Optimization of Excitation in FDTD Method and Corresponding Source Modeling

Source and excitation modeling in FDTD formulation has a significant impact on the method performance and the required simulation time. Since the abrupt source introduction yields intensive numerical variations in whole computational domain, a generally accepted solution is to slowly introduce the source, using appropriate shaping functions in time. The main goal of the optimization presented in this paper is to find balance between two opposite demands: minimal required computation time and acceptable degradation of simulation performance. Reducing the time necessary for source activation and deactivation is an important issue, especially in design of microwave structures, when the simulation is intensively repeated in the process of device parameter optimization. Here proposed optimized source models are realized and tested within an own developed FDTD simulation environment

Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging - Replaces CentER DP 2010-62

This article uses a sequentialized experimental design to select simulation input com- binations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer code). This design and analysis adapt the clas- sic "expected improvement" (EI) in "efficient global optimization" (EGO) through the introduction of an unbiased estimator of the Kriging predictor variance; this estimator uses parametric bootstrapping. Classic EI and bootstrapped EI are com- pared through various test functions, including the six-hump camel-back and several Hartmann functions. These empirical results demonstrate that in some applications bootstrapped EI finds the global optimum faster than classic EI does; in general, however, the classic EI may be considered to be a robust global optimizer.Simulation;Optimization;Kriging;Bootstrap

Modeling of Radiation Damage Effects in Silicon Detectors at High Fluences HL-LHC with Sentaurus TCAD

In this work we propose the application of an enhanced radiation damage model based on the introduction of deep level traps / recombination centers suitable for device level numerical simulation of silicon detectors at very high fluences (e.g. 2.0x10E16 1 MeV equivalent neutrons/cm2). We present the comparison between simulation results and experimental data for p-type substrate structures in different operating conditions (temperature and biasing voltages) for fluences up to 2.2x10E16 neutrons/cm2. The good agreement between simulation findings and experimental measurements fosters the application of this modeling scheme to the optimization of the next silicon detectors to be used at HL-LHC.Comment: Supported by the H2020 project AIDA-2020, GA no. 65416

Dynamics simulation of human box delivering task

Thesis (M.S.) University of Alaska Fairbanks, 2018The dynamic optimization of a box delivery motion is a complex task. The key component is to achieve an optimized motion associated with the box weight, delivering speed, and location. This thesis addresses one solution for determining the optimal delivery of a box. The delivering task is divided into five subtasks: lifting, transition step, carrying, transition step, and unloading. Each task is simulated independently with appropriate boundary conditions so that they can be stitched together to render a complete delivering task. Each task is formulated as an optimization problem. The design variables are joint angle profiles. For lifting and carrying task, the objective function is the dynamic effort. The unloading task is a byproduct of the lifting task, but done in reverse, starting with holding the box and ending with it at its final position. In contrast, for transition task, the objective function is the combination of dynamic effort and joint discomfort. The various joint parameters are analyzed consisting of joint torque, joint angles, and ground reactive forces. A viable optimization motion is generated from the simulation results. It is also empirically validated. This research holds significance for professions containing heavy box lifting and delivering tasks and would like to reduce the chance of injury.Chapter 1 Introduction -- Chapter 2 Skeletal Human Modeling -- Chapter 3 Kinematics and Dynamics -- Chapter 4 Lifting Simulation -- Chapter 5 Carrying Simulation -- Chapter 6 Delivering Simulation -- Chapter 7 Conclusion and Future Research -- Reference

A Survey of Stochastic Simulation and Optimization Methods in Signal Processing

International audienceModern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are anal ytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, area as of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed

Foreword

This report reviews the Expectation Maximization EM algorithm and applies it to the data segmentation problem yielding the Expectation Maximization Segmentation EMS algorithm The EMS algorithm requires batch processing of the data and can be applied to mode switching or jumping linear dynamical state space models The EMS algorithm consists of an optimal fusion of fixed interval Kalman smoothing and discrete optimization. The next section gives a short introduction to the EM algorithm with some background and convergence results In Section the data segmentation problem is dened and in Section the EM algorithm is applied to this problem Section contains simulation results and Section some conclusive remarks

Energy Efficient Spectrum Sensing for State Estimation over A Wireless Channel

The performance of remote estimation over wireless channel is strongly affected by sensor data losses due to interference. Although the impact of interference can be alleviated by performing spectrum sensing and then transmitting only when the channel is clear, the introduction of spectrum sensing also incurs extra energy expenditure. In this paper, we investigate the problem of energy efficient spectrum sensing for state estimation of a general linear dynamic system, and formulate an optimization problem which minimizes the total sensor energy consumption while guaranteeing a desired level of estimation performance. The optimal solution is evaluated through both analytical and simulation results.Comment: 4 pages, 6 figures, accepted to IEEE GlobalSIP 201

Optimization of Fluidized Bed Isothermal Reactor in a Styrene Production Process

The focus of this thesis is to explain the optimization of a fluidized bed isothermal reactor in a styrene production process. The first section of the thesis gives a summary of chemical process optimization in general. The next portion of the thesis gives an introduction to chemical process simulation software, and it explains how simulation software aids in the design and optimization of chemical processes. The third section of the thesis gives a brief overview of an optimization project of a styrene production process that was completed in the previous semester with a group of three. The final section explains the optimization of a fluidized bed reactor in the styrene production process discussed in the previous section of the thesis. The results of the reactor optimization produced a reactor system that has a total fluidized catalyst bed volume of 75.4 m3 with 15 reactors in parallel. The optimized reactor operates at a temperature of 715°C and a pressure of 75 kPa, and it produces a total flowrate of styrene of 193 kmol/hr and yield of ethylbenzene to styrene of 68 %