Deep reinforcement learning based IRS-assisted mobile edge computing under physical-layer security

et al. 2022

The development of information technology has changed the mode of communication of social information, and this change has put forward new requirements on the contents, methods and even objects of information science research. Knowledge service in the information service process can extract knowledge and information content from various explicit and implicit knowledge resources according to people’s needs, build knowledge networks, and provide knowledge content or solutions for users’ problems. Hence, it is very important to investigate how to analyze and design the advanced standard knowledge service system based on deep learning. To this end, we firstly introduce the typical deep learning networks of convolutional neural network (CNN) for the knowledge service system, and then employ the CNN to implement the knowledge classification based on deep learning. Finally, some simulation results on the knowledge service system are presented to validate the proposed studies in this paper.

Section: System Model Of Deep Learning For Standard Knowledge Service...mentioning

confidence: 99%

Analysis and Design of Standard Knowledge Service System based on Deep Learning

Zhou

et al. 2022

“…In this subsection, our goal is to measure the performance of the devised network in terms of outage probability, where the analytical expression for the network outage probability is derived. Since the eavesdropper can overhear the network and may cause physical-layer security problems, the outage probability is defined by [20]…”

Section: One Kg Nodementioning

confidence: 99%

Analysis and Design of Power System Transformer Standard Based on Knowledge Graph

Zhou

et al. 2022

The transformer can convert one kind of electric energy such as AC current and AC voltage into another kind of electric energy with the same frequency. Knowledge graph (KG) can describe various entities and concepts in the real world and their relationships, and it can be considered as a semantic network for power system transformer. Hence, it is of vital importance to analyze and design the power system transformer standard based on the knowledge graph. To this end, we firstly examine the power system transformer with one KG node and one eavesdropper E, where the eavesdropper E can overhear the network from the source, which may cause physical-layer secure issue and an outage probability event. To deal with the issue, we analyze and design the system secure performance under the eavesdropper and define the outage probability for system security, by providing analytical expression of outage probability. We further investigate the power system transformer with multiple KG nodes which can help strengthen the system security and reliability. For such a system, we analyze and design the system secure performance under the eavesdropper and define the outage probability for system security, by providing analytical expression of outage probability. Finally, we give some simulations to analyze the impact of secure transformer standard on the power system, and verify the accuracy of our proposed analytical expression for the the power system transformer standard based on the knowledge graph.

“…Notably, in order to further accelerate the convergence and improve the model accuracy, the batch normalization (BN) is applied before the ReLU function, given by [19][20][21]…”

Section: Eai Endorsed Transactions On Scalable Information Systems On...mentioning

confidence: 99%

Research on Knowledge Management of Novel Power System Based on Deep Learning

2022

With the rapid development of information technology, power system has been developed and applied rapidly. In the power system, fault detection is very important and is one of the key means to ensure the operation of power system. How to effectively improve the ability of fault detection is the most important issue in the research of power system. Traditional fault detection mainly relies on manual daily inspection, and power must be cut off during maintenance, which affects the normal operation of the power grid. In case of emergency, the equipment can not be powered off, which may lead to missed test and bury potential safety hazards. To solve these issues, in this paper, we study the knowledge management based power system by employing the deep learning technique. Specifically, we firstly introduce the data augmentation in the knowledge management based power system and the associated activated functions. We then develop the deep network architecture to extract the local spatial features among the data of the knowledge management based power system. We further provide several training strategies for the data classification in the knowledge management based power system, where the cross entropy based loss function is used. Finally, some experimental results are demonstrated to show the effectiveness of the proposed studies for the knowledge management based power system.