Road Network Simulation Using FLAME GPU

Demand for high performance road network simulation is increasing due to the need for improved traffic management to cope with the globally increasing number of road vehicles and the poor capacity utilisation of existing infrastructure. This paper demonstrates FLAME GPU as a suitable Agent Based Simulation environment for road network simulations, capable of coping with the increasing demands on road network simulation. Gipps’ car following model is implemented and used to demonstrate the performance of simulation as the problem size is scaled. The performance of message communication techniques has been evaluated to give insight into the impact of runtime generated data structures to improve agent communication performance. A custom visualisation is demonstrated for FLAME GPU simulations and the techniques used are described

Sensornet checkpointing: enabling repeatability in testbeds and realism in simulations

When developing sensor network applications, the shift from simulation to testbed causes application failures, resulting in additional time-consuming iterations between simulation and testbed. We propose transferring sensor network checkpoints between simulation and testbed to reduce the gap between simulation and testbed. Sensornet checkpointing combines the best of both simulation and testbeds: the nonintrusiveness and repeatability of simulation, and the realism of testbeds

Constant DC output boost converter for solar system sourace model

A Neural network controller of DC-DC boost converter is designed and presented in this project. In order to control the output voltage of the boost converter, the controller is designed to change the duty cycle of the converter. The mathematical model of boost converter and neural network controller are derived to design simulation model. The simulation is developed on Matlab simulation program. To verity the effectiveness of the simulation model, an experimental set up is developed. The boost circuit with mosfet as a switching component is developed. The neural network controller to generate duty cycle of PWM signal is programmed. The simulation and experimental results show that the output voltage of the boost converter can be controlled according to the value of duty cycle

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic

Performance, Validation and Testing with the Network Simulation Cradle

Much current simulation of TCP makes use of simplified models of TCP, which is a large and complex protocol with many variations possible between implementations. We use direct execution of real world network stacks in the network simulator ns-2 to compare TCP performance between implementations and reproduce existing work. A project called The Network Simulation Cradle provides the real world network stacks and we show how it can be used for performance evaluation and validation. There are large differences in performance between simplified TCP models and TCP implementations in some situations. Such differences are apparent in some reproduced research, with results using the Network Simulation Cradle very different from the results produced with the ns-2 TCP models. In other cases, using the real implementations gives very similar results, validating the original research

Simulation Platform for Wireless Sensor Networks Based on Impulse Radio Ultra Wide Band

Impulse Radio Ultra Wide Band (IR-UWB) is a promising technology to address Wireless Sensor Network (WSN) constraints. However, existing network simulation tools do not provide a complete WSN simulation architecture, with the IR-UWB specificities at the PHYsical (PHY) and the Medium Access Control (MAC) layers. In this paper, we propose a WSN simulation architecture based on the IR-UWB technique. At the PHY layer, we take into account the pulse collision by dealing with the pulse propagation delay. We also modelled MAC protocols specific to IRUWB, for WSN applications. To completely fit the WSN simulation requirements, we propose a generic and reusable sensor and sensing channel model. Most of the WSN application performances can be evaluated thanks to the proposed simulation architecture. The proposed models are implemented on a scalable and well known network simulator: Global Mobile Information System Simulator (GloMoSim). However, they can be reused for all other packet based simulation platforms