As urbanization accelerates, the issue of traffic noise escalates. Efficiently harnessing this prevalent acoustic energy and facilitating its collection and conversion has emerged as a notable challenge in contemporary research. This paper introduces a piezoelectric self-powered system anchored on a Conical-Neck Helmholtz Resonator-Based Piezoelectric Self-Powered System (CNHR-PSS) which places the piezoelectric device inside a Conical-Neck Helmholtz resonator. This system amalgamates acoustic energy harvesting, traffic noise abatement, 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, seizes noise and transmutes it into electrical energy, showcasing robust scalability. Multiple PSNs coalesce to form a sound barrier for traffic noise mitigation. Concurrently, the voltage signals emanated by the PSN also encapsulate traffic status information. The algorithm extracts feature from the output signal and employs machine learning to decipher traffic conditions. Simulative and theoretical analyses affirm that the system can efficaciously harvest acoustic energy from urban traffic noise and attenuate noise, with a pinnacle output power of 0.52mW and an average noise reduction of 13.16 %. The recognition accuracy of traffic conditions via SVM attained 100 %. The investigative outcomes underscore the exemplary performance of this self-powered system, rendering it a viable solution for applications in traffic noise mitigation and intelligent transportation.

中文翻译：

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用于城市交通的高性能锥颈亥姆霍兹谐振器压电自供电系统

随着城市化进程的加快，交通噪声问题日益突出。有效利用这种普遍的声能并促进其收集和转换已成为当代研究中的一个显着挑战。本文介绍了一种基于锥颈亥姆霍兹谐振器的压电自供电系统（CNHR-PSS）的压电自供电系统，该系统将压电器件放置在锥颈亥姆霍兹谐振器内。该系统融合了声能收集、交通噪音消除和交通状况识别。它由两部分组成，包括压电自供电节点（PSN）和机器学习算法。 PSN 采用锥颈亥姆霍兹谐振器和压电模块，捕获噪声并将其转化为电能，展现出强大的可扩展性。多个 PSN 结合起来形成缓解交通噪音的声屏障。同时，PSN发出的电压信号还封装了交通状态信息。该算法从输出信号中提取特征，并利用机器学习来解读交通状况。仿真和理论分析表明，该系统能够有效地从城市交通噪声中收集声能并衰减噪声，峰值输出功率为0.52mW，平均降噪13.16%。通过SVM对交通状况的识别准确率达到100%。研究结果强调了该自供电系统的典范性能，使其成为交通噪声缓解和智能交通应用的可行解决方案。