scholarly journals Ternary optimization for designing metasurfaces

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Azin Hojjati ◽  
Mohammad Soleimani ◽  
Vahid Nayyeri ◽  
Omar M. Ramahi
Keyword(s):  
Design Method ◽  
Pso Algorithm ◽  
Frequency Selective Surface ◽  
Unit Cell ◽  
Polarization Conversion ◽  
Proof Of Concept ◽  
Polarization Converter ◽  
Swarm Optimization ◽  
Via Hole ◽  
Comparison Of The Results

AbstractA fully automated approach for designing metasurfaces whose unit cell may include metallic vias is proposed. Towards this aim, a ternary version of the particle swarm optimization (PSO) algorithm is employed in order to find the optimal metallic pattern and via-hole positions simultaneously. In the proposed design method, the upper surface of the unit cell is first pixelated. One of the possible three states of a metallic covered pixel, an uncovered etched pixel and a pixel containing a centered metalized via-hole is assigned to each pixel. The optimal state of each pixel is then determined by utilizing a ternary PSO algorithm to achieve favorable design goals. This method can be used for designing various metasurfaces as well as other via-assisted electromagnetic structures. As a proof of concept, the proposed method was applied to design two surfaces: a frequency selective surface with a minimum resonance frequency, and a linear-to-circular polarization converter with a maximum polarization conversion bandwidth. Comparison of the results with previous works confirms the efficiency and capability of the proposed method to design diverse metasurfaces in an automated fashion without the need for any theoretical or physical model.

scholarly journals Experimental Characterization of 2 × 2 Electronically Reconfigurable Polarization Converter Unit Cells at X-Band

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Biswarup Rana ◽  
In-Gon Lee ◽  
Ic-Pyo Hong
Keyword(s):  
Phase Shifter ◽  
Unit Cell ◽  
Polarization Conversion ◽  
Experimental Characterization ◽  
Polarization Converter ◽  
Pin Diodes ◽  
Transition Section ◽  
X Band ◽  
Unit Cells ◽  
Converter Unit

In this paper, an electronically reconfigurable polarization converter unit cell operating at X-band is proposed. The polarization converter unit cell consists of a passive patch, a phase shifter, and an active patch. There are two PIN diodes on the active patch. By switching the bias conditions of those PIN diodes, an electronically reconfigurable polarization converter is conceived. Both the passive and active patches are circular, and there are circular types of slots on both patches to enhance the operating bandwidth. To compensate for the capacitance introduced by PIN diodes, an equivalent capacitance structure is introduced on the active patch. 2 × 2 unit cells are designed to check the performance of the unit cell for polarization conversion applications. In addition, a novel type of experimental characterization technique is proposed to check the performance of polarization conversion using 2 × 2 unit cells. Two WR-90 waveguide sections, two rectangular to square sections, and a power supply are taken for the measurements. The rectangular to square waveguide transition section is designed in such a way so that 2 × 2 unit cells can be perfectly adjusted on the transition section and the performance of the 2 × 2 unit cells can be measured. The simulation results of the 8 × 8 array are also added to a miniaturized X-band horn antenna to check the performance of the overall array.

scholarly journals Robust Performance of PID Controller by using Artificial Intelligence

2018 ◽  
Author(s):  
Shachi Tiwary ◽  
Ashraf Jafri ◽  
Kushal Tiwari ◽  
Richa Tiwari ◽  
Chaman Yadav
Keyword(s):  
Pid Controller ◽  
Dynamic Behaviour ◽  
Design Method ◽  
Pso Algorithm ◽  
Proportional Integral Derivative ◽  
Robust Performance ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Modern Methods ◽  
Selection Of

This paper is meant to design method for determining the optimal proportional-integral-derivative (PID) controller parameters of plant system using the particle swarm optimization (PSO) algorithm and bacterial Foraging Optimization (BFO). There are several methods which are used to tune the controller parameters. They are categorized into two types known as classical methods and modern methods. In this paper the use of PSO method tuned the PID parameter to make them more general and to achieve the steady state error limit, also to improve the dynamic behaviour of the system. The performance and design criteria of automatic selection of controller constants are discussed below.

scholarly journals Particle Swarm Optimization Algorithm-Based Design Method for Ultrasonic Transducers

Micromachines ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 715
Author(s):  
Dongdong Chen ◽  
Jianxin Zhao ◽  
Chunlong Fei ◽  
Di Li ◽  
Yuanbo Zhu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Equivalent Circuit ◽  
Design Method ◽  
Particle Swarm ◽  
Equivalent Circuit Model ◽  
Pso Algorithm ◽  
Circuit Model ◽  
Center Frequency ◽  
Swarm Optimization ◽  
And Performance

In order to improve the fabrication efficiency and performance of an ultrasonic transducer (UT), a particle swarm optimization (PSO) algorithm-based design method was established and combined with an electrically equivalent circuit model. The relationship between the design and performance parameters of the UT is described by an electrically equivalent circuit model. Optimality criteria were established according to the desired performance; then, the design parameters were iteratively optimized using a PSO algorithm. The Pb(ZrxTi1−x)O3 (PZT) ceramic UT was designed by the proposed method to verify its effectiveness. A center frequency of 6 MHz and a bandwidth of −6 dB (70%) were the desired performance characteristics. The optimized thicknesses of the piezoelectric and matching layers were 255 μm and 102 μm. The experimental results agree with those determined by the equivalent circuit model, and the center frequency and −6 dB bandwidth of the fabricated UT were 6.3 MHz and 68.25%, respectively, which verifies the effectiveness of the developed optimization design method.

scholarly journals Simple design of efficient broadband multifunctional polarization converter for X-band applications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thi Kim Thu Nguyen ◽  
Thi Minh Nguyen ◽  
Hong Quang Nguyen ◽  
Thanh Nghia Cao ◽  
Dac Tuyen Le ◽  
...  
Keyword(s):  
Circular Polarization ◽  
Incident Angle ◽  
Polarization Conversion ◽  
Simple Design ◽  
Cross Polarization ◽  
Polarization Converter ◽  
Frequency Bands ◽  
X Band ◽  
Cost Efficient ◽  
Patch Resonators

AbstractA simple design of a broadband multifunctional polarization converter using an anisotropic metasurface for X-band application is proposed. The proposed polarization converter consists of a periodic array of the two-corner-cut square patch resonators based on the FR-4 substrate that achieves both cross-polarization and linear-to-circular polarization conversions. The simulated results show that the polarization converter displays the linear cross-polarization conversion in the frequency range from 8 to 12 GHz with the polarization conversion efficiency above 90%. The efficiency is kept higher than 80% with wide incident angle up to 45°. Moreover, the proposed design achieves the linear-to-circular polarization conversion at two frequency bands of 7.42–7.6 GHz and 13–13.56 GHz. A prototype of the proposed polarization converter is fabricated and measured, showing a good agreement between the measured and simulated results. The proposed polarization converter exhibits excellent performances such as simple structure, multifunctional property, and large cost-efficient bandwidth and wide incident angle insensitivity in the linear cross polarization conversion, which can be useful for X-band applications. Furthermore, this structure can be extended to design broadband polarization converters in other frequency bands.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

scholarly journals Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Mashallah Rezakazemi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Pso Algorithm ◽  
Cfd Modeling ◽  
Swarm Optimization ◽  
Inference System ◽  
Mesh Density

AbstractHerein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.

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.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

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