首页|New Findings on Machine Learning Described by Investigators at University of Sus sex (A High-performance Conical-neck Helmholtz Resonator-based Piezoelectric Sel f-powered System for Urban Transportation)
New Findings on Machine Learning Described by Investigators at University of Sus sex (A High-performance Conical-neck Helmholtz Resonator-based Piezoelectric Sel f-powered System for Urban Transportation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Brighton, United Kingd om, by NewsRx correspondents, research stated, “As urbanization accelerates, the issue of traffic noise escalates. Efficiently harnessing this prevalent acousti c energy and facilitating its collection and conversion has emerged as a notable challenge in contemporary research.” Our news journalists obtained a quote from the research from the University of S ussex, “This paper introduces a piezoelectric self -powered system anchored on a Conical -Neck Helmholtz Resonator -Based Piezoelectric Self -Powered System (CN HR-PSS) which places the piezoelectric device inside a Conical -Neck Helmholtz r esonator. This system amalgamates acoustic energy harvesting, traffic noise abat ement, and traffic condition discernment. It combines by two parts, including a Piezoelectric Self -Powered Node (PSN) and a machine learning algorithm. The PSN , employing the Conical Neck Helmholtz Resonator and piezoelectric module, seize s noise and transmutes it into electrical energy, showcasing robust scalability. Multiple PSNs coalesce to form a sound barrier for traffic noise mitigation. Co ncurrently, the voltage signals emanated by the PSN also encapsulate traffic sta tus information. The algorithm extracts feature from the output signal and emplo ys machine learning to decipher traffic conditions.”
BrightonUnited KingdomEuropeCyborg sEmerging TechnologiesMachine LearningUniversity of Sussex
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