16 research outputs found
Innovation or introduction? The impact of technological progress sources on industrial green transformation of resource-based cities in China
Adaptive Digital Twin for UAV-Assisted Integrated Sensing, Communication, and Computation Networks
In this paper, we study a digital twin (DT)-empowered integrated sensing,
communication, and computation network. Specifically, the users perform radar
sensing and computation offloading on the same spectrum, while unmanned aerial
vehicles (UAVs) are deployed to provide edge computing service. We first
formulate a multi-objective optimization problem to minimize the beampattern
performance of multi-input multi-output (MIMO) radars and the computation
offloading energy consumption simultaneously. Then, we explore the prediction
capability of DT to provide intelligent offloading decision, where the DT
estimation deviation is considered. To track this challenge, we reformulate the
original problem as a multi-agent Markov decision process and design a
multi-agent proximal policy optimization (MAPPO) framework to achieve a
flexible learning policy. Furthermore, the Beta-policy and attention mechanism
are used to improve the training performance. Numerical results show that the
proposed method is able to balance the performance tradeoff between sensing and
computation functions, while reducing the energy consumption compared with the
existing studies.Comment: 14 pages, 11 figures
FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing
Integrating unmanned aerial vehicles (UAVs) into vehicular networks have
shown high potentials in affording intensive computing tasks. In this paper, we
study the digital twin driven vehicular edge computing networks for adaptively
computing resource management where an unmanned aerial vehicle (UAV) named
FlexEdge acts as a flying server. In particular, we first formulate an energy
consumption minimization problem by jointly optimizing UAV trajectory and
computation resource under the practical constraints. To address such a
challenging problem, we then build the computation offloading process as a
Markov decision process and propose a deep reinforcement learning-based
proximal policy optimization algorithm to dynamically learn the computation
offloading strategy and trajectory design policy. Numerical results indicate
that our proposed algorithm can achieve quick convergence rate and
significantly reduce the system energy consumption.Comment: 6 pages, 6 figure
Do Environmental Regulations Facilitate a Low-Carbon Transformation in China’s Resource-Based Cities?
Resource-based cities (RBCs) are not only important for ensuring national resource and energy security, but they also face serious ecological and environmental problems. To achieve China’s carbon peaking and neutrality goals in the coming years, RBCs’ achievement of a low-carbon transformation has become increasingly significant. The core of this study is an investigation as to whether governance, including environmental regulations, can facilitate the low-carbon transformation of RBCs. Based on RBC data from 2003 to 2019, we establish a dynamic panel model to research the influence and mechanism of environmental regulations on low-carbon transformation. We found that China’s environmental regulations facilitate a low-carbon transformation in RBCs. Mechanism analysis identified that the environmental regulations facilitate the low-carbon transformation in RBCs by strengthening foreign direct investment, enhancing green technology innovation and promoting industrial structure upgrading. Heterogeneity analysis found that the environmental regulations play a greater role in facilitating the low-carbon transformation of RBCs in regions with more developed economies and less dependence on resources. Our research provides theoretical and policy implications for environmental regulations for the low-carbon transformation of RBCs in China, applicable to other resource-based areas
Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images
Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas. Considering the inhomogeneity and randomness, resulting from human management difference, the high spatial resolution Chinese GF-1 B/D image and developed MSCU-net+C deep learning method are used to mapping corn residue covered area (CRCA) in this study. The developed MSCU-net+C is joined by a multiscale convolution group (MSCG), the global loss function, and Convolutional Block Attention Module (CBAM) based on U-net and the full connected conditional random field (FCCRF). The effectiveness of the proposed MSCU-net+C is validated by the ablation experiment and comparison experiment for mapping CRCA in Lishu County, Jilin Province, China. The accuracy assessment results show that the developed MSCU-net+C improve the CRCA classification accuracy from IOUAVG = 0.8604 and KappaAVG = 0.8864 to IOUAVG = 0.9081 and KappaAVG = 0.9258 compared with U-net. Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG/KappaAVG with 0.0091/0.0058, 0.0133/0.0091, 0.044/0.0345, 0.0104/0.0069, and 0.0107/0.0072 compared with U-net, respectively. The classification accuracies of IOUAVG/KappaAVG of traditional machine learning methods, including support vector machine (SVM) and neural network (NN), are 0.576/0.5526 and 0.6417/0.6482, respectively. These results reveal that the developed MSCU-net+C can be used to map CRCA for monitoring black soil protection
Monitoring the Damage of Armyworm as a Pest in Summer Corn by Unmanned Aerial Vehicle Imaging
BACKGROUND:
The timely, rapid, and accurate near real-time observations are urgent to monitor the damage of corn armyworm, because the rapid expansion of armyworm would lead to severe yield losses. Therefore, the potential of machine learning algorithms for identifying the armyworm infected areas automatically and accurately by multispectral Unmanned Aerial Vehicle (UAV) dataset is explored in this study. And the study area is in Beicuizhuang Village, Langfang City, Hebei Province, which is the main corn-producing area in the North China Plain.
RESULTS:
Firstly, we identified the optimal combination of image features by Gini-importance and the comparation of four kinds of machine learning methods including Random Forest (RF), Multilayer Perceptron (MLP), Naive Bayes Classifier (NB) and Support Vector Machine (SVM) was done. And RF was proved to be the most potential with the highest Kappa and OA of 0.9709 and 0.9850, respectively. Secondly, the armyworm infected areas and healthy corn areas were predicted by an optimized RF model in the UAV dataset, and the armyworm incidence levels were classified subsequently. Thirdly, the relationship between the spectral characteristics of different bands and pest incidence levels within the Sentinel-2 and UAV images were analyzed, and the B3 in UAV images and the B6 in Sentinel-2 image were less sensitive for armyworm incidence levels. So the Sentinel-2 image was used to monitor armyworm in two towns.
CONCLUSIONS:
The optimized dataset and RF model are effective and reliable, which can be used for identifying the corn damage by armyworm using UAV images accurately and automatically in field-scale