Excess heat significantly reduces the efficiency and lifespan of electrical and optoelectronic devices. While passive radiative cooling is becoming more common, achieving active thermal control without physical reconfiguration remains challenging. Unlike conventional static absorbers, we propose a broadband plasmonic solar absorber designed to regulate energy absorption in the near-infrared (NIR) region without modifying the geometrical parameters. The design utilizes a coaxial cylindrical metal-insulator-metal (MIM) configuration, combining refractory copper (Cu), silicon dioxide (SiO2), and a trilayer graphene (Gr) that allows electrical tuning in a broadband solar absorber. Simulation results show a maximum broadband absorption efficiency of 93.54% when the Gr Fermi level is set to 0.1 eV. By electrically modulating the Fermi level to 0.7 eV, the proposed structure exhibits good reflection characteristics in the NIR region, and the absorption is actively suppressed to 67.92%, thereby transitioning the device into a thermal-protection mode. Furthermore, thermal analysis confirms that this active modulation achieves a significant temperature reduction from 55.9 °C to 31 °C at a wavelength of 1100 nm. The structure also exhibits remarkable environmental robustness, including polarization insensitivity and angular stability up to 55°, owing to its symmetrical coaxial geometry. This study presents a compact absorber with a high-performance solution for dynamic sunlight harvesting and precision thermal management. The proposed work provides a versatile framework for next-generation solar cells and integrated optoelectronic systems.
This study presents an adaptive control scheme based on synergetic control theory for suppressing the vibration of building structures due to earthquake. The control key for the proposed controller is based on a magneto-rheological (MR) damper, which supports the building. According to Lyapunov-based stability analysis, an adaptive synergetic control (ASC) strategy was established under variation of the stiffness and viscosity coefficients in the vibrated building. The control and adaptive laws of the ASC were developed to ensure the stability of the controlled structure. The proposed controller addresses the suppression problem of a single-degree-of-freedom (SDOF) building model, and an earthquake control scenario was conducted and simulat
... Show MoreThis paper presents a newly developed method with new algorithms to find the numerical solution of nth-order state-space equations (SSE) of linear continuous-time control system by using block method. The algorithms have been written in Matlab language. The state-space equation is the modern representation to the analysis of continuous-time system. It was treated numerically to the single-input-single-output (SISO) systems as well as multiple-input-multiple-output (MIMO) systems by using fourth-order-six-steps block method. We show that it is possible to find the output values of the state-space method using block method. Comparison between the numerical and exact results has been given for some numerical examples for solving different type
... Show MoreThis paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number
... Show MoreThe evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed
... Show MoreIn this review of literature, the light will be concentrated on the local drugs delivery systems for treating the periodontal diseases. Principles, types, advantages and indications of each type will be discussed in this paper.
Magnetic levitation (Maglev) systems are employed in a wide range of applications and are therefore of significant practical importance, which has led to growing research interest. This paper presents the design of a terminal synergetic control (TSC) and feedback linearization-based proportional-integral-derivative plus second-order derivative (FL-PIDD2) controller for the Maglev system. For developing the control law of both controllers, the mathematical model of the Maglev system is converted into a canonical system where the expression of the nonlinearity is displayed in the last differential dynamic equation of the system. The determination of the TSC and FL-PIDD2 gains for achieving the desired dynamic response is carried out using the
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