The use of software tools and applications progressively became a standard in both education and industry. A solution for hand-drawn electrical scheme digitization has been proposed to match the fast-paced dynamic of ...
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The use of software tools and applications progressively became a standard in both education and industry. A solution for hand-drawn electrical scheme digitization has been proposed to match the fast-paced dynamic of ...
The use of software tools and applications progressively became a standard in both education and industry. A solution for hand-drawn electrical scheme digitization has been proposed to match the fast-paced dynamic of the modern world in the field of electrical engineering. The aim is to notably reduce time-consuming and error-prone electrical scheme tracing from hand-drawn to simulating software. The means have been achieved through the usage of state-of-the-art deep learning model YOLOv5 for electrical elements detection along with Python and OpenCV library for data processing. The user’s input is an image of a hand-drawn circuit, and the end result is an LTspice digitized electrical scheme ready for simulation.
Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the industrial production of complex and high-criticality parts for aerospace, power generation, medical, transportation, and...
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Open-loop control of laser powder bed fusion (LPBF) additive manufacturing (AM) has enabled the industrial production of complex and high-criticality parts for aerospace, power generation, medical, transportation, and other industries. This approach relies on static parameter sets obtained through extensive experimentation and a priori simulation on analog parts, with the hope that they remain stable and defect-free once transferred to the production parts. Closed-loop control of LPBF has the potential to enhance process stability further and reduce defect formation in the face of complex thermal histories, stochastic process noise, hardware drift, and unexpected perturbations. The controllers can be classified based on the spatial and temporal scales in which they operate, designated as layer-to-layer and in-layer controllers. However, the performance and effectiveness of controllers largely depend on the tuning of their parameters. Traditionally, controller tuning has been a manual, expertise-driven process that does not guarantee optimal controller performance and is often constrained by the non-transferability of settings between different systems. This study proposes the use of Bayesian Optimization (BO), a sample-efficient algorithm, to automate the tuning of an in-layer controller by leveraging the layer-to-layer repetitive nature of the LPBF process. Two alternative approaches are introduced: online tuning, which adjusts parameters iteratively during the process, and offline tuning, conducted in a representative setup such as laser exposures on a bare metal plate. The proposed methods are experimentally implemented on an in-layer PI controller and the performance of the resulting tuned controllers is investigated on two different wedge geometries that are prone to overheating. The results demonstrate that BO effectively tunes controllers using either method, where both significantly reduced overheating in controlled wedge specimens compared to those uncontro
Automotive powertrain mainly consisting of combustion engine,motor and battery(*** for hybrid powertrain)is a very complicated integration system,and the research on the automotive powertrain control techniques remain...
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Automotive powertrain mainly consisting of combustion engine,motor and battery(*** for hybrid powertrain)is a very complicated integration system,and the research on the automotive powertrain control techniques remains hot-spot in past *** paper proposes some challenging issues and control solutions of automotive powertrain system from the perspective of the dynamic system *** typical characteristics of automotive powertrain system are analysed for control development,and the several control applications using model-based and model-free control design are demonstrated with sufficient experimental *** addition,some open issues for future powertrain control development are summarised.
Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements ...
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Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements ...
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Long-range optimal prediction algorithms use the predicted output for several steps ahead. The prediction based on traditionally estimated model parameters does not result in an optimal prediction if the measurements are noisy or/and model structure differs from real process structure. In this paper two different identification schemes are presented and compared: long-range predictive single-model identification and simultaneous multi-step-ahead prediction identification. It is shown that the first method is easier to realize but the second one leads to more accurate results. Both methods are derived for a first-order model in details. Simulation runs and a level control example illustrate the algorithms presented.
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