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
Objective:To prevent and treat various types of cancer safely,reliably,and at low cost.Method:Early and mid-stage cancer patients took praziquantel and albendazole every day,late cancer patients only took albendazole ...Objective:To prevent and treat various types of cancer safely,reliably,and at low cost.Method:Early and mid-stage cancer patients took praziquantel and albendazole every day,late cancer patients only took albendazole every day,while with the traditional Chinese medicine“ginseng jade bamboo particle”to eliminate the adverse reactions and side effects caused by the above two western medicines,continue for more than three months.Conclusion:Praziquantel and albendazole have good therapeutic and cancer prevention effects in actual clinical trials.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.
文摘Objective:To prevent and treat various types of cancer safely,reliably,and at low cost.Method:Early and mid-stage cancer patients took praziquantel and albendazole every day,late cancer patients only took albendazole every day,while with the traditional Chinese medicine“ginseng jade bamboo particle”to eliminate the adverse reactions and side effects caused by the above two western medicines,continue for more than three months.Conclusion:Praziquantel and albendazole have good therapeutic and cancer prevention effects in actual clinical trials.
