Search

With the rapid development of power grid infrastructure, especially the increasing number of ultra-high voltage (UHV) projects, knowledge extracted from historical engineering data is collected and can be potentially used to assist in the review of power transmission and transformation projects. However, conventional knowledge modeling and knowledge reasoning methods cannot meet the current needs of power grid construction. In this paper, considering the more supernumerary and distinctive information brought by multi-view data which could be beneficial for feature representation and knowledge reasoning from the constructed knowledge base, a multi-view graph convolutional network (GCN) based on knowledge graph is proposed to make classification for power grid infrastructure projects. Specifically, several views are constructed based on attribute information of a knowledge graph. In addition, a Haar convolution-based pooling mechanism is employed to capture the structural features represented by a chain of subgraphs. And then an aggregator that combines both attribute and structural information is used to classify UHV projects. Results from both UHV and NCI-1 datasets indicate that our proposed method is more has higher accuracy and generalization ability. [ABSTRACT FROM AUTHOR]

Soil visible-near-infrared reflection (Vis-NIR; 350 to 2500 nm) spectra is a comprehensive reflection of various soil physical and chemical properties, resulting in significant differences of hyperspectral characteristics and diversity of soil property prediction models. Calcium carbonate regulates several soil properties that were widely used to describe soil types and to quantify vulnerability to erosion. Therefore, spectral-based determination of soil calcium carbonate content is essential for agricultural management and environmental evaluation. Based on their reflectance similarity, the soil Vis-NIR spectra data itself can be classified into different spectral groups to predict soil calcium carbonate. To obtain the best hyperspectral quantitative prediction model for soil calcium carbonate based on soil group spectral data and spectral group data, a total of 246 soil samples were collected from seven soil types in Shaanxi province, China, and the soil Vis-NIR spectra were measured. All the soil spectral data were smoothed using Savitzky-Golay and continuum removal method. In this paper, we combined spectral angle cosine (SAC) algorithm with spectral correlation coefficient (SCC) algorithm to classify soil spectra. The results indicated that the coefficient of correlations between measured and predicted soil properties for seven prediction models based on random regression forest (RFR) were low (Ratio of performance to deviation, RPD < 2.00). When soil spectra were classified into three types based on the SAC-SCC spectral similarity measurement method, accuracy of all the prediction models increased significantly (R2 ≥ 0.96; RPD > 2.00) compared with the individual soil type models. These results provide a novel method for soil spectral classification and soil attribute estimation. [ABSTRACT FROM AUTHOR]