摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者从英国布莱顿发回的新闻报道，研究表明，“随着城市化进程的加快，交通噪音问题也在升级。有效利用这种普遍存在的声能并促进其收集和转换已经成为当代研究的一个显著挑战。”本文介绍了一种基于锥颈Helmholtz谐振器的压电自供电系统(CN HR-PSS),该系统将压电元件置于锥颈Helmholtz谐振器内,将声能量采集、交通噪声抑制和交通状况识别结合起来,由两部分组成。摘要:包括压电自供电节点(PSN)和机器学习算法。PSN采用锥颈Helmholtz谐振器和压电模块,捕获s噪声并将其转化为电能,显示出良好的可扩展性。多个PSN联合起来形成一个声屏障,用于交通噪声的抑制。PSN发出的电压信号也封装了交通状态信息，该算法从输出信号中提取特征，并利用机器学习对交通状况进行解读。
Abstract
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.”
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