To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installa...To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.展开更多
Rain streaks in an image appear in different sizes and orientations,resulting in severe blurring and visual quality degradation.Previous CNNbased algorithms have achieved encouraging deraining results although there a...Rain streaks in an image appear in different sizes and orientations,resulting in severe blurring and visual quality degradation.Previous CNNbased algorithms have achieved encouraging deraining results although there are certain limitations in the description of rain streaks and the restoration of scene structures in different environments.In this paper,we propose an efficient multi-scale enhancement and aggregation network(MEAN)to solve the single-image deraining problem.Considering the importance of large receptive fields and multi-scale features,we introduce a multi-scale enhanced unit(MEU)to capture longrange dependencies and exploit features at different scales to depict rain.Simultaneously,an attentive aggregation unit(AAU)is designed to utilize the informative features in spatial and channel dimensions,thereby aggregating effective information to eliminate redundant features for rich scenario details.To improve the deraining performance of the encoder–decoder network,we utilized an AAU to filter the information in the encoder network and concatenated the useful features to the decoder network,which is conducive to predicting high-quality clean images.Experimental results on synthetic datasets and real-world samples show that the proposed method achieves a significant deraining performance compared to state-of-the-art approaches.展开更多
We investigated forest road networks and forestry operations before and after mechanization on aggregated forestry operation sites. We developed equations to estimate densities of road networks with average slope angl...We investigated forest road networks and forestry operations before and after mechanization on aggregated forestry operation sites. We developed equations to estimate densities of road networks with average slope angles, operational efficiency of bunching operations with road network density, and average forwarding distances with operation site areas. Subsequently, we analyzed the effects of aggregating forests, establishing forest road networks, and mechanization on operational efficiency and costs. Six ha proved to be an appropriate operation site area with minimum operation expenses. The operation site areas of the forest owners＇ cooperative in this region aggregated approximately 6 ha and the cooperative conducted forestry operations on aggregated sites. Therefore, 6 ha would be an appropriate operation site area in this region. Regarding road network density, higher-density road networks increased operational expenses due to the higher direct operational expenses of strip road establishment. Therefore, road network density should be reduced to approximately 200 m.展开更多
多聚焦图像3维形貌重建旨在利用不同聚焦水平的图像序列恢复场景的3维结构信息.现有的3维形貌重建方法大多从单一尺度对图像序列的聚焦水平进行评价,通过引入正则化或后处理方法引导重建过程,由于深度信息选择空间的局限性往往导致重建...多聚焦图像3维形貌重建旨在利用不同聚焦水平的图像序列恢复场景的3维结构信息.现有的3维形貌重建方法大多从单一尺度对图像序列的聚焦水平进行评价,通过引入正则化或后处理方法引导重建过程,由于深度信息选择空间的局限性往往导致重建结果无法有效收敛.针对上述问题,提出一种多尺度代价聚合的多聚焦图像3维形貌重建框架(multi-scale cost aggregation framework for 3D shape reconstruction from multi-focus images,MSCAS),该框架首先引入非降采样的多尺度变换增加输入图像序列的深度信息选择空间,然后联合尺度内序列关联与尺度间信息约束进行代价聚合,通过这种扩张-聚合模式实现了场景深度表征信息的倍增与跨尺度和跨序列表征信息的有效融合.作为一种通用框架,MSCAS框架可实现已有模型设计类方法和深度学习类方法的嵌入进而实现性能提升.实验结果表明:MSCAS框架在嵌入模型设计类SFF方法后4组数据集中的均方根误差(root mean squared error,RMSE)平均下降14.91个百分点,结构相似度(structural similarity index measure,SSIM)平均提升56.69个百分点,嵌入深度学习类SFF方法后4组数据集中的RMSE平均下降1.55个百分点,SSIM平均提升1.61个百分点.验证了MSCAS框架的有效性和通用性.展开更多
基金supported by the Fujian University of Technology under Grant GYZ20016,GY-Z18183,and GY-Z19005partially supported by the National Science and Technology Council under Grant NSTC 113-2221-E-224-056-.
文摘To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.
基金supported by the National Natural Science Foundation of China(No.61972227)the Natural Science Foundation of Shandong Province(No.ZR201808160102)+4 种基金Shandong Provincial Natural Science Foundation Key Project(No.ZR2020KF015)the Key Research and Development Project of Shandong Province(No.2019GSF109112)the Science and Technology Plan for Young Talents in Colleges and Universities of Shandong Province(No.2020KJN007)the Scientific Research Studio in Colleges and Universities of Ji’nan City(No.2021GXRC092)the Science and Technology Research Program for Colleges and Universities in Shandong Province(No.KJ2018BZN029).
文摘Rain streaks in an image appear in different sizes and orientations,resulting in severe blurring and visual quality degradation.Previous CNNbased algorithms have achieved encouraging deraining results although there are certain limitations in the description of rain streaks and the restoration of scene structures in different environments.In this paper,we propose an efficient multi-scale enhancement and aggregation network(MEAN)to solve the single-image deraining problem.Considering the importance of large receptive fields and multi-scale features,we introduce a multi-scale enhanced unit(MEU)to capture longrange dependencies and exploit features at different scales to depict rain.Simultaneously,an attentive aggregation unit(AAU)is designed to utilize the informative features in spatial and channel dimensions,thereby aggregating effective information to eliminate redundant features for rich scenario details.To improve the deraining performance of the encoder–decoder network,we utilized an AAU to filter the information in the encoder network and concatenated the useful features to the decoder network,which is conducive to predicting high-quality clean images.Experimental results on synthetic datasets and real-world samples show that the proposed method achieves a significant deraining performance compared to state-of-the-art approaches.
文摘We investigated forest road networks and forestry operations before and after mechanization on aggregated forestry operation sites. We developed equations to estimate densities of road networks with average slope angles, operational efficiency of bunching operations with road network density, and average forwarding distances with operation site areas. Subsequently, we analyzed the effects of aggregating forests, establishing forest road networks, and mechanization on operational efficiency and costs. Six ha proved to be an appropriate operation site area with minimum operation expenses. The operation site areas of the forest owners＇ cooperative in this region aggregated approximately 6 ha and the cooperative conducted forestry operations on aggregated sites. Therefore, 6 ha would be an appropriate operation site area in this region. Regarding road network density, higher-density road networks increased operational expenses due to the higher direct operational expenses of strip road establishment. Therefore, road network density should be reduced to approximately 200 m.
文摘多聚焦图像3维形貌重建旨在利用不同聚焦水平的图像序列恢复场景的3维结构信息.现有的3维形貌重建方法大多从单一尺度对图像序列的聚焦水平进行评价,通过引入正则化或后处理方法引导重建过程,由于深度信息选择空间的局限性往往导致重建结果无法有效收敛.针对上述问题,提出一种多尺度代价聚合的多聚焦图像3维形貌重建框架(multi-scale cost aggregation framework for 3D shape reconstruction from multi-focus images,MSCAS),该框架首先引入非降采样的多尺度变换增加输入图像序列的深度信息选择空间,然后联合尺度内序列关联与尺度间信息约束进行代价聚合,通过这种扩张-聚合模式实现了场景深度表征信息的倍增与跨尺度和跨序列表征信息的有效融合.作为一种通用框架,MSCAS框架可实现已有模型设计类方法和深度学习类方法的嵌入进而实现性能提升.实验结果表明:MSCAS框架在嵌入模型设计类SFF方法后4组数据集中的均方根误差(root mean squared error,RMSE)平均下降14.91个百分点,结构相似度(structural similarity index measure,SSIM)平均提升56.69个百分点,嵌入深度学习类SFF方法后4组数据集中的RMSE平均下降1.55个百分点,SSIM平均提升1.61个百分点.验证了MSCAS框架的有效性和通用性.
