检索结果-内蒙古大学图书馆

A smart grid computational offloading policy generation method for end-edge-cloud environments

Journal of Reliable Intelligent Environments 2025年第1期11卷 1-13页

作者： Liu, Weiwei Wang, Yue Xu, Chenxi Zheng, Min State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China

With the continuous progress of the Internet of Things (IoT) technology, smart grid has become the key to improve the efficiency of the power system. At present, how to reasonably distribute the massive power computing tasks under the premise of guaranteeing the quality of service is an urgent problem of the current smart grid. Therefore, this paper focuses on the smart grid computation task offloading decision problem based on the collaboration of edge computing and cloud computing. An end-edge-cloud task scheduling model with task execution delay, energy consumption and load balance as optimization objectives is proposed. In addition, the Dyna-Actor Critic (Dyna-AC) algorithm is proposed using Deep Reinforcement Learning (DRL), which integrates the advantages of model-based and model-free learning and optimizes the performance of this algorithm through heuristic sample selection and asynchronous parameter update strategies. Simulation results show that the algorithm is very effective in reducing power business execution delay, terminal energy consumption and improving system load. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Research on Key Technologies of Intelligent Detection of Substation Equipment Based on Cloud Edge Collaboration 24

Research on Key Technologies of Intelligent Detection of Sub...

引用

2024 International Conference on Computer Vision and Deep Learning, CVDL 2024

作者： Chang, Ke Zheng, Min Wu, Chunpeng State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd Beijing China

ISBN: (纸本)9798400718199

The traditional manual inspection has some problems, such as strong subjectivity, time consuming and poor efficiency. With the remarkable progress of artificial intelligence, advanced communication and other technologies, and the wide application of deep learning models, the intensity of daily work has been reduced, but at the same time, new problems have been created. At present, the memory and computing power of the terminal hardware are limited, and when the model is deployed to the terminal side, the processing efficiency of the model is slow and the accuracy is low due to the limited hardware conditions. Aiming at the limited memory and computing power of the edge hardware, the lightweight network structure and model compression strategy are studied, and a channel structured pruning algorithm is proposed. A lightweight deep learning network model based on structured pruning of VGG-16 model was constructed. Experiments show that the optimized pruning model can obtain the same detection performance as the original model, and can reduce the size of the model by about 55.3% and increase the speed of 33FPS when the average accuracy mAP is lost by 8.1%, which provides effective support for the intelligent detection of substation equipment. © 2024 ACM.

关键词： computing power

来源：评论

学校读者我要写书评

暂无评论

Enhancing Data Consistency in Distribution Networks Leveraging Graph Contrastive Representation Learning 6

Enhancing Data Consistency in Distribution Networks Leveragi...

引用

6th International Conference on Power and Energy Technology, ICPET 2024

作者： Liu, Siyan Shi, Danni Tang, Zhuang State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co.Ltd. Beijing China

ISBN: (纸本)9798350375855

The rapid integration of distributed renewable energy sources and the diversification of loads have precipitated profound changes in the configuration and operation of power distribution networks in the 21st century, underscoring the necessity for accurate and standardized power grid topology model data. This manuscript introduces a novel graph contrastive learning-based approach for the representation of node features in distribution networks, specifically targeting the alignment of identical nodes across different Common Information Model (CIM) documents. By combining self-supervised learning with graph neural networks, a feature extraction framework that integrates topological structural features and attribute features of the distribution network is proposed in the paper. Besides, the study designs a graph view generation strategy considering distribution network characteristics. The proposed method is validated by an 11-feeder distribution network dataset, and the results show the effectivity of the graph contrastive learning. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Lightweight Method for Adaptive Power grid Operation Safety Control Model Based on Knowledge Distillation 24

Lightweight Method for Adaptive Power Grid Operation Safety ...

引用

2024 International Conference on Computer Vision and Deep Learning, CVDL 2024

作者： Wei, Wei Zhang, Yi Du, Zexu Chu, Zongbo Zhang, Guoliang Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798400718199

Safety control is one of the most important tasks for power grid operation. However, safety control and risk warning models with high accuracy always have multitudinous parameters and need scaled resources for calculation. So as to sort out this problem, we proposed a novel and adaptive model lightweight method based on knowledge distillation. In our method, the intermediate features of the teacher network is utilized to guide the learning of the student network and a regressor is introduced behind the selected middle layer of teacher network to realize dimension match. The prediction confidence of teacher network is mapped to the feature space for the regressor initialization. Besides, the prediction confidence is used to construct the distillation temperature to improve the adaptability of information transfer. Experiments on ImageNet, CIFAR 100 and grid-specific datasets demonstrate the lightweight model obtained by our method possesses powerful and adaptive feature extraction and representation ability. © 2024 ACM.

关键词： Safety engineering

来源：评论

学校读者我要写书评

暂无评论

Masked Image Modeling Knowledge Distillation Based on Mutual Learning 24

Masked Image Modeling Knowledge Distillation Based on Mutual...

引用

2024 International Conference on Computer Vision and Deep Learning, CVDL 2024

作者： Zhang, Yi Du, Zexu Wei, Wei Chu, Zongbo State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798400718199

With the rapid improvement of the performance of large models, pre-training a large-scale model and fine-tuning on downstream has become a trend. However, previous knowledge distillation methods mainly concentrates on a specific downstream task. This paper proposes a novel distillation framework Mutual Distillation for Masked Autoencoder Model(MMIM) which transfers knowledge from large-scale pre-trained visual models to provide smaller backbones for downstream tasks, thereby saving time. In our model MMIM, to maintain the advantages of mutual learning, two different student models learn collaboratively to boost both feature representation capabilities simultaneously. At the same time, in order to utilize powerful feature representation capability of pre-trained teacher model, we use the intermediate features of the teacher model to supervise the learning of the student models. Experiments on ImageNet demonstrates the compressed model obtained by our framework possesses powerful feature representation comparable to teacher model. In addition, MMIM also outperforms state-of-the-art knowledge distillation methods which directly distills fine-tuned models. © 2024 ACM.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Vertex Centric Weighted Edge Index to Accelerate Weighted Graph Traversal 4

Vertex Centric Weighted Edge Index to Accelerate Weighted Gr...

引用

4th International Conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Lin, Jianchao Ren, Chunhui Liu, Siyan State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Grid Advanced Computing and Applications Beijing China

ISBN: (纸本)9798350384062

Indexing is an important technique to optimize graph database performance. However, indexing in existing graph databases focuses mainly on property-value based query, and has not drawn much attention on graph traversal. We propose a new indexing technique, which maintains sorted indexing of vertex and weight values of the vertex's adjacent edges. With this proposed vertex centric adjacent weighted edge index, graph traversal algorithms based on weight values like Dijkstra's algorithm and Prim's algorithm have significant performance gains. © 2024 IEEE.

关键词： Graph Databases

来源：评论

学校读者我要写书评

暂无评论

Application of Self-Distillation Framework in Transmission Line Defect Recognition 7

Application of Self-Distillation Framework in Transmission L...

引用

7th IEEE advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Yang, He Shi, Cuncun Lin, Long State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China

ISBN: (纸本)9798350339161

Drones have become very common in inspections of transmission lines across the country, but the images and videos captured by drones still require manual processing, which is time-consuming and labor-intensive, not intelligent enough to meet real-time requirements, and the response is relatively passive;currently in the power sector artificial intelligence has been used to process drone footage, but it has not yet reached the level of human experts. In recent years, methods to improve model performance have focused on conceiving new network structures and improving the quantity and quality of sample sets. Both methods are costly. This paper proposes a training framework for the model based on self-distillation. Without changing the model structure or changing the data set, you can learn the knowledge of your own network to improve model performance and use smaller models under the same performance requirements. Based on this framework, we implemented a self-distillation system on ResNet and verified the framework by comparing original training and self-distillation training. Of course, this framework is also applicable to other networks. Experiments show that the self-distillation method can significantly improve the accuracy of recognition. © 2024 IEEE.

关键词： Electric lines

来源：评论

学校读者我要写书评

暂无评论

Transformer Fault Detection Method Based on Multi-Source Data Fusion of Transformer and RetNet 1

Transformer Fault Detection Method Based on Multi-Source Dat...

引用

1st International Conference on Artificial Intelligence and Human-Computer Interaction, ArtInHCI 2023

作者： Chang, Ke Gan, Jinrui Xia, Weishang State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing102209 China

ISBN: (纸本)9781643685083

Current fault methods for transformers are all based on a single signal and fail to make full use of fault data. In order to improve the accuracy of state judgment, this paper proposes a Transformer fault detection method based on multi-source data fusion based on Transformer and RetNet, which integrates four fault data: gas in oil, voltage and current, sound and oil temperature. The Transformer fault diagnosis is carried out jointly. Considering the degree of correlation between different data, the four kinds of data are divided into two categories and processed by different models respectively. Transformer is used to focus the data of strong correlation between gas and voltage and current in oil, fuse the characteristic information, and utilize the multi-scale attention mechanism of RetNet. The correlation between sound and oil temperature data is widely mined in different spatio-Temporal ranges. Finally, two models are combined with four data sources to judge transformer *** accuracy of the new method proposed in this paper is 81.36%, which is 2.98% higher than RetNet and 5.03% higher than Transformer. The experimental results show that the method is effective. © 2024 The Authors.

关键词： Failure analysis

来源：评论

学校读者我要写书评

暂无评论

Research on Pseudo Measurement Generation Method Based on 1D-CNN and Transfer Learning for Medium-Voltage Distribution Networks 2

Research on Pseudo Measurement Generation Method Based on 1D...

引用

2nd International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Sun, Zhiqing Lei, Shuya Yi, Xuan Zhang, Wense Wang, Yishen Ni, Xiabing Wang, Qifeng Liang, Xiao Huang, Yan State grid Hangzhou power supply company Zhejiang China State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Grid Advanced Computing and Applications Zhejiang China State Grid Smart Grid Research State Grid Laboratory of Grid Advanced Computing and Applications Zhejiang China Tsinghua University Sichuan Energy Internet Research Institute Chengdu China

ISBN: (纸本)9798350373318

With the increasing scale and complexity of new power systems, ensuring the safe and optimized operation of distribution networks becomes particularly important. Due to constraints such as economic factors, the comprehensive deployment of real-time measurement devices in mediumvoltage distribution networks faces significant challenges, resulting in insufficient observability of the distribution network. To address this issue, this paper proposes a method that combines one-dimensional convolutional neural networks (1D-CNN) with transfer learning for pseudo measurement generation. This method first constructs a 1D-CNN pseudo measurement generation model using a large amount of historical measurement data, and then trains and optimizes the 1D-CNN model through transfer learning to achieve pseudo measurement generation even under topological changes. The test results of actual engineering data demonstrate that the proposed method can generate high-frequency pseudo measurement data for nodes in medium-voltage distribution networks in real time, accurately, and efficiently. Additionally, the proposed method exhibits good adaptability when the distribution network topology changes. In this way, this paper provides a new technical path for the perception of the operational status of medium-voltage distribution networks and provides technical support for the safe and optimized operation of distribution networks. © 2024 IEEE.

关键词： Power distribution networks

来源：评论

学校读者我要写书评

暂无评论

Image Quality Intelligent Detection Method on Electric Power Video Surveillance 6

Image Quality Intelligent Detection Method on Electric Power...

引用

6th IEEE advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Wang, Jin Peng, Yuanlong Chen, Jiangqi Liu, Hao Yan, Longchuan State Grid Smart Grid Research Institute Co.Ltd. State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Information and Telecommunication Branch Beijing China

ISBN: (纸本)9798350316520

At present, artificial intelligence technology in power has achieved certain application results in the field of equipment defect recognition and *** article proposes a joint self-supervised learning method based on contrastive learning and sorting learning, and applies it to pre-training of power inspection image quality feature extraction models. By effectively utilizing massive unlabeled image samples, the training of feature extraction models is achieved, effectively improving the model's perception of image quality deterioration and scene generalization ability;Then, based on the pre-training model of joint learning, further supervised fine-tuning is carried out, and the results of general data pre-training, contrastive learning, and joint learning test sets are compared through experiments to verify the effectiveness of joint learning in intelligent detection of power image quality deterioration. © 2024 IEEE.

关键词： Sorting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：