检索结果-内蒙古大学图书馆

20th international conference on Intelligent computing (ICIC)

作者： Liu, Bingqian Wu, Xinxin Pan, Deng Chen, Yanyu Huang, Jianye Liao, Feilong Lin, Shuang State Grid Fujian Elect Power Res Inst Fuzhou 350000 Peoples R China Xiamen Univ Sch Informat Xiamen 361005 Peoples R China

ISBN: (纸本)9789819756179;9789819756186

The increasing complexity of power distribution networks necessitates advanced methods for fault attribution analysis to uphold system reliability and stability. This paper introduces a novel approach that integrates large language models (LLMs) with domain-specific knowledge graphs to tackle the challenges posed by high-dimensional and intricate fault data in power distribution networks. A multi-dimensional fault ontology [13] is proposed to efficiently structure the fault data, facilitating the creation of a comprehensive fault knowledge graph. To enhance the diagnostic predictions of the LLM, a reinforcement learning-based node selection algorithm is implemented, strategically choosing pertinent nodes from the fault graph to enhance the model's reasoning abilities. Experimental findings illustrate that this approach surpasses traditional statistical methods and direct LLM reasoning, achieving superior accuracy and efficiency in fault diagnosis. By incorporating selective knowledge graph node sampling, irrelevant noise is filtered out from the fault data, sharpening the LLM's focus and eradicating "AI hallucinations," thereby improving analytical precision. Validation on a real-world dataset from a power company confirms the method's efficacy, promising swift and accurate fault analysis while reducing the time required for power grid fault diagnosis.

关键词： Fault Attribution Analysis Knowledge Graph Large Language Models

来源：评论

学校读者我要写书评

暂无评论

international conference for Technological Engineering and its applications in Sustainable Development, ICTEASD 2023

International Conference for Technological Engineering and i...

引用

2023 international conference for Technological Engineering and its applications in Sustainable Development, ICTEASD 2023

ISBN: (纸本)9798350336474

The proceedings contain 69 papers. The topics discussed include: IoT based exam hall surveillance robot;immersive technologies: a literature review on brand engagement and consumer behavior;fog computing in IoT: architecture, applications, and research challenges;empowering aquarists comprehensive study on IoT-enabled smart aquarium systems for remote monitoring and control;artificial intelligence law for Malaysia;efficient and resilient placement strategy for electric vehicle charging stations, incorporating renewable energy and storage equipment in distribution networks;utilizing flexible, robust optimization for the integration of renewable energy sources and electric cars in a large-scale vehicle-to-grid (V2G) network;and enhancing data quality in image pre-processing: a case study on plant disease classification.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Non-targeted Adversarial Attacks on Object Detection Models 20th

Non-targeted Adversarial Attacks on Object Detection Models

引用

20th international conference on Intelligent computing (ICIC)

作者： Mi, Jian-Xun Zhao, Xiangjin Chen, Yongtao Cheng, Xiao Tian, Peng Lv, Xiaohong Zhong, Jiayong Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing 400065 Peoples R China State Grid Chongqing Elect Power Res Inst Chongqing 401123 Peoples R China

ISBN: (纸本)9789819756056;9789819756063

Adversarial attacks involve introducing imperceptible noise into images to induce incorrect outputs from models, serving as a means to assess model security. Object detection, as a crucial task in the field of computer vision, has garnered significant attention regarding its security. NMS mechanisms in object detection models suppress low-confidence detection boxes. So, most current adversarial attacks on object detection are for targeted attacks. However, some object detection datasets designed for specific scenarios contain only one class, rendering targeted attacks unsuitable. If the original image's category is very different from the target category, it will be hard to attack. This is true even if a large perturbation is made. This paper introduces a non-targeted adversarial attack method capable of effectively compromising object detection models. We conduct experiments on a power dataset from the national power grid, demonstrating promising results. Furthermore, we prove that utilizing the proposed UnTargeted Attack (UTA) method from this paper enables the generation of more stealthy perturbations with fewer iterations.

关键词： Adversarial Attack Object Detection Non-targeted Adversarial Attacks

来源：评论

学校读者我要写书评

暂无评论

Dynamic load balancing method based on optimal complete matching of weighted bipartite graph for simulation tasks in multi-energy system digital twin applications

引用

ENERGY REPORTS 2022年 8卷 1423-1431页

作者： Tang, Xueyong Ding, Yi Lei, Jinyong Yang, He Song, Yankan Zhejiang Univ Hangzhou 310058 Peoples R China Elect Power Res Inst CGS Guangzhou 510663 Peoples R China Tsinghua Sichuan Energy Internet Res Inst Chengdu 610200 Peoples R China

As demands of renewable energy and more energy storages in power systems promote, the digital twin technology for multi-energy system draws attention from the researchers in power system and energy area. The load balancing algorithm of the digital twin server cluster significantly impacts the computing performance of the computing nodes. Optimal complete matching of a weighted bipartite graph is proposed to allocate computing task blocks to each computing node evenly. In the proposed algorithm, the computation times of simulation computing task blocks are estimated and the computation time of one single step in the task blocks are used as dynamic load indicators of the computing nodes. The indices are fed back to the scheduling server in real time. With the total steps of the simulation computing task blocks and the computation time of each single step for the task blocks, a fully weighted bipartite graph of computing nodes and simulation computing task blocks is constructed, greedy expansion of the Hungarian algorithm is used to optimize the complete matching of this weighted bipartite graph. Based on the optimization results, the task blocks are allocated to the computing nodes correspondingly in real time. Simulation-driven model parameter correction function, the representative simulation computing applications of digital twin for multi-energy system was used to test the proposed algorithm and the results show a significant improvement of the load balancing effect at the computing nodes. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Digital twin Simulation computing task blocks Load balancing Weighted bipartite graph

来源：评论

学校读者我要写书评

暂无评论

Precision high power wide bandwidth AC/DC current shunt 4

Precision high power wide bandwidth AC/DC current shunt

引用

4th international conference on Smart grid Metrology, SMAGRIMET 2023

作者： Sala, Alan Konjevod, Jure Mostarac, Petar Gasparini, Stipe University of Zagreb Faculty of Electrical Engineering and Computing Department of Electrical Engineering Fundamentals and Measurements Zagreb10000 Croatia KONČAR-Electrical Engineering Institute Ltd. ZagrebHR-10000 Croatia

ISBN: (纸本)9798350323597

As new calibration equipment becomes available, there is an increasing demand for calibrations at higher currents and wider frequency ranges of up to 100 kHz and 100 A [1]. In response to this demand, we have expanded our ac-dc capability and developed a new set of standard shunts suitable for measuring currents in laboratories, as well as for use in power electronics applications [2]. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Data Augmentation Techniques for Medical Image Segmentation - A Review

Data Augmentation Techniques for Medical Image Segmentation ...

引用

2024 international conference on Computer and applications, ICCA 2024

作者： Kozah, Naney Dornaika, Fadi Charafeddine, Jinan El Jaam, Jihad University of the Basque Country UPV/EHU Department of Computer Science and Artificial Intelligence San Sebastian Spain University of the Basque Country UPV/EHU Ikerbasque Basque Foundation for Science San Sebastian Spain Léonard de Vinci Pôle Universitaire Research Center Paris La Défense92916 France School of Computing and Data Liverpool J. Mores University Liverpool United Kingdom

ISBN: (纸本)9798350367560

Segmentation of medical images is crucial for accu-rate diagnosis, treatment planning and intervention. However, the scarcity of data and the variability of different imaging modalities, including MRI, CT, PET and ultrasound, pose a major challenge for deep learning models. This review discusses traditional and advanced data augmentation techniques used to improve segmentation performance. In addition to geometric transformations such as rotation and scaling, advanced approaches such as Generative Adversarial Networks (GANs) and superpixel-based augmentations are also evaluated. The review also highlights novel methods such as Local-and-Contour Aware grid Mixing (LCAMix) and Hard and Soft Mixing (HSMix) that preserve anatomical boundaries and effectively augment datasets. The analysis concludes with practical insights for clinical applications and suggests integrating these techniques into data pipelines to increase the generalizability of models. Future research directions focus on DICOM-specific augmentation strategies and optimization of computational requirements to improve clinical applicability. © 2024 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Grouped federated learning for time-sensitive tasks in industrial IoTs

引用

PEER-TO-PEER NETWORKING AND applications 2024年第2期17卷 819-833页

作者： Hao, Jiangshan Zhang, Linghao Zhao, Yanchao Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Jiangjun Ave Nanjing 211106 Jiangsu Peoples R China State Grid Sichuan Elect Power Res Inst Power Internet Things Key Lab Jinhui West Second St Chengdu 610094 Sichuan Peoples R China

The Industrial Internet of Things (IoT) is a crucial part of Industry 4.0 and constantly calls for intelligence to improve productivity. Meanwhile, the federated learning scheme has recently been proposed to provide both distributed learning ability and inherent privacy protection functions. Despite its original advantages, the unique features of industrial IoT posed several challenges to federated learning regarding efficiency and scalability. Due to the heterogeneity of data under IoT and the large difference in computing efficiency of each device, the training time of federated learning will be affected by stragglers. To this end, an effective device selection mechanism is urgently needed to improve the training efficiency of global model. Additionally, the communication coordination problem among various devices should be considered to accelerate global convergence. To solve the abovementioned problems, we proposed an Upper Confidence Bound (UCB)-based grouping federated learning (UGFL) where a data scheduling method effectively reduces the stragglers. We combined Lyapunov optimization with the UCB (Garivier and Moulines in international conference on Algorithmic Learning Theory, pp. 178-188, 2011) algorithm to design an efficient device selection method to choose the best device to participate in each global epoch. In addition, we theoretically analyzed the upper bound of convergence of our proposed method. Experimental results show that our method has higher accuracy and faster convergence speed than the existing methods.

关键词： Internet of things Federated learning Device selection Data scheduling

来源：评论

学校读者我要写书评

暂无评论

Enhancing Privacy Strategy in New Power Systems: Problem Assessment, Comparative Analysis and Suggestions 5th

Enhancing Privacy Strategy in New Power Systems: Problem Ass...

引用

5th international conference on Big Data and Security (ICBDS)

作者： Chen, Li Yao, Xuyan Zhang, Xin Cao, Yuxin Su, Jiahe Tian, Yuan Nanjing Inst Technol Nanjing 211167 Jiangsu Peoples R China Univ Oulu Sch Software Engn Oulu 90014 Finland

ISBN: (纸本)9789819743896;9789819743902

This paper meticulously examines the privacy challenges stemming from the escalating volume of data within the new power systems. It thoroughly explores various privacy computing techniques, including encryption and statistical security, with a particular emphasis on applications such as homomorphic encryption, federated learning and differential privacy within the power grid. The analysis underscores the pivotal role of privacy computing in addressing privacy-related concerns within the power industry, particularly focusing on issues like data transmission security and multi-party collaboration. Furthermore, the paper proposes innovative solutions aimed at bolstering privacy protection, fostering data sharing initiatives, and enhancing cost-effectiveness through the strategic implementation of privacy computing technologies. Recognizing the paramount importance of understanding privacy protection in the context of the digital transformation of power systems, this paper seeks to provide valuable insights to guide further research and application of privacy computing. Ultimately, these insights aim to facilitate secure data circulation within the energy sector.

关键词： New power systems Private computing Homomorphic encryption Federated learning Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Quantum-Secure Communication for Trusted Edge computing with IoT Devices 1

引用

38th international conference on Information Security and Privacy Protection (SEC)

作者： Kornaros, George Berki, Georgia Grammatikakis, Miltos Hellen Mediterranean Univ Iraklion 71410 Greece Intelligent Syst & Comp Architecture Lab Iraklion 71410 Greece

ISBN: (数字)9783031563263

ISBN: (纸本)9783031563287;9783031563263;9783031563256

Internet-of-Things(IoT)-based edge computing in smart factories, smart grid, agriculture, constructions and autonomous vehicles include service-oriented gateways that connect with the cloud, perform machine-to-machine communication, often transmiting large amount data up and down the network, performing time-sensitive processing and involving intelligent local decision-making. In view of a sharp increase in cyberattacks today targeting edge computing, these gateways need to provide digital signing and key negotiation for ensuring reliable data sources, trusted applications and authentic devices and connections. In contrast to common perception, we show that post-quantum cryptography methods do not necessitate extensive modification to adopt in such environments;further, the cryptography algorithm's hardness is preserved while fulfilling the IoT device's resource limitations. In particular, we demonstrate an efficient method and an implementation on a 32-bit ARMCortex-M4, 64KB memory microcontroller, based on post-quantum key encapsulation mechanisms (KEMs), for secure communication and authentication in an industrial IoT environment.

关键词： trustworthy edge computing IoT secure communications post-quantum KEM FrodoKEM firmware-over-the-air updating

来源：评论

学校读者我要写书评

暂无评论

Research on security monitoring of distributed heterogeneous network environment based on microservice architecture

Research on security monitoring of distributed heterogeneous...

引用

2024 international conference on Cloud computing and Communication Engineering, CCCE 2024

作者： Wei, Wei Zhong, Ye Wang, Ce Wang, Xingchuan Long, Na Information Center of Guizhou Power Grid Co. Ltd Guizhou Guiyang550000 China

ISBN: (数字)9781510686717

ISBN: (纸本)9781510686700

In a distributed network environment, multiple mutually independent trust domains are formed between different enterprises. During network access, access requests for resources may come from local trust domains or from field trust domains. Therefore, when a user from a local trust domain accesses a resource from a foreign trust domain, or a user from a foreign trust domain accesses a resource from a local trust domain, it involves cross-trust domain authentication and session key negotiation. In this paper, we propose a distributed heterogeneous network environment security monitoring study based on microservice architecture, which collects and analyzes the horizontal traffic of microservices in real time to monitor the network performance. By collecting the virtual network packet capture data under the microservice architecture of the SouthNet cloud environment, the network communication quality between microservice applications is monitored and alerted in real time, and the network communication fault localization between microservice nodes is supported to assist in troubleshooting. The experimental results show that the distributed heterogeneous network environment security monitoring system based on microservice architecture can improve the flexibility, scalability and security of the system, which is applicable to the monitoring needs in different fields. © 2024 SPIE.

关键词： Network security Environmental monitoring Computer security Network architectures Clouds Analytical research Cloud computing Databases Environmental sensing Information security

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：