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High efficiency second and third harmonic generation from magnetic metamaterials by using a grating
Sajedian, I.,Zakery, A.,Rho, J. North-Holland Pub. Co 2017 OPTICS COMMUNICATIONS - Vol.397 No.-
Metamaterials can be used to generate harmonic signals in small thicknesses, but they suffer from low efficiency. Here, we introduce a new method for amplifying second and third harmonic generation from magnetic metamaterials. We show numerically that by using a grating structure under the metamaterial, the grating and the metamaterial form a resonator which leads to a higher absorption in the metamaterial. By this method we could increase the absorption of the structure in the magnetic resonance up to 25% of the initial value. This leads to the generation of second and third harmonic signals with a higher efficiency from this metamaterial-based nonlinear media. We confirmed this idea in the nanostrip metamaterials and saw the amplitude of the second harmonic generation was doubled and the amplitude of the third harmonic generation increased by a factor of 4 in comparison to the same structure without grating.
The role of current loop in harmonic generation from magnetic metamaterials in two polarizations
Sajedian, I.,Kim, I.,Zakery, A.,Rho, J. North-Holland Pub. Co 2017 OPTICS COMMUNICATIONS - Vol.401 No.-
In this paper, we investigate the role of current loop in the generation of second and third harmonic signals from magnetic metamaterials and we are clarifying why two polarized harmonics are generated from magnetic metamaterials. We show that the current loop formed in the magnetic resonant frequency acts as a source for nonlinear effects. The current loop that has a circular shape can be divided into two orthogonal parts, where each of these parts acts as a source for generating a harmonic signal parallel to itself. The type of harmonic signal is determined by the metamaterial's inversion symmetry in that direction. This claim is also supported by the experimental results of another group.
Deep Q-network to produce polarization-independent perfect solar absorbers: a statistical report
Sajedian Iman,Badloe Trevon,이헌,노준석 나노기술연구협의회 2020 Nano Convergence Vol.7 No.26
Using reinforcement learning, a deep Q-network was used to design polarization-independent, perfect solar absorbers. The deep Q-network selected the geometrical properties and materials of a symmetric three-layer metamaterial made up of circular rods on top of two films. The combination of all the possible permutations gives around 500 billion possible designs. In around 30,000 steps, the deep Q-network was able to produce 1250 structures that have an integrated absorption of higher than 90% in the visible region, with a maximum of 97.6% and an integrated absorption of less than 10% in the 8–13 µm wavelength region, with a minimum of 1.37%. A statistical analysis of the distribution of materials and geometrical parameters that make up the solar absorbers is presented.
Design of high transmission color filters for solar cells directed by deep Q-learning
Sajedian, Iman,Lee, Heon,Rho, Junsuk Elsevier 2020 SOLAR ENERGY -PHOENIX ARIZONA THEN NEW YORK- Vol.195 No.-
<P><B>Abstract</B></P> <P>In this paper, we have used deep Q-learning networks (DQN) to find a colored coating for solar cells with high transmission. A basic structure with a huge range of possibilities was given to DQN, and it was designed to find the best structures fitting our purpose. The number of possibilities given to the model was more than 12 billion. Our model could find the structures with higher transmission and deeper colors compared to other human researchers in around 32,000 steps. Our numerical results cover a large area of color gamut which can be used for aesthetic purposes.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We described how to modify deep Q-learning algorithm, so it can find the best colors for solar cells. </LI> <LI> We used a big range of materials and geometrical parameters to cover a huge amount of design possibilities. </LI> <LI> Our found results are noticeably stronger than the best results found by other researchers. </LI> <LI> Our code could find the desired results in a reasonable time. </LI> </UL> </P>
Accurate and instant frequency estimation from noisy sinusoidal waves by deep learning
노준석,Iman Sajedian 나노기술연구협의회 2019 Nano Convergence Vol.6 No.27
We used a deep learning network to find the frequency of a noisy sinusoidal wave. A three-layer neural network was designed to extract the frequency of sinusoidal waves that had been combined with white noise at a signal-to-noise ratio of 25 dB. One hundred thousand waves were prepared for training and testing the model. We designed a neural network that could achieve a mean squared error of 4 × 10−5 for normalized frequencies. This model was written for the range 1 kHz ≤ f ≤ 10 kHz but also shown how to easily be generalized to other ranges. The algorithm is easy to rewrite and the final results are highly accurate. The trained model can find frequency of any previously-unseen noisy wave in less than a second.
Moshiran, Vahid Ahmadi,Karimi, Ali,Golbabaei, Farideh,Yarandi, Mohsen Sadeghi,Sajedian, Ali Asghar,Koozekonan, Aysa Ghasemi Occupational Safety and Health Research Institute 2021 Safety and health at work Vol.12 No.3
Background: Styrene is one of the aromatic compounds used in acetonitrile-butadiene-styrene (ABS) producing petrochemicals, which has an impact on health of workers. Therefore, this study aimed to investigate the health risks of styrene emitted from the petrochemical industry in Iran. Methods: Air samples were collected based on NIOSH 1501 method. The samples were analyzed by the Varian-cp3800 gas chromatograph. Finally, risk levels of styrene's health effects on employees were assessed by the quantitative method of the U.S. Environmental Protection Agency (U.S. EPA) and the semiquantitative way by the Singapore Occupational Safety and Health Association. Results: Based on the results, the employees had the highest average exposure to styrene vapors (4.06 × 10<sup>-1</sup>mg.(kg - day)<sup>-1</sup>) in the polybutadiene latex (PBL) unit. Therefore, the most top predictors of cancer and non-cancer risk were 2.3×10<sup>-4</sup> and 7.26 × 10<sup>-1</sup>, respectively. Given that the lowest average exposure (1.5 × 10<sup>-2</sup>mg.(kg - day)<sup>-1</sup>) was in the dryer unit, the prediction showed a moderate risk of cancer (0.8 × 10<sup>-6</sup>) and non-cancer (2.3 × 10<sup>-3</sup>) for the employees. The EPA method also predicted that there would be a definite cancer risk in 16% and a probable risk in 76% of exposures. However, according to the semiquantitative approach, the rate of risk was at the "low" level for all staff. The results showed that there was a significant difference (p < 0.05) between the units in exposure and health risk of styrene (p < 0.05). Conclusion: Given the high risk of styrene's health effects, appropriate control measures are required to reduce the exposure level.