<div style="text-align:justify;"> <span style="font-family:Verdana;">Positively and negatively charged polyelectrolytes, namely, Poly(diallyldimethylammonium chloride) and Poly(styrene ...<div style="text-align:justify;"> <span style="font-family:Verdana;">Positively and negatively charged polyelectrolytes, namely, Poly(diallyldimethylammonium chloride) and Poly(styrene sulfonate), respectively, were employed to disperse and deploy negatively charged quantum dots on an otherwise passive metamaterial structure with a resonant frequency of 0.62 THz, by employing a layer-by-layer, self-assembly scheme. Upon exposure to a UV source with a wavelength of 365 nm the amplitude modulation was observed to increase with increases in the number of deposited bi-layers, until a modulation maximum of 2.68% was recorded enabling an all-optical, dynamically reconfigurable metamaterial geometry. Furthermore, amplitude modulation was subsequently observed to decrease with further increases in the number of layers employed due to quenching and shadowing effects. The experimental observations reported herein will enable the utilization of all-optical reconfigurable THz devices for communication and data transmission applications.</span> </div>展开更多
This paper presents an integrated on line learning system to evolve programmable logic array (PLA) controllers for navigating an autonomous robot in a two dimensional environment. The integrated on line learning sy...This paper presents an integrated on line learning system to evolve programmable logic array (PLA) controllers for navigating an autonomous robot in a two dimensional environment. The integrated on line learning system consists of two learning modules: one is the module of reinforcement learning based on temporal difference learning based on genetic algorithms, and the other is the module of evolutionary learning based on genetic algorithms. The control rules extracted from the module of reinforcement learning can be used as input to the module of evolutionary learning, and quickly implemented by the PLA through on line evolution. The on line evolution has shown promise as a method of learning systems in complex environment. The evolved PLA controllers can successfully navigate the robot to a target in the two dimensional environment while avoiding collisions with randomly positioned obstacles.展开更多
基金supported by the National Natural Science Foundations of China(62274093,62005119,61991431,62341408 and 61921005)the Excellent Youth Foundation of Jiangsu Scientific Committee(BK20211538)。
文摘<div style="text-align:justify;"> <span style="font-family:Verdana;">Positively and negatively charged polyelectrolytes, namely, Poly(diallyldimethylammonium chloride) and Poly(styrene sulfonate), respectively, were employed to disperse and deploy negatively charged quantum dots on an otherwise passive metamaterial structure with a resonant frequency of 0.62 THz, by employing a layer-by-layer, self-assembly scheme. Upon exposure to a UV source with a wavelength of 365 nm the amplitude modulation was observed to increase with increases in the number of deposited bi-layers, until a modulation maximum of 2.68% was recorded enabling an all-optical, dynamically reconfigurable metamaterial geometry. Furthermore, amplitude modulation was subsequently observed to decrease with further increases in the number of layers employed due to quenching and shadowing effects. The experimental observations reported herein will enable the utilization of all-optical reconfigurable THz devices for communication and data transmission applications.</span> </div>
文摘This paper presents an integrated on line learning system to evolve programmable logic array (PLA) controllers for navigating an autonomous robot in a two dimensional environment. The integrated on line learning system consists of two learning modules: one is the module of reinforcement learning based on temporal difference learning based on genetic algorithms, and the other is the module of evolutionary learning based on genetic algorithms. The control rules extracted from the module of reinforcement learning can be used as input to the module of evolutionary learning, and quickly implemented by the PLA through on line evolution. The on line evolution has shown promise as a method of learning systems in complex environment. The evolved PLA controllers can successfully navigate the robot to a target in the two dimensional environment while avoiding collisions with randomly positioned obstacles.
