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Encoding Protest Duration In An Agent-Based Model As Characteristic Phase Transitions 基于智能体模型的抗议持续时间编码为特征相变
2022 Annual Modeling and Simulation Conference (ANNSIM) Pub Date : 2022-07-18 DOI: 10.23919/ANNSIM55834.2022.9859477
Brian J. Goode, Bianica Pires
{"title":"Encoding Protest Duration In An Agent-Based Model As Characteristic Phase Transitions","authors":"Brian J. Goode, Bianica Pires","doi":"10.23919/ANNSIM55834.2022.9859477","DOIUrl":"https://doi.org/10.23919/ANNSIM55834.2022.9859477","url":null,"abstract":"Protests and civil unrest events carry high societal impact and are examples of complex interactions and collective behavior. Agent-based modeling (ABM) is one approach to simulating emergent phenomena seen in protests by leveraging individual behaviors derived from theory or observation. The utility of these models is immense; however, techniques for understanding the theoretical consonance of these complex aggregate behaviors is missing. For example, protest dynamics can range from small-scale, more frequent protests to nation-wide events that can last several days. This work focuses on characterizing the duration between population level shifts from protest and non-protest states using a characteristic, stylized, network model of individual interactions. The model encodes the population (macro-)level protest states as a well-known phase transition (double-well potential function) dependent on individual (micro-)level interaction characteristics. The model is fit to the ABM in distribution and the process of rioting is captured by a reduced set of parameters.","PeriodicalId":374469,"journal":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116888666","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}
引用次数: 0
Predicting Cooling Energy Demands of Adaptive Facades Using Artificial Neural Network 基于人工神经网络的自适应立面冷却能量需求预测
2022 Annual Modeling and Simulation Conference (ANNSIM) Pub Date : 2022-07-18 DOI: 10.23919/ANNSIM55834.2022.9859413
Ammar Alammar, W. Jabi
{"title":"Predicting Cooling Energy Demands of Adaptive Facades Using Artificial Neural Network","authors":"Ammar Alammar, W. Jabi","doi":"10.23919/ANNSIM55834.2022.9859413","DOIUrl":"https://doi.org/10.23919/ANNSIM55834.2022.9859413","url":null,"abstract":"Adaptive Façades (AFs) have proven to be effective as a building envelope that can enhance energy efficiency and thermal comfort. However, evaluating the performance of these AFs using the current building performance simulation (BPS) tools is complex, time-consuming, and computationally intensive. These limitations can be overcome by using a machine learning (ML) model as a method to assess the AF system efficiently during the early design stage. This study presents an alternative approach using an Artificial Neural Network (ANN) model that can predict the hourly cooling loads of AF in significantly less time compared to BPS. To construct the model, a generative parametric simulation of office tower spaces with an AF shading system were simulated in terms of energy consumption using Honeybee add-on in Grass-hopper which are linked to EnergyPlus for training the ANN model. The prediction results showed a highly accurate model that can estimate cooling loads within seconds.","PeriodicalId":374469,"journal":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122445815","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}
引用次数: 1
Boolean Logical Operator Driven Selective Data Filtering For Large Datasets 布尔逻辑运算符驱动的大数据集选择性数据过滤
2022 Annual Modeling and Simulation Conference (ANNSIM) Pub Date : 2022-07-18 DOI: 10.23919/ANNSIM55834.2022.9859309
G. Davidson, S. Majumdar
{"title":"Boolean Logical Operator Driven Selective Data Filtering For Large Datasets","authors":"G. Davidson, S. Majumdar","doi":"10.23919/ANNSIM55834.2022.9859309","DOIUrl":"https://doi.org/10.23919/ANNSIM55834.2022.9859309","url":null,"abstract":"Specific users of a system processing large data sets are often interested in only a small subset of the large volumes of available data. This paper presents research on a parallel processing based data filtering technique that filters out and stores only the subset of data that is of interest to a given user. A user’s preferences reflecting her/his interest are captured in a set of keywords or phrases which may be used in conjunction with Boolean operators. An Apache Spark based prototype is built and deployed on an Amazon EC2 cloud to demonstrate the viability of the approach and to analyze the performance of the proposed technique.","PeriodicalId":374469,"journal":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","volume":"31 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114122232","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}
引用次数: 1
Modeling Cardiac Cell Biophysics Using Long-Short-Term Memory Networks 利用长短期记忆网络建模心脏细胞生物物理学
2022 Annual Modeling and Simulation Conference (ANNSIM) Pub Date : 2022-07-18 DOI: 10.23919/ANNSIM55834.2022.9859442
Bruna Gonçalves, M. Deo
{"title":"Modeling Cardiac Cell Biophysics Using Long-Short-Term Memory Networks","authors":"Bruna Gonçalves, M. Deo","doi":"10.23919/ANNSIM55834.2022.9859442","DOIUrl":"https://doi.org/10.23919/ANNSIM55834.2022.9859442","url":null,"abstract":"Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are being used to investigate mechanisms of various life-threatening cardiac disorders and drug effects. Long Short-Term Memory (LSTM) is a type of artificial neural network that has become popular for finding patterns from previous time steps to help predict the future response. In this paper, we developed LSTM-based predictive models to represent the electrophysiological response of a typical hiPSC-CM. Our model is able to reproduce experimentally observed effects on the action potential (AP) morphology for alterations in 5 main ionic currents in hiPSC-CMs. The proposed model achieved an impressive accuracy of 98.5% when compared to a detailed mathematical model of hiPSC-CM based on AP duration parameters (APD50, APD75, APD90). The proposed approach has the potential to reduce the computational time of multiscale cardiac electrophysiology simulations and can be used in mechanistic studies to investigate inherited arrhythmia syndromes and patient-specific drug therapies.","PeriodicalId":374469,"journal":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122240537","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}
引用次数: 0
ESS: EMF-Based Simulation Specification, A Domain-Specific Language For Model Validation Experiments ESS:基于电磁场的仿真规范，一种用于模型验证实验的领域特定语言
2022 Annual Modeling and Simulation Conference (ANNSIM) Pub Date : 2022-07-18 DOI: 10.23919/ANNSIM55834.2022.9859307
Joost Mertens, J. Denil
{"title":"ESS: EMF-Based Simulation Specification, A Domain-Specific Language For Model Validation Experiments","authors":"Joost Mertens, J. Denil","doi":"10.23919/ANNSIM55834.2022.9859307","DOIUrl":"https://doi.org/10.23919/ANNSIM55834.2022.9859307","url":null,"abstract":"In the modeling and simulation process of systems, the verification and validation activities are necessary to give credibility to model results. For similar types of models, the applicable validation techniques are reusable and automatable. In this paper, we present ESS, the EMF-Based Simulation Specification. ESS is a domain-specific language, workflow, and accompanying code generators built on top of the Eclipse Modeling Framework. Existing approaches focus on specifying how experiments and simulations must be executed, ESS extends that functionality with the ability to specify and reuse model validation techniques. We provide an initial implementation of ESS that works with ordinary differential equations in Matlab and demonstrate this implementation with the validation of an electronic filter circuit. Though this initial version is limited to only one simulator and one validation metric, our aim is to provide support for an exhaustive list of validation metrics in the future.","PeriodicalId":374469,"journal":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133031979","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}
引用次数: 3
Impact Assessment Of Energy Conservation Measures On Building Energy Consumption, Carbon Emission, And Adaptation Cost Using Future Weather Data 基于未来气象数据的节能措施对建筑能耗、碳排放及适应成本的影响评价
2022 Annual Modeling and Simulation Conference (ANNSIM) Pub Date : 2022-07-18 DOI: 10.23919/ANNSIM55834.2022.9859308
Zahra Zolfaghari, Twisha Raja, Patricia Kusumadjaja, Daniel Salinas, Udiksha Nagaraj Kapini, P. Pease
{"title":"Impact Assessment Of Energy Conservation Measures On Building Energy Consumption, Carbon Emission, And Adaptation Cost Using Future Weather Data","authors":"Zahra Zolfaghari, Twisha Raja, Patricia Kusumadjaja, Daniel Salinas, Udiksha Nagaraj Kapini, P. Pease","doi":"10.23919/ANNSIM55834.2022.9859308","DOIUrl":"https://doi.org/10.23919/ANNSIM55834.2022.9859308","url":null,"abstract":"The sixth Intergovernmental Panel on Climate Change assessment report shows a 1.5° Celsius increase in the earth’s temperature by 2040 which could lead to catastrophic weather events. Keeping this projection to the lower range through mitigation efforts is therefore of paramount importance. Buildings account for a significant amount of carbon emissions. Therefore, predicting buildings’ energy consumption and carbon emission in advance and implementing the most cost and energy-efficient strategies accordingly can help to combat climate change. This paper analyzed the energy consumption and carbon emission of an office building in 16 US climate zones now and in 2050 using future weather data. Also, the impact of three energy conservation measures and their implementation cost has been assessed in the cities with higher future energy use to suggest the most energy and cost-effective building upgrade that can offset the additional energy consumption caused by global warming with a reasonable payback period.","PeriodicalId":374469,"journal":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132441364","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}
引用次数: 3
Multi-Paradigm Modelling For Model Based Systems Engineering: Extending The FTG + PM 基于模型的系统工程多范式建模:扩展FTG + PM
2022 Annual Modeling and Simulation Conference (ANNSIM) Pub Date : 2022-07-18 DOI: 10.23919/ANNSIM55834.2022.9859391
Randy Paredis, Joeri Exelmans, H. Vangheluwe
{"title":"Multi-Paradigm Modelling For Model Based Systems Engineering: Extending The FTG + PM","authors":"Randy Paredis, Joeri Exelmans, H. Vangheluwe","doi":"10.23919/ANNSIM55834.2022.9859391","DOIUrl":"https://doi.org/10.23919/ANNSIM55834.2022.9859391","url":null,"abstract":"Model Based Systems Engineering (MBSE) supports Cyber-Physical Systems (CPS) design, by experts from various domains, through complex workflows, manipulating models in different formalisms, each with their own methods, techniques and tools. We refer to often-used combinations of workflow and formalism patterns as modelling paradigms. The Formalism Transformation Graph (FTG) and Process Model (PM) are key components in Multi-Paradigm Modelling (MPM) based MBSE, which combines multiple modelling paradigms. This paper extends the FTG + PM framework: a Process Trace (PT) records all start/end events of engineering activities as well as all versions of all artifacts consumed/produced by them. A PT results from the enactment of a PM. The artifacts and activities in the PT are “virtual”: though referred to in a technology-agnostic manner, they are realized using various technologies. Adapters translate between both, making the FTG + PM federated. An Automated Guided Vehicle (AGV) example is used.","PeriodicalId":374469,"journal":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134461719","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}
引用次数: 1
Infomorphism: Urban Planning For Renewable Energy Integration Via Simulated Energy Exchange Networks 信息形态:通过模拟能源交换网络实现可再生能源整合的城市规划
2022 Annual Modeling and Simulation Conference (ANNSIM) Pub Date : 2022-07-18 DOI: 10.23919/ANNSIM55834.2022.9859300
Feng Li, A. Tsamis, K. Schell
{"title":"Infomorphism: Urban Planning For Renewable Energy Integration Via Simulated Energy Exchange Networks","authors":"Feng Li, A. Tsamis, K. Schell","doi":"10.23919/ANNSIM55834.2022.9859300","DOIUrl":"https://doi.org/10.23919/ANNSIM55834.2022.9859300","url":null,"abstract":"Increasing renewable energy efficiency is a crucial part of developing a sustainable city. While current Urban Building Energy Modeling frameworks have been developed for analyzing and improving urban energy efficiency, these tools have not integrated systemic optimization modeling to develop and evaluate the performance of potential urban environments from generative planning models. In this study, we present Infomorphism, a computational planning framework that joins a morphological generative process with an energy network optimization model, to explore potential planning policies and constraints associated with renewable energy integration. This paper takes Manhattan as a case study to show local energy networks that maximize the city’s overall efficiency to share local renewable energy - generated thermal and electric energy - maximize renewable energy penetration rates and minimize energy exchange costs. We show how geothermal and solar drive a future city’s collective form and infrastructure to achieve up to 74% local renewable energy integration.","PeriodicalId":374469,"journal":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115996616","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}
引用次数: 1
Validation Of EPSim - An Embedded Platform Simulator For Control-Embedded Co-Design 嵌入式控制协同设计平台模拟器EPSim的验证
2022 Annual Modeling and Simulation Conference (ANNSIM) Pub Date : 2022-07-18 DOI: 10.23919/ANNSIM55834.2022.9859452
Ken Vanherpen, P. D. Meulenaere, Yon Vanommeslaeghe, Davy Maes
{"title":"Validation Of EPSim - An Embedded Platform Simulator For Control-Embedded Co-Design","authors":"Ken Vanherpen, P. D. Meulenaere, Yon Vanommeslaeghe, Davy Maes","doi":"10.23919/ANNSIM55834.2022.9859452","DOIUrl":"https://doi.org/10.23919/ANNSIM55834.2022.9859452","url":null,"abstract":"The engineering processes for the design of Cyber-Physical Systems (CPS) are often suboptimal due to - among others - inefficient collaboration between the involved engineering domains. This may lead to increased development time and cost, oversized hardware, suboptimal product performance, etc. One of the factors contributing to this inefficient collaboration is that the integration of product components in the final stages of the development process was not sufficiently specified at design time. In this paper, we propose the Embedded Platform Simulator, EPSim, enabling the virtual integration of the control and the embedded domain in early stages of the development process. We elaborate on the architecture of the EPSim-tool and validate its precision with traditional control-embedded Hardware-in-the-Loop simulations.","PeriodicalId":374469,"journal":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122143068","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}
引用次数: 0
Synthesizing Diagnostic Burn Images For Deep Learning Applications 为深度学习应用合成诊断烧伤图像
2022 Annual Modeling and Simulation Conference (ANNSIM) Pub Date : 2022-07-18 DOI: 10.23919/ANNSIM55834.2022.9859295
Bernhard Schenkenfelder, Sophie Kaltenleithner, Bertram Sabrowsky-Hirsch, Christoph Klug, D. Lumenta, J. Scharinger
{"title":"Synthesizing Diagnostic Burn Images For Deep Learning Applications","authors":"Bernhard Schenkenfelder, Sophie Kaltenleithner, Bertram Sabrowsky-Hirsch, Christoph Klug, D. Lumenta, J. Scharinger","doi":"10.23919/ANNSIM55834.2022.9859295","DOIUrl":"https://doi.org/10.23919/ANNSIM55834.2022.9859295","url":null,"abstract":"The treatment of burns is supported by accurately estimating measurements such as total body surface area burned (TBSA-b) and total body surface area (TBSA). Computing these values automatically can lead to faster decisions and reduce human error. Deep learning-based methods rely on big data to perform well in practical applications. This is especially true for burn medicine, where real data is scarce. In this paper, we present a pipeline for synthesizing diagnostic burn images and wound annotations from virtual 3D models. We demonstrate how to generate a heterogeneous dataset by combining such features as body shape, real skin and wound textures, background scenes, camera settings and illumination. The resulting images can be used for various deep-learning tasks such as wound detection, segmentation or classification. Solely with these synthetic images, we train models for burn wound segmentation and show that they learn distinctive features of burn wounds.","PeriodicalId":374469,"journal":{"name":"2022 Annual Modeling and Simulation Conference (ANNSIM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129383481","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}
引用次数: 0
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