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7th International Conference on Computer network, Electronic and Automation, ICCNEA 2024

作者： Li, Ao Wang, Zhongsheng 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

Gray scale picture colorization, a research hotspot in the domain of computer vision, seeks to assign reasonable colours to every single pixel of a grayscale picture, enriching the visual information of the coloured picture. Neural networks take grayscale images as input and output colorized images. From a psychological perspective, colours can provide observers with a more pleasant perceptual experience. Old photos can also be coloured, which are important documentary resources for recording real history and restoring the social landscape of the time. Additionally, due to the limitations of imaging mechanisms, most medical images are grayscale. Therefore, research on grayscale image colorization is a task of significant importance. However, existing automatic image colorization systems have issues such as "blurry boundaries", "colour overflow", "inappropriate colour choices" and "incorrect colour regions". This paper proposes a model combining adversarial neural networks and U-Net networks, optimizing them by incorporating global feature fusion modules, Pathway emphasis mechanisms and spatial focus mechanisms. The idea of Markov discriminator was added to the discriminator, and the loss function was optimized. The ultimate investigative conclusions demonstrate that the PSNR and SSIM of the suggested system are 24.45 and 0.941, accordingly, in relation to the preceding protocol, which are 3% and 5% higher than the previous algorithm, and the example has been ameliorated to some degree. And the loss function is modified, and the final algorithm can improve the effect of picture colouring, which encompasses a broad spectrum of application prospects. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Discriminators

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Research on Multilevel Degradater Model under Unsupervised Learning 6

Research on Multilevel Degradater Model under Unsupervised L...

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6th International Conference on Computer network, Electronic and Automation, ICCNEA 2023

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

ISBN: (纸本)9798350305388

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. © 2023 IEEE.

关键词： Gaussian distribution

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DV-Hop Localization Strategy Based on Beacon Node Confidence Analysis 6

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

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6th International Conference on Computer network, Electronic and Automation, ICCNEA 2023

作者： Shen, Han Wang, Zhongsheng Xi'an Technological University School of Computer Science and Engineering Xi'an710021 China Xi'an Technological University State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an China

ISBN: (纸本)9798350305388

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. © 2023 IEEE.

关键词： Sensor nodes

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Research and Implementation of Image Defogging Based on Deep Learning 6

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

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6th International Conference on Computer network, Electronic and Automation, ICCNEA 2023

作者： Li, Ao Wang, Zhongsheng Xi'an Technological University School of Computer Science and Engineering Xi'an710021 China Xi'an Technological University State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an China

ISBN: (纸本)9798350305388

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. © 2023 IEEE.

关键词： Generative adversarial networks

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Reinforcement Learning Scheduling Research for Edge Servers 6

Reinforcement Learning Scheduling Research for Edge Servers

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6th International Conference on Computer network, Electronic and Automation, ICCNEA 2023

作者： Han, Yonggui Wang, Zhongsheng Xi'an Technological University School of Computer Science and Engineering Xi'an710021 China Monitoring and Control xi'An Technological University State and Provincial Joint Engineering Lab. of Advanced Network Xi'an China

ISBN: (纸本)9798350305388

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. © 2023 IEEE.

关键词： Reinforcement learning

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Research of Image De-noising Based on Deep Learning 6

Research of Image De-noising Based on Deep Learning

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6th International Conference on Computer network, Electronic and Automation, ICCNEA 2023

作者： Li, Kai Wang, Zhongsheng Xi'an Technological University School of Computer Science and Engineering Xi'an710021 China Xi'an Technological University State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an710021 China

ISBN: (纸本)9798350305388

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. © 2023 IEEE.

关键词： Image denoising

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Research on improved full-factor deep information mining algorithm

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Cognitive Robotics 2022年 2卷 30-38页

作者： Man, Yun Fei, Xu Jun, Liu Qian, Zhang School of Computer Science and Engineering Xi'an Technological University Xi'an China Engineering Laboratory The State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'an China 63768 Unit of the Chinese People's Liberation Army

In the use of fire-fighting physics platform for fire alarm data correlation analysis, there are often problems such as too much data volume and insufficient accuracy of the analysis results. For such questions, this paper establishes a full-factor secondary mining mechanism for fire accidents based on the fire big data based on the correlation analysis algorithm and the clustering algorithm. The association algorithm is used to conduct full-factor primary mining on the fire-related factors in the data warehouse, and the common-sense accident attributes in the association rules are extracted. Then use the K-means clustering algorithm, where the cluster center is the relevant attribute in the fire accident record, and perform the second combined clustering of the accident elements to achieve in-depth information mining of all factors of the fire accident. Experimental results show that the improved full-factor deep information mining algorithm proposed in this paper can effectively filter 31.6% of meaningless mining results compared to the traditional single mining algorithm. It shows that the algorithm in this paper can more accurately dig out the relationship between data, and can provide more effective decision support for fire management and other work. © 2022

关键词： Fires

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Resource allocation in UAV assisted air ground intelligent inspection system

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Cognitive Robotics 2022年 2卷 1-12页

作者： Zhang, Zhuoya Xu, Fei Qin, Zengshi Xie, Yue School of ordnance science and technology Xi'an Technological University Xi'An China Engineering Laboratory The State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'An China School of Computer Science and Engineering Xi'an Technological University Xi'An China

With the progress of power grid technology and intelligent technology, intelligent inspection robot (IR) came into being and are expected to become the main force of substation inspection in the future. Among them, mobile edge computing provides a promising architecture to meet the explosive growth of communication and computing needs of inspection robot. Inspection robot can transmit the collected High Definition (HD) video to adjacent edge servers for data processing and state research and judgment. However, the communication constraints of long-distance transmission, high reliability and low delay pose challenges to task offloading optimization. Therefore, this paper introduced Unmanned Aerial Vehicle (UAV) and established UAV assisted mobile edge computing system. UAV assisted and mobile edge computing are combined to form edge computing nodes. In this way, it provided communication and computing services to the IR for fast data processing. Specifically, in order to optimize the system energy consumption, a resource allocation strategy based on genetic algorithm is proposed. By optimizing the offloading decision and computing resource allocation of the IRs, the computing task of the IRs are offloaded to an energy-efficient UAV. The experimental results show that the resource allocation strategy based on genetic algorithm can effectively reduce the energy consumption and cost of UAVs and IRs, and effectively realize the reasonable allocation of resources. The results verify the effectiveness and reliability of the algorithm in the real scene. © 2021

关键词： Resource allocation

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Data Crawling and Research Based on Topic Web Crawler 5

Data Crawling and Research Based on Topic Web Crawler

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5th International Conference on Computer network, Electronic and Automation, ICCNEA 2022

作者： Wang, Zhongsheng Lv, Jieyi Xi'an Technological University School of Computer Science and Engineering Shaanxi China State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control No.2 Xuefu Middle Road Weiyang district Shaanxi Xi'an China

ISBN: (数字)9781665491099

ISBN: (纸本)9781665491099

With the popularity of big data, efficient acquisition of existing massive data and multi-angle analysis has become a key technology. In this paper, compared with the traditional general web crawler, the main web crawler strategy adopted in network crawling can be more efficient for grasping targets, so as to carry out data grasping operations more efficiently. Based on the film review data on ***, this paper analyzes and studies the film review data without damaging the operation of the website. By using jieba, matplotlib, wordcloud and other libraries in python library, data can be visualized into wordcloud map, pie chart and line chart, which is helpful for users to see key words and the proportion of favorable and negative comments in movie works directly, which is of great significance to users' preference selection. It also has certain reference significance for accurate software recommendation in the era of big data. © 2022 IEEE.

关键词： Python

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A Defect Detection Method Based on Improved Mask R-CNN for Wafer Maps 4

A Defect Detection Method Based on Improved Mask R-CNN for W...

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

作者： Li, Yang Wang, Jianguo School Of Computer Science And Engineering Xian Technological University Xian China State And Provincial Joint Engineering Lab. Of Advanced Network Monitoring And Control Xian China

ISBN: (纸本)9781665444866

Pattern recognition of defects in wafer drawing is very important to determine the root cause of production defects, which can provide insights for the production improvement of wafer plants. In wafer manufacturing process, there will be mixed defects, detection of mixed defects is much more complex. Deep learning method has become a good choice to solve the problem of complex pattern recognition. In this paper, an improved Mask R-CNN model for wafer defect map is proposed. The ability of small target recognition is improved by fusing residual network and feature pyramid network, and the target feature can be fully extracted in the shallow model, so that the efficiency of feature extraction is improved. Secondly, an improved non-maximum suppression (NMS) algorithm is added to solve the error detection problem caused by traditional algorithms. The experimental results show that the improved Mask R-CNN model is superior to the conventional model, and has better recognition effect for small targets and shallow models. Secondly, the improved NMS algorithm is added to improve the detection precision. © 2021 IEEE.

关键词： Defects

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