With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key te...With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.展开更多
In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using new...In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for recognition.One such method exploits the difference in substancedensity,leading to excellent coal/gangue recognition.Therefore,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the samples.Our training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the classification.The prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the volume.The classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,respectively.The test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was measured.The useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained range.Then,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),respectively.Although obtaining a classification with high accuracy is the main motive,this workhas a remarkable reduction in the data preprocessing time compared to related works.The CGVP-CNN modelmanaged to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classificationaccuracy using the estimated volume value.展开更多
Sugarcane/soybean intercropping with reduced nitrogen addition is an important sustainable agricultural pattern that can alter soil ecological functions,thereby affecting straw decomposition in the soil.However,the me...Sugarcane/soybean intercropping with reduced nitrogen addition is an important sustainable agricultural pattern that can alter soil ecological functions,thereby affecting straw decomposition in the soil.However,the mechanisms underlying changes in soil organic carbon(SOC)composition and microbial communities during straw decomposition under long-term intercropping with reduced nitrogen addition remain unclear.In this study,we conducted an in-situ microplot incubation experiment with^(13)C-labeled soybean straw residue addition in a two-factor(cropping pattern:sugarcane monoculture(MS)and sugarcane/soybean intercropping(SB);nitrogen addition levels:reduced nitrogen addition(N1)and conventional nitrogen addition(N2))long-term experimental field plot.The results showed that the SBN1 treatment significantly increased the residual particulate organic carbon(POC)and residual microbial biomass carbon(MBC)contents during straw decomposition,and the straw carbon in soil was mainly conserved as POC.Straw addition changed the structure and reduced the diversity of the soil microbial community,but microbial diversity gradually recovered with decomposition time.During straw decomposition,the intercropping pattern significantly increased the relative abundances of Firmicutes and Ascomycota.In addition,straw addition reduced microbial network complexity in the sugarcane/soybean intercropping pattern but increased it in the sugarcane monoculture pattern.Nevertheless,microbial network complexity remained higher in the SBN1 treatment than in the MSN1 treatment.In general,the SBN1 treatment significantly increased the diversity of microbial communities and the relative abundance of microorganisms associated with organic matter decomposition,and the changes in microbial communities were mainly driven by the residual labile SOC fractions.These findings suggest that more straw carbon can be sequestered in the soil under sugarcane/soybean intercropping with reduced nitrogen addition to maintain microbial diversity and contribute to the development of sustainable agriculture.展开更多
The rapid development of the Internet of Things(IoT)and modern information technology has led to the emergence of new types of cyber-attacks.It poses a great potential danger to network security.Consequently,protectin...The rapid development of the Internet of Things(IoT)and modern information technology has led to the emergence of new types of cyber-attacks.It poses a great potential danger to network security.Consequently,protecting against network attacks has become a pressing issue that requires urgent attention.It is crucial to find practical solutions to combat such malicious behavior.A network intrusion detection(NID)method,known as GMCE-GraphSAGE,was proposed to meet the detection demands of the current intricate network environment.Traffic data is mapped into gaussian distribution,which helps to ensure that subsequent models can effectively learn the features of traffic samples.The conditional generative adversarial network(CGAN)can generate attack samples based on specified labels to create balanced traffic datasets.In addition,we constructed a communication interaction graph based on the connection patterns of traffic nodes.The E-GraphSAGE is designed to capture both the topology and edge features of the traffic graph.From it,global behavioral information is combined with traffic features,providing a solid foundation for classifying and detecting.Experiments on the UNSW-NB15 dataset demonstrate the great detection advantage of the proposed method.Its binary and multi-classification F1-score can achieve 99.36%and 89.29%,respectively.The GMCE-GraphSAGE effectively improves the detection rate of minority class samples in the NID task.展开更多
基金supported by National Natural Science Foundation of China under Grants No.62076249,62022092,62293545.
文摘With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.
基金the National Natural Science Foundation of China under Grant No.52274159 received by E.Hu,https://www.nsfc.gov.cn/Grant No.52374165 received by E.Hu,https://www.nsfc.gov.cn/the China National Coal Group Key Technology Project Grant No.(20221CY001)received by Z.Guan,and E.Hu,https://www.chinacoal.com/.
文摘In the coal mining industry,the gangue separation phase imposes a key challenge due to the high visual similaritybetween coal and gangue.Recently,separation methods have become more intelligent and efficient,using newtechnologies and applying different features for recognition.One such method exploits the difference in substancedensity,leading to excellent coal/gangue recognition.Therefore,this study uses density differences to distinguishcoal from gangue by performing volume prediction on the samples.Our training samples maintain a record of3-side images as input,volume,and weight as the ground truth for the classification.The prediction process relieson a Convolutional neural network(CGVP-CNN)model that receives an input of a 3-side image and then extractsthe needed features to estimate an approximation for the volume.The classification was comparatively performedvia ten different classifiers,namely,K-Nearest Neighbors(KNN),Linear Support Vector Machines(Linear SVM),Radial Basis Function(RBF)SVM,Gaussian Process,Decision Tree,Random Forest,Multi-Layer Perceptron(MLP),Adaptive Boosting(AdaBosst),Naive Bayes,and Quadratic Discriminant Analysis(QDA).After severalexperiments on testing and training data,results yield a classification accuracy of 100%,92%,95%,96%,100%,100%,100%,96%,81%,and 92%,respectively.The test reveals the best timing with KNN,which maintained anaccuracy level of 100%.Assessing themodel generalization capability to newdata is essential to ensure the efficiencyof the model,so by applying a cross-validation experiment,the model generalization was measured.The useddataset was isolated based on the volume values to ensure the model generalization not only on new images of thesame volume but with a volume outside the trained range.Then,the predicted volume values were passed to theclassifiers group,where classification reported accuracy was found to be(100%,100%,100%,98%,88%,87%,100%,87%,97%,100%),respectively.Although obtaining a classification with high accuracy is the main motive,this workhas a remarkable reduction in the data preprocessing time compared to related works.The CGVP-CNN modelmanaged to reduce the data preprocessing time of previous works to 0.017 s while maintaining high classificationaccuracy using the estimated volume value.
基金supported by the China National Key R&D Program during the 14th Five-year Plan Period(2022YFD1901603)。
文摘Sugarcane/soybean intercropping with reduced nitrogen addition is an important sustainable agricultural pattern that can alter soil ecological functions,thereby affecting straw decomposition in the soil.However,the mechanisms underlying changes in soil organic carbon(SOC)composition and microbial communities during straw decomposition under long-term intercropping with reduced nitrogen addition remain unclear.In this study,we conducted an in-situ microplot incubation experiment with^(13)C-labeled soybean straw residue addition in a two-factor(cropping pattern:sugarcane monoculture(MS)and sugarcane/soybean intercropping(SB);nitrogen addition levels:reduced nitrogen addition(N1)and conventional nitrogen addition(N2))long-term experimental field plot.The results showed that the SBN1 treatment significantly increased the residual particulate organic carbon(POC)and residual microbial biomass carbon(MBC)contents during straw decomposition,and the straw carbon in soil was mainly conserved as POC.Straw addition changed the structure and reduced the diversity of the soil microbial community,but microbial diversity gradually recovered with decomposition time.During straw decomposition,the intercropping pattern significantly increased the relative abundances of Firmicutes and Ascomycota.In addition,straw addition reduced microbial network complexity in the sugarcane/soybean intercropping pattern but increased it in the sugarcane monoculture pattern.Nevertheless,microbial network complexity remained higher in the SBN1 treatment than in the MSN1 treatment.In general,the SBN1 treatment significantly increased the diversity of microbial communities and the relative abundance of microorganisms associated with organic matter decomposition,and the changes in microbial communities were mainly driven by the residual labile SOC fractions.These findings suggest that more straw carbon can be sequestered in the soil under sugarcane/soybean intercropping with reduced nitrogen addition to maintain microbial diversity and contribute to the development of sustainable agriculture.
基金funded by the National Natural Science Foundation of China(grant number.62171228)National Key Research and Development Program of China(grant number.2021YFE0105500).
文摘The rapid development of the Internet of Things(IoT)and modern information technology has led to the emergence of new types of cyber-attacks.It poses a great potential danger to network security.Consequently,protecting against network attacks has become a pressing issue that requires urgent attention.It is crucial to find practical solutions to combat such malicious behavior.A network intrusion detection(NID)method,known as GMCE-GraphSAGE,was proposed to meet the detection demands of the current intricate network environment.Traffic data is mapped into gaussian distribution,which helps to ensure that subsequent models can effectively learn the features of traffic samples.The conditional generative adversarial network(CGAN)can generate attack samples based on specified labels to create balanced traffic datasets.In addition,we constructed a communication interaction graph based on the connection patterns of traffic nodes.The E-GraphSAGE is designed to capture both the topology and edge features of the traffic graph.From it,global behavioral information is combined with traffic features,providing a solid foundation for classifying and detecting.Experiments on the UNSW-NB15 dataset demonstrate the great detection advantage of the proposed method.Its binary and multi-classification F1-score can achieve 99.36%and 89.29%,respectively.The GMCE-GraphSAGE effectively improves the detection rate of minority class samples in the NID task.
基金National Natural Science Foundation of China(No.62204127)the Natural Science Foundation of Jiangsu Province(No.BK20215093)State Key Laboratory of Luminescence and Applications(No.SKLA‒2021‒04)。
