A method of compressor valve fault diagnosis using information entropy and SVM is proposed in this paper. The main obstacle in the fault diagnosis focuses on the low non-linear pattern recognition performance and small sample number. Therefore, the information entropy, which is flexible and tolerant to the non-linearity problem, is applied to analyze the characteristic of the signals. SVM is employed in the fault classification because of its superiority in dealing with smaller sample problem. The information entropy features and the optimization test of the SVM model are detailed analyzed. The experiment shows the good performance of the information entropy SVM method in compressor valve fault diagnosis. 相似文献
Objective: Although identification of factors that influence helmet use during bicycle riding is necessary for the selection of groups that require safe cycling education, limited baseline data are available. The aim of the present study was to analyze the rate of helmet use and the demographic factors that were independently associated with helmet use among Korean bicycle riders.
Methods: In this cross-sectional study, we used public data from the Sixth Korean National Health and Nutrition Examination Survey conducted in 2013 and 2014. Helmet users were defined as subjects who always, usually, or frequently wore helmets when cycling. Independent factors associated with helmet use were determined using odds ratios (ORss) adjusted for 5 demographic factors via multivariate logistic regression analysis.
Results: In the total population, 4,103 individuals were bicycle riders; among these, 782 individuals (19.1%) wore helmets. A total of 21.1% of male riders used helmets, compared to 15.5% of female riders (P <.001). The adjusted logistic regression model revealed that female sex (OR = 0.665; 95% confidence interval [CI], 0.554–0.797), teenage status (OR = 0.475, 95% CI, 0.333–0.678), and low household income (OR = 0.657, 95% CI 0.513–0.841) were significantly associated with nonuse of helmets.
Conclusions: Female sex, teenage status, and low household income were independent factors associated with the nonuse of helmets. We identified factors associated with helmet use during bicycle riding through analysis of baseline data on helmet usage. 相似文献
● State-of-the-art applications of machine learning (ML) in solid waste (SW) is presented.● Changes of research field over time, space, and hot topics were analyzed.● Detailed application seniors of ML on the life cycle of SW were summarized.● Perspectives towards future development of ML in the field of SW were discussed. Due to the superiority of machine learning (ML) data processing, it is widely used in research of solid waste (SW). This study analyzed the research and developmental progress of the applications of ML in the life cycle of SW. Statistical analyses were undertaken on the literature published between 1985 and 2021 in the Science Citation Index Expanded and Social Sciences Citation Index to provide an overview of the progress. Based on the articles considered, a rapid upward trend from 1985 to 2021 was found and international cooperatives were found to have strengthened. The three topics of ML, namely, SW categories, ML algorithms, and specific applications, as applied to the life cycle of SW were discussed. ML has been applied during the entire SW process, thereby affecting its life cycle. ML was used to predict the generation and characteristics of SW, optimize its collection and transportation, and model the processing of its energy utilization. Finally, the current challenges of applying ML to SW and future perspectives were discussed. The goal is to achieve high economic and environmental benefits and carbon reduction during the life cycle of SW. ML plays an important role in the modernization and intellectualization of SW management. It is hoped that this work would be helpful to provide a constructive overview towards the state-of-the-art development of SW disposal. 相似文献
