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4th International Conference on Computer network, Electronic and Automation, ICCNEA 2021

作者： Ding, Ziwei Zhou, Jiangwei Liu, Bailin Bai, Wanmin School Of Computer Science And Engineering Xi'an Technological University Xi'an710021 China State And Provincial Joint Engineering Lab Of Advanced Network Monitoring And Control Xi'an710021 China

ISBN: (纸本)9781665444866

Large-scale equipment in different industries is becoming more and more electronic, complicated and integrated. In order to provide technical support for the normal operation of industrial equipment, fault diagnosis becomes extremely important. Intelligent fault diagnosis, as the name suggests, is to combine fault diagnosis and artificial intelligence theory to replace traditional fault diagnosis methods in a smarter way to obtain more accurate diagnosis results. Machine learning has become a current technical hotspot. In view of its advantages in efficiently extracting information and processing large amounts of data, more and more attention has been paid in the field of fault diagnosis. This paper systematically introduces the related concepts of machine learning and fault diagnosis, and summarizes fault diagnosis methods and applications based on support vector machines and neural networks. Finally, this paper makes a further outlook on the development of intelligent fault diagnosis technology based on machine learning. © 2021 IEEE.

关键词： Failure analysis

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Research and Design of Decimal network DDNS 4

Research and Design of Decimal Network DDNS

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4th International Conference on Computer network, Electronic and Automation, ICCNEA 2021

作者： Wang, Zhongsheng Liu, Zhang School Of Computer Science And Engineering Xi'an Technological University Xi'an710021 China State And Provincial Joint Engineering Lab. Of Advanced Network Monitoring And Control Xi'an China

ISBN: (纸本)9781665444866

Internet domain name is consists of characters, numbers, or both of them. when a client access to a website, it first send a request to local domain name server (DNS), if there is no record, local DNS request to higher authorities, until the root name servers, and get the correct record after return to local, and the cache to the local server, the process logic is complex. It's expensive in time and space. Decimal network digital domain name system(DDNS) can directly find the target network terminal through the routing rules, without the need for domain name resolution and other conversion process, and then quickly establish the connection, reducing the scale of routing table of each forwarding node in the network, thus saving huge time and space costs. This paper introduces the concept of digital domain name system, the structure of digital domain name system, the grammar and extension of digital domain name system, the mapping of digital domain name system and IP address, and the resource record in digital domain name system. It will show the characteristics and advantages of digital domain name in a more comprehensive and detailed way. © 2021 IEEE.

关键词： Internet protocols

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Research of Image De-noising Based on Deep Learning

Research of Image De-noising Based on Deep Learning

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International Conference on Computer network, Electronic and Automation (ICCNEA)

作者： Kai Li Zhongsheng Wang School of Computer Science and Engineering Xi’an Technological University Xi’an China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an Technological University Xi'an China

Image denoising is a technology to restore the image by changing the information before and after the image sequence. The restored image will become very clear, and it is used a lot now. Due to the imperfect equipment, when the image is transmitted, it will be affected by noise, thus losing the original details. In order to solve this problem and make the noise image clear and obvious, this paper proposes an image denoising method based on deep learning, and constructs the image denoising network structure based on convolutional neural network. At the same time, the directional propagation algorithm is optimized to speed up the training speed of the model and effectively improve the denoising effect. By comparing the experimental data of the classical algorithm experiment, it is concluded that this study has achieved a better denoising effect. Finally, the key issues of image denoising research are summarized, and the development trend of future research in this field is discussed.

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Research and Implementation of Image Defogging Based on Deep Learning

Research and Implementation of Image Defogging Based on Deep...

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International Conference on Computer network, Electronic and Automation (ICCNEA)

作者： Ao Li Zhongsheng Wang School of Computer Science and Engineering Xi’an Technological University Xi’an China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an Technological University Xi'an China

Image degradation due to fog and haze is a common problem in the field of life and image research. This article will be designed and implemented of image haze removal algorithm. The traditional methods have some problems such as low brightness, artifact, halo and distortion, but the existing methods based on deep learning have better effect. In this paper, we will propose a de-fogging network model, which is based on adversarial network. The generator in adversarial network is a convolutional neural network with residual network, which reduces the model training parameters and improves the parameter utilization. The discriminator uses the full convolutional network as the discriminator, which enhances the spatial sensitivity of the model to the image. In order to improve efficiency, the discriminator is simplified on the original structure. The loss function of the model is improved so that the model can get better training effect.

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Reinforcement Learning Scheduling Research for Edge Servers

Reinforcement Learning Scheduling Research for Edge Servers

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International Conference on Computer network, Electronic and Automation (ICCNEA)

作者： Yonggui Han Zhongsheng Wang School of Computer Science and Engineering Xi’an Technological University Xi’an China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an Technological University Xi'an China

The rapid development of the Internet of Things and big data has brought about massive amounts of data and the need for real-time responses, which traditional computing models can no longer meet. Edge computing comes into being, which pushes computing resources and services closer to the source of data generation so that data can be processed and analyzed closer to the user. The scheduling of siting strategies for edge servers is crucial for improving the performance of edge computing networks. Traditional scheduling methods for siting strategies are often based on rules or optimization algorithms, which lack adaptive and intelligent management of the network *** paper proposes a reinforcement learning DQN-based edge server siting policy scheduling method, which aims to achieve intelligent management and resource optimization of edge computing networks by learning the optimal policy. Experimental results show that the method has significant performance advantages in terms of delay, load balancing and energy consumption. The experiment adopts DQN algorithm, which is based on Q-learning and guides the agent to make decisions by optimizing Q-value function. In this paper, the reinforcement learning scheduling algorithm of edge server is studied and compared with other three algorithms. According to the experimental results, the proposed reinforcement learning scheduling algorithm reduces the delay by 7%, the energy consumption by 3%, and the load balancing by 20% compared with the other three algorithms. This shows that the algorithm has significant advantages in improving system performance.

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Research on Multilevel Degradater Model under Unsupervised Learning

Research on Multilevel Degradater Model under Unsupervised L...

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International Conference on Computer network, Electronic and Automation (ICCNEA)

作者： Jieyi Lv Zhongsheng Wang School of Computer Science and Engineering Xi'an Technological University Xi'an China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an Technological University Xi'an China

In most super-resolution experiments, because the data set in the real scene cannot meet the quantity requirements, it is necessary to downsample the high-resolution images using the degradation method to obtain the same proportion of low-resolution images. However, because the same degradation method is used to process images, the processing process of low-resolution images is relatively simple, which is easy to cause model solidification in the subsequent training process. After the training is completed, the generalization ability will be generally low due to the single underlying degradation method. This paper uses the SRMD method to build an unsupervised multilevel degradation, and takes advantage of the adduction network generated in unsupervised learning. After extracting image features, it adopts anisotropic Gaussian fuzzy kernel, bi-cubic subsampling and additive Gaussian noise to compose the degradation training data set. Compared with the traditional bi-cubic PNSR, the performance is improved by 5%. The SSIM value is increased by 3%, and the running speed is increased by 7% on average.

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DV-Hop Localization Strategy Based on Beacon Node Confidence Analysis

DV-Hop Localization Strategy Based on Beacon Node Confidence...

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International Conference on Computer network, Electronic and Automation (ICCNEA)

作者： Han Shen Zhongsheng Wang School of Computer Science and Engineering Xi'an Technological University Xi'an China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an Technological University Xi'an China

To address the problem that the DV-Hop localization model for wireless sensor networks can no longer meet the current localization accuracy requirements, this paper proposes the concept of beacon node trustworthiness to provide ideas and basis for improving the optimization model. Based on the principle of least squares in the localization model, weighted least squares is introduced to present the trustworthiness for beacon nodes with different characteristics. To analyze the characteristics of the trustworthiness of beacon nodes, 18 functions are designed in terms of function instrumentality and decrement able, hierarchical faultiness, etc. Finally, the conclusions obtained from the credibility test are tested with weighted matrix and average hop distance weighting, and the localization error is reduced from 32.68% to 31.78%, with a role rate of 100%. The credibility characteristics of beacon nodes discussed in this paper provide ideas for subsequent optimization of the localization model.

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Quantitative evaluation modeling of mobile Ad Hoc networkcommunication service availability under failure|

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International Journal of Modeling, Simulation, and Scientific Computing 2020年第6期11卷 250-267页

作者： Yanfang Fu Yichang Su Zan Wang Dengdeng Guo Fei Dai Lianjiong Zhong Jianing Wei Xi’an Technological University State and Provincial Joint Engineering Lab.of Advanced Network Monitoring and Control Xi’an 710021P.R.China Beijing Electromechanical Engineering Institute Beijing 100074 P.R.China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing 100074 P.R.China

The availability of network communication services is an important evaluation standard to measure the ability of the network to meet the user’s business *** current related research mostly focuses on the evaluation of network reliability,which cannot reflect the logical relationship between failure/maintenance and network performance *** Ad Hoc network is a multi-hop,centerless and energy-constrained distributed *** to the dynamic change of communication environment and the instability of wireless link,Ad Hoc network is facing great challenges in service avail*** this thesis,a fault-based quantitative evaluation model of traffic availability in Ad Hoc networks is *** studying the multi-state Markov fault model of communication networks and the performance analysis of delay index based on CSMA\CA protocol,the quantitative evaluation of traffic availability in Ad Hoc networks is *** the availability of tactical unit network under different typical network configuration conditions is analyzed through experiments.

关键词： network reliability service availability fault model quantitative evaluation

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Message from the General Chair

Proceedings - 2020 International Conference on Computer Netw...

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Proceedings - 2020 International Conference on Computer network, Electronic and Automation, ICCNEA 2020 2020年 xiii页

作者： Yaping, Lei Xi'An Technological University China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control China

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Welcome Message

Proceedings - 2020 International Conference on Computer Netw...

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Proceedings - 2020 International Conference on Computer network, Electronic and Automation, ICCNEA 2020 2020年 xv页

作者： Jianguo, Wang Xi'An Technological University China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control China

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