69results about How to "Low data requirements" patented technology

A method for use in video compression is disclosed. In particular, the claimed invention relates to a method of more efficient fractional-pixel interpolation in two steps by a fixed filter (240) and an adaptive filter (250) for fractional-pixel motion compensation.

System and method for parameterizing one or more steady-state models each having a plurality of model parameters for mapping model input to model output through a stored representation of an in-situ hydrocarbon reservoir. For each model, training data representing operation of the reservoir is provided including input values and target output values. A next input value(s) and next target output value are received from the training data. The model is parameterized with the input value(s) and target output value, and derivative constraints imposed to constrain relationships between the input value(s) and a resulting model output value, using an optimizer to perform constrained optimization on the parameters to satisfy an objective function subject to the derivative constraints. The receiving and parameterizing are performed iteratively, generating a parameterized model. Multiple models form an aggregate model of the system / process, which may be optimized to satisfy a second objective function subject to operational constraints.

A lithographic method and apparatus used to pattern an object. An illumination system supplies a beam of radiation. An array of individually controllable elements patterns the beam. The patterned beam is projected by a projection system on to a substrate supported on a substrate table. Individual elements of the array are periodically addressed to load an image frame on to the array appropriate to the pattern to be imparted to the beam at any given instant in time taking into account the image to be projected onto the substrate. In any given frame loading operation, only those individual controllable elements that must change state are addressed to reduce the amount of data required to be transferred during the frame loading operation.

The invention relates to a method for predicting a low-porosity and low-permeability clasolite effective reservoir at a high-diagenesis stage and belongs to the technical field of oil and gas exploration and development. The method is characterized in that a geological process response relationship is established through carrying out analysis, simulation and quantitative evaluation on a geological process affecting porosity, the original pore preservation and induced pore increase / decrease conditions of a reservoir are determined, thus, the current porosity of a low-porosity and low-permeability reservoir at the high-diagenesis stage is quantitatively evaluated, the development of the effective reservoir is predicted, and a basis is provided for reservoir evaluation. The method for predicting the low-porosity and low-permeability clasolite effective reservoir at the high-diagenesis stage has the advantages that the predictability is strong, the distribution of various clasolite effective reservoirs at different stages of high-diagenesis can be accurately predicted, the fine prediction on the distribution of effective reservoirs of a research target section can be achieved, and the predicted unit plane can reach a hectometer level; the theoretical basis is solid, and the condition of over reliance on the data of sampled points in the traditional method is avoided; the quantitative performance is good, the applicability is wide, and the information requirements are relatively low; and the evaluation, with different information foundations, on different exploration and development stages is facilitated.

The invention discloses a wind power forecasting method based on a genetic algorithm optimization BP neural network, comprising the steps: acquiring forecasting reference data from a data processing module of a wind power forecasting system; establishing a forecasting model of the BP neural network to the reference data, adopting a plurality of population codes corresponding to different structures of the BP neural network, encoding the weight number and threshold of the neural network by every population to generate individuals with different lengths, evolving and optimizing every populationby using selection, intersection and variation operations of the genetic algorithm, and finally judging convergence conditions and selecting optimal individual; then initiating the neural network, further training the network by using momentum BP algorithm with variable learning rate till up to convergence, forecasting wind power by using the network; and finally, repeatedly using a forecasted valve to carry out a plurality of times of forecasting in a circle of forecast for realizing multi-step forecasting with spacing time interval. In the invention, the forecasting precision is improved, the calculation time is decreased, and the stability is enhanced.