半干旱地区矿区的土地利用格局在采矿干扰下发生着巨大变化,以全国八大煤炭生产基地之一的山西省大同矿区为研究对象,分析1985—2015年土地利用类型的时空变化以及影响土地利用变化的驱动因子,构建RF(Random Forest,RF)-FLUS(Future Lan...半干旱地区矿区的土地利用格局在采矿干扰下发生着巨大变化,以全国八大煤炭生产基地之一的山西省大同矿区为研究对象,分析1985—2015年土地利用类型的时空变化以及影响土地利用变化的驱动因子,构建RF(Random Forest,RF)-FLUS(Future Land Use Simulation,FLUS)模型模拟预测半干旱区矿区未来土地利用变化,结果表明:(1)1985—2015年,矿区的林地、耕地和水域面积减少,草地和建设用地面积增加。(2)林地、草地分布受气候及距离水系和设施点的距离影响较大;耕地分布受气候、高程及距水域、居民点的距离影响较大;水域分布最重要的影响因子是降水;建设用地分布主要受生产能力和距设施点的距离影响较大。(3)FLUS模型和RF-FLUS模型拟合精度均较高,但RF-FLUS模型比FLUS模型精度更高,更接近实际土地格局变化结果。(4)根据RF-FLUS模型对矿区2025年土地利用变化预测表明,矿区内林地、草地和耕地均呈下降趋势,下降速率变化不大;水域保持不变,建设用地与其他类型(裸地和未利用地)保持稳定上升的趋势。本研究为探究矿区土地格局复杂动态演变机制、探索小尺度土地资源优化路径、促进区域生态健康发展提供有利的科学依据。展开更多
Satellite communication systems are facing serious electromagnetic interference,and interference signal recognition is a crucial foundation for targeted anti-interference.In this paper,we propose a novel interference ...Satellite communication systems are facing serious electromagnetic interference,and interference signal recognition is a crucial foundation for targeted anti-interference.In this paper,we propose a novel interference recognition algorithm called HDCGD-CBAM,which adopts the time-frequency images(TFIs)of signals to effectively extract the temporal and spectral characteristics.In the proposed method,we improve the Convolutional Long Short-Term Memory Deep Neural Network(CLDNN)in two ways.First,the simpler Gate Recurrent Unit(GRU)is used instead of the Long Short-Term Memory(LSTM),reducing model parameters while maintaining the recognition accuracy.Second,we replace convolutional layers with hybrid dilated convolution(HDC)to expand the receptive field of feature maps,which captures the correlation of time-frequency data on a larger spatial scale.Additionally,Convolutional Block Attention Module(CBAM)is introduced before and after the HDC layers to strengthen the extraction of critical features and improve the recognition performance.The experiment results show that the HDCGD-CBAM model significantly outper-forms existing methods in terms of recognition accuracy and complexity.When Jamming-to-Signal Ratio(JSR)varies from-30dB to 10dB,it achieves an average accuracy of 78.7%and outperforms the CLDNN by 7.29%while reducing the Floating Point Operations(FLOPs)by 79.8%to 114.75M.Moreover,the proposed model has fewer parameters with 301k compared to several state-of-the-art methods.展开更多
文摘半干旱地区矿区的土地利用格局在采矿干扰下发生着巨大变化,以全国八大煤炭生产基地之一的山西省大同矿区为研究对象,分析1985—2015年土地利用类型的时空变化以及影响土地利用变化的驱动因子,构建RF(Random Forest,RF)-FLUS(Future Land Use Simulation,FLUS)模型模拟预测半干旱区矿区未来土地利用变化,结果表明:(1)1985—2015年,矿区的林地、耕地和水域面积减少,草地和建设用地面积增加。(2)林地、草地分布受气候及距离水系和设施点的距离影响较大;耕地分布受气候、高程及距水域、居民点的距离影响较大;水域分布最重要的影响因子是降水;建设用地分布主要受生产能力和距设施点的距离影响较大。(3)FLUS模型和RF-FLUS模型拟合精度均较高,但RF-FLUS模型比FLUS模型精度更高,更接近实际土地格局变化结果。(4)根据RF-FLUS模型对矿区2025年土地利用变化预测表明,矿区内林地、草地和耕地均呈下降趋势,下降速率变化不大;水域保持不变,建设用地与其他类型(裸地和未利用地)保持稳定上升的趋势。本研究为探究矿区土地格局复杂动态演变机制、探索小尺度土地资源优化路径、促进区域生态健康发展提供有利的科学依据。
基金This work was supported by the Beijing Natural Science Foundation(L202003).
文摘Satellite communication systems are facing serious electromagnetic interference,and interference signal recognition is a crucial foundation for targeted anti-interference.In this paper,we propose a novel interference recognition algorithm called HDCGD-CBAM,which adopts the time-frequency images(TFIs)of signals to effectively extract the temporal and spectral characteristics.In the proposed method,we improve the Convolutional Long Short-Term Memory Deep Neural Network(CLDNN)in two ways.First,the simpler Gate Recurrent Unit(GRU)is used instead of the Long Short-Term Memory(LSTM),reducing model parameters while maintaining the recognition accuracy.Second,we replace convolutional layers with hybrid dilated convolution(HDC)to expand the receptive field of feature maps,which captures the correlation of time-frequency data on a larger spatial scale.Additionally,Convolutional Block Attention Module(CBAM)is introduced before and after the HDC layers to strengthen the extraction of critical features and improve the recognition performance.The experiment results show that the HDCGD-CBAM model significantly outper-forms existing methods in terms of recognition accuracy and complexity.When Jamming-to-Signal Ratio(JSR)varies from-30dB to 10dB,it achieves an average accuracy of 78.7%and outperforms the CLDNN by 7.29%while reducing the Floating Point Operations(FLOPs)by 79.8%to 114.75M.Moreover,the proposed model has fewer parameters with 301k compared to several state-of-the-art methods.
