MEMS accelerometers are widely used in various fields due to their small size and low cost,and have good application prospects.However,the low accuracy limits its range of applications.To ensure data accuracy and safe...MEMS accelerometers are widely used in various fields due to their small size and low cost,and have good application prospects.However,the low accuracy limits its range of applications.To ensure data accuracy and safety we need to calibrate MEMS accelerometers.Many authors have improved accelerometer accuracy by calculating calibration parameters,and a large number of published calibration methods have been confusing.In this context,this paper introduces these techniques and methods,analyzes and summarizes the main error models and calibration procedures,and provides useful suggestions.Finally,the content of the accelerometer calibration method needs to be overcome.展开更多
In recent years,the rapid development of big data technology has also been favored by more and more scholars.Massive data storage and calculation problems have also been solved.At the same time,outlier detection probl...In recent years,the rapid development of big data technology has also been favored by more and more scholars.Massive data storage and calculation problems have also been solved.At the same time,outlier detection problems in mass data have also come along with it.Therefore,more research work has been devoted to the problem of outlier detection in big data.However,the existing available methods have high computation time,the improved algorithm of outlier detection is presented,which has higher performance to detect outlier.In this paper,an improved algorithm is proposed.The SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data,which can give the number of clusters and reduce the number of iterations and improve the accuracy of clustering.In this paper,several experiments are performed to compare and analyze multiple performances of the algorithm.Through analysis,we know that the proposed algorithm is superior to the existing algorithms.展开更多
基金This work has received funding from 5150 Spring Specialists(05492018012)the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No.701697,Major Program of the National Social Science Fund of China(Grant No.17ZDA092)+1 种基金Basic Research Programs(Natural Science Foundation)of Jiangsu Province(BK20180794)333 High-Level Talent Cultivation Project of Jiangsu Province(BRA2018332)and the PAPD fund.
文摘MEMS accelerometers are widely used in various fields due to their small size and low cost,and have good application prospects.However,the low accuracy limits its range of applications.To ensure data accuracy and safety we need to calibrate MEMS accelerometers.Many authors have improved accelerometer accuracy by calculating calibration parameters,and a large number of published calibration methods have been confusing.In this context,this paper introduces these techniques and methods,analyzes and summarizes the main error models and calibration procedures,and provides useful suggestions.Finally,the content of the accelerometer calibration method needs to be overcome.
基金This work was supported by the National Natural Science Foundation of China(Grant number 42077204)the National Key Research and Development Project(Grant number 2017YFC0210103)with data support provided by the National Earth System Science Data Center,National Science&Technology Infrastructure of China(http://www.geodata.cn).
基金This study was funded by the National Natural Science Foundation of China[grant numbers 41771291 and 21806080]the Jiangsu Specially-Appointed Professor Program,the Six Talent Peaks Project in Jiangsu Province[grant number NY-083]the Startup Foundation for Introducing Talent of NUIST,and the Innovation and Entrepreneurship Training Program for College Students in Jiangsu Province.
文摘In recent years,the rapid development of big data technology has also been favored by more and more scholars.Massive data storage and calculation problems have also been solved.At the same time,outlier detection problems in mass data have also come along with it.Therefore,more research work has been devoted to the problem of outlier detection in big data.However,the existing available methods have high computation time,the improved algorithm of outlier detection is presented,which has higher performance to detect outlier.In this paper,an improved algorithm is proposed.The SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data,which can give the number of clusters and reduce the number of iterations and improve the accuracy of clustering.In this paper,several experiments are performed to compare and analyze multiple performances of the algorithm.Through analysis,we know that the proposed algorithm is superior to the existing algorithms.
