scholarly journals Modeling and Analysis of Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway

Computation ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 53 ◽  
Author(s):  
Shengkun Xie ◽  
Anna T. Lawniczak ◽  
Chong Gan
Keyword(s):  
Simulation Model ◽  
Cellular Automaton ◽  
Knowledge Base ◽  
Autonomous Agents ◽  
Regression Tree ◽  
Simulation Models ◽  
Traffic Density ◽  
Cognitive Agents ◽  
Modeling And Analysis ◽  
Traffic Environment

For a better understanding of the nature of complex systems modeling, computer simulations and the analysis of the resulting data are major tools which can be applied. In this paper, we study a statistical modeling problem of data coming from a simulation model that investigates the correctness of autonomous agents’ decisions in learning to cross a cellular automaton-based highway. The goal is a better understanding of cognitive agents’ performance in learning to cross a cellular automaton-based highway with different traffic density. We investigate the effects of parameters’ values of the simulation model (e.g., knowledge base transfer, car creation probability, agents’ fear and desire to cross the highway) and their interactions on cognitive agents’ decisions (i.e., correct crossing decisions, incorrect crossing decisions, correct waiting decisions, and incorrect waiting decisions). We firstly utilize canonical correlation analysis (CCA) to see if all the considered parameters’ values and decision types are significantly statistically correlated, so that no considered dependent variables or independent variables (i.e., decision types and configuration parameters, respectively) can be omitted from the simulation model in potential future studies. After CCA, we then use the regression tree method to explore the effects of model configuration parameters’ values on the agents’ decisions. In particular, we focus on the discussion of the effects of the knowledge base transfer, which is a key factor in the investigation on how accumulated knowledge/information about the agents’ performance in one traffic environment affects the agents’ learning outcomes in another traffic environment. This factor affects the cognitive agents’ decision-making abilities in a major way in a new traffic environment where the cognitive agents start learning from existing accumulated knowledge/information about their performance in an environment with different traffic density. The obtained results provide us with a better understanding of how cognitive agents learn to cross the highway, i.e., how the knowledge base transfer as a factor affects the experimental outcomes. Furthermore, the proposed methodology can become useful in modeling and analyzing data coming from other computer simulation models and can provide an approach for better understanding a factor or treatment effect.

scholarly journals Modeling and analysis of the characteristics of SiC MOSFET

2021 ◽  
Vol 2125 (1) ◽  
pp. 012051
Author(s):  
Guoqing Qiu ◽  
Kedi Jiang ◽  
Shengyou Xu ◽  
Xin Yang ◽  
Wei Wang
Keyword(s):  
Simulation Model ◽  
Circuit Simulation ◽  
Power Conversion ◽  
Simulation Models ◽  
Data Sheet ◽  
Superior Performance ◽  
Power Devices ◽  
Matlab Simulink ◽  
Modeling And Analysis ◽  
Mathematical Processing

Abstract Although the superior performance of SiC MOSFET devices has beenvalidated by many studies, it is necessary to overcome many technical bottlenecks to make SiC MOSFET gradually replace Si-based power devices into the mainstream. In view of the current situation where the performance of SiC MOSFETs in power conversion devices cannot be evaluated well at this stage, it is necessary to carry out fine modeling of SiC MOSFETs and establish accurate simulation models. In this paper, the powerful mathematical processing capability and rich modules of Matlab/Simulink are used to build a SiC MOSFET model, and then the product data sheet is compared with the fitted data. The results show that the switching simulation waveforms are in general agreement with the data sheet waveforms, and the error is less than 7%. Verifing the accuracy of the model and reducing the difficulty of modeling, it provides a new idea for establishing the circuit simulation model of SiC MOSFET in Matlab/Simulink.

Ontology-Based Unified Representation of Dynamic Simulation Models in Engineering Design

2018 ◽  
Author(s):  
Jun Yu ◽  
Zhenjun Ming ◽  
Guoxin Wang ◽  
Yan Yan ◽  
Xiaoping Lan
Keyword(s):  
Simulation Model ◽  
Knowledge Base ◽  
Engineering Design ◽  
Dynamic Simulation ◽  
Collaborative Design ◽  
Simulation Models ◽  
Heterogeneous Data ◽  
Automation Systems ◽  
Dynamic Simulation Model ◽  
Definition Of

The development of complex product dynamic simulation models and the integration of design automation systems require knowledge from multiple heterogeneous data sources and tools. Because of the heterogeneity of model data, the integration of tools and data is a time-consuming and error-prone task. The main objective of this study is to provide a unified model of dynamic simulation for engineering design, which serves as a knowledge base to support the development of a dynamic simulation model. The integration of knowledge is realized through (i) definition of the structure and interface during the design phase of the dynamic simulation model, and (ii) definition of a model-driven integrated environment configuration process during the runtime phase. In order to achieve interoperability among the different simulation models in a collaborative design environment, we build a “Demand-Resources-Service-Knowledge-Process (DKRSP)” ontology that formally represents the semantics of dynamic simulation models. Based on the ontology, a knowledge base is created for the management of dynamic simulation knowledge. The efficacy of the ontology and the knowledge base are demonstrated using a transmission design example.

Knowledge-Based Integrated Manufacturing Flexible Simulation System

1996 ◽  
Author(s):  
Ming Dong ◽  
Jianzhong Cha ◽  
Mingcheng E
Keyword(s):  
Simulation Model ◽  
Knowledge Base ◽  
System Architecture ◽  
Procedural Knowledge ◽  
Object Oriented ◽  
Simulation Models ◽  
Simulation System ◽  
Integrated Manufacturing ◽  
Knowledge Based ◽  
Inference Engines

Abstract In this paper, we present a knowledge-based flexible simulation system for integrated manufacturing. The simulation model knowledge base of a CIMS is constituted of five parts: FBS models of the CIM-OSA system architecture, entity classes library, procedural knowledge base, database and inference engines. The knowledge-based simulation models are represented by the object-oriented frame language and their behaviours are generated by inference engines reasoning about the sets of procedural rules. Because of the use of various classes libraries which make this system flexible, we call it a knowledge-based integrated manufacturing flexible simulation system.

scholarly journals Simulation modeling and analysis of primary health center operations

SIMULATION ◽  
2021 ◽  
pp. 003754972110309
Author(s):  
Mohd Shoaib ◽  
Varun Ramamohan
Keyword(s):  
Resource Utilization ◽  
Waiting Times ◽  
Discrete Event ◽  
Simulation Models ◽  
Modeling And Analysis ◽  
Healthcare Facilities ◽  
Event Simulation ◽  
Analytical Approximations ◽  
Primary Health ◽  
Generic Modeling

We present discrete-event simulation models of the operations of primary health centers (PHCs) in the Indian context. Our PHC simulation models incorporate four types of patients seeking medical care: outpatients, inpatients, childbirth cases, and patients seeking antenatal care. A generic modeling approach was adopted to develop simulation models of PHC operations. This involved developing an archetype PHC simulation, which was then adapted to represent two other PHC configurations, differing in numbers of resources and types of services provided, encountered during PHC visits. A model representing a benchmark configuration conforming to government-mandated operational guidelines, with demand estimated from disease burden data and service times closer to international estimates (higher than observed), was also developed. Simulation outcomes for the three observed configurations indicate negligible patient waiting times and low resource utilization values at observed patient demand estimates. However, simulation outcomes for the benchmark configuration indicated significantly higher resource utilization. Simulation experiments to evaluate the effect of potential changes in operational patterns on reducing the utilization of stressed resources for the benchmark case were performed. Our analysis also motivated the development of simple analytical approximations of the average utilization of a server in a queueing system with characteristics similar to the PHC doctor/patient system. Our study represents the first step in an ongoing effort to establish the computational infrastructure required to analyze public health operations in India and can provide researchers in other settings with hierarchical health systems, a template for the development of simulation models of their primary healthcare facilities.

scholarly journals Guidelines to Calibrate a Multi-Residential Building Simulation Model Addressing Overheating Evaluation and Residents’ Influence

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 242
Author(s):  
Christoph Schünemann ◽  
David Schiela ◽  
Regine Ortlepp
Keyword(s):  
Simulation Model ◽  
Time Window ◽  
Residential Building ◽  
Simulation Models ◽  
Building Simulation ◽  
Night Time ◽  
Building Performance Simulation ◽  
Starting Point ◽  
3D Simulation Model ◽  
Building Physics

Can building performance simulation reproduce measured summertime indoor conditions of a multi-residential building in good conformity? This question is answered by calibrating simulated to monitored room temperatures of several rooms of a multi-residential building for an entire summer in two process steps. First, we did a calibration for several days without the residents being present to validate the building physics of the 3D simulation model. Second, the simulations were calibrated for the entire summer period, including the residents’ impact on evolving room temperature and overheating. As a result, a high degree of conformity between simulation and measurement could be achieved for all monitored rooms. The credibility of our results was secured by a detailed sensitivity analysis under varying meteorological conditions, shading situations, and window ventilation or room use in the simulation model. For top floor dwellings, a high overheating intensity was evoked by a combination of insufficient use of night-time window ventilation and non-heat-adapted residential behavior in combination with high solar gains and low heat storage capacities. Finally, the overall findings were merged into a process guideline to describe how a step-by-step calibration of residential building simulation models can be done. This guideline is intended to be a starting point for future discussions about the validity of the simplified boundary conditions which are often used in present-day standard overheating assessment.

Settings, Experimentation and Evaluation of the Simulation Models

2013 ◽  
Vol 309 ◽  
pp. 366-371 ◽  
Author(s):  
František Manlig ◽  
Radek Havlik ◽  
Alena Gottwaldova
Keyword(s):  
Simulation Model ◽  
New Technology ◽  
Simulation Models ◽  
Manufacturing Processes ◽  
Customer Requirements ◽  
Evaluation Functions ◽  
Operational Management ◽  
Minimum Number ◽  
User Friendly ◽  
Setting Parameters

This paper deals with research in computer simulation of manufacturing processes. The paper summarizes the procedures associated with developing the model, experimenting with and evaluating the model results. The key area is of experimentation with the simulation model and evaluation using indicators or multi-criteria functions. With regards to the experiment the crucial variables are the simulation model. The key ideas are to set the number of variables, depending on what a given simulation will be. For example, when introducing new technology into production, modify the type of warehouse, saving workers, thus economizing. The simulation models for the operational management uses simplified models, if possible, a minimum number of variables to obtain the result in shortest possible time. These models are more user friendly and the course will be conducted mostly in the background. An example of a criteria function is the number of parts produced or production time. Multi-criteria function has given us the opportunity to make better quality decisions. It is based on the composition of several parameters, including their weight to one end point. The type of evaluation functions, whether it is an indicator or criteria function is selected and based on customer requirements. In most cases it is recommended to use the multi-dimensional function. It gives us a more comprehensive view of the results from the model and facilitates decision-making. The result of this paper is a display of setting parameters for the experimentation on a sample model. Furthermore, the comparisons of results with a multi-criteria objective function and one-criterion indicator.

A Method to Define the Best Weld Sequence Using a Limited Number of Welding Simulation Analysis

2015 ◽  
Author(s):  
Mahyar Asadi ◽  
Ghazi Alsoruji
Keyword(s):  
Simulation Model ◽  
Surrogate Model ◽  
Simulation Analysis ◽  
Combinatorial Problem ◽  
Fem Analysis ◽  
Simulation Models ◽  
Computational Time ◽  
Optimal Sequence ◽  
Welded Structure ◽  
Welding Sequence

Weld sequence optimization, which is determining the best (and worst) welding sequence for welding work pieces, is a very common problem in welding design. The solution for such a combinatorial problem is limited by available resources. Although there are fast simulation models that support sequencing design, still it takes long because of many possible combinations, e.g. millions in a welded structure involving 10 passes. It is not feasible to choose the optimal sequence by evaluating all possible combinations, therefore this paper employs surrogate modeling that partially explores the design space and constructs an approximation model from some combinations of solutions of the expensive simulation model to mimic the behavior of the simulation model as closely as possible but at a much lower computational time and cost. This surrogate model, then, could be used to approximate the behavior of the other combinations and to find the best (and worst) sequence in terms of distortion. The technique is developed and tested on a simple panel structure with 4 weld passes, but essentially can be generalized to many weld passes. A comparison between the results of the surrogate model and the full transient FEM analysis all possible combinations shows the accuracy of the algorithm/model.

Rotorcraft Lateral-Directional Oscillations: The Anatomy of a Nuisance Mode

2021 ◽  
Author(s):  
Dheeraj Agarwal ◽  
Linghai Lu ◽  
Gareth D. Padfield ◽  
Mark D. White ◽  
Neil Cameron
Keyword(s):  
Simulation Model ◽  
Simulation Models ◽  
Flight Test ◽  
Flight Simulation ◽  
Stability And Control ◽  
Systems Research ◽  
Control Derivatives ◽  
Research Aircraft ◽  
Level Of Understanding ◽  
And Control

High-fidelity rotorcraft flight simulation relies on the availability of a quality flight model that further demands a good level of understanding of the complexities arising from aerodynamic couplings and interference effects. One such example is the difficulty in the prediction of the characteristics of the rotorcraft lateral-directional oscillation (LDO) mode in simulation. Achieving an acceptable level of the damping of this mode is a design challenge requiring simulation models with sufficient fidelity that reveal sources of destabilizing effects. This paper is focused on using System Identification to highlight such fidelity issues using Liverpool's FLIGHTLAB Bell 412 simulation model and in-flight LDO measurements from the bare airframe National Research Council's (Canada) Advanced Systems Research Aircraft. The simulation model was renovated to improve the fidelity of the model. The results show a close match between the identified models and flight test for the LDO mode frequency and damping. Comparison of identified stability and control derivatives with those predicted by the simulation model highlight areas of good and poor fidelity.

scholarly journals Model Driven Combat Effectiveness Simulation Systems Engineering

2020 ◽  
Vol 70 (1) ◽  
pp. 54-59
Author(s):  
Zhi Zhu ◽  
Yonglin Lei ◽  
Yifan Zhu
Keyword(s):  
Decision Making ◽  
Simulation Model ◽  
Conceptual Model ◽  
Systems Engineering ◽  
Simulation Models ◽  
Proof Of Concept ◽  
Model Driven Engineering ◽  
Model Driven ◽  
Combat Effectiveness ◽  
Simulation Systems

Model-driven engineering has become popular in the combat effectiveness simulation systems engineering during these last years. It allows to systematically develop a simulation model in a composable way. However, implementing a conceptual model is really a complex and costly job if this is not guided under a well-established framework. Hence this study attempts to explore methodologies for engineering the development of simulation models. For this purpose, we define an ontological metamodelling framework. This framework starts with ontology-aware system conceptual descriptions, and then refines and transforms them toward system models until they reach final executable implementations. As a proof of concept, we identify a set of ontology-aware modelling frameworks in combat systems specification, then an underwater targets search scenario is presented as a motivating example for running simulations and results can be used as a reference for decision-making behaviors.

Measurement and Numerical Simulation of the Velocity Profile in the Thin Film of an Impinging Water Jet

2021 ◽  
Vol 144 (3) ◽  
Author(s):  
Matthias Joppa ◽  
Mike Bermuske ◽  
Frank Rüdiger ◽  
Lars Büttner ◽  
Jochen Fröhlich ◽  
...  
Keyword(s):  
Velocity Profile ◽  
Simulation Model ◽  
Process Optimization ◽  
Low Cost ◽  
Simulation Models ◽  
Fluid Simulation ◽  
Doppler Velocity ◽  
Measurement Technology ◽  
Mean Velocity ◽  
Validation Data

Abstract Impinging circular free-surface water jets are used in challenging cooling and cleaning tasks. In order to develop simulation models for process optimization, validation data are required, which are currently not available. Therefore, the flow field of these jets is studied for the first time with the novel laser Doppler velocity profile sensor. The mean velocity field and fluctuations are measured within the stagnation and adjacent redirection region for radial coordinates up to three times the nozzle diameter. In the examined parameter range with jet velocities up to 17 m/s and nozzle diameters up to 5.2 mm, i.e., Reynolds numbers up to 69 500, thin films of a few hundred micrometers are formed, which hinder the measurement with common optical measuring systems. Based on the measurement results, a comparatively low-cost volume of fluid simulation model is developed and validated that presumes a relaminarized film flow. The profiles measured and the simulated flow show very good agreement. In the future, the simulation model provides a basis for process optimization and the innovative measurement technology used will prospectively provide further detailed insights into other flows with high velocity gradients.

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