With the rapid increase of educational resources, how to search for necessary educational resource quickly is one of most important issues. Educational resources have the characters of distribution and heterogeneity, ...With the rapid increase of educational resources, how to search for necessary educational resource quickly is one of most important issues. Educational resources have the characters of distribution and heterogeneity, which are the same as the characters of Grid resources. Therefore, the technology of Grid resources search was adopted to implement the educational resources search. Motivated by the insufficiency of currently resources search methods based on metadata, a method of extracting semantic relations between words constituting metadata is proposed. We mainly focus on acquiring synonymy, hyponymy, hypernymy and parataxis relations. In our schema, we extract texts related to metadata that will be expanded from text spatial through text extraction templates. Next, metadata will be obtained through metadata extraction templates. Finally, we compute semantic similarity to eliminate false relations and construct a semantic expansion knowledge base. The proposed method in this paper has been applied on the education grid.展开更多
To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range(BVR)air combat,a hierarchical strategy framewo...To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range(BVR)air combat,a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks.Firstly,an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established.Secondly,the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models.Finally,the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences,to enhance the training effectiveness and improve the algorithm convergence speed.Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.展开更多
文摘With the rapid increase of educational resources, how to search for necessary educational resource quickly is one of most important issues. Educational resources have the characters of distribution and heterogeneity, which are the same as the characters of Grid resources. Therefore, the technology of Grid resources search was adopted to implement the educational resources search. Motivated by the insufficiency of currently resources search methods based on metadata, a method of extracting semantic relations between words constituting metadata is proposed. We mainly focus on acquiring synonymy, hyponymy, hypernymy and parataxis relations. In our schema, we extract texts related to metadata that will be expanded from text spatial through text extraction templates. Next, metadata will be obtained through metadata extraction templates. Finally, we compute semantic similarity to eliminate false relations and construct a semantic expansion knowledge base. The proposed method in this paper has been applied on the education grid.
基金supported by the Open Research Subject of State Key Laboratory of Intelligent Game,China(No.ZBKF-23-04)。
文摘To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range(BVR)air combat,a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks.Firstly,an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established.Secondly,the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models.Finally,the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences,to enhance the training effectiveness and improve the algorithm convergence speed.Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.
