38results about How to "Reduce the difficulty of forecasting" patented technology

The invention relates to an electric energy measuring error estimation method. The method comprises the following steps: preprocessing metering data measured by an electric energy metering device to obtain a metering error sequence; performing wavelet conversion decomposition on the metering error sequence to obtain multiple groups of error sequence components; constructing a robust extreme learning machine model, regarding each group of error sequence component and the measured metering data as the input quantity to add in the robust extreme learning machine model as the input quantity, thereby obtaining an error prediction value of each group of error sequence component; adding the obtained error prediction values to predict the next generation of error value; preprocessing the error value to obtain the new metering error sequence, updating the measured metering data, performing iterative updating and cyclically predicting. Through the electric energy metering error estimation methodprovided by the invention, the rule in the original signal is extracted through the application of the wavelet conversion decomposition and the robust extreme learning machine model, the prediction difficulty is greatly reduced, the prediction precision and operation speed under the existence of the noise condition are improved.

The invention relates to the field of intelligent driving, specifically discloses a driving assistance system-oriented dangerous target detection method and device, and aims at solving the problem oflow dangerous target detection precision under field complicated traffic scenes. For the aim, the dangerous target detection method comprises the following steps of: identifying a detection frame position of a dangerous target in an obtained vehicle body external image and a Cartesian product of a target type and a distance type according to a preset dangerous target detection model; obtaining a danger level of the target according to the Cartesian product and a danger level matching table; and marking a detection frame of the target in the image according to a color corresponding to the danger level. Meanwhile, the dangerous target detection device, a dangerous target storage device and a dangerous target processing device can execute the steps in the dangerous target detection method. Through above technical scheme, the accuracy of image-based dangerous target detection can be enhanced under real complicated traffic scenes, so as to prevent traffic accidents.

The invention discloses a multi-label classification method for image data. Decompose the input image, use the neural network to extract the high-order correlation of the features, decompose the label data, use the neural network to extract the high-order correlation of the label, and use the neural network containing multiple fully connected layers to convert the feature code of the input image Decode from the input space to the label space; construct a loss function, initialize the training parameters, use the stochastic gradient descent method to minimize the final loss function as the goal, and train and solve to obtain the optimal training parameters; then input the image data to be tested into the trained The prediction is made in the model, and the label result is output to realize multi-label classification. The invention solves the problem that previous work on image data cannot simultaneously extract the secondary correlation and multiple correlation of tags, reduces the prediction difficulty caused by too sparse image data, and improves the accuracy of multi-label classification.