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spring simulation multiconference, springsim 2020

Spring Simulation Multiconference, SpringSim 2020

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2020 spring simulation multiconference, springsim 2020

ISBN: (纸本)9781713812883

The proceedings contain 68 papers. The topics discussed include: reinforcement learning from simulated environments: an encoder decoder approach;feature transformation and simulation of short term price variability in reinforcement learning for portfolio management;a synergistic approach for deep learning and knowledge engineered solutions;predicting the resource needs and outcomes of computationally intensive biological simulations;studying communications resiliency in emergency plans;estimating effects of the decision support system on educational agents with simulations;scenario-based generation of ontologies for domain-specific languages;strategic airlift operationalizing constructive simulations;coupling weap and leap models using interaction modeling;and exploratory analysis to address deep uncertainty – using calibratable system models for exploratory simulation of complex missions.

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Proceedings of the 2020 spring simulation Conference, springsim 2020

Proceedings of the 2020 Spring Simulation Conference, Spring...

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2020 spring simulation Conference, springsim 2020

ISBN: (纸本)9781565553705

The proceedings contain 68 papers. The topics discussed include: a framework for composable cellular automata DEVS modeling, simulation, and visualization;modeling and simulating pedestrian social group behavior with heterogeneous social relationships;towards real-time cyber-physical systems instrumentation for creating digital twins;'surgical GPS' proof of concept for scoliosis surgery;experimental wargames to address the complexityscarcity gap;a model library for finite state machines in cadmium;reinforcement learning from simulated environments: an encoder decoder framework;simulation and analysis of animal movement paths using NUMERUS model builder;and a phase transition model and temporal logic specifications for smart energy systems - revisited.

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Feature transformation and simulation of short term price variability in reinforcement learning for portfolio management

Feature transformation and simulation of short term price va...

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2020 spring simulation multiconference, springsim 2020

作者： Lin, Yen-Chih Blum, Jeremy Department of Computer Science and Mathematics Pennsylvania State University Harrisburg 777 W. Harrisburg Pike MiddletownPA United States

ISBN: (纸本)9781713812883

Reinforcement learning has been shown capable of learning optimal strategies from imperfect information environments in order to create robust decision support systems. This paper shows that two automatic feature transformation techniques - Bayesian recurrent neural network (BRNN) for modelling future price trends and Generative Adversarial Networks (GANs) for modelling short-term realistic price variability - are able to improve the performance of reinforcement learning agents in solving portfolio management problem effectively, when measured in terms of increasing profitability and reducing risks. © 2020 Society for Modeling & simulation International (SCS)

关键词： Reinforcement learning

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Machine learning of an approximate morphism of an electronic warfare simulation component

Machine learning of an approximate morphism of an electronic...

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2020 spring simulation multiconference, springsim 2020

作者： Jarvis, Donald E. Tactical Electronic Warfare Division U. S. Naval Research Laboratory 4555 Overlook Ave. SW WashingtonDC United States

ISBN: (纸本)9781713812883

Electromagnetic waveforms are an essential component of radar and electronic warfare digital computer simulations. Sampled representations of radar waveforms are widely used in high-fidelity applications for their physical realism and suitability for algorithmic processing. However, this fidelity comes at a price because operations on sampled representations are often a computationally costly simulation bottleneck. In this paper, we propose a generic framework for constructing a reduced, feature-based model component derived from a given high-fidelity component, and demonstrate the approach on a simplified radar waveform model. The reduced model is related to the original through an approximate morphism. Both supervised and unsupervised machine learning are key components of the framework. © 2020 Society for Modeling & simulation International (SCS)

关键词： Machine learning

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Deploy mechanism for virtual-machine based vehicular ad hoc network simulation

Deploy mechanism for virtual-machine based vehicular ad hoc ...

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2020 spring simulation multiconference, springsim 2020

作者： Kohiga, Akihito Shinoda, Yoichi School of Information Science Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (纸本)9781713812883

The virtual-machine (VM) based vehicular ad hoc network (VANET) simulation executes VMs as vehicles, on which actual telecom servers and routing protocols are implemented. We would like to create massive VM based VANET simulation to verify that VANET is feasible for emergency communication facility. However, network congestion increased with the number of VMs as the ad hoc network routing protocols created multiple network sessions over a certain part of physical machines. Such network congestion limited scalability. We propose a VM deployment mechanism to reduce network sessions over the physical machines. Our mechanism executes geographically neighboring VMs on the same physical machine. The VMs moves to another physical machine when the simulated vehicles changed positions. Our approach reduced the network sessions by approximately one-sixth. This realize the extensive VM-based VANET simulation. © 2020 Society for Modeling & simulation International (SCS)

关键词： Vehicular ad hoc networks

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Scalability of sensor simulation in ros-gazebo platform with and without using GPU

Scalability of sensor simulation in ros-gazebo platform with...

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2020 spring simulation multiconference, springsim 2020

作者： Saglam, Ahmet Papelis, Yiannis Department of Computational Modeling and Simulation Engineering Old Dominion University 5115 Hampton Blvd NorfolkVA United States Virginia Modeling Analysis and Simulation Center Old Dominion University 1030 University Blvd SuffolkVA United States

ISBN: (纸本)9781713812883

Autonomous mobile robots depend heavily on sensors for interpreting the external environment and planning their movement. Given the complexity of even simple environments, the use of simulation is necessary to test sensory perception and movement strategies efficiently. Accurate and efficient sensor simulation is key to the successful development and fielding of autonomous mobile robots. In this paper, we investigate the scalability of LIDAR-type sensor simulation in ROS-supported Gazebo, an open-source multi-robot simulator. Specifically, the performance and scalability of the simulator are assessed with and without support from GPU in two virtual worlds, one highly-complex in terms of the number of collision targets and one not-complex. In both cases, we increase the number of sensors and evaluate system performance. The results show that the use of GPU helps improve performance, providing better speedups in the complex scene. However, the number of sensors that can be used is limited when using GPU. © 2020 Society for Modeling & simulation International (SCS)

关键词： Multipurpose robots

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Enforcing security and privacy in distributed ledgers by intel SGX

Enforcing security and privacy in distributed ledgers by int...

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2020 spring simulation multiconference, springsim 2020

作者： Liang, Xueping Shetty, Sachin Foytik, Peter Tosh, Deepak Department of Engineering and Computer Science Virginia State University 1 Hayden St PetersburgVA23806 United States Virginia Modeling Analysis and Simulation Center Old Dominion University 1030 University Blvd SuffolkVA23435 United States Department of Computer Science University of Texas at El Paso 500 W University Ave El PasoTX79968 United States

ISBN: (纸本)9781713812883

Distributed Ledger Technology (DLT) utilizes an architecture that can host a large number of nodes without pre-established trust to provide decentralized services. The blockchain is the most widely used architecture of distributed ledger, where transactions across the whole network are visible to all participants in a chain to prevent tampering. However, transactions may contain sensitive information such as business contract. To secure the system and protect user privacy, we propose a multi-channel architecture that leverages Intel Software Guard Extensions (SGX). We illustrate how SGX capabilities help to defend against attacks on distributed ledgers, by way of SGX enforcement on the participating machines. We adopt the design and implementation of a two layer architecture for securing the blockchain mining process and enhancing the transaction privacy. The security analysis and performance evaluation show that the design and protocols are capable of protecting privacy, defending against adversarial attacks and scalable. © 2020 Society for Modeling & simulation International (SCS)

关键词： Blockchain

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simulation of new healthcare delivery to evaluate impacts on patient access to care: A telehealth supply and demand use case

Simulation of new healthcare delivery to evaluate impacts on...

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2020 spring simulation multiconference, springsim 2020

作者： Henchey, Matthew Ercolini, Deborah Klaus, Sybil Modeling Simulation Experimentation and Analytics MITRE Corporation 7515 Colshire Drive McLeanVA United States Data and Human Centered Solutions MITRE Corporation 202 Burlington Road BedfordMA United States Health Transformation MITRE Corporation 7515 Colshire Drive McLeanVA United States

ISBN: (纸本)9781713812883

We propose a framework, Modeling HealthCARE: Capacity, Access, and Resource Evaluation, that uses hybrid simulation to help healthcare organizations evaluate supply and demand as they implement alternative healthcare services. The focus of this tool is on patients and their access to care. We integrate agent-based and discrete event simulation to model the patient as an agent adopting and requesting new services, following a discrete-event-driven process based on the existing and new healthcare journey. We model a use case in which a regional Department of Veterans Affairs healthcare organization is expanding its telehealth capabilities to meet growing demand for Post-Traumatic Stress Disorder treatment and is using telehealth to deliver services to Veterans, which saves them from traveling long distances. Results of our proof of concept model show that as adoption of telehealth increases, wait times and travel distances decrease, as service capacity and resources are configured to meet demand. © 2020 Society for Modeling & simulation International (SCS)

关键词： Discrete event simulation

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Modeling the modeler: An empirical study on how modelers learn to create simulations

Modeling the modeler: An empirical study on how modelers lea...

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2020 spring simulation multiconference, springsim 2020

作者： Kavak, Hamdi Padilla, Jose Diallo, Saikou Barraco, Anthony Department of Computational and Data Sciences George Mason University 4400 University Dr. FairfaxVA United States Virginia Modeling Analysis and Simulation Center Old Dominion University 1030 University Blvd. SuffolkVA United States

ISBN: (数字)9781565553705

ISBN: (纸本)9781713812883

This paper presents our novel efforts on automatically capturing and analyzing user data from a discrete-event simulation environment. We collected action data such as adding/removing blocks and running a model that enable creating calculated data fields and examining their relations across expertise groups. We found that beginner-level users use more blocks/edges and make more build errors compared to intermediate-level users. When examining the users with higher expertise, we note differences related to time spent in the tool, which could be linked to user engagement. The model running failure of beginner-level users may suggest a trial and error approach to building a model rather than an established process. Our study opens a critical line of inquiry focused on user engagement instead of process establishment, which is the current focus in the community. In addition to these findings, we report other potential uses of such user action data and lessons learned. © 2020 Society for Modeling & simulation International (SCS)

关键词： Discrete event simulation

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Exploratory analysis to address deep uncertainty - Using calibratable system models for exploratory simulation of complex missions

Exploratory analysis to address deep uncertainty - Using cal...

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2020 spring simulation multiconference, springsim 2020

作者： Tolk, Andreas Dinh, Khuong Comer, Kevin Scott, Steve MITRE Corporation 903 Enterprise Parkway #200 HamptonVA23666 United States MITRE Corporation 7515 Colshire Drive McLeanVA22102 United States

ISBN: (纸本)9781713812883

Traditional optimization methods and sometimes even the underlaying concept of experimentation design for optimization problems are not always successful in complex solution spaces. This is not only driven by the complexity itself, but also by the deep uncertainty in estimating the model parameters. Applying exploratory analysis to better understand the solution space has been recognized as a promising approach for several years, but with the new generation of computers the community now has the means to practically apply it as well. We define the terms, derive a conceptual model, provide an application case from the defense domain, and show the possibility to generalize the results, namely that parameter sweeps are possible and lead to deeper insights of the solution space, allowing for stable and robust solutions that often are more important in practice than optimized point solutions. © 2020 Society for Modeling & simulation International (SCS)

关键词： Uncertainty analysis

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