Aviation electrical connectors are critically important components in electrical wiring interconnection systems. Currently, connector defect detection relies entirely on manual inspection, which is error-prone and tim...
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This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian, demonstrating that obtaining a fine-t...
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This paper develops a performance improvement control strategy based on residual generator to improve the control performance of the multi-inverter parallel system in the presence of load disturbances and line paramet...
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In recent developments, autonomous racing has garnered attention as it aims to overcome the limitations of standard autonomous driving systems. Achieving safe racing conditions necessitates both fast and long-range pe...
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The main motivation behind the present work was to validate the impact of pendulum mass, cart mass, and length of pendulum on stabilization and swing-up of cart-inverted pendulum. Inverted pendulum system is a classic...
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Computer aided control in biomedical applications is gaining more and more popularity due to numerous research studies that have proven the efficiency of automatic control over manual dosing, which is highly susceptib...
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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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In recent years, underwater vehicle-manipulator systems(UVMSs) have captured researchers' attention. Inspired by fish, bionic mechanisms were introduced into a *** underwater biomimetic vehicle-manipulator system(...
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In recent years, underwater vehicle-manipulator systems(UVMSs) have captured researchers' attention. Inspired by fish, bionic mechanisms were introduced into a *** underwater biomimetic vehicle-manipulator system(UBVMS) is actuated by two symmetrically arranged biomimetic undulatory fin propulsors [1]. Based on the UBVMS, some topics have been researched, such as freefloating autonomous operation [2, 3], three-dimensional helical path following [4], and underwater video processing [5].
The model of the 3-layer feed-forward neural network is introduced whose first hidden layer consists of bithreshold neurons and the other layers - of single-threshold ones. The proposed network is capable to recognize...
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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
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