No abstract

Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher.

Advanced three-dimensional (3D) imaging techniques can acquire high-resolution 3D biomedical and biological data, but available digital display methods show this data in restricted two dimensions. 3D light-field displays optically reconstruct realistic 3D image by carefully tailoring light fields, and a natural and comfortable 3D sense of real objects or scenes is expected. An interactive floating full-parallax 3D light-field display with all depth cues is demonstrated with 3D biomedical and biological data, which are capable of achieving high efficiency and high image quality. A compound lens-array with two pieces of lens in each lens unit is designed and fabricated to suppress the aberrations and increase the viewing angle. The optimally designed holographic functional screen is used to recompose the light distribution from the lens-array. The imaging distortion can be decreased to less than 1.9% from more than 20%. The real time interactive floating full-parallax 3D light-field image with the clear displayed depth of 30 cm can be perceived with the right geometric occlusion and smooth parallax in the viewing angle of 45°, where 9216 viewpoints are used.

No abstract

A high-efficient computer-generated integral imaging (CGII) method is presented based on the backward ray-tracing technique. In traditional CGII methods, the total rendering time is long, because a large number of cameras are established in the virtual world. The ray origin and the ray direction for every pixel in elemental image array are calculated with the backward ray-tracing technique, and the total rendering time can be noticeably reduced. The method is suitable to create high quality integral image without the pseudoscopic problem. Real time and non-real time CGII rendering images and optical reconstruction are demonstrated, and the effectiveness is verified with different types of 3D object models. Real time optical reconstruction with 90 × 90 viewpoints and the frame rate above 40 fps for the CGII 3D display are realized without the pseudoscopic problem.