Genetic algorithm for true negative index in plasmonic metamaterials

2015 ◽  
Author(s):  
Ian A. Goforth ◽  
Daniel B. Fullager ◽  
Hossein Alisafaee ◽  
Michael A. Fiddy
Keyword(s):  
Genetic Algorithm ◽  
Negative Index ◽  
True Negative ◽  
Plasmonic Metamaterials

scholarly journals Backward Phase Matching for Second Harmonic Generation in Negative-Index Conformal Surface Plasmonic Metamaterials

2018 ◽  
Vol 5 (11) ◽  
pp. 1800661 ◽  
Author(s):  
Liangliang Liu ◽  
Lin Wu ◽  
Jingjing Zhang ◽  
Zhuo Li ◽  
Baile Zhang ◽  
...  
Keyword(s):  
Second Harmonic Generation ◽  
Harmonic Generation ◽  
Second Harmonic ◽  
Phase Matching ◽  
Negative Index ◽  
Plasmonic Metamaterials

scholarly journals Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3222 ◽  
Author(s):  
Di Wang ◽  
Lin Xie ◽  
Simon Yang ◽  
Fengchun Tian
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Near Infrared ◽  
Spectral Response ◽  
Sensor Data ◽  
Support Vector ◽  
True Negative ◽  
Svm Model ◽  
Tobacco Cultivation ◽  
Effective Principal

Near-infrared (NIR) spectral sensors deliver the spectral response of the light absorbed by materials for quantification, qualification or identification. Spectral analysis technology based on the NIR sensor has been a useful tool for complex information processing and high precision identification in the tobacco industry. In this paper, a novel method based on the support vector machine (SVM) is proposed to discriminate the tobacco cultivation region using the near-infrared (NIR) sensors, where the genetic algorithm (GA) is employed for input subset selection to identify the effective principal components (PCs) for the SVM model. With the same number of PCs as the inputs to the SVM model, a number of comparative experiments were conducted between the effective PCs selected by GA and the PCs orderly starting from the first one. The model performance was evaluated in terms of prediction accuracy and four parameters of assessment criteria (true positive rate, true negative rate, positive predictive value and F1 score). From the results, it is interesting to find that some PCs with less information may contribute more to the cultivation regions and are considered as more effective PCs, and the SVM model with the effective PCs selected by GA has a superior discrimination capacity. The proposed GA-SVM model can effectively learn the relationship between tobacco cultivation regions and tobacco NIR sensor data.

scholarly journals Genetic-algorithm-aided ultra-broadband perfect absorbers using plasmonic metamaterials

2021 ◽  
Author(s):  
Alexandre Mayer ◽  
bi hai ◽  
Sarah Griesse-Nascimento ◽  
benoit hackens ◽  
Jerome Loicq ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Plasmonic Metamaterials ◽  
Perfect Absorbers

THE MICROSTRUCTURE DESIGN OPTIMIZATION OF NEGATIVE INDEX METAMATERIALS USING GENETIC ALGORITHM

2011 ◽  
Vol 22 ◽  
pp. 95-108 ◽  
Author(s):  
Yongxiang Zhao ◽  
Fei Chen ◽  
Haiya Chen ◽  
Na Li ◽  
Qiang Shen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Negative Index ◽  
Microstructure Design

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

Over-All Accuracy of 99mTc-Pertechnetate Brain Scanning for Brain Tumours – Study of 471 Patients

1978 ◽  
Vol 17 (06) ◽  
pp. 249-253
Author(s):  
E. Pétursson ◽  
B. Sigurbjörnsson ◽  
D. Davidsson ◽  
O.G. Björnsson
Keyword(s):  
Brain Metastases ◽  
Brain Tumours ◽  
True Negative ◽  
Diagnostic Reliability ◽  
Radioisotope Scanning ◽  
Primary Brain Tumours ◽  
99Mtc Pertechnetate

A 3-year follow-up and re-evaluation of all scans on all patients referred for brain scanning in Iceland during 1 year was performed in order to assess the diagnostic reliability of radioisotope scanning for brain tumours. The study included 471 patients. Of these 25 had primary brain tumours and 7 brain metastases. Scans were positive and correctly interpreted in68 % of the patients with primary brain tumours and in 3 of the 7 patients with metastases. The over-all accuracy of brain scanning for brain tumours defined as the total number of correct positive scans and correct negative scans versus total number of scans examined was 96%, this figure being mainly influenced by the high number of true negative scans.

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