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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Hui Huang Aoying Ji Jichao Ye Wu Lu Yonghai Xu Ping Wang Science and Technology Digitization Department State Grid Zhejiang Electric Power Co. LTD. Lishui Power Supply Company Lishui China Center of Conviction State Grid Zhejiang Electric Power Corporation Information & Telecommunication Branch Hangzhou China

ISBN: (数字)9798331596651

ISBN: (纸本)9798331596668

With the growing integration of Smart Microgrids, cybersecurity concerns have become increasingly critical. Micro-grids face a variety of sophisticated cyber threats, including False Data Injection Attacks (FDIA), Denial of Service (DoS) Attacks, and Privilege Escalation Attacks, which compromise secure operations by obstructing communication networks and control systems. Existing security architectures lack the flexibility and adaptability to address the unique demands of microgrids. This paper proposes a novel Key Distribution Security Association-Based Confidentiality Isolation Control Framework, leveraging a dynamic GDOI multicast security association allocation method. The framework introduces fine-grained cryptographic boundary definitions and secure interconnection mechanisms to enhance microgrid security. By employing distinct Security Parameter Indexes (SPI) and integrating heartbeat signals, the framework achieves independent Security Associations (SA), effectively addressing issues like source authentication, replay attacks, and resource inefficiencies. Simulations and security analyses validate the proposed framework’s ability to defend against FDIA and replay attacks while reducing storage overhead and supporting dynamic adaptability in complex environments. This framework provides a scalable and practical solution for the secure operation of Smart Microgrids.

关键词： Heart beat Microgrids Dynamic scheduling Smart grids Security Resource management Reliability Indexes Usability Faces

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CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer

arXiv

引用

arXiv 2023年

作者： Shao, Zhiwen Su, Yuchen Zhou, Yong Meng, Fanrong Zhu, Hancheng Liu, Bing Yao, Rui The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China The Engineering Research Center of Mine Digitization Ministry of Education of the People’s Republic of China Xuzhou221116 China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Contour based scene text detection methods have rapidly developed recently, but still suffer from inaccurate front-end contour initialization, multi-stage error accumulation, or deficient local information aggregation. To tackle these limitations, we propose a novel arbitrary-shaped scene text detection framework named CT-Net by progressive contour regression with contour transformers. Specifically, we first employ a contour initialization module that generates coarse text contours without any post-processing. Then, we adopt contour refinement modules to adaptively refine text contours in an iterative manner, which are beneficial for context information capturing and progressive global contour deformation. Besides, we propose an adaptive training strategy to enable the contour transformers to learn more potential deformation paths, and introduce a re-score mechanism that can effectively suppress false positives. Extensive experiments are conducted on four challenging datasets, which demonstrate the accuracy and efficiency of our CT-Net over state-of-the-art methods. Particularly, CT-Net achieves F-measure of 86.1 at 11.2 frames per second (FPS) and F-measure of 87.8 at 10.1 FPS for CTW1500 and Total-Text datasets, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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SG-LRA: Self-Generating Automatic Scoliosis Cobb Angle Measurement with Low-Rank Approximation

arXiv

引用

arXiv 2024年

作者： Shao, Zhiwen Yuan, Yichen Ma, Lizhuang Yeung, Dit-Yan Zhu, Xiaojia School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Mine Digitization Engineering Research Center The Ministry of Education Xuzhou221116 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Xuzhou Central Hospital The Xuzhou Clinical School of Xuzhou Medical University Xuzhou221009 China Department of Orthopedics The First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Hefei230001 China

Automatic Cobb angle measurement from X-ray images is crucial for scoliosis screening and diagnosis. However, most existing regression-based methods and segmentation-based methods struggle with inaccurate spine representations or mask connectivity/fragmentation issues. Besides, landmark-based methods suffer from insufficient training data and annotations. To address these challenges, we propose a novel framework including Self-Generation pipeline and Low-Rank Approximation representation (SG-LRA) for automatic Cobb angle measurement. Specifically, we propose a parameterized spine contour representation based on LRA, which enables eigen-spine decomposition and spine contour reconstruction. We can directly obtain spine contour with only regressed LRA coefficients, which form a more accurate spine representation than rectangular boxes. Also, we combine LRA coefficient regression with anchor box classification to solve inaccurate predictions and mask connectivity issues. Moreover, we develop a data engine with automatic annotation and automatic selection in an iterative manner, which is trained on a private Spinal2023 dataset. With our data engine, we generate the largest scoliosis X-ray dataset named Spinal-AI2024 largely without privacy leaks. Extensive experiments on public AASCE2019, private Spinal2023, and generated Spinal-AI2024 datasets demonstrate that our method achieves state-of-the-art Cobb angle measurement performance. Our code and Spinal-AI2024 dataset are available at https://***/Ernestchenchen/SG-LRA and https://***/Ernestchenchen/Spinal-AI2024, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Angle measurement

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Data Driven based Malicious URL Detection using Explainable AI

Data Driven based Malicious URL Detection using Explainable ...

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Saranda Poddar Deepraj Chowdhury Ashutosh Dhar Dwivedi Raghava Rao Mukkamala Department of Computer Science RCC Institute of Information Technology Kolkata India Department of Electronics and Communication International Institute of Information Technology Naya Raipur India Department of Digitization Centre for Business Data Analytics Copenhagen Business School Frederiksberg Denmark

With the ever-increasing reach of the internet, and its increasing access through various types of devices, the spread of malware, phishing attempts, etc. have steadily been increasing, along with their level of sophistication. Thus it becomes very important to conduct research on different methods to prevent such harmful attacks on systems and users. Using a malicious URL is the common way for hackers to attack a system, thus, to accommodate the variety attack vectors of malicious websites, 21 features were extracted from 651,191 URLs to train the proposed model. A two-stage stacked ensemble learning model, based on gradient boosting methods and random forest, has been trained and tested in the 70:30 ratio of the 651,191 URLs, and an accuracy of 97% has been achieved. Then Explainable AI (XAI) has been used to clearly explain the working of the model, and study the impact of each of the 21 features on the 4 class predictions (benign, defacement, phishing and malware).

关键词： Uniform resource locators Phishing Forestry Predictive models Feature extraction Malware Security

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AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G network using Automatic Deep Learning

AUTODEEPSLICE: A Data Driven Network Slicing Technique of 5G...

引用

IEEE Globecom Workshops

作者： Deepraj Chowdhury Rupanjan Das Risav Rana Ashutosh Dhar Dwivedi Pushpita Chatterjee Raghava Rao Mukkamala Department of Electronics and Communication International Institute of Information Technology Naya Raipur India Department of Digitization Centre for Business Data Analytics Copenhagen Business School Frederiksberg Denmark Department of Computer Science Tennessee State University Nashville USA

ISBN: (纸本)9781665459761

With the advancement of mobile networks, and the revolution of mobile communication from 1G to 5G, human dependency has increased, so it has become the most priority to provide an efficient and highly reliable network. The 5G networks need high capacity and high-speed data transfer, with the least latency and increased security. 5G service providers are trying to find a programmable solution that can handle a huge number of independent service users on the same physical infrastructure. Network slicing is used to provide end-to-end network resource allocation. To accelerate the 5G network performance, Artificial Intelligence (AI) powered data-driven based decisions will be used for future communication. In this proposed work, Automatic deep learning has been implemented, where the hyperparameter will automatically select the algorithm and tune the hyperparameter. The proposed model AUTODEEPSLICE efficiently makes intelligent decisions and will choose the best network slice at the time of network breakdown.

关键词： Deep learning Wireless communication 5G mobile communication Network slicing Load management Security Resource management

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Resource Demand Analysis of IoT Terminal Chips Based on Atomic Service Modeling

Resource Demand Analysis of IoT Terminal Chips Based on Atom...

引用

IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Yigong Zhang Yongjie Nie Siyang Liu Jian Li Yuxing Mao Tianyan Jiang Xianping Zhao Tiejun Cao School of Electrical Engineering Chongqing University Chongqing China Electric Power Science Research Institute of Yunnan Power Grid Co. Ltd. Kunming China Department of Technology Innovation and Digitization Yunnan Power Grid Co. Ltd. Kunming China

The diversity of smart city scenarios makes it difficult for Internet of Things terminals (IoTT) to accurately match their differential demands for computing, storage, and communication resources with general-purpose MCU chips, leading to resource redundancy and cost increases. Due to the lack of resource demand analysis for IoT application scenarios, traditional customized chip design methods cannot be directly used for the design of IoTT chips. To address this issue, we propose building a decoupled atomic service set to analyze the resource demands of practical IoT application scenarios. Specifically, we use timed automata to model the atomic services of IoTT, characterize the event attributes and time constraints of atomic services, and use UPPAAL to verify the reachability of the timed automata model of the proposed atomic services. Finally, these atomic services are mapped to the resource call situation of RISC-V instructions to guide the architecture design of specialized chips for IoTT.

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Design and application of new distributed photovoltaic anti-islanding protection device

Design and application of new distributed photovoltaic anti-...

引用

Energy, Power and Grid (ICEPG), International Conference on

作者： Shengzuo Liu Shuang Ji Hongyan Yao Yingnan Xian Lei Wang Liang Tao Fengjun Yuan Science and technology digitization department State Grid Siping power supply company Siping Jilin China State Grid Siping power supply company Substation secondary maintenance center Siping Jilin China State Grid Siping power supply company Electric power equipment manufacturing installation Co. LTD Siping Jilin China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In recent years, the proportion of investment in distributed photovoltaic power generation has been increasing. However, when grid-connected power generation systems experience islanding effects, the quality of power supply to the entire power network and the personal safety of grid staff can be severely compromised. To address this issue, this paper proposes a new type of distributed photovoltaic anti-islanding protection device. The device is characterized by its simplicity and ease of implementation, reliable operation, and low cost. It eliminates the potential pitfalls associated with “islanding operation” and resolves the “island” risk that has plagued grid systems for years.

关键词： Photovoltaic systems Islanding Costs Power grids Hazards Reliability Protection Investment

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Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM

引用

Computers, Materials & Continua 2020年第1期62卷 199-216页

作者： Fangming Bi Xuanyi Fu Wei Chen Weidong Fang Xuzhi Miao Biruk Assefa College of Computer Science and Technology China University of Mining and TechnologyXuzhou221116China College of Computer Science and Technology Mine Digitization Engineering Research Center of the Ministry of EducationChina University of Mining and TechnologyXuzhou221116China College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China Key Laboratory of Wireless Sensor Network&Communication Shanghai Institute of Micro-System and Information TechnologyChinese Academy of SciencesShanghai201899China Infromation Communication Technology Department Wollo UniversityDessie EthiopiaP.o Box 1145Ethiopia

Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is *** algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image,which can eliminate most non-fire ***,the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively ***,based on the segmented image,the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire ***,the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine,and the recognition results were *** video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios.

关键词： Fire detection image segmentation feature extraction fruit fly optimization support vector machine

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Point-Snamae: Att-Masked Autoencoder Based on Self-Distillation And Normal Vector Estimation for Self-Supervised Point Cloud Representation Learning

SSRN

引用

SSRN 2024年

作者： Liu, Jie Yang, Mengna Tian, Yu Li, Yancui Song, Da Li, Kang Cao, Xin College of Computer and Information Engineering Henan Normal University Henan Xinxiang453007 China Big Data Engineering Laboratory for Teaching Resources & Assessment of Education Quality Henan Xinxiang453007 China School of Information Science and Technology Northwest University Shaanxi Xi’an China National and Local Joint Engineering Research Center for Cultural Heritage Digitization Shaanxi Xi’an710127 China Bioresource Engineering Department McGill University MontrealQC Canada

Masked signal modeling (MSM) has gained considerable attention in self-supervised learning. Considering that the existing masked strategies may cause the model to damage the structural or semantic understanding of key regions, we present an improved masked auto-encoder based on self-distillation and normal vector prediction that can augment the capacity to recognize local regions. Firstly, given a point cloud with a normal vector, our approach involves the grouping and embedding of point clouds lacking a corresponding normal vector. Secondly, based on self-distillation, the teacher branch guides the student branch to perform regional masking according to an embedded self-attention matrix. Due to the point cloud’s redundancy, the encoder utilizes a high ratio as input and greatly alleviates the input. At the same time, to prevent the model learning difficulty caused by too few inputs from affecting the robustness of model learning, our method only retains a small number of high score regions. Finally, the feature representation of the teacher-student branches is supervised by patch and global losses to comprehensively evaluate the learning performance of the mask patches. Taking normal vector estimation as a supervisory signal to improve global representation. Extensive experiments have shown that our approach is effective and well-transferable to a variety of downstream tasks. Specifically, our model achieves an accuracy of 94.12% on ModelNet40 and 87.16% on the PB-T50-RS setting of ScanObjectNN, outperforming other self-supervised learning methods. Our method achieved a remarkable accuracy of 98.4% in the 5-way 20-shot setting of Few Shot Learning. For part segmentation, our method achieves competitive results with other self-supervised methods. © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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Multi-Source Distributed Photovoltaic Anti-Islanding Protection Device Based on Impedance Angle Measurement

Multi-Source Distributed Photovoltaic Anti-Islanding Protect...

引用

Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Yongan Ju Shengzuo Liu Rui Zhang Shiyu Fu Kuo Chen Xiaobin Zhang Lei Wang Power Dispatching and Control Center State Grid Siping power supply company Siping Jilin China Science and technology digitization department State Grid Siping power supply company Siping Jilin China Electric power design institute State Grid Siping power supply company Siping Jilin China Substation Secondary System Maintenance Center State Grid Siping power supply company Siping Jilin China Ministry of Construction State Grid Siping power supply company Siping Jilin China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

With the increasing penetration of distributed generators (DGs) in distribution networks, the issue of unintentional islanding caused by grid outages has become increasingly severe. Impedance-based anti-islanding protection methods, which offer advantages such as small detection blind zones, low false operation rates, and minimal impact on power quality, have attracted significant attention. However, in multi-source distribution networks, variations in DG numbers and mutual interference among injected signals can lead to protection failure. This paper first designs an islanding identification criterion based on impedance angle measurement. Then, a “one-point multi-address” encoding scheme is adopted, where the tripping of any phase simultaneously acts on all receivers in the same district to disconnect photovoltaic (PV) stations. Field experiments demonstrate that the proposed method effectively avoids misoperation and refusal-to-operate issues in existing protections, eliminates potential risks of “islanding operation,” and resolves the long-standing “islanding” hazard in power systems.

关键词： Photovoltaic systems Technical requirements Islanding Impedance measurement Distribution networks Receivers Smart grids Impedance Protection Testing

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