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Structural Health Monitoring (SHM) has brought various benefits into the industry, such as in economic, life-safety and lightweight design aspects. The recent development of SHM techniques leads to the optimization of the sensor arrangement, enhancement in the damage identification accuracy and increases in computational efficiency. Vibration-based SHM has shown to be practical and requires a relatively small number of sensors. In this approach, the dynamic responses at vibration nodes (node displacement, or NODIS) are adopted for damage identification in plate structures. The method utilizes the framework of Bayesian inference to overcome the drawbacks of traditional deterministic approaches so that the presence of various uncertainties and errors can be taken into consideration. Furthermore, the Markov chain Monte Carlo sampling technique with adaptive Metropolis algorithm is integrated into the framework, achieving a considerable reduction of computational costs. In this paper, the principles of the NODIS method for plate structure is elaborated. Then, a thorough explanation of the MCMC-based Bayesian framework with NODIS and its theoretical background are presented. At last, the performance of the proposed method is evaluated with numerical results.

A scientific review is a type of article that summarizes the current state of a specific field, which is crucial for promoting the advancement of our science community. Authors need to read hundreds of research articles to prepare the data and insights for a comprehensive review, which is time-consuming and labor-intensive. In this work, we present an algorithm that can automatically extract keywords from the meta-information of each article and generate the basic data for review articles. Two different fields&mdash, communication engineering, and lab on a chip technology&mdash, were analyzed as examples. We first built an article library by downloading all the articles from the target journal using a python-based crawler. Second, the rapid automatic keyword extraction algorithm was implemented on the title and abstract of each article. Finally, we classified all extracted keywords into class by calculating the Levenshtein distance between each of them. The results demonstrated its capability of not only finding out how communication engineering and lab on a chip were evolved in the past decades but also summarizing the analytical outcomes after data mining of the extracted keywords. Our algorithm is more than a useful tool for researchers during the preparation of a review article, it can also be applied to quantitatively analyze the past, present and help authors predict the future trend of a specific research field.