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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： A. Vineela G. Lavanya Devi G. Vinay Deekshit Department of Computer Science and Engineering GMR Institute of Technology Rajam India Department of Computer Science and System Engineering AU College of Engineering(A) Vishakhapatnam India

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask.

关键词： Visualization Human factors Cameras Social factors Vaccines Object recognition IP networks

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Pricing Policy for a Dynamic Spectrum Allocation Scheme with Batch Requests and Impatient Packets in Cognitive Radio Networks

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Journal of systems science and systems engineering 2022年第2期31卷 133-149页

作者： Haixing Wu Shunfu Jin Wuyi Yue School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan UniversityQinhuangdao 066004China Department of Intelligence and Informatics Konan UniversityKobe 658-8501Japan

In cognitive radio networks(CRNs),multiple secondary users may send out requests simultaneously and one secondary user may send out multiple requests at one time,i.e.,request arrivals usually show an aggregate ***,a secondary user packet waiting in the buffer may leave the system due to impatience before it is transmitted,and this impatient behavior inevitably has an impact on the system *** to investigate the influence of the aggregate behavior of requests and the likelihood of impatience on a dynamic spectrum allocation scheme in CRNs,in this paper a batch arrival queueing model with possible reneging and potential transmission interruption is *** constructing a Markov chain and presenting a transition rate matrix,the steady-state distribution of the queueing model along with a dynamic spectrum allocation scheme is derived to analyze the stochastic behavior of the ***,some important performance measures such as the loss rate,the balk rate and the average delay of secondary user packets are ***,system experiments are carried out to show the change trends of the performance measures with respect to batch arrival rates of secondary user packets for different impatience parameters,different batch sizes of secondary user packets,and different arrival rates of primary user ***,a pricing policy for secondary users is presented and the dynamic spectrum allocation scheme is socially optimized.

关键词： Cognitive radio networks dynamic spectrum scheme batch arrival impatient packets Markov chain social optimization

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Few-Shot Gearbox Fault Diagnosis Based on Semi-Supervised Prototypical Network and Contrastive Learning

Few-Shot Gearbox Fault Diagnosis Based on Semi-Supervised Pr...

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2023 China Automation Congress, CAC 2023

作者： Cheng, Shulie Liang, Yingjie Ping, Zuowei Liao, Xuechao Wang, Maolin Zhang, Yong School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Naval University of Engineering National Key Laboratory of Science and Technology on Vessel Integrated Power System Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350303759

Due to the scarcity of labeled faulty data in industrial practice, fault diagnosis models often face challenges related to overfitting and limited accuracy. This article introduces a novel solution to tackle the problem - a fault diagnosis method that leverages a combination of a semi-supervised prototype network and contrastive learning. Firstly, a limited number of labeled samples is employed to construct pairs of positive and negative samples. And the training method of contrastive learning is used to provide appropriate initial parameters for the autoencoder. Secondly, the autoencoder is employed as a feature mapping function within the prototype network and obtains the prototype using a limited number of labeled samples. Finally, a prototype refinement method fine-tunes the prototype through unlabeled data, reducing the impact of anomalous data and obtaining a more stable and accurate prototype. The effectiveness of the proposed method is evaluated using gearbox data as a validation dataset. Through comparative analysis with other methods, the results demonstrate that the proposed approach achieves superior diagnostic accuracy. © 2023 IEEE.

关键词： Failure analysis

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EdgeThemis: Ensuring Model Integrity for Edge Intelligence 25

EdgeThemis: Ensuring Model Integrity for Edge Intelligence

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34th ACM Web Conference, WWW 2025

作者： Yang, Jiyu He, Qiang Zhou, Zheyu Dai, Xiaohai Chen, Feifei Tian, Cong Yang, Yun Swinburne University of Technology Melbourne Australia Huazhong University of Science and Technology Wuhan China Deakin University Melbourne Australia Xidian University Xi’an China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400712746

Machine learning (ML) models are widely deployed on edge nodes, such as mobile phones and edge servers, to power a wide range of AI applications over the web. Ensuring the integrity of these edge models is paramount, as they are subject to corruption caused by software/hardware exceptions and malicious tampering, which may undermine model performance, incur economic losses, and pose health risks. Existing data integrity mechanisms designed for files stored on disks cannot properly verify the integrity of models running in GPUs or mitigate the new integrity threats against edge models. This paper proposes EdgeThemis, a novel mechanism for verifying the integrity of edge models through sentinel verification. To enable verifiability for a model M, EdgeThemis embeds a sentinel backdoor and a verification module into M. Then, a challenger can send verification requests to the edge node hosting M to verify its integrity. Next, the sentinel activates the verification module to generate a unique integrity proof tied to the identity of the edge node for verification. Finally, the challenger can verify the integrity proof to detect model corruption. Theoretical analysis proves that EdgeThemis can properly mitigate potential integrity threats against edge models. Experiments demonstrate that EdgeThemis achieves a verification accuracy of 100.00% across various models and different types of model corruption with robustness against replay attacks, theft attacks, and replacement attacks. © 2025 Copyright held by the owner/author(s).

关键词： Backpropagation

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Quantitative Estimation of Pipeline Slope Disaster Risk in China

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International Journal of Disaster Risk science 2023年第2期14卷 298-312页

作者： Yan Yan Jiaojiao Zhou Cheng Xie Shuyao Yin Sheng Hu Renchao Wang Key Laboratory of High-Speed Railway Engineering Ministry of Education/School of Civil EngineeringSouthwest Jiaotong UniversityChengdu610031China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing100101China Pipe China South Branch Guangzhou510623China Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity Northwest UniversityXi’an 710127China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

China’s economic development is closely related to oil and gas resources,and the country is investing heavily in pipeline *** geological hazards seriously affect the long-term safe operation of buried pipelines,usually causing pipeline leakage,property and environmental losses,and adverse social *** ensure the safety of pipelines and reduce the probability of pipeline disasters,it is necessary to predict and quantitatively evaluate slope *** there has been much research focus in recent years on the evaluation of pipeline slope disasters and the stress calculation of pipelines under hazards,existing methods only provide information on the occurrence probability of slope events,not whether a slope disaster will lead to pipeline *** the 2015 Xinzhan landslide in Guizhou Province,China,as an example,this study used discrete elements to simulate landslide events and determine the risk level and scope for pipeline damage,and then established a pipe-soil coupling model to quantitatively evaluate the impact of landslide hazards for pipelines in medium-and high-risk *** results provide a reference for future pipeline disaster prevention and control.

关键词： China Coupled pipe-soil model Landslides Pipeline slope disasters Pipe stress Risk classification

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OpticE: A Coherence Theory-Based Model for Link Prediction 29

OpticE: A Coherence Theory-Based Model for Link Prediction

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29th International Conference on Computational Linguistics, COLING 2022

作者： Gui, Xiangyu Zhao, Feng Jin, Langjunqing Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs). During the learning process, the semantics of each entity are embedded by a vector or a point in a feature space. The distance between these points is a measure of semantic similarity. However, in a KG, while two entities may have similar semantics in some relations, they have different semantics in others. It is ambiguous to assign a fixed distance to depict the variant semantic similarity of entities. To alleviate the semantic ambiguity in KGs, we design a new embedding approach named OpticE, which is derived from the well-known physical phenomenon of optical interference. It is a lightweight and relation-adaptive model based on coherence theory, in which each entity’s semantics vary automatically regarding different relations. In addition, a unique negative sampling method is proposed to combine the multimapping properties and self-adversarial learning during the training process. The experimental results obtained on practical KG benchmarks show that the OpticE model, with elegant structures, can compete with existing link prediction methods. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Drone-Assisted Load Distribution Framework for Traffic Optimization in IoT Networks

Drone-Assisted Load Distribution Framework for Traffic Optim...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Priyanka Soni Omkar Manohar Mense Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Device connectivity has been redefined by the rapid development of the Internet of Things (IoT) technology, enabling diverse applications in areas such as smart cities, smart homes, and healthcare. These applications produce massive amounts of data, making it a significant challenge to offload tasks to cloud servers. However, the physical distance between devices often leads to latency issues for IoT systems, impacting time-sensitive applications. Fog computing tackles this issue by processing data closer to IoT devices. However, a high user density can strain the capacity of a macro base station, potentially leading to congestion. Drone Base Stations (DBSs) provide a flexible solution by acting as mobile relays to reduce latency and traffic burden. To address this issue, this work proposes a drone-assisted load distribution strategy in IoT networks. In our proposed approach, we deploy DBS efficiently using a greedy optimization technique. However, after the deployment of DBS, an optimal base station assignment framework dynamically connects user equipment to the most suitable base station, helping to minimize latency and enhance network performance. This dual-phase approach provides a scalable and practical solution for realtime IoT applications. Through extensive simulations, ensures that our proposed approach achieves a more balanced assignment compared to baseline algorithms.

关键词： Base stations Smart cities Scalability Smart homes Internet of Things Servers Relays Optimization Drones Strain

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Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

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computer systems science & engineering 2022年第12期43卷 1219-1230页

作者： Yanman Li Jun Zhang Yang Hu Yingnan Zhao Yi Cao NARI Nanjing Control System Ltd. Nanjing210032China School of Computer Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and computer Engineering University of WindsorWindsorN9B 3P4Canada

Safety helmet-wearing detection is an essential part of the intelligentmonitoring system. To improve the speed and accuracy of detection, especiallysmall targets and occluded objects, it presents a novel and efficient detectormodel. The underlying core algorithm of this model adopts the YOLOv5 (YouOnly Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (CompleteIntersection Over Union) Loss function, and the Mish activation function. First,it applies the attention mechanism in the feature extraction. The network can learnthe weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accuratebounding box regression. Third, it utilizes Mish activation function to improvedetection accuracy and generalization ability. It builds a safety helmet-wearingdetection data set containing more than 10,000 images collected from the Internetfor preprocessing. On the self-made helmet wearing test data set, the averageaccuracy of the helmet detection of the proposed algorithm is 96.7%, which is1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.

关键词： Safety helmet wearing detection object detection deep learning YOLOv5 Attention Mechanism

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Exploring Deep Insights from Vast Data: An Overview of Deep Learning Techniques for Big Data

Exploring Deep Insights from Vast Data: An Overview of Deep ...

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International Conference on Electrical engineering and computer science (ICECOS)

作者： Sarhad Baez Hasan Shahab Wahhab Kareem Didar Dlshad Hamad Ameen Computer Science Department Soran University Faculty of Science Soran Iraq Department of Information System Engineering Department of Computer Engineering Erbil Polytechnic University College of Engineering Knowledge University Erbil Iraq

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

Big data has emerged very fast, and this has brought both opportunities and problems that are related to the application of deep learning. This paper explores how deep learning can be implemented using big data and in particular the Apache Mahout library. Unlike most other machine learning tools, Mahout can perform distributed computation, especially while using the MapReduce frameworks including Apache Hadoop and Apache Spark. However, this study does not only focus on the explanation of the need for Mahout's integration and employment but also carries out a high-fidelity argument on how deep learning algorithms can be incorporated in the Mahout framework to optimize the manipulation of large and complicated datasets. Mahout-based contributions include the comparative analysis of key deep learning paradigms including distributed processing, parallel processing and transfer learning. This paper also includes examples of the Mahout deep learning integration use and its application in real contexts. This article describes the challenges that currently organizations face in handling big data and indeed, the potential threats these challenges pose to the success of deep learning techniques when used in organizations’ big data processing and analysis.

关键词： Deep learning Scalability Transfer learning Cluster computing Organizations Big Data Parallel processing Sparks Security Research and development

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High Energy Efficiency Dynamic Connected Hybrid Precoding for mm Wave Massive MIMO systems

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China Communications 2024年第5期21卷 36-44页

作者： Du Ruiyan Liu Huajing Li Tiangui Liu Fulai Lab of Key Technology of Millimeter-wave Large-scale MIMO System Northeastern University at QinhuangdaoQinhuangdao066004China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China

This paper considers a high energy efficiency dynamic connected(HEDC)structure,which promotes the practicability and reduces the power consumption of hybrid precoding system by lowresolution phase shifters(PSs).Based on the proposed structure,a new hybrid precoding algorithm is presented to optimize the energy efficiency,namely,HP-HEDC ***,via a new defined effective optimal precoding matrix,the problem of optimizing the analog switch precoding matrix is formulated as a sparse representation ***,the optimal analog switch precoding matrix can be readily obtained by the branch-and-bound ***,the digital precoding matrix optimization problem is modeled as a dictionary update problem and solved by the method of optimal direction(MOD).Finally,the diagonal entries of the analog PS precoding matrix are optimized by exhaustive search independently since PS and antenna is *** results show that the HEDC structure enjoys low power consumption and satisfactory spectral *** proposed algorithm presents at least 50%energy efficiency improvement compared with other algorithms when the PS resolution is set as 3-bit.

关键词： energy efficiency hybrid precoding mmWave optimized resolution phase shifter

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