Pub Date : 2022-09-01Epub Date: 2022-05-06DOI: 10.1007/s11356-022-20375-y
Hong Yang, Junlin Zhao, Guohui Li
As air pollution worsens, the prediction of PM2.5 concentration becomes increasingly important for public health. This paper proposes a new hybrid prediction model of PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), amplitude-aware permutation entropy (AAPE), variational mode decomposition improved by marine predators algorithm (MPA-VMD), and extreme learning machine optimized by chimp optimization algorithm (ChOA-ELM), named CEEMDAN-AAPE-MPA-VMD-ChOA-ELM. Firstly, CEEMDAN is used to decompose the original data, and AAPE is used to quantify the complexity of all IMF components. Secondly, MPA-VMD is used to decompose the IMF component with the maximum AAPE. Lastly, ChOA-ELM is used to predict all IMF components, and all prediction results are reconstructed to obtain the final prediction results. The proposed model combines the advantages of secondary decomposition technique, feature analysis, and optimization algorithm, which can predict PM2.5 concentration accurately. PM2.5 concentrations at hourly intervals collected from March 1, 2021, to March 31, 2021, in Shanghai and Shenyang, China, are used for experimental study and DM test. The experimental results in Shanghai show that the RMSE, MAE, MAPE, and R2 of the proposed model are 1.0676, 0.7685, 0.0181, and 0.9980 respectively, which is better than all comparison models at 90% confidence level. In Shenyang, the RMSE, MAE, MAPE, and R2 of the proposed model are 1.4399, 1.1258, 0.0389, and 0.9976, respectively, which is better than all comparison models at 95% confidence level.
{"title":"A new hybrid prediction model of PM<sub>2.5</sub> concentration based on secondary decomposition and optimized extreme learning machine.","authors":"Hong Yang, Junlin Zhao, Guohui Li","doi":"10.1007/s11356-022-20375-y","DOIUrl":"10.1007/s11356-022-20375-y","url":null,"abstract":"<p><p>As air pollution worsens, the prediction of PM<sub>2.5</sub> concentration becomes increasingly important for public health. This paper proposes a new hybrid prediction model of PM<sub>2.5</sub> concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), amplitude-aware permutation entropy (AAPE), variational mode decomposition improved by marine predators algorithm (MPA-VMD), and extreme learning machine optimized by chimp optimization algorithm (ChOA-ELM), named CEEMDAN-AAPE-MPA-VMD-ChOA-ELM. Firstly, CEEMDAN is used to decompose the original data, and AAPE is used to quantify the complexity of all IMF components. Secondly, MPA-VMD is used to decompose the IMF component with the maximum AAPE. Lastly, ChOA-ELM is used to predict all IMF components, and all prediction results are reconstructed to obtain the final prediction results. The proposed model combines the advantages of secondary decomposition technique, feature analysis, and optimization algorithm, which can predict PM<sub>2.5</sub> concentration accurately. PM<sub>2.5</sub> concentrations at hourly intervals collected from March 1, 2021, to March 31, 2021, in Shanghai and Shenyang, China, are used for experimental study and DM test. The experimental results in Shanghai show that the RMSE, MAE, MAPE, and R<sup>2</sup> of the proposed model are 1.0676, 0.7685, 0.0181, and 0.9980 respectively, which is better than all comparison models at 90% confidence level. In Shenyang, the RMSE, MAE, MAPE, and R<sup>2</sup> of the proposed model are 1.4399, 1.1258, 0.0389, and 0.9976, respectively, which is better than all comparison models at 95% confidence level.</p>","PeriodicalId":16565,"journal":{"name":"Journal of Northeast Agricultural University","volume":"19 1","pages":"67214-67241"},"PeriodicalIF":5.8,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78284778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-04-01DOI: 10.4028/www.scientific.net/AMR.507.112
Yan Yu, X. Liu, S. Shang
A test soil-bin is an effective device to test the whole or components of the agricultural machinery. A new computer control and test system featuring PLC control and PXI bus has been designed to control the whole soil bin system including motion control and correlative date processing. This paper introduces the software design as well as the hardware design of the electric control system and test system. The result of the present work implied that soil-bin testing platform can provide reliable data for agricultural machinery research, which has a significant effect on the agricultural machinery technology innovation and saving the cost of research and development.
{"title":"Design and Application of New Testing Slots in the Tracking Control System","authors":"Yan Yu, X. Liu, S. Shang","doi":"10.4028/www.scientific.net/AMR.507.112","DOIUrl":"https://doi.org/10.4028/www.scientific.net/AMR.507.112","url":null,"abstract":"A test soil-bin is an effective device to test the whole or components of the agricultural machinery. A new computer control and test system featuring PLC control and PXI bus has been designed to control the whole soil bin system including motion control and correlative date processing. This paper introduces the software design as well as the hardware design of the electric control system and test system. The result of the present work implied that soil-bin testing platform can provide reliable data for agricultural machinery research, which has a significant effect on the agricultural machinery technology innovation and saving the cost of research and development.","PeriodicalId":16565,"journal":{"name":"Journal of Northeast Agricultural University","volume":"507 1","pages":"112 - 116"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4028/www.scientific.net/AMR.507.112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70642888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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