Aiming at the extraction of cavern surface deformation from three-dimensional laser scanning dense point clouds,we propose a method integrating the Multiscale Model-to-Model Cloud Comparison(M3C2)with an im-proved Alpha Shapes algorithm.First,the two-phase surface point cloud data are registered,and the improved Alpha Shapes algorithm is used to identify the outer contour point clouds.After the fine registration of these two-phase outer contour point clouds,the M3C2 algorithm calculates the deformation value of each point,and finally the continuous deformation regions are extracted through distance clustering.Experimental results show that the proposed method ef-fectively eliminates the points at small furrows as well as those affected by mixed pixels.Specifically,the removal rates of point clouds in the two phases within 10 m from the scanner to the cavern section are 14.17%and 13.52%,respectively,which are 6.25%and 6.42%within 70 m.This method accurately and efficiently extracts the cavern surface deformation regions with more than twice the registration error.
近年来,越来越多的科研工作者试图将催化活性与催化剂中氧空位的酸碱性联系起来,通过结合路易斯酸碱活性位点对氧空位缺陷在确定催化特性和电荷载流子动力学中所起作用的深入理解,可以为合理开发更有效的催化剂提供有价值的见解.氧空位是过渡金属氧化物中最常见的缺陷,含氧空位的催化剂与表面酸碱性如何影响反应物、中间体和固体表面产物的吸附、反应和解吸过程密切相关.氧空位位置和氧空位的酸碱性在多相催化的过程中一直起着核心作用,研究基于氧空位缺陷构建路易斯酸碱位点的催化剂,对于各类分子活化、催化转化反应的性能提升具有重要意义.多相催化剂的表面性质将在很大程度上决定表面活性位点的电子结构,因此本文以含氧空位的固体催化剂为研究对象,对关于氧空位构筑路易斯酸碱位点的研究进行综述,探究相应活性位点对于催化剂的催化效率、稳定性和化学选择性的影响,并总结了其在光催化领域中的相关应用.
The flight test data is characterized by multiple types,large data volume and complex algorithms,thus data processing is a key link in flight test.The flight test data processing system needs to have capabilities such as rapid analysis of test data and timely sharing of cross-regional data.Here,on the basis of analyzing current flight test data processing system,we propose new approaches such as data processing center&diversity points,sharing be-tween diversity points,horizontal flight data splitting,and separated processing,then design a new flight test data processing system based on edge cloud collaboration,which realizes functions of data processing,cross-regional data sharing,and performance deduction&analysis for the whole-process subject implementation.
To improve the accuracy and reliability of solar irradiance prediction for photovoltaic power system,we propose a model to forecast short-term solar irradiance based on improved Stacking ensemble learning and error cor-rection.First,the Gradient Boosting Decision Tree(GBDT)is used to perform feature selection and remove redun-dant characteristics of original data set,thus increase prediction accuracy and computing efficiency.Then,an im-proved Stacking irradiance prediction model is established.In accordance with the difference in prediction accuracy of prediction models in the primary layer under K-fold cross-validation,the prediction results are weighted,and the Box-Cox is employed to transform and process the training set data input from the first layer to the second layer of Stacking,so as to increase the normality and homoscedasticity of prediction.Finally,the historical prediction error data are extracted,and Random Forest is applied to construct an error model to further improve the prediction accu-racy.The experimental results show that,compared with traditional models and classic Stacking models,the proposed method significantly improves the prediction performance on solar irradiance.
In view of the complicated factors influencing the stock price,we revised the Long Short-Term Memory(LSTM)network,which is commonly used in time series,to predict stock prices under the condition of multivari-able.First,the Variance Inflation Factor(VIF)was used to screen variables,and then the adaptive promotion(Ada-boost)model was combined to check the importance of characteristic variables.Second,the crawler was used to con-duct text analysis of investor sentiment,calculate indicators including sentiment index,and reveal the relationship between them and stock price.Then,prices of three stocks including Gree Electric Appliances,Flyco Electric Appli-ances and Midea Group were predicted by Multilayer Perceptron(MLP)and LSTM,and the appropriate model was selected as the benchmark model.Finally,indicators of sentiment index and investor concern were added to the benchmark model to construct the LSTM-EM model,and the GM(1,1)model was used to correct the residual term after considering investor sentiment.The empirical results show that the proposed model can predict the stock price accurately.
