We present a new method for automatically forecasting the occurrence of solar flares based on photospheric magnetic measurements. The method is a cascading combination of an ordinal logistic regression model and a sup...We present a new method for automatically forecasting the occurrence of solar flares based on photospheric magnetic measurements. The method is a cascading combination of an ordinal logistic regression model and a support vector machine classifier. The predictive variables are three photospheric magnetic parameters, i.e., the total unsigned magnetic flux, length of the strong-gradient magnetic polarity inversion line, and total magnetic energy dissipation. The output is true or false for the occurrence of a certain level of flares within 24 hours. Experimental results, from a sample of 230 active regions between 1996 and 2005, show the accuracies of a 24- hour flare forecast to be 0.86, 0.72, 0.65 and 0.84 respectively for the four different levels. Comparison shows an improvement in the accuracy of X-class flare forecasting.展开更多
When a video camera is mounted on a vehicle’s frame, it experiences the same ride as a passenger and is subject to vertical displacement as the vehicle hits bumps on the road. This results in a captured video that ma...When a video camera is mounted on a vehicle’s frame, it experiences the same ride as a passenger and is subject to vertical displacement as the vehicle hits bumps on the road. This results in a captured video that may be difficult to watch because the bumps are transferred to the recorded video. This paper presents a new image stabilization model for vehicle navigation that can remove the effect of vertical vehicular motion due to road bumps. It uses a wheel sensor that monitors the wheel’s reaction with respect to road disturbances prior to the vehicle’s suspension system. This model employs an inexpensive sensor and control circuitry. The vehicle’s suspension system, bumpy road, and the compensation control system are modeled analytically. Experimental results show that the proposed model works suc-cessfully. It can eliminate 10 cm of drift and results in only 1 cm disturbance at the onset and the end of bumps.展开更多
基金supported by NSF under grants ATM-071 6950,ATM-0745744NASA under grant NNXO-8 AQ90G
文摘We present a new method for automatically forecasting the occurrence of solar flares based on photospheric magnetic measurements. The method is a cascading combination of an ordinal logistic regression model and a support vector machine classifier. The predictive variables are three photospheric magnetic parameters, i.e., the total unsigned magnetic flux, length of the strong-gradient magnetic polarity inversion line, and total magnetic energy dissipation. The output is true or false for the occurrence of a certain level of flares within 24 hours. Experimental results, from a sample of 230 active regions between 1996 and 2005, show the accuracies of a 24- hour flare forecast to be 0.86, 0.72, 0.65 and 0.84 respectively for the four different levels. Comparison shows an improvement in the accuracy of X-class flare forecasting.
文摘When a video camera is mounted on a vehicle’s frame, it experiences the same ride as a passenger and is subject to vertical displacement as the vehicle hits bumps on the road. This results in a captured video that may be difficult to watch because the bumps are transferred to the recorded video. This paper presents a new image stabilization model for vehicle navigation that can remove the effect of vertical vehicular motion due to road bumps. It uses a wheel sensor that monitors the wheel’s reaction with respect to road disturbances prior to the vehicle’s suspension system. This model employs an inexpensive sensor and control circuitry. The vehicle’s suspension system, bumpy road, and the compensation control system are modeled analytically. Experimental results show that the proposed model works suc-cessfully. It can eliminate 10 cm of drift and results in only 1 cm disturbance at the onset and the end of bumps.
