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23rd IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2024

作者： Sarvesh, Muskan Kang, Ryan Nam, Hyeongil Park, Simon Hugo, Ron Maurer, Frank Kim, Kangsoo University of Calgary Department of Electrical and Software Engineering Canada University of Calgary Department of Mechanical and Manufacturing Engineering Canada University of Calgary Department of Computer Science Canada

ISBN: (纸本)9798331516475

Immersive and simulation technologies, such as Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), and Digital Twins (DTs), have proven effective in enhancing decision-making, streamlining operations, and improving safety through intuitive visualizations. Unsurprisingly, the oil and gas (O&G) pipeline industry is increasingly turning to these technologies to tackle complex construction and resource management challenges. This paper reviews the applications, benefits, and future trends of these technologies, focusing on integration and emerging trends in monitoring, training, maintenance, and testing. It identifies potential gaps, offers guidance for future research, and emphasizes adapting to technological evolution, providing insights to improve system reliability and sustainability in O&G pipeline operations using immersion and simulation methods. © 2024 IEEE.

关键词： Gas pipelines

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A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

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computer Modeling in engineering & Sciences 2024年第7期140卷 907-927页

作者： Yong Ma Han Zhao Kunyin Guo Yunni Xia Xu Wang Xianhua Niu Dongge Zhu Yumin Dong School of Computer Information Engineering Jiangxi Normal UniversityNanchang330000China School of Digital Industry Jiangxi Normal UniversityShangrao334000China The College of Computer Science Chongqing UniversityChongqing400044China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Computer and Software Engineering Xihua UniversityChengdu610039China Electric Power Research Institute of State Grid Ningxia Electric Power Company Ltd. Yinchuan750002China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a technology designed for the on-demand provisioning of computing and storage services,strategically positioned close to *** the MEC environment,frequently accessed content can be deployed and cached on edge servers to optimize the efficiency of content delivery,ultimately enhancing the quality of the user ***,due to the typical placement of edge devices and nodes at the network’s periphery,these components may face various potential fault tolerance challenges,including network instability,device failures,and resource *** the dynamic nature ofMEC,making high-quality content caching decisions for real-time mobile applications,especially those sensitive to latency,by effectively utilizing mobility information,continues to be a significant *** response to this challenge,this paper introduces FT-MAACC,a mobility-aware caching solution grounded in multi-agent deep reinforcement learning and equipped with fault tolerance *** approach comprehensively integrates content adaptivity algorithms to evaluate the priority of highly user-adaptive cached ***,it relies on collaborative caching strategies based onmulti-agent deep reinforcement learningmodels and establishes a fault-tolerancemodel to ensure the system’s reliability,availability,and *** results unequivocally demonstrate that FTMAACC outperforms its peer methods in cache hit rates and transmission latency.

关键词： Mobile edge networks mobility fault tolerance cooperative caching multi-agent deep reinforcement learning content prediction

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Impacts of Earthing Variations on Overvoltage in Meshed Multi-terminal DC Grid 50

Impacts of Earthing Variations on Overvoltage in Meshed Mult...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bader, Amir Ar-Rahman Ukil, Abhisek Dept. of Engineering Science The University of Auckland Auckland New Zealand Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (纸本)9781665464543

This paper investigates the impact of different earthing methods on overvoltage within modular multilevel Voltage Source Converter (MMC VSC) based Direct Current (DC) transmission and distribution grids, particularly in meshed multi-terminal (MTDC) configurations. A bipolar MTDC grid is modelled and simulated in PSCAD/EMTDC to investigate the influence of earthing on midpoint overvoltages. The transient response of the grid under DC fault scenarios is analysed through various case-studies, including solid earthing placement, variations in earthing impedance, and the application of surge arresters. The findings show the critical role of earthing in mitigating overvoltage, with low-resistive earthing options proving to be the most effective trade-off. © 2024 IEEE.

关键词： bipolar dedicated metallic return earthing meshed MTDC overvoltage

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PaddyShield: A CNN-Based Rice Leaf Diseases Detection and Management System 8

PaddyShield: A CNN-Based Rice Leaf Diseases Detection and Ma...

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8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Chathuranga, Shalika Subhashini, Pavithra Ekanayake, Jeeva NSBM Green University Department of Software Engineering Colombo Sri Lanka The Open University of Sri Lanka Electrical and Computer Engineering Department Nawala Sri Lanka

ISBN: (纸本)9798331509170

Sri Lankan paddy farmers face numerous challenges due to limited access to resources and knowledge of pests and diseases effectively. This often leads to wasting money on pesticides without addressing the problem, affecting crop yield. Existing systems excel at swift disease detection but do not acknowledge farmers in preventing and mitigating diseases. Farmers expect information on the diseases and suitable mechanisms to get rid of the diseases. Further, we conducted a pre-survey with paddy farmers in the Anuradhapura district. We understood the importance of acknowledging the pests and diseases and the methods to mitigate them. Therefore, we developed a 'PaddyShield' system to identify diseases and provide comprehensive disease details on prevention mechanisms for the identified diseases through rice leaf. The 'PaddyShield' system was implemented after employing the sophisticated Convolutional Neural Network (CNN) as the linchpin for precise disease detection. The system's strength lies in its adept handling of a diverse dataset encompassing various paddy leaf diseases, ensuring accuracy (95%) in early disease identification. Through rigorous testing, the system indisputably highlights its superiority over existing solutions. Accessibility is prioritized through a user-friendly Flaskpowered interface. Overcoming hurdles like data preprocessing and model complexity with inventive solutions, the system achieves seamless backend-frontend integration. Optimal GPU resource utilization further accentuates its efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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A Deep Reinforcement Learning based VM Scheduling Strategy Decreasing Data Center Communication costs 24

A Deep Reinforcement Learning based VM Scheduling Strategy D...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xiao, Zhijiao Liu, Xiao Ming, Zhong Software Engineering Research Center College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798350319934

With the rapid development of cloud computing, more and more complicated services are deployed on the cloud. The frequent communication between services places a great burden on the network of the data center and leads to a decline in the quality of service. Researches have shown that the costs of communication between services vary with the placements of virtual machines on which services are running. Therefore, the virtual machine strategy should be optimized by migrating virtual machines with high communication frequency closer. Based on fat-tree network topology, a Deep Q network based online virtual machines scheduling strategy(DQN-VMS) is proposed. The objective is to obtain an optimized virtual machine scheduling strategy that brings down communication costs while guaranteeing the quality of service. The calculation model for communication costs is built for the fat-tree network topology. A reward function that takes into account the communication costs and Service Level Agreements violation(SLAV) rate is designed. The experiment shows that the proposed strategy performs about 26.81% and 40.55% better in comparison with two virtual machine scheduling strategies based on graph partition algorithm and greedy algorithm, which are commonly used in existing data centers. © 2022 IEEE.

关键词： Virtual machine

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Review of Power Grid Optimization Scheduling Methods Based On Deep Reinforcement Learning 7

Review of Power Grid Optimization Scheduling Methods Based O...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Zherui, Yang Zhen, Zhang Fangzhou, Li Nan, Dai Yiying, Yan Ke, Wang Dayu College Hohai University China College of Energy and Electrical Engineering Hohai University China College of Computer Science and Software Engineering Hohai University China

ISBN: (纸本)9798350359558

In recent years, with the introduction of energy conservation and emission reduction targets, the introduction of renewable energy has become an important initiative. However, due to the many factors that affect the new energy units, the traditional scheduling method becomes inappropriate. If a more intelligent scheduling method is not developed, the lack of flexible energy scheduling capacity will further lead to problems such as insufficient wind power consumption level, inefficient power grid decision-making, high operating costs, and unstable energy supply. In order to solve the above problems and improve the environmental protection, efficiency, economy and safety of the power grid, this paper summarizes the application of artificial intelligence in the field of power grid scheduling, analyzes SAC, A3C and TD3 algorithms in detail, analyzes their mechanism through the algorithm structure, and summarizes the advantages and disadvantages of each algorithm and its application scenarios. In addition, this paper also analyzes the application of artificial intelligence in unit combination, economic scheduling optimization and optimal power flow optimization. Different from other review papers, this paper deeply analyzes several problems that may arise in the application of the algorithm, and specifically analyzes the future optimization direction and corresponding optimization methods, so as to improve the level of experience combination, fault handling, comprehensive consideration, interpretability and other aspects of the future agent. © 2024 IEEE.

关键词： Reinforcement learning

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Hybrid Deep Learning Framework for Stock Price Prediction Incorporating Technical and Macroeconomic Indicators 9

Hybrid Deep Learning Framework for Stock Price Prediction In...

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9th International Conference on computer Science and engineering, UBMK 2024

作者： Turan, Ali Can Kokach, Vael Kocaçinar, Büşra Şengel, Öznur Akbulut, Fatma Patlar Istanbul Kültür University Department of Computer Engineering Istanbul Turkey Istanbul Kültür University Department of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350365887

The complicated interaction of several elements makes accurate stock price prediction still difficult. This study suggests a hybrid deep learning model to improve stock market prediction by integrating Bidirectional Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNNs). Incorporating both technical and macroeconomic variables helps the model to adequately identify complex trends and interconnections inside the financial data. While the bidirectional LSTM models temporal dependencies, the CNN component shines in retrieving pertinent features from the input data. Experimental findings show that the suggested model beats conventional approaches, therefore offering traders and investors important new perspectives. © 2024 IEEE.

关键词： Prediction models

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NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework 24

NNVISR: Bring Neural Network Video Interpolation and Super R...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Tong, Yuan Hu, Mengshun Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

We present NNVISR - an open-source filter plugin for the VapourSynth video processing framework, which facilitates the application of neural networks for various kinds of video enhancing tasks, including denoising, super resolution, interpolation, and spatio-temporal super-resolution. NNVISR fills the gap between video enhancement neural networks and video processing pipelines, by accepting any network that enhances a group of frames, and handling all other network agnostic details during video processing. NNVISR is publicly released at https://***/tongyuantongyu/vs-NNVISR. © 2024 ACM.

关键词： Open systems

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Survival Prediction of Glioma Patients using Machine Learning

Survival Prediction of Glioma Patients using Machine Learnin...

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Fadlallah, Nouran Boluk, Pinar Sarisaray Akyuz, Sureyya Department of Computer Engineering Bahcesehir University Istanbul Turkey Department of Software Engineering Bahcesehir University Istanbul Turkey Department of Mathematics Bahcesehir University Istanbul Turkey

ISBN: (纸本)9798350379433

Gliomas are one of the most common types of brain tumors with varying prognoses, treatment responses, and clinical outcomes. Predicting the survival of patients with glioma is a challenging task due to the complexity and variety of parameters that contribute to the outcome, such as genetic, molecular, and clinical factors. Machine learning has been increasingly used in medical applications in recent years due to its ability to understand or approximate complex relationships of multidimensional data. In this work, we aim to predict the survival of glioma patients by training and comparing the performance of different machine-learning approaches on multi-feature patient data. Our tests included simple models such as support vector machines SVMs and decision trees, as well as multiple ensemble models. Using a small private dataset of 81 samples, our best-performing models: Random Forest, and Stacking classifier achieve an accuracy of 0.97, f1-score of 0.97, precision of 0.97, and recall of 0.97. Finally, we utilize explainability methods to further describe the effects of different features of patient data on the predicted outcome. © 2024 IEEE.

关键词： Support vector machines

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Triplet MAML for Few-Shot Classification Problems 2nd

Triplet MAML for Few-Shot Classification Problems

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2nd International Conference on Advanced engineering, Technology and Applications, ICAETA 2023

作者： Gülcü, Ayla Özkan, İsmail Taha Samed Kuş, Zeki Karakuş, Osman Furkan Department of Software Engineering Bahçeşehir University Istanbul Turkey Department of Computer Engineering Bahçeşehir University Istanbul Turkey Department of Computer Engineering Fatih Sultan Mehmet Vakif University Istanbul Turkey Department of Computer Engineering Yıldız Technical University Istanbul Turkey

ISBN: (纸本)9783031509193

In this study, we propose a TripletMAML algorithm as an extension to Model-Agnostic Meta-Learning (MAML) which is the most widely-used optimization-based meta-learning algorithm. We approach MAML from a metric-learning perspective and train it using meta-learning tasks composed of triplets of images. The idea of meta-learning is preserved while generating the meta-learning tasks and training our novel meta-model. The experimental results obtained on four few-shot classification datasets show that TripletMAML that is trained using a combined loss yields in high quality results. We compared the performance of TripletMAML to several metric learning-based methods and a baseline method, in addition to MAML. For fair comparison, we used the reported results of those algorithms that were obtained using the same shallow backbone. The results show that TripletMAML improves MAML by a large margin, and yields better results than most of the compared algorithms in both 1-shot and 5-shot settings. Moreover, when we consider the classification performance of other meta-learning algorithms that use much deeper backbones, we conclude that TripletMAML is not only competitive in terms of the classification performance but also very efficient in terms of the complexity. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Few-Shot Image Classification MAML Meta-learning Metric Learning Triplet Networks

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