The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their *** introducing innovative solutions for energy harvesting and autonomous sensing,this research presents...
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The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their *** introducing innovative solutions for energy harvesting and autonomous sensing,this research presents a cost-effective strategy to enhance the performance of triboelectric nanogenerators(TENGs).The TENG was fabricated from polyvinylidene fluoride(PVDF)and N,N'-poly(methyl methacrylate)(PMMA)blend with a porous structure via a novel optimized quenching *** developed approach results in a highβ-phase content(85.7%)PVDF/3wt.%PMMA porous blend,known for its superior piezoelectric ***/3wt.%PMMA modified porous TENG demonstrates remarkable electrical output,with a dielectric constant of 40 and an open-circuit voltage of approximately 600 *** porous matrix notably increases durability,enduring over 36000 operational cycles without performance ***,practical applications were explored in this research,including powering LEDs and pacemakers with a maximum power output of 750mWm^(-2).Also,TENG served as a self-powered tactile sensor for robotic applications in various temperature *** work highlights the potential of the PVDF/PMMA porous blend to utilize the next-generation self-powered sensors and power small electronic devices.
The present paper introduces a mathematical model for the cross-talking between microRNA and Protein. Studying the qualitative properties of the proposed model, we infer that the microRNA is an inhibitor for the Prote...
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Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may ...
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Methods of mathematical modeling of the dependence of fuel consumption on the load of diesel gensets are considered. Accounting for such dependence in simulating the operation of solar-diesel hybrid power systems can ...
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Learning algorithms have become an integral component to modern engineering solutions. Examples range from self-driving cars and recommender systems to finance and even critical infrastructure, many of which are typic...
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Learning algorithms have become an integral component to modern engineering solutions. Examples range from self-driving cars and recommender systems to finance and even critical infrastructure, many of which are typically under the purview of control theory. While these algorithms have already shown tremendous promise in certain applications [1], there are considerable challenges, in particular, with respect to guaranteeing safety and gauging fundamental limits of operation. Thus, as we integrate tools from machine learning into our systems, we also require an integrated theoretical understanding of how they operate in the presence of dynamic and system-theoretic phenomena. Over the past few years, intense efforts toward this goal - an integrated theoretical understanding of learning, dynamics, and control - have been made. While much work remains to be done, a relatively clear and complete picture has begun to emerge for (fully observed) linear dynamical systems. These systems already allow for reasoning about concrete failure modes, thus helping to indicate a path forward. Moreover, while simple at a glance, these systems can be challenging to analyze. Recently, a host of methods from learning theory and high-dimensional statistics, not typically in the control-theoretic toolbox, have been introduced to our community. This tutorial survey serves as an introduction to these results for learning in the context of unknown linear dynamical systems (see 'Summary'). We review the current state of the art and emphasize which tools are needed to arrive at these results. Our focus is on characterizing the sample efficiency and fundamental limits of learning algorithms. Along the way, we also delineate a number of open problems. More concretely, this article is structured as follows. We begin by revisiting recent advances in the finite-sample analysis of system identification. Next, we discuss how these finite-sample bounds can be used downstream to give guaranteed performa
In this paper, a nonlinear guidance law for impact angle-constrained interception of nonmaneuvering targets, respecting the bounds on the control input - the commanded interceptor lateral acceleration, is proposed. By...
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In recent years, deep learning methods have been widely applied to fault classification in chemical processes, but previous studies have encountered significant challenges. Firstly, actual chemical processes often hav...
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This paper proposes an estimator to provide precise position, velocity, and orientation for a landing aircraft relative to the runway based on image sensors, IMU sensor data, and barometric sensor measurements. This w...
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作者：
Mahmoud, AbdulrahmanBabiker, AhmedMohamed, MazenAhmed, IjazKhalid, Muhammad
Control and Instrumentation Engineering Department Saudi Arabia
Department of Electrical Engineering Islamabad Pakistan
Electrical Engineering Department Dhahran31261 Saudi Arabia KFUPM
Interdisciplinary Research Center for Sustainable Energy Systems Saudi Arabia
DC microgrids (MGs) have recently garnered significant interest due to their efficient power conversion and simpler controlsystems compared to AC MGs. However, managing DC MGs presents specific challenges, especially...
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Dynamic control for aerial tracking can be complicated by the addition of secondary goals such as concealment, which may be necessary in intelligence, surveillance, and reconnaissance (ISR) mission. Therefore, a RL ag...
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