Detailed land cover change in multitemporal images is an important application for earth science. Many techniques have been proposed to solve this problem in different ways. However, accurately identifying changes still remains a challenge due to the difficulties in describing the characteristics of various change categories by using single-level features. In this article, a multilevel feature representation framework was designed to build robust feature set for complex change detection task. First, four different levels of information from low level to high level, including pixel-level, neighborhood-level, object-level, and scene-level features, were extracted. Through the operation of extracting different level features from multitemporal images, the differences between them can be described comprehensively. Second, multilevel features were fused to reduce the dimension and then used as the input for supervised change detector with initial limited labels. Finally, for reducing the labeling cost and improving the change detection results simultaneously, active learning was conducted to select the most informative samples for labeling, and this step together with the previous steps were iteratively conducted to improve the results in each round. Experimental results of three pairs of real remote sensing datasets demonstrated that the proposed framework outperformed the other state-of-the-art methods in terms of accuracy. Moreover, the influences of scene scale for high-level semantic features in the proposed approach on change detection performance were also analyzed and discussed.
To enable the accurate assessment of landslide susceptibility in the upper reaches of the Minjiang River Basin, this research intends to spatially compare landslide susceptibility maps obtained from unclassified landslides directly and the spatial superposition of different types of landslide susceptibility map, and explore interpretability using cartographic principles of the two methods of map-making. This research using the catalogs of rainfall and seismic landslides selected nine background factors those affect the occurrence of landslides through correlation analysis finally, including lithology, NDVI, elevation, slope, aspect, profile curve, curvature, land use, and distance to faults, to assess rainfall and seismic landslide susceptibility, respectively, by using a WOE-RF coupling model. Then, an evaluation of landslide susceptibility was conducted by merging rainfall and seismic landslides into a dataset that does not distinguish types of landslides; a comparison was also made between the landslide susceptibility maps obtained through the superposition of rainfall and seismic landslide susceptibility maps and unclassified landslides. Finally, confusion matrix and ROC curve were used to verify the accuracy of the model. It was found that the accuracy of the training set, testing set, and the entire data set based on the WOE-RF model for predicting rainfall landslides were 0.9248, 0.8317, and 0.9347, and the AUC area were 1, 0.949, and 0.955; the accuracy of the training set, testing set, and the entire data set for seismic landslides prediction were 0.9498, 0.9067, and 0.8329, and the AUC area were 1, 0.981, and 0.921; the accuracy of the training set, testing set, and the entire data set for unclassified landslides prediction were 0.9446, 0.9080, and 0.8352, and the AUC area were 0.9997, 0.9822, and 0.9207. Both of the confusion matrix and the ROC curve indicated that the accuracy of the coupling model is high. The southeast of the line from Mount Xuebaoding to Lixian County is a high landslide prone area, and through the maps, it was found that the extremely high susceptibility area of seismic landslides is located at a higher elevation than rainfall landslides by extracting the extremely high susceptibility zones of both. It was also found that the results of the two methods of evaluating landslide susceptibility were significantly different. As for a same background factor, the distribution of the areas occupied by the same landslide occurrence class was not the same according to the two methods, which indicates the necessity of conducting relevant research on distinguishing landslide types.
With the rapid growth of 5G technology, the increase of base stations not noly brings high energy consumption, but also becomes new flexibility resources for power system. For high energy consumption and low utilization of energy storage of base stations, the strategy of energy storage regulation of macro base station and sleep to save energy of micro base station based on genetic algorithm is proposed. Firstly, the variational mode decomposition and long short-term memory are combined in the model in which the parameters are optimized by using the genetic algorithm. By applying the model, the traffic of micro base station can be predicted, and the low load is selected to sleep. Then, a multi-objective optimization model of macro base station energy storage regulation is established, and the optimal charging power and discharging power are solved by genetic algorithm. Finally, collaborative working mode is given combining the charging and discharging power of macro base station and the power reduced of sleep of micro base station. The simulation shows that the strategy can reduce the energy consumption of the base station, and it can simultaneously assist power system to cut peak and fill valley to gain income.
Urban agglomeration is an important model for promoting global economic development and has made important contributions to global economic integration. However, as the core area of urbanization and industrialization, urban agglomerations also contribute to air pollutant emissions primarily due to the agglomeration of population and industry. The mutual influence of air pollution between different cities in urban agglomerations has brought significant challenges to global environmental governance. The Fenwei Plain is one of the most severely polluted areas in China. We collected daily average PM2.5 concentration data of 11 cities in the Fenwei Plain, China in 2019. We then developed an interpretive structural model to statistically analyze the spatial correlation and hierarchical transmission of haze pollution between the 11 cities. The results showed that haze pollution has a strong systematic correlation between the 11 cities, and a regional haze pollution community has formed throughout the region. Haze pollution also exhibits evident transmission and spatial correlations between the cities. The transmission starts from Baoji and ends at Sanmenxia, with mutual interactions between the cities of Xi’an, Xianyang, Weinan, Tongchuan, Jinzhong, Luliang, Linfen, Yuncheng, and Luoyang. Thus, air pollution prevention and control in the Fenwei Plain should consider the spatial correlation of haze pollution between different cities, especially in autumn and winter, and should rationally be implemented in key urban cluster areas. We recommend building a coordinated governance between cities to improve the overall air quality. Our findings shed a light for coordinated pollution management in urban agglomerations worldwide.
Based on the bed temperature fluctuation problem in circulating fluidized bed furnace,the reason resulting this fluctuation is discussed and some control measures are suggested.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)