scholarly journals Diffractive Efficiency Optimization in Metasurface Design via Electromagnetic Coupling Compensation

Materials ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1005 ◽  
Author(s):  
Yang Li ◽  
Minghui Hong
Keyword(s):  
Electric Fields ◽  
Electric Field Intensity ◽  
Electromagnetic Coupling ◽  
Optical Component ◽  
Computational Method ◽  
Optical Elements ◽  
Swarm Optimization ◽  
Efficiency Optimization ◽  
Diffractive Efficiency

Metasurface is an advanced flat optical component that can flexibly manipulate the electromagnetic wave in an ultrathin dimension. However, electromagnetic coupling among neighbored optical elements decreases the diffractive efficiency and increases the noise. In this paper, a novel computational method is proposed to optimize the coupling of the metasurface. The coupled electric fields in metasurface design are decomposed into various coupling orders and then restructured to replace the whole metasurface simulation. This method is applied to optimize a metasurface that consisted of conventional nanorod plasmonic antennas as a case study. The convergence of this method in calculation is demonstrated. The electric field intensity deviation of a nanoantenna array can be reduced from 112.2% to 0.5% by the second-order coupling correction. The diffractive efficiency of a three-level phase meta-deflector is optimized from 73% to 86% by optimized coupling compensation via particle swarm optimization (PSO). This process opens a new area of metasurface design by the detailed field distribution of optical elements.

scholarly journals Scattering Analysis and Efficiency Optimization of Dielectric Pancharatnam–Berry-Phase Metasurfaces

Nanomaterials ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 586
Author(s):  
Chen-Yi Yu ◽  
Qiu-Chun Zeng ◽  
Chih-Jen Yu ◽  
Chien-Yuan Han ◽  
Chih-Ming Wang
Keyword(s):  
Phase Modulation ◽  
Rotation Angle ◽  
Berry Phase ◽  
Fine Tuning ◽  
Design Rule ◽  
Polarization Conversion ◽  
Swarm Optimization ◽  
Efficiency Optimization ◽  
Phase Type ◽  
Gradient Phase

In this study, the phase modulation ability of a dielectric Pancharatnam–Berry (PB) phase metasurface, consisting of nanofins, is theoretically analyzed. It is generally considered that the optical thickness of the unit cell of a PB-phase metasurface is λ/2, i.e., a half-waveplate for polarization conversion. It is found that the λ/2 is not essential for achieving a full 2π modulation. Nevertheless, a λ/2 thickness is still needed for a high polarization conversion efficiency. Moreover, a gradient phase metasurface is designed. With the help of the particle swarm optimization (PSO) method, the wavefront errors of the gradient phase metasurface are reduced by fine-tuning the rotation angle of the nanofins. The diffraction efficiency of the gradient phase metasurface is thus improved from 73.4% to 87.3%. This design rule can be utilized to optimize the efficiency of phase-type meta-devices, such as meta-deflectors and metalenses.

Reliability-Based Analysis and Optimization of the Gravity Balancing Performance of Spring-Articulated Serial Robots with Uncertainties

2021 ◽  
pp. 1-32
Author(s):  
Vu Linh Nguyen ◽  
Chin-Hsing Kuo ◽  
Po Ting Lin
Keyword(s):  
Pso Algorithm ◽  
Reliability Based Design Optimization ◽  
Swarm Optimization ◽  
Reliability Based Design ◽  
Serial Robots ◽  
Simulation Based ◽  
Serial Robot ◽  
Simulation Results ◽  
Uncertainty Level

Abstract This article proposes a method for analyzing the gravity balancing reliability of spring-articulated serial robots with uncertainties. Gravity balancing reliability is defined as the probability that the torque reduction ratio (the ratio of the balanced torque to the unbalanced torque) is less than a specified threshold. The reliability analysis is performed by exploiting a Monte Carlo simulation (MCS) with consideration of the uncertainties in the link dimensions, masses, and compliance parameters. The gravity balancing begins with a simulation-based analysis of the gravitational torques of a typical serial robot. Based on the simulation results, a gravity balancing design for the robot using mechanical springs is realized. A reliability-based design optimization (RBDO) method is also developed to seek a reliable and robust design for maximized balancing performance under a prescribed uncertainty level. The RBDO is formulated with consideration of a probabilistic reliability constraint and solved by using a particle swarm optimization (PSO) algorithm. A numerical example is provided to illustrate the gravity balancing performance and reliability of a robot with uncertainties. A sensitivity analysis of the balancing design is also performed. Lastly, the effectiveness of the RBDO method is demonstrated through a case study in which the balancing performance and reliability of a robot with uncertainties are improved with the proposed method.

Thermodynamic aspects of epitaxial self-assembly and magnetoelectric response in multiferroic nanostructures

2007 ◽  
Vol 22 (8) ◽  
pp. 2087-2095 ◽  
Author(s):  
Julia Slutsker ◽  
Zhuopeng Tan ◽  
Alexander L. Roytburd ◽  
Igor Levin
Keyword(s):  
Electric Fields ◽  
Self Assembly ◽  
Electromagnetic Coupling ◽  
Hysteretic Behavior ◽  
Microscopic Mechanism ◽  
Magnetic Nanorods ◽  
External Electric Fields ◽  
Multiferroic Films ◽  
Magnetoelectric Response ◽  
Theoretical Results

A thermodynamic approach was used to describe the formation and magnetoelectric response of composite multiferroic films. Experimental and theoretical results that address the origins of different phase morphologies in epitaxial spinel-perovskite nanostructures grown on differently oriented substrates are presented. A theoretical model of magnetoelectric coupling in multiferroic nanostructures that considers a microscopic mechanism of magnetization in single-domain magnetic nanorods is described. This model explains a discontinuous electromagnetic coupling, as observed experimentally, and predicts a hysteretic behavior of magnetization under external electric fields.

scholarly journals Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement

2017 ◽  
Vol 10 (2) ◽  
pp. 67
Author(s):  
Vina Ayumi ◽  
L.M. Rasdi Rere ◽  
Mohamad Ivan Fanany ◽  
Aniati Murni Arymurthy
Keyword(s):  
Contrast Enhancement ◽  
Harmony Search ◽  
Search Space ◽  
Optimization Method ◽  
Large Solution ◽  
Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Simulation Results ◽  
Better Than

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem's characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.

Complex optical elements for scanning helium microscopy through 3D printing

2021 ◽  
Author(s):  
Matthew Bergin ◽  
Thomas Myles ◽  
Aleksandar Radić ◽  
Christopher Hatchwell ◽  
Sam Lambrick ◽  
...  
Keyword(s):  
3D Printing ◽  
Multiple Scattering ◽  
Low Cost ◽  
Optical Element ◽  
Next Generation ◽  
Background Signal ◽  
Optical Elements ◽  
Cost Components

Abstract Developing the next generation of scanning helium microscopes requires the fabrication of optical elements with complex internal geometries. We show that resin stereolithography (SLA) 3D printing produces low-cost components with the requisite convoluted structures whilst achieving the required vacuum properties, even without in situ baking. As a case study, a redesigned pinhole plate optical element of an existing scanning helium microscope was fabricated using SLA 3D printing. In comparison to the original machined component, the new optical element minimised the key sources of background signal, in particular multiple scattering and the secondary effusive beam.

scholarly journals Bilevel Traffic Evacuation Model and Algorithm Design for Large-Scale Activities

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Danwen Bao ◽  
Jiayu Gu ◽  
Junhua Jia
Keyword(s):  
Large Scale ◽  
Algorithm Design ◽  
Arrival Rate ◽  
Planning Model ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Evacuation Time ◽  
Classic Model ◽  
The Road

This paper establishes a bilevel planning model with one master and multiple slaves to solve traffic evacuation problems. The minimum evacuation network saturation and shortest evacuation time are used as the objective functions for the upper- and lower-level models, respectively. The optimizing conditions of this model are also analyzed. An improved particle swarm optimization (PSO) method is proposed by introducing an electromagnetism-like mechanism to solve the bilevel model and enhance its convergence efficiency. A case study is carried out using the Nanjing Olympic Sports Center. The results indicate that, for large-scale activities, the average evacuation time of the classic model is shorter but the road saturation distribution is more uneven. Thus, the overall evacuation efficiency of the network is not high. For induced emergencies, the evacuation time of the bilevel planning model is shortened. When the audience arrival rate is increased from 50% to 100%, the evacuation time is shortened from 22% to 35%, indicating that the optimization effect of the bilevel planning model is more effective compared to the classic model. Therefore, the model and algorithm presented in this paper can provide a theoretical basis for the traffic-induced evacuation decision making of large-scale activities.

A HYBRID SUPPORT VECTOR REGRESSION APPROACH FOR RAINFALL FORECASTING USING PARTICLE SWARM OPTIMIZATION AND PROJECTION PURSUIT TECHNOLOGY

2010 ◽  
Vol 09 (02) ◽  
pp. 87-104 ◽  
Author(s):  
JIANSHENG WU ◽  
MINGZHE LIU ◽  
LONG JIN
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Support Vector Regression ◽  
Projection Pursuit ◽  
Particle Swarm ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Support Vector Regression Model ◽  
Swarm Optimization

In this paper, a hybrid rainfall-forecasting approach is proposed which is based on support vector regression, particle swarm optimization and projection pursuit technology. The projection pursuit technology is used to reduce dimensions of parameter spaces in rainfall forecasting. The particle swarm optimization algorithm is for searching the parameters for support vector regression model and to construct the support vector regression model. The observed data of daily rainfall values in Guangxi (China) is used as a case study for the proposed model. The computing results show that the present model yields better forecasting performance in this case study, compared to other rainfall-forecasting models. Our model may provide a promising alternative for forecasting rainfall application.

Sign in / Sign up

Export Citation Format

Share Document