Intermittent Prediction Method Based On Marcov Method And Grey Prediction Method

This paper concentrates on the intermittent demand for electric power supply and studies the method of demand prediction. This chapter first divides the demand for electric power supply into two statistical sequences: (1) sequence of demand occurrence, among which “1”stands for the occurrence of demand,“0”means that the demand fails to occur; (2) sequence of demand quantity. Next the author predicts the moment of time and the number of times n that demand occurs within a specific time interval in the future based on 0-1 sequence using Markov arrival process (MAP). Then the paper forecasts the demand quantity in subsequent n intervals using Grey prediction model GM (1, 1) based on the sequence of demand quantity. Finally, the author places the demand quantity in the n intervals in order at the moments where demand occurs to get the predicted result of demand for electric material with intermittent demand. According to instance analysis, the integrated approach mentioned in this paper surpasses existing methods in providing accurate prediction on data of product with intermittent demand

Prompted Contextual Transformer for Incomplete-View CT Reconstruction

Incomplete-view computed tomography (CT) can shorten the data acquisition time and allow scanning of large objects, including sparse-view and limited-angle scenarios, each with various settings, such as different view numbers or angular ranges. However, the reconstructed images present severe, varying artifacts due to different missing projection data patterns. Existing methods tackle these scenarios/settings separately and individually, which are cumbersome and lack the flexibility to adapt to new settings. To enjoy the multi-setting synergy in a single model, we propose a novel Prompted Contextual Transformer (ProCT) for incomplete-view CT reconstruction. The novelties of ProCT lie in two folds. First, we devise a projection view-aware prompting to provide setting-discriminative information, enabling a single ProCT to handle diverse incomplete-view CT settings. Second, we propose artifact-aware contextual learning to sense artifact pattern knowledge from in-context image pairs, making ProCT capable of accurately removing the complex, unseen artifacts. Extensive experimental results on two publicly available clinical CT datasets demonstrate the superior performance of ProCT over state-of-the-art methods -- including single-setting models -- on a wide range of incomplete-view CT settings, strong transferability to unseen datasets and scenarios, and improved performance when sinogram data is available. The code is available at: https://github.com/Masaaki-75/proc

Catalysis on singly dispersed bimetallic sites

A catalytic site typically consists of one or more atoms of a catalyst surface that arrange into a configuration offering a specific electronic structure for adsorbing or dissociating reactant molecules. The catalytic activity of adjacent bimetallic sites of metallic nanoparticles has been studied previously. An isolated bimetallic site supported on a non-metallic surface could exhibit a distinctly different catalytic performance owing to the cationic state of the singly dispersed bimetallic site and the minimized choices of binding configurations of a reactant molecule compared with continuously packed bimetallic sites. Here we report that isolated Rh1Co3 bimetallic sites exhibit a distinctly different catalytic performance in reduction of nitric oxide with carbon monoxide at low temperature, resulting from strong adsorption of two nitric oxide molecules and a nitrous oxide intermediate on Rh1Co3 sites and following a low-barrier pathway dissociation to dinitrogen and an oxygen atom. This observation suggests a method to develop catalysts with high selectivity