The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification.The combination of deep learning methods and RGB optical images obtained by unmanned aer...The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification.The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles(UAVs) provides a new research direction for urban tree species classification.We proposed an RGB optical image dataset with 10 urban tree species,termed TCC10,which is a benchmark for tree canopy classification(TCC).TCC10 dataset contains two types of data:tree canopy images with simple backgrounds and those with complex backgrounds.The objective was to examine the possibility of using deep learning methods(AlexNet,VGG-16,and ResNet-50) for individual tree species classification.The results of convolutional neural networks(CNNs) were compared with those of K-nearest neighbor(KNN) and BP neural network.Our results demonstrated:(1) ResNet-50 achieved an overall accuracy(OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16.(2) The classification accuracy of KNN and BP neural network was less than70%,while the accuracy of CNNs was relatively higher.(3)The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds.For the deciduous tree species in TCC10,the classification accuracy of ResNet-50 was higher in summer than that in autumn.Therefore,the deep learning is effective for urban tree species classification using RGB optical images.展开更多
A hierarchical clustering model for a wireless sensor network (WSN) which takes into account the node fault and the balance of energy consumption is built. With the sensing and transmission as the main tasks of the WS...A hierarchical clustering model for a wireless sensor network (WSN) which takes into account the node fault and the balance of energy consumption is built. With the sensing and transmission as the main tasks of the WSN, its working mode is converted to a modular k-out-of-n system whose each modular is also a k-out-of-n structure. By using the theory of signature and its extending, the reliability of the WSN is computed. Finally, the reliability of WSN changing with the number of nodes, coverage requirements and node lifetimes is given by the numerical analysis. The proposed reliability analysis model is suitable for a large-scale WSN. ? 2017 Beijing Institute of Aerospace Information.展开更多
Measurements are always interfered with glint noise in a radar target tracking system, which makes the performance of traditional filtering fall sharply and even divergent.Against this problem, a new Interactive Multi...Measurements are always interfered with glint noise in a radar target tracking system, which makes the performance of traditional filtering fall sharply and even divergent.Against this problem, a new Interactive Multiple Model Particle Filter (IMMPF) algorithm is proposed for target tracking by introducing PF into Interactive Multiple Model (IMM).Different from the general method to select importance density function from PF, the particles are extracted from observation likelihood function within depending on observation noises.Observation noise is modelled, and the latest observation is fused, then the target can be effectively tracked.Finally, the optimized method is simulated with respect to bearings-only tracking of maneuvering target in a glint noise environment.Compared with the existing filtering algorithms, it turns out that the developed filtering algorithm is more efficient and closer to the real-time tracking requirement of high maneuvering targets.展开更多
An oil and gas pipeline monitoring platform uses internet of things(IoT)to ensure safe operation in remote and unattended areas,through automatic monitoring and systematic control on equipment such as the cut-off valv...An oil and gas pipeline monitoring platform uses internet of things(IoT)to ensure safe operation in remote and unattended areas,through automatic monitoring and systematic control on equipment such as the cut-off valves and cathodic protection systems.The continuity and stability of power supplies for various equipment of an oil and gas pipeline IoT monitoring platform is crucial.There is no single universal off-grid power supply method that is optimal for an oil and gas pipeline IoT monitoring platform in all different contexts.Therefore,it is necessary to select a suitable one according to the specific geographical location and meteorological conditions.This paper proposes an off-grid power supply system comprised of a reversible solid oxide fuel cell(RESOC),photovoltaic(PV)and battery.Minimum operating costs and the reliability of system operations under constraint conditions are the key determining objectives.A“PV+battery+RESOC”system operational optimization model is established.Based on the model,three types of off-grid power supply schemes are proposed,and three geographical locations with different meteorological conditions are selected as practical application scenarios.The Matlab Cplex solver is used to solve the different power supply modes of the three regions.And finally,the power supply scheme with the best reliability and economy under different geographical environments and meteorological conditions is obtained.展开更多
This paper presents an imperfect maintenance strategy for the multi-component systems.The proposed maintenance strategy takes into account two types of maintenance actions,namely preventive maintenance(PM)and correcti...This paper presents an imperfect maintenance strategy for the multi-component systems.The proposed maintenance strategy takes into account two types of maintenance actions,namely preventive maintenance(PM)and corrective maintenance(CM).The imperfect effect of PM is modeled on the basis of the hybrid hazard rate model.Meanwhile,a new structure importance measure based on the survival signature is presented.Using this new importance measure method,an adjustment function is designed to update the PM maintenance threshold.For CM actions,a decision rule relying on the criticality level of components is introduced.In order to judge the criticality level of components,a novel structure updating method based on the survival signature is proposed.Moreover,a maintenance model considering economic dependence among components is developed.A 10-component system is finally introduced to illustrate the use and advantages of the proposed maintenance strategy.展开更多
Trees are an integral part of the forestry ecosystem.In forestry work,the precise acquisition of tree morphological parameters and attributes is affected by complex illumination and tree morphology.In order to minimiz...Trees are an integral part of the forestry ecosystem.In forestry work,the precise acquisition of tree morphological parameters and attributes is affected by complex illumination and tree morphology.In order to minimize a series of inestimable problems,such as yield reduction,ecological damage,and destruction,caused by inaccurate acquisition of tree location information,this paper proposes a ground tree detection method GMOSTNet.Based on the four types of tree species in the GMOST dataset and Faster R-CNN,it extracted the features of the trees,generate candidate regions,classification,and other operations.By reducing the influence of illumination and occlusion factors during experimentation,more detailed information of the input image was obtained.Meanwhile,regarding false detections caused by inappropriate approximations,the deviation and proximity of the proposal were adjusted.The experimental results showed that the AP value of the four tree species is improved after using GMOSTNet,and the overall accuracy increases from the original 87.25%to 93.25%.展开更多
基金supported by Joint Fund of Natural Science Foundation of Zhejiang-Qingshanhu Science and Technology City(Grant No.LQY18C160002)National Natural Science Foundation of China(Grant No.U1809208)+1 种基金Zhejiang Science and Technology Key R&D Program Funded Project(Grant No.2018C02013)Natural Science Foundation of Zhejiang Province(Grant No.LQ20F020005).
文摘The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification.The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles(UAVs) provides a new research direction for urban tree species classification.We proposed an RGB optical image dataset with 10 urban tree species,termed TCC10,which is a benchmark for tree canopy classification(TCC).TCC10 dataset contains two types of data:tree canopy images with simple backgrounds and those with complex backgrounds.The objective was to examine the possibility of using deep learning methods(AlexNet,VGG-16,and ResNet-50) for individual tree species classification.The results of convolutional neural networks(CNNs) were compared with those of K-nearest neighbor(KNN) and BP neural network.Our results demonstrated:(1) ResNet-50 achieved an overall accuracy(OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16.(2) The classification accuracy of KNN and BP neural network was less than70%,while the accuracy of CNNs was relatively higher.(3)The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds.For the deciduous tree species in TCC10,the classification accuracy of ResNet-50 was higher in summer than that in autumn.Therefore,the deep learning is effective for urban tree species classification using RGB optical images.
基金supported by the National Natural Science Foundation of China(71271165)
文摘A hierarchical clustering model for a wireless sensor network (WSN) which takes into account the node fault and the balance of energy consumption is built. With the sensing and transmission as the main tasks of the WSN, its working mode is converted to a modular k-out-of-n system whose each modular is also a k-out-of-n structure. By using the theory of signature and its extending, the reliability of the WSN is computed. Finally, the reliability of WSN changing with the number of nodes, coverage requirements and node lifetimes is given by the numerical analysis. The proposed reliability analysis model is suitable for a large-scale WSN. ? 2017 Beijing Institute of Aerospace Information.
基金Sponsored by the National Natural Science Foundation of China(Grant No.71271165)
文摘Measurements are always interfered with glint noise in a radar target tracking system, which makes the performance of traditional filtering fall sharply and even divergent.Against this problem, a new Interactive Multiple Model Particle Filter (IMMPF) algorithm is proposed for target tracking by introducing PF into Interactive Multiple Model (IMM).Different from the general method to select importance density function from PF, the particles are extracted from observation likelihood function within depending on observation noises.Observation noise is modelled, and the latest observation is fused, then the target can be effectively tracked.Finally, the optimized method is simulated with respect to bearings-only tracking of maneuvering target in a glint noise environment.Compared with the existing filtering algorithms, it turns out that the developed filtering algorithm is more efficient and closer to the real-time tracking requirement of high maneuvering targets.
基金This work was supported by the Zhejiang A&F University Talent Startup Project(2017FR025)the Science and Technology Project in Jinyun(JYKJZDSJ-2018-1)and the Key R&D Program of Sichuan Province(2017GZ0391).
文摘An oil and gas pipeline monitoring platform uses internet of things(IoT)to ensure safe operation in remote and unattended areas,through automatic monitoring and systematic control on equipment such as the cut-off valves and cathodic protection systems.The continuity and stability of power supplies for various equipment of an oil and gas pipeline IoT monitoring platform is crucial.There is no single universal off-grid power supply method that is optimal for an oil and gas pipeline IoT monitoring platform in all different contexts.Therefore,it is necessary to select a suitable one according to the specific geographical location and meteorological conditions.This paper proposes an off-grid power supply system comprised of a reversible solid oxide fuel cell(RESOC),photovoltaic(PV)and battery.Minimum operating costs and the reliability of system operations under constraint conditions are the key determining objectives.A“PV+battery+RESOC”system operational optimization model is established.Based on the model,three types of off-grid power supply schemes are proposed,and three geographical locations with different meteorological conditions are selected as practical application scenarios.The Matlab Cplex solver is used to solve the different power supply modes of the three regions.And finally,the power supply scheme with the best reliability and economy under different geographical environments and meteorological conditions is obtained.
基金Supported by the Research Fund of Xijing University(XJ200204)the National Natural Science Foundation of China(11726623,11726624)the Natural Science Basic Research Plan in Shaanxi Province of China(2020JM-646,2021JQ-867)。
文摘This paper presents an imperfect maintenance strategy for the multi-component systems.The proposed maintenance strategy takes into account two types of maintenance actions,namely preventive maintenance(PM)and corrective maintenance(CM).The imperfect effect of PM is modeled on the basis of the hybrid hazard rate model.Meanwhile,a new structure importance measure based on the survival signature is presented.Using this new importance measure method,an adjustment function is designed to update the PM maintenance threshold.For CM actions,a decision rule relying on the criticality level of components is introduced.In order to judge the criticality level of components,a novel structure updating method based on the survival signature is proposed.Moreover,a maintenance model considering economic dependence among components is developed.A 10-component system is finally introduced to illustrate the use and advantages of the proposed maintenance strategy.
基金National Natural Science Foundation of China(U1809208).
文摘Trees are an integral part of the forestry ecosystem.In forestry work,the precise acquisition of tree morphological parameters and attributes is affected by complex illumination and tree morphology.In order to minimize a series of inestimable problems,such as yield reduction,ecological damage,and destruction,caused by inaccurate acquisition of tree location information,this paper proposes a ground tree detection method GMOSTNet.Based on the four types of tree species in the GMOST dataset and Faster R-CNN,it extracted the features of the trees,generate candidate regions,classification,and other operations.By reducing the influence of illumination and occlusion factors during experimentation,more detailed information of the input image was obtained.Meanwhile,regarding false detections caused by inappropriate approximations,the deviation and proximity of the proposal were adjusted.The experimental results showed that the AP value of the four tree species is improved after using GMOSTNet,and the overall accuracy increases from the original 87.25%to 93.25%.