Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enh...Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.展开更多
本文隶属于一个更大的研究项目。该项目着重关注历史社会网络分析在中国佛教史研究中的应用。研究课题包括:社会网络分析(Social Network Analysis)^(①)的度量是否适用于佛教史,网络可视化能否使我们更好地理解历史人物群体并发现新的...本文隶属于一个更大的研究项目。该项目着重关注历史社会网络分析在中国佛教史研究中的应用。研究课题包括:社会网络分析(Social Network Analysis)^(①)的度量是否适用于佛教史,网络可视化能否使我们更好地理解历史人物群体并发现新的规律。本作采用中国佛教历史社会网络(Historical Social Network of Chinese Buddhism)数据集中的数据,该数据集涵盖了佛教人物及其传承关系,其数据量在过去的三十年间稳步增长,收录超过一万七千五百个中国佛教历史中的人物关系。此数据集开源。理论上,本文所有可视化结果和计量皆可复现。本文着眼于由道安(314-385)、慧远(334-416)、鸠摩罗什(约344-413)及其团体组成的“三角形”样关系。该关系形成于主组件的起始。本文首先阐述了相互联动的节点如何促成大乘佛教在中国的建立,其次研究了如何使用社会网络分析来定位迄今为止在佛教历史上被忽视但重要的人物。展开更多
基金National Science Foundation of Zhejiang under Contract(LY23E010001)。
文摘Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.
文摘本文隶属于一个更大的研究项目。该项目着重关注历史社会网络分析在中国佛教史研究中的应用。研究课题包括:社会网络分析(Social Network Analysis)^(①)的度量是否适用于佛教史,网络可视化能否使我们更好地理解历史人物群体并发现新的规律。本作采用中国佛教历史社会网络(Historical Social Network of Chinese Buddhism)数据集中的数据,该数据集涵盖了佛教人物及其传承关系,其数据量在过去的三十年间稳步增长,收录超过一万七千五百个中国佛教历史中的人物关系。此数据集开源。理论上,本文所有可视化结果和计量皆可复现。本文着眼于由道安(314-385)、慧远(334-416)、鸠摩罗什(约344-413)及其团体组成的“三角形”样关系。该关系形成于主组件的起始。本文首先阐述了相互联动的节点如何促成大乘佛教在中国的建立,其次研究了如何使用社会网络分析来定位迄今为止在佛教历史上被忽视但重要的人物。
