Abstract: In one embodiment, a Kalman filter based capacity forecasting method includes acquiring a capacity time sequence of an object to be forecasted; establishing a dynamical model for the capacity time sequence, and extracting a state transition parameter and a process noise parameter of the dynamical model; performing Kalman filter estimation on the capacity time sequence by using the state transition parameter and the process noise parameter to generate at least one state characteristic signal; segmenting the capacity time sequence according to the at least one state characteristic signal, and determining at least one corresponding segmentation point; and forecasting the capacity at future time according to the at least one segmentation point determined in the capacity time sequence. By adopting the technical solutions of the invention, accurate forecasting of the capacity growth is achieved, to facilitate operation and maintenance personnel making a reasonable capacity expansion plan.

Abstract: Embodiments of the present disclosure provide a disk capacity predicting method, apparatus, equipment and non-volatile computer storage medium. On the one hand, the change data of the disk capacity is obtained according to the historical capacity data of the disk; then the target inflection point in the historical capacity data is obtained according to the change data of the disk capacity; and then the linear relationship between the time and disk capacity is obtained according to the historical capacity data after the target inflection point. Therefore, technical solutions provided by embodiments of the present disclosure may achieve improvement of accuracy of prediction of disk capacity trend and reduce the cost needed in disk capacity prediction.

Abstract: In one embodiment, a Kalman filter based capacity forecasting method includes acquiring a capacity time sequence of an object to be forecasted; establishing a dynamical model for the capacity time sequence, and extracting a state transition parameter and a process noise parameter of the dynamical model; performing Kalman filter estimation on the capacity time sequence by using the state transition parameter and the process noise parameter to generate at least one state characteristic signal; segmenting the capacity time sequence according to the at least one state characteristic signal, and determining at least one corresponding segmentation point; and forecasting the capacity at future time according to the at least one segmentation point determined in the capacity time sequence. By adopting the technical solutions of the invention, accurate forecasting of the capacity growth is achieved, to facilitate operation and maintenance personnel making a reasonable capacity expansion plan.

Abstract: Embodiments of the present disclosure provide a disk capacity predicting method, apparatus, equipment and non-volatile computer storage medium. On the one hand, the change data of the disk capacity is obtained according to the historical capacity data of the disk; then the target inflection point in the historical capacity data is obtained according to the change data of the disk capacity; and then the linear relationship between the time and disk capacity is obtained according to the historical capacity data after the target inflection point. Therefore, technical solutions provided by embodiments of the present disclosure may achieve improvement of accuracy of prediction of disk capacity trend and reduce the cost needed in disk capacity prediction.

Abstract: An "arrested nucleation" jet vapor deposition system uses sonic jets and moving substrates to produce nanocluster-embedded films in which semiconductor or metal nanoclusters are trapped in a film of hard, inorganic host materials. CdSe films fabricated at high rate and room temperature in accordance with the present invention show unusual nonlinear optical behavior. Nanoclusters have unique size dependent properties and nanocluster based films have applications in optics and electronics.