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FDSC-YOLOv8:Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering

computer Modeling in Engineering & Sciences 2024年第9期140卷 3035-3049页

作者： Rui Wang Zhihui Liu Hongdi Liu Baozhong Su Chuanyi Ma Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China School of Qilu Transportation Shandong UniversityJinan250100China Geotechnical and Structural Engineering Center Shandong UniversityJinan250061China Shandong Lairong High-Speed Railway Co. Ltd.Weihai264200China Shandong High-Speed Group Co. Ltd.Jinan250014China

In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel ***,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack *** paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural ***,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional ***,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small ***,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak ***,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target *** FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.

关键词： Crack segmentation improved YOLOv8 engineering applications feature extraction

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E-MAEL: Efficient Multi-Cloud Data Auditing With Error Localization for IoT Consumer Devices in Metaverse

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Liu, Mingxi Fan, Kuan Lu, Ning Shi, Wenbo Alenazi, Mohammed J.F Chen, Chien-Ming Kumari, Saru Northeastern University at Qinhuangdao College of Computer Science and Engineering Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Shenyang China Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao China Northeastern University College of Computer Science and Engineering Shenyang China Northeastern University at Qinhuangdao sHebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao China Department of Computer Engineering Riyadh11451 Saudi Arabia Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Chaudhary Charan Singh University Department of Mathematics Uttar Pradesh Meerut India

By integrating multi-cloud (MC) storage with the "edge-to-cloud" architecture of consumer IoT, the metaverse paradigm achieves enhanced efficiency in real-time data collection and interactive virtual feedback. However, data stored across multiple clouds is vulnerable to tampering and theft due to the inherent insecurity of cloud computing, and cloud service providers, driven by economic interests, may conceal data losses. Existing data integrity auditing techniques fail to ensure prompt and reliable verification for distributed data in the MC-enabled metaverse paradigm, limiting their effectiveness in eliminating erroneous data swiftly. To address these challenges, we propose E-MAEL (Efficient Multi-cloud Data Auditing with Error Localization) for IoT devices in the metaverse. We design an MC self-auditing framework based on blockchain to eliminate audit delays caused by third-party auditors. A variable-length auditing protocol based on Schnorr is introduced, reducing computation overhead by up to 21% compared to schemes relying on bilinear pairing and RSA assumptions. We develop a single-round error localization strategy based on symmetric unbalanced block design, enabling efficient identification and removal of erroneous data while resisting collusion attacks. A dynamic reputation update strategy using convex functions is proposed, providing robust reputation management for MCs and effectively penalizing audit errors. Theoretical security analyses and experimental results demonstrate the effectiveness of EMAEL in improving computational efficiency and enhancing reliability in MC auditing systems. © 1975-2011 IEEE.

关键词： Cloud storage

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LAGNet: A Hybrid Deep Learning Model for Automatic Modulation Recognition

LAGNet: A Hybrid Deep Learning Model for Automatic Modulatio...

引用

IEEE Conference on Wireless Communications and networking

作者： Zhuo Li Guangyue Lu Yuxin Li Hao Zhou Huan Li Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Automatic Modulation Recognition (AMR) is becoming increasingly crucial in the industrial internet, and it ensures more reliable communication for devices within this vast and intricate network. Although recent application of neural networks, notably Graph Convolutional network (GCN) in the AMR domain shows promising results, it fails to account for temporal features within modulation signals, resulting in a loss of recognition precision. Given that modulation signals possess both time and space features, this paper proposes a hybrid deep learning model named LAGNet, which combines Long Short-Term Memory (LSTM) and GCN. The model uses an attention mechanism to map the LSTM outputs into a graph and then employs the GCN to extract the spatial features of the signal. Subsequently, the temporal features and the spatial features are combined together for modulation signal classification. Experimental results show that LAGNet not only outperforms several advanced models in classification accuracy across all signal-to-noise ratios, but also requires fewer learnable parameters.

关键词： Deep learning Accuracy Neural networks Modulation Pattern classification Feature extraction Reliability

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Smart Contract-Based Auditing of Edge Data for Vehicular networks

Smart Contract-Based Auditing of Edge Data for Vehicular Net...

引用

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

作者： Yu Zhao Yangguang Tian Chunbo Wang Xiaoqiang Di Hui Qi Jilin Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun China School of Computer Science and Electronic Engineering University of Surrey Guildford United Kingdom

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the development of vehicular networks and cloud-edge collaborative technologies, a large amount of vehicle data is collected at edge nodes (Edge Node, EN) for analysis and decision-making. However, edge data faces challenges in terms of integrity and security. Data owners (Data Owner, DO) should delegate auditors to periodically verify the integrity of the data. However, existing verification methods have not yet addressed issues related to verifiers forging evidence and fair payment. This paper proposes a smart contract-based edge data integrity verification scheme. An audit tree based on lattice hashing is designed, allowing the smart contract to initiate multiple verification challenges while only storing a complete label, thus reducing storage overhead. The homomorphic additivity of lattice hashing supports arbitrary data segmentation as challenges, effectively preventing EN from forging evidence. This scheme also designs two smart contract arbitration algorithms to ensure fair payment. Experimental comparisons show that this scheme effectively resolves the trust issues related to EN and ensures fair payment among EN, CSP, and DO.

关键词： Fabrication Cloud computing Data integrity Smart contracts Collaboration Lattices Security Sustainable development Faces Internet of Vehicles

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An SDN Oriented Loop Detection Mechanism Based on TTL Statistics

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China Communications 2020年第6期17卷 1-12页

作者： Tao Yu Longfei Yu Diyue Chen Hongyan Cui Jilong Wang Institute of Network Science and Cyberspace Tsinghua UniversityBeijing 100084China Beijing Advanced Information Network Key Laboratory School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China Network System Architecture and Integration Beijing Key Laboratory Beijing 100876China

The rise of the network has injected new impetus into the development of traditional *** to the complexity of the network itself and its network programmability,there is a risk of routing loops occurring in the SDN *** paper proposes a loop detection *** to the Time To Live(TTL)value of the loop packet,there is approximately periodicity in the same *** use sFlow to count the number of packets corresponding to each TTL value of a switch in the loop over a period of time,and perform discrete Fourier transform on the obtained finite-length sequence to observe its frequency domain performance and determine whether there are periodic *** doing so,it is determined whether there is a routing loop,and the purpose of passively detecting the routing loop is *** to existing algorithms,it has advantages in real-time,scalability and false positive *** experimental results show that the routing loop detection algorithm based on TTL statistics in this paper still maintains high judgment accuracy under the scenarios of lower stream sampling rate and smaller detection period.

关键词： SDN Loop Detection TTL sFlow

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Deep Video Cascade Clustering from Video Structures

SSRN

引用

SSRN 2024年

作者： Wang, Zihua Zhu, Hongyuan Mi, Siya Zhang, Yu Geng, Xin School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China A*STAR Singapore School of Cyber Science and Engineering Southeast University Nanjing211189 China Purple Mountain Laboratories Nanjing211189 China

Video clustering is a critical unsupervised learning task where, unlike in supervised video classification, category labels are not *** main challenge lies in learning an effective video representation without annotations to group similar *** this paper, we introduce a novel unsupervised video learning paradigm called Deep Video Cascade Clustering ($\text{DVC}^2$).Unlike existing image-based clustering methods, $\text{DVC}^2$ first learns an initial video representation through frame clustering, which serves as guidance. Subsequently, the video clustering results are aligned with both the long-term and short-term video structures and their nearest *** conduct a comprehensive evaluation of $\text{DVC}^2$ on benchmark video datasets, including UCF101 and Kinetic-400, and achieve state-of-the-art video clustering results. Additionally, we evaluate the performance of combining self-supervised learning with K-means *** findings validate the effectiveness of $\text{DVC}^2$ in unsupervised video clustering scenarios. © 2024, The Authors. All rights reserved.

关键词： Unsupervised learning

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A Computing and network Resource Aware Mapping Algorithm for MEC Systems Interconnected by Metro Optical networks

A Computing and Network Resource Aware Mapping Algorithm for...

引用

Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Jing Zhao Yuanmou Chen Yi Huang Cloud Network and Integration of Heaven and Earth Research Center of Institute of Strategic Development China Telecom Research Institute Beijing China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China

Aiming at the resource allocation in MEC system, a computing and network resource aware mapping algorithm (CN-ACO) is proposed, which can reduce the network blocking probability, improve the spectrum resource utilizat... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Optical interconnections Simulation Computational modeling Asia computer architecture Routing Load management Computational efficiency Resource management Photonics

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Evolutionary Multitasking Based on Team Learning Strategy 6

Evolutionary Multitasking Based on Team Learning Strategy

引用

6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Li, Wei Xu, Qingzheng Gao, Xinyu Wang, Lei Xi'an University of Technology School of Computer Science and Engineering Xi'an China National University of Defense Technology College of Information and Communication Wuhan China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665453110

Multi-factorial optimization (MFO) is a popular optimization mechanism recently, which aims to optimize multiple tasks in a single run. Generally, the multi-factorial optimization algorithm uses the random learning strategy to generate offspring, which leads to an inefficient exploration ability of the algorithm. To address this problem, this paper proposed a simple and effective strategy called team learning strategy (TLS). The proposed learning strategy divided the population into excellent team and ordinary team. Different teams employ different learning strategies. Moreover, nine MFO problems were used to assess the effectiveness of the proposed strategy. The experimental results show that the team learning strategy can improve the performance of the algorithm. © 2022 IEEE.

关键词： Evolutionary algorithms

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Experimental secure entanglement-free quantum remote sensing over 50 km of optical fiber

arXiv

引用

arXiv 2024年

作者： He, Wenjie Huang, Chunfeng Guan, Rui Chen, Ye Zhang, Zhenrong Wei, Kejin Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning530004 China

Secure quantum remote sensing (SQRS) uses quantum states to gather information about distant objects or environments while ensuring secure data transmission against eavesdropping. It has potential applications in various fields, including environmental monitoring, military surveillance, and disaster response, where both data accuracy and transmission security are critical. Recent experiments have demonstrated the feasibility of SQRS using entanglement states. Here, we experimentally demonstrate an SQRS that can estimate a phase without requiring entanglement, offering the practical advantage that single-qubit states are easier to prepare. We successfully estimate the preset phase information at a remote site over a fiber distance of 50 km, which serves as a key step toward long-distance applications. © 2024, CC BY-NC-ND.

关键词： Optical fibers

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Refrence-Frame-Independent Quantum key Distribution over 250 km of Optical Fiber

arXiv

引用

arXiv 2024年

作者： Liu, Xin Luo, Di Luo, Zhicheng Li, Shizhuo Zhang, Zhenrong Wei, Kejin Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning530004 China

The reference-frame-independent quantum key distribution (RFI QKD) protocol enables QKD systems to function effectively despite slowly varying reference frames, offering a distinct advantage in practical scenarios, particularly in mobile platforms. In this study, we successfully distribute secure key bits over a 250 km optical fiber distance by developing an RFI QKD system with a repetition rate of 150 MHz. Benefiting from high repetition rate, we achieve a finite-key secret key rate of 49.65 bit/s at a distance of 200 km, which is more than three times higher than state-of-the-art systems. Our work dramatically extends the transmission distance and enhances the secret key rate of RFI QKD, significantly promoting its practical application. © 2024, CC BY-NC-ND.

关键词： Optical fibers

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