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A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

Computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

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Deep Learning-Based Golf Swing Sequence Analysis 38

Deep Learning-Based Golf Swing Sequence Analysis

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38th IEEE Region 10 Conference, TENCON 2023

作者： Hajian, Amir Sookpreedee, Karit Phairoh, Kingrak Ruangsang, Watchara Aramvith, Supavadee Chulalongkorn University Department of Electrical Engineering Bangkok Thailand Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Faculty of Engineering Department of Electrical Engineering Bangkok10330 Thailand

ISBN: (纸本)9798350302196

Golf is widely recognized as one of the most popular sports globally. However, one drawback of playing golf is the relatively high cost of equipment and coaching. While numerous training programs are available to assist players in their practice, there is currently no swing analysis program developed by Thai professionals. In this project, advanced deep learning models were employed: SwingNet, capable of predicting the sequence of eight golf swing events in videos and determining the confidence level of each swing, and MoveN et, designed to identify joint positions on the body and represent them as skeletons. These models were integrated into a customized template-matching algorithm that utilized angle-based measurements to analyze the sequence of golf swings. This analysis assessed the similarity score, represented as a percentage, between two individuals for each golf swing event. Furthermore, various techniques were implemented to enhance the efficiency of SwingN et. Through performance evaluation, it was observed that the efficiency of SwingN et surpassed by one percent compared to the pre-trained model. © 2023 IEEE.

关键词： Golf

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Big OLAP data Cube Compression Algorithms in Column-Oriented Cloud/Edge data Infrastructures 9

Big OLAP Data Cube Compression Algorithms in Column-Oriented...

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9th IEEE International Conference on Multimedia Big data, BigMM 2023

作者： Cuzzocrea, Alfredo University of Calabria The Big Data Engineering and Analytics Laboratory Rende Italy University of Paris City Dept. of Computer Science Paris France

This paper provides a brief overview on the innovative problem of devising and implementing big OLAP data cube compression algorithms in column-oriented Cloud/Edge data infrastructures, an emerging need for next-gener... 详细信息

ISBN: (纸本)9798350360004

关键词： data analytics

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Minu, M.S. Talapalli, Srikar Chevuri, Saahithi Medhasri, T.S. SRM Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science Engineering with Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331542344

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience. © 2024 IEEE.

关键词： Collaborative filtering

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A Method of Emergency Volunteer Team Internal Participation in Rescue Decision-Making Considering Psychological Behavior

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Journal of Systems Science and Information 2024年第1期12卷 64-80页

作者： Jie BAI Shengqun CHEN College of Computer and Information Sciences Fujian Agriculture and Forestry UniversityFuzhou 350002China School of Information Engineering Fujian Business UniversityFuzhou 350506China Engineering Research Center of Big Data Analytics for Business Intelligence Fujian Province UniversityFuzhou 350506China

A method for internal participation in rescue decision-making of emergency volunteer teams considering psychological behavior is proposed to address the time sequence of rescue ***,the problem of multi-tasking and multi-operation within the emergency volunteer team is ***,considering that task leaders are influenced by behavioral and psychological factors in the evaluation,the required time for the job is used as a reference point,and the expected time that volunteers can complete the job is used as an attribute *** task leader's prospect satisfaction value for each volunteer is calculated based on prospect theory,and the perceived utility values of disappointment theory and regret theory are calculated to measure the task leader's satisfaction with each ***,a multilayer coded genetic algorithm is used to construct an optimization model for emergency volunteer decision-making with the objective of maximizing the satisfaction ***,the feasibility and effectiveness of this method are illustrated by an example *** result shows that the efficiency of rescue tasks can be improved through decision optimization within the volunteer team.

关键词： emergency volunteers rescue task multi-layer coding genetic algorithm satisfaction value psychological behavior

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A Collaborative Trajectory-Oriented Viewport Prediction for on-Demand and Live 360° VR Video Streaming 20

A Collaborative Trajectory-Oriented Viewport Prediction for ...

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20th IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2023

作者： Yaqoob, Abid Muntean, Gabriel-Miro School of Electronic Engineering Dublin City University Performance Engineering Lab and Insight Sfi Centre for Data Analytics Ireland School of Electronic Engineering Dublin City University Performance Engineering Lab Ireland

ISBN: (纸本)9798350331110

Viewport prediction is critical in delivering high-quality 360° virtual reality (VR) videos to a large audience across diverse networks. Traditional computationally expensive viewport prediction mechanisms that rely on static content analysis and individual viewing preferences have proven inadequate in adapting to dynamic content and user preferences. This paper introduces TOPVR, a novel trajectory-oriented viewport prediction approach for both on-demand and live 360° VR video streaming. TOPVR overcomes the challenges introduced by diverse user preferences by leveraging the collaborative behavior of viewers and smartly capturing the relationship between different trajectories. It incorporates a user management system that identifies potential users with similar preferences and the closest trajectory changes to the current user over time. Additionally, a collaborative viewport prediction method estimates future viewing positions for each user based on recent viewing information and similar trajectories and trend changes of other users watching the same content. We evaluate the performance of TOPVR using a real-world head movement dataset recorded using on-demand and live streaming experiments. Our experimental results demonstrate that TOPVR, with its viewer management system and collaborative trajectory prediction mechanisms, outperforms existing benchmark algorithms in terms of higher prediction performance. © 2023 IEEE.

关键词： Forecasting

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Numerical prediction of ureter stone size using an integrated CFD-ML approach

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Neural Computing and Applications 2025年第7期37卷 5325-5341页

作者： Ashraf, Muhammad Mubashar Kamal, Khurram Fahad, Muhammad Noor, N.F.M. Ratlamwala, Tahir Abdul Hussain Islamabad Pakistan College of Science and Engineering Hamad bin Khalifa University Doha Qatar Institute of Mathematical Sciences Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Center for Data Analytics Consultancy & Services Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia

Ureteral flow parameters provide significant details about its physical attributes. Ureter is a single transport medium for urine transmission from kidney to ureter and its health is very important for a healthy human body. Understanding the fluid flow behavior can contribute toward the ureter health monitoring including estimation of any kind of blockage in the flow. Using ANSYS Fluent, Computational Fluid Dynamics (CFD) analysis and the grid independence study are carried out through iterative simulation process to achieve the solution independence. The CFD modeling provides tools and techniques to observe varying fluid parameters such as pressure, velocity and effect of the flow on smooth walls. Fluid Structure Interaction (FSI), an effective technique to analyze the effects of such flows on the ureter walls is also employed. Although the exact modeling of the ureter wall is not possible due to its complex physical parameters, some of its available physiological properties can be used to visualize the model of the ureter numerically. The present study is intended to predict the ureter stone size by using the FSI analysis. The simulations are carried out by increasing the stone size gradually from 1.7 to 3.4 mm and the input flow parameters are compared with the output flow parameters within the same solution setup and boundary conditions via artificial neural network in MATLAB. The output results obtained from the FSI simulations are then utilized to generate a prediction model for the ureter stone size. It is observed that the increasing stone size has a significant effect on the ureter wall, causing high stress regions in the point of interaction. The findings also revealed that the predicted size of the ureter stone is the closest to the actual size and with the least mean squared error at 80 optimal neurons. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computational fluid dynamics

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data-Driven Analysis of Climate Change Impacts on Public Health: A Systematic Review and Future Directions

Data-Driven Analysis of Climate Change Impacts on Public Hea...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Yarmohammadtoosky, Sahar Xie, Ying Kennesaw State University School of Data Science and Analytics MariettaGA United States Kennesaw State University College of Computing and Software Engineering MariettaGA United States

ISBN: (纸本)9798350362480

Climate change profoundly impacts human health, ecosystems, and food and water security. This review provides a comprehensive analysis of climate change's effects on human health using data science methods, including statistical and machine learning models, to assess current knowledge. It identifies critical gaps in data collection and methodological limitations across different regions and climates. Key recommendations include improving data resolution and duration to enhance model accuracy, as well as fostering collaboration between data scientists, public health experts, and climate specialists. The review underscores the need for innovative, data-driven models to better predict and address the health impacts of climate change. © 2024 IEEE.

关键词： Electronic health record

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Influence of Bay Span Length on Near-Field Seismic Response of Steel Moment Resisting Frames 2nd

Influence of Bay Span Length on Near-Field Seismic Response ...

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2nd International Conference on Materials, Mechanics and Structures, ICMMS 2022

作者： Kottalage, Bahubali Jayapal Pandikkadavath, Muhamed Safeer Mangalathu, Sujith Department of Civil Engineering National Institute of Technology Calicut Calicut India Data Analytics Division Equifax Inc AtlantaGA United States

ISBN: (纸本)9789811933707

Near-field seismic disturbances having high-frequency content along with large magnitude velocity pulses are more detrimental to *** present study investigates the near-field seismic response of steel moment-resisting frames designed using equivalent lateral force method for different bay span *** two-dimensional study frames are developed in the OpenSees modelling platform by incorporating relevant inelastic parameters, and nonlinear time history analyses are carried out under a suite of near-field pulse-type ground motion *** seismic responses of the case study frames are evaluated in terms of inter-storey drift ratio and residual drift *** seismic demand models are successively derived, allowing the definition of fragility curves for four conventional damage *** results obtained for the different case study structures are compared and critically discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Seismic response

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IOTM: Iterative Optimization Trigger Method - A Runtime data-Free Backdoor Attacks on Deep Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4562-4573页

作者： Arshad, Iram Alsamhi, Saeed Hamood Qiao, Yuansong Lee, Brian Ye, Yuhang Technological University of Shannon Midlands Midwest N37 HD68 Ireland University of Galway Insight Centre for Data Analytics GalwayH91 TK33 Ireland Ibb University Faculty of Engineering Ibb70270 Yemen

Deep neural networks are susceptible to various backdoor attacks, such as training time attacks, where the attacker can inject a trigger pattern into a small portion of the dataset to control the model's predictions at runtime. Backdoor attacks are dangerous because they do not degrade the model's performance. This article explores the feasibility of a new type of backdoor attack, a data-free backdoor. Unlike traditional backdoor attacks that require poisoning data and injection during training, our approach, the iterative optimization trigger method (IOTM), enables trigger generation without compromising the integrity of the models and datasets. We propose an attack based on an IOTM technique, guided by an adaptive trigger generator (ATG) and employing a custom objective function. ATG dynamically refines the trigger using feedback from the model's predictions. We empirically evaluated the effectiveness of IOTM with three deep learning models (CNN, VGG16, and ResNet18) using the CIFAR10 dataset. The achieved runtime-attack success rate (R-ASR) varies across different classes. For some classes, the R-ASR reached 100%;whereas, for others, it reached 62%. Furthermore, we conducted an ablation study to investigate critical factors in the runtime backdoor, including optimizer, weight, 'REG,' and trigger visibility on R-ASR using the CIFAR100 dataset. We observed significant variations in the R-ASR by changing the optimizer, including Adam and SGD, with and without momentum. The R-ASR reached 81.25% with the Adam optimizer, whereas the SGD with momentum and without results reached 46.87% and 3.12%, respectively. © 2020 IEEE.

关键词： Iterative methods

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