In this paper, a combined method of unsupervised clustering and learning vector quantity (LVQ) is presented to forecast the occurrence of solar flare. Three magnetic parameters including the maximum horizontal gradien...In this paper, a combined method of unsupervised clustering and learning vector quantity (LVQ) is presented to forecast the occurrence of solar flare. Three magnetic parameters including the maximum horizontal gradient, the length of the neutral line, and the number of singular points are extracted from SOHO/MDI longitudinal magnetograms as measures. Based on these pa- rameters, the sliding-window method is used to form the sequential data by adding three days evolutionary information. Con- sidering the imbalanced problem in dataset, the K-means clustering, as an unsupervised clustering algorithm, is used to convert imbalanced data to balanced ones. Finally, the learning vector quantity is employed to predict the flares level within 48 hours. Experimental results indicate that the performance of the proposed flare forecasting model with sequential data is improved.展开更多
For enhancing performances and increasing functions of PD radar, High PRF, medium PRF and low PRF are commonly applied into system ambiguity appeared in range and velocity in some PRF. Based on clustering, a slidin...For enhancing performances and increasing functions of PD radar, High PRF, medium PRF and low PRF are commonly applied into system ambiguity appeared in range and velocity in some PRF. Based on clustering, a sliding window correlator algorithm for resolving the radar object ambiguity in range and velocity is described. Slide window algorithm is a searching algorithm. The probability of ambiguity resolution for targets and the computational efficiency are discussed. The relations between the probability of ambiguity resolution of this algorithm and PRF, the range of interest, and the width of sliding window are analyzed. Simulational results are also given.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 10973020)the Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (Grant No. PHR200906210)+1 种基金the Funding Project for Base Construction of Scientific Research of Beijing Municipal Commission of Education (Grant No. WYJD200902)Beijing Philosophy and Social Science Planning Project (Grant No. 09BaJG258)
文摘In this paper, a combined method of unsupervised clustering and learning vector quantity (LVQ) is presented to forecast the occurrence of solar flare. Three magnetic parameters including the maximum horizontal gradient, the length of the neutral line, and the number of singular points are extracted from SOHO/MDI longitudinal magnetograms as measures. Based on these pa- rameters, the sliding-window method is used to form the sequential data by adding three days evolutionary information. Con- sidering the imbalanced problem in dataset, the K-means clustering, as an unsupervised clustering algorithm, is used to convert imbalanced data to balanced ones. Finally, the learning vector quantity is employed to predict the flares level within 48 hours. Experimental results indicate that the performance of the proposed flare forecasting model with sequential data is improved.
文摘For enhancing performances and increasing functions of PD radar, High PRF, medium PRF and low PRF are commonly applied into system ambiguity appeared in range and velocity in some PRF. Based on clustering, a sliding window correlator algorithm for resolving the radar object ambiguity in range and velocity is described. Slide window algorithm is a searching algorithm. The probability of ambiguity resolution for targets and the computational efficiency are discussed. The relations between the probability of ambiguity resolution of this algorithm and PRF, the range of interest, and the width of sliding window are analyzed. Simulational results are also given.
