This article mainly explores and analyzes the application of network broadcast in the lecture service of university library under the cloudization background. By using the online survey method to investigate the lectu...This article mainly explores and analyzes the application of network broadcast in the lecture service of university library under the cloudization background. By using the online survey method to investigate the lectures in some universities, it introduces the classification and characteristics of the network broadcast and obtains the advantage of network broadcast applied to lecture training through making contrast between the means of network multimedia. The result shows that the introduction of network broadcast can realize more customized, virtual and specialized lecture services, and facilitate the transformation of university library services in the digital era.展开更多
This paper focuses on the problem of active object detection(AOD).AOD is important for service robots to complete tasks in the family environment,and leads robots to approach the target ob ject by taking appropriate m...This paper focuses on the problem of active object detection(AOD).AOD is important for service robots to complete tasks in the family environment,and leads robots to approach the target ob ject by taking appropriate moving actions.Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy.Therefore,an AOD model based on a deep Q-learning network(DQN)with a novel training algorithm is proposed in this paper.The DQN model is designed to fit the Q-values of various actions,and includes state space,feature extraction,and a multilayer perceptron.In contrast to existing research,a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy.In addition,a method of generating the end state is presented to judge when to stop the AOD task during the training process.Sufficient comparison experiments and ablation studies are performed based on an AOD dataset,proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.展开更多
文摘This article mainly explores and analyzes the application of network broadcast in the lecture service of university library under the cloudization background. By using the online survey method to investigate the lectures in some universities, it introduces the classification and characteristics of the network broadcast and obtains the advantage of network broadcast applied to lecture training through making contrast between the means of network multimedia. The result shows that the introduction of network broadcast can realize more customized, virtual and specialized lecture services, and facilitate the transformation of university library services in the digital era.
基金supported by the National Natural Science Foundation of China(Nos.U1813215 and 62273203)。
文摘This paper focuses on the problem of active object detection(AOD).AOD is important for service robots to complete tasks in the family environment,and leads robots to approach the target ob ject by taking appropriate moving actions.Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy.Therefore,an AOD model based on a deep Q-learning network(DQN)with a novel training algorithm is proposed in this paper.The DQN model is designed to fit the Q-values of various actions,and includes state space,feature extraction,and a multilayer perceptron.In contrast to existing research,a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy.In addition,a method of generating the end state is presented to judge when to stop the AOD task during the training process.Sufficient comparison experiments and ablation studies are performed based on an AOD dataset,proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.
