems. Accurate forecasts of baseline loads are essential for DR programs. In the era of big data, machine learning-based approaches present a unique opportunity for baseline load forecasting. Thus, this paper presents a machine learning-based approach using a relatively less explored algorithm, Gaussian process regression (GPR), to forecast aggregate baseline loads. As such, a dataset was generated using a set of EnergyPlus simulations. Using the generated dataset, a GPR-based forecasting model was developed. In addition, support vector regression (SVR)-, artificial neural network (ANN)-, and averaging-based models were developed as baseline models for comparison. These models were compared in terms of accuracy, simplicity, and integrity. The prediction performance of the models showed that the GPR-based model is more accurate and reliable than the others. Such high performance shows the potential of the GPR in baseline load forecasting. GPR, therefore, can be used for DR applications.
{"title":"Gaussian Process Regression for Aggregate Baseline Load Forecasting","authors":"Kadir Amasyali, M. Olama","doi":"10.23919/ANNSIM52504.2021.9552156","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552156","url":null,"abstract":"Demand response (DR) is one of the most effective ways to maintain the reliability and improve the flexibility of power systems. Accurate forecasts of baseline loads are essential for DR programs. In the era of big data, machine learning-based approaches present a unique opportunity for baseline load forecasting. Thus, this paper presents a machine learning-based approach using a relatively less explored algorithm, Gaussian process regression (GPR), to forecast aggregate baseline loads. As such, a dataset was generated using a set of EnergyPlus simulations. Using the generated dataset, a GPR-based forecasting model was developed. In addition, support vector regression (SVR)-, artificial neural network (ANN)-, and averaging-based models were developed as baseline models for comparison. These models were compared in terms of accuracy, simplicity, and integrity. The prediction performance of the models showed that the GPR-based model is more accurate and reliable than the others. Such high performance shows the potential of the GPR in baseline load forecasting. GPR, therefore, can be used for DR applications.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"1 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82870455","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 : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552052
Xiaohan Wang, Lin Zhang, Y. Laili, Kunyu Xie, H. Lu, Chun Zhao
Agent-Based Simulation (ABS) provides distributed entities for simulating agent emergence or interactive behaviors, but the agent behaviors usually rely on the hard rules, thus lacking the intelligent decision-making capability. With the development of artificial intelligence, Multi-Agent Reinforcement Learning (MARL) has shown positive potential in robot control, autonomous driving, and human-machine battles as its powerful learning capability for making intelligent decisions. There are many challenges in applying MARL directly to ABS, and there is no unified framework that integrates them. The paper proposed the Multi-Agent Discrete Event Simulation (MADES) framework based on several DEVS atomic models to construct the multi-agent system, which has advantages for representing various MARL architectures. A predator-prey system simulation with a mainstream MARL algorithm is built under our framework, the training curves and event transition time figure have verified the learning and the simulation performance of the framework.
{"title":"MADES: A Unified Framework for Integrating Agent-Based Simulation with Multi-Agent Reinforcement Learning","authors":"Xiaohan Wang, Lin Zhang, Y. Laili, Kunyu Xie, H. Lu, Chun Zhao","doi":"10.23919/ANNSIM52504.2021.9552052","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552052","url":null,"abstract":"Agent-Based Simulation (ABS) provides distributed entities for simulating agent emergence or interactive behaviors, but the agent behaviors usually rely on the hard rules, thus lacking the intelligent decision-making capability. With the development of artificial intelligence, Multi-Agent Reinforcement Learning (MARL) has shown positive potential in robot control, autonomous driving, and human-machine battles as its powerful learning capability for making intelligent decisions. There are many challenges in applying MARL directly to ABS, and there is no unified framework that integrates them. The paper proposed the Multi-Agent Discrete Event Simulation (MADES) framework based on several DEVS atomic models to construct the multi-agent system, which has advantages for representing various MARL architectures. A predator-prey system simulation with a mainstream MARL algorithm is built under our framework, the training curves and event transition time figure have verified the learning and the simulation performance of the framework.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"13 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79556694","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 : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552075
Jean-Pierre Müller
DEVS (Discrete EVent System) has been proposed to formalize discrete dynamical systems and is widely used for modeling and simulation. Although the operational semantics of DEVS models is well defined, and it exists some attempt to characterize their behavior using temporal logics, there is no attempt to define their denotational semantics. The meaning of a DEVS model is the set of possible coupled input, output and state trajectories. Therefore, denotational semantics is a mapping from DEVS models onto an algebra of trajectories. In this paper, we use category theory to define this algebra. This algebra, called Dyn, is made of trajectories as objects, and the DEVS behavior and structure specifications are mapped onto morphisms between trajectories, exhibiting their coupling. This result opens the way to algebraic manipulations of DEVS models, as well as the access to the results and proof mechanisms available in category theory.
{"title":"Towards a Categorical Semantics of DEVS","authors":"Jean-Pierre Müller","doi":"10.23919/ANNSIM52504.2021.9552075","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552075","url":null,"abstract":"DEVS (Discrete EVent System) has been proposed to formalize discrete dynamical systems and is widely used for modeling and simulation. Although the operational semantics of DEVS models is well defined, and it exists some attempt to characterize their behavior using temporal logics, there is no attempt to define their denotational semantics. The meaning of a DEVS model is the set of possible coupled input, output and state trajectories. Therefore, denotational semantics is a mapping from DEVS models onto an algebra of trajectories. In this paper, we use category theory to define this algebra. This algebra, called Dyn, is made of trajectories as objects, and the DEVS behavior and structure specifications are mapped onto morphisms between trajectories, exhibiting their coupling. This result opens the way to algebraic manipulations of DEVS models, as well as the access to the results and proof mechanisms available in category theory.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"32 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83540331","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 : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552044
H. Khalil, G. Wainer
Measuring Carbon Dioxide and studying its diffusion indoors has many applications, which include, but not limited to, maintaining air quality, conserving energy, and minimizing viral infections. On the one hand, many of the experiments needed to conduct studies, like the indoor diffusion of Carbon Dioxide, are complex to implement. On the other hand, adjusting each model manually to mimic the studied space is difficult and prone to error. We propose an automation tool for modeling, simulating, and visualizing Carbon Dioxide. The tool automates the simulation of Cellular Discrete Event Simulation models of Carbon Dioxide dispersion indoors. We discuss the tool's applications, the software architecture, related tools, and a case study. The case study compares simulations of a closed space with different configurations. The results show how such configurations affect Carbon Dioxide concentration.
{"title":"CD2: An Automation Tool for Cell-Devs CO2 Diffusion Models","authors":"H. Khalil, G. Wainer","doi":"10.23919/ANNSIM52504.2021.9552044","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552044","url":null,"abstract":"Measuring Carbon Dioxide and studying its diffusion indoors has many applications, which include, but not limited to, maintaining air quality, conserving energy, and minimizing viral infections. On the one hand, many of the experiments needed to conduct studies, like the indoor diffusion of Carbon Dioxide, are complex to implement. On the other hand, adjusting each model manually to mimic the studied space is difficult and prone to error. We propose an automation tool for modeling, simulating, and visualizing Carbon Dioxide. The tool automates the simulation of Cellular Discrete Event Simulation models of Carbon Dioxide dispersion indoors. We discuss the tool's applications, the software architecture, related tools, and a case study. The case study compares simulations of a closed space with different configurations. The results show how such configurations affect Carbon Dioxide concentration.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"18 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85134493","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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