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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제16권 제4호
- 발행연도
- 2020.1
- 수록면
- 832 - 844 (13page)
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초록· 키워드
오류제보하기
The degree of dispersion of a random variable can be described by the variance, which reflects the distance of the random variable from its mean. However, the time complexity of the traditional variance calculation algorithm is O(n), which results from full calculation of all samples. When the number of samples increases oron the occasion of high speed signal processing, algorithms with O(n) time complexity will cost huge amount of time and that may results in performance degradation of the whole system. A novel multistep recursive algorithm for variance calculation of the timevarying data series with O(1) time complexity (constant time) is proposed in this paper. Numerical simulation and experiments of the algorithm is presented and the results demonstrate that the proposed multistep recursive algorithm can effectively decrease computing time and hence significantly improve the variance calculation efficiency for timevarying data, which demonstrates the potential value for timeconsumption data analysis or high speed signal processing.
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참고문헌 (33)
참고문헌 신청
R. A. Fisher / 1919 / XV. —The correlation between relatives on the supposition of Mendelian inheritance / Earth and Environmental Science Transactions of the Royal Society of Edinburgh 52(2):399~433
S. Orcioni / 2017 / Multivariance nonlinear system identification using wiener basis functions and perfect sequences / Proceedings of 2017 25th European Signal Processing Conference(EUSIPCO) :2679~2683
S. Baek / 2019 / Abrupt variance and discernibility analyses of multi-sensor signals for fault pattern extraction / Computers & Industrial Engineering 128:999~1007
S. Durant / 2017 / Automatic detection of orientation variance / Neuroscience Letters 658:43~47
J. Y. Gotoh / 2018 / Robust empirical optimization is almost the same as mean–variance optimization / Operations Research Letters 46(4):448~452
S. Orcioni / 2017 / Multivariance nonlinear system identification using wiener basis functions and perfect sequences / Proceedings of 2017 25th European Signal Processing Conference(EUSIPCO) :2679~2683
S. Baek / 2019 / Abrupt variance and discernibility analyses of multi-sensor signals for fault pattern extraction / Computers & Industrial Engineering 128:999~1007
S. Durant / 2017 / Automatic detection of orientation variance / Neuroscience Letters 658:43~47
J. Y. Gotoh / 2018 / Robust empirical optimization is almost the same as mean–variance optimization / Operations Research Letters 46(4):448~452
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