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Fairness-Aware Beam Placement Scheduling for LEO Satellites

Fairness-Aware Beam Placement Scheduling for LEO Satellites

引用

2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Eftetahi, Pooria Seyed Cai, Lin Sepahi, Amir University of Victoria Dept. Electrical and Computer Engineering VictoriaBC Canada

ISBN: (纸本)9798350362312

One of the primary objectives of the next-generation communication systems is to establish ubiquitous coverage. In this regard, Low Earth Orbit (LEO) satellite networks have attracted significant attention as a promising technology to achieve global coverage. However, fairly and efficiently managing resource allocation, especially beam placement scheduling, poses a significant challenge due to the large number of ground terminals and the vast coverage of LEO satellites. To address this challenge, we model the beam placement scheduling as a problem of minimizing terminal payload at the LEO satellite while considering fairness among terminals. In our proposed solution, we divide the coverage area into segments and use the advantage actor-critic (A2C) to schedule beams within each segment. Our simulations demonstrate that our approach outperforms the greedy method in terms of both throughput and fairness while improving first-time access to the LEO satellite for terminals. Moreover, it outperforms round-robin (RR) scheduling in terms of throughput. © 2024 IEEE.

关键词： Satellite communication systems

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Controller Design for Water Treatment Plants Using Data-Driven Control 50

Controller Design for Water Treatment Plants Using Data-Driv...

引用

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

作者： Furukawa, Masaki Fujimoto, Yasutaka Dept. of Electrical and Computer Engineering Yokohama National University Yokohama Japan

ISBN: (纸本)9781665464543

This study explores methods for improving energy efficiency in wastewater treatment facilities to address the pressure of rising electricity tariffs. As the energy consumption by water treatment facilities accounting for about 50% of the total electricity consumption in wastewater treatment facilities, particular attention is paid to the energy utilization of blowers within water treatment facilities [1], [2]. The aim is to achieve appropriate control of aeration equipment based on the facility’s conditions and environment, without relying on plant models but using data-driven control. This approach addresses the problem of excessive electricity consumption in current scenarios that rely on manual control or simple control loops. By employing VRFT for the PID controller parameter tuning, a successful improvement in the target response performance was achieved. The proposed method achieved an error in the DO response comparable to that of conventional methods and reduced the operating costs of aeration at wastewater treatment plants by about 12%. © 2024 IEEE.

关键词： data-driven control virtual reference feedback tuning wastewater treatment plant

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BDA: Blockchain-Based Decentralized Authentication System for Autonomous Vehicle Under Ad-Hoc Network

BDA: Blockchain-Based Decentralized Authentication System fo...

引用

2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Bae, Ji-Sung Seo, Seung-Woo Seoul National University Dept. of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350371888

Autonomous driving technology has made substantial development in various platforms and fields. However, a single mistake of an autonomous vehicle can lead to a severe accident or a life-threatening event. Protecting autonomous vehicle data must be considered and guarantee real-time data management, robust security systems, and safe communication systems between vehicle nodes. Since these features can be satisfied by blockchain-based algorithms, combining these two big main themes will make a safer and secure connected-vehicle (CV) system. In this paper we propose a decentralized authentication system in order to make a robust and safe autonomous driving environment. © 2024 IEEE.

关键词： Blockchain

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An Analysis of Library Usage in the C++ Code Base of Fedora Linux 37

An Analysis of Library Usage in the C++ Code Base of Fedora ...

引用

2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Deng, Jiachao Adams, Michael D. University of Victoria Dept. of Electrical and Computer Engineering VictoriaBC Canada

ISBN: (纸本)9798350362312

Several aspects of library usage in C++ are explored. A framework is proposed for analyzing the C++ code base of Linux distributions that employ the DNF package manager, such as Fedora Linux and Red Hat Enterprise Linux. This framework is used in conjunction with a C++ source-code analysis tool (developed by the authors) to study library usage with a fine level of granularity, considering instances of uses of types, type aliases, member/nonmember functions, variables, and enumerators. The effectiveness of our proposed framework and C++ analysis tool is demonstrated by applying them to the C++ code base of Fedora Linux 37. Using this approach, we were able to analyze library usage in the C++ source code of over 2300 software packages. Numerous observations are made about various aspects of library usage that can facilitate improved teaching of C++, allow more effective design/implementation of C++ libraries, and help to direct the future evolution of the C++ standard. © 2024 IEEE.

关键词： Linux

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An Interpretable Framework for Predicting Type 2 Diabetes using ML and Explainable AI 26

An Interpretable Framework for Predicting Type 2 Diabetes us...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahmed, Kazi Fardeen Uz Zaman, Md Shahid Peyal, Hasibul Islam Hossain, Anika Rahman Ratul, Md. Toyaha Abdal, Md. Nazmul Islam, Md. Injamul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Uttara University Dept. of Computer Science & Engineering Bangladesh Khulna University Dept. of Computer Science & Engineering Discipline Bangladesh

ISBN: (纸本)9798350359015

Diabetes Mellitus is an incurable disease and stands as a major universe cause of destruction. With the universal prevalence of diabetes increasing rapidly, accurate detection and identification of the disease have become crucial. In this paper, we present an explainable AI-based diabetes indicative methodology that is carefully constructed, effective, and, most importantly, interpretable. Using two real-world diabetes datasets, the three most well-known machine learning classifiers in the literature - Random Forest (RF), Decision Tree (DT), and XGBoost - were subjected with quantitative assessments. After training and assessment of all classification models, the suggested method achieved the best results in the XGBoost classifier for the Sylhet dataset with 99.4% accuracy and the Pima Indian dataset with 92.59% accuracy. The datasets were normalized across observations to provide standardized values for improved analytical applicability and comparability. Explainable AI has been implemented for the XGBoost machine learning model by generating both global and local explanations using Shapley additive explanations (SHAP) and Local interpretable model-agnostic explanations (LIME). The elements that contribute to diabetes are explained and shown in graphs to help medical professionals make decisions about clinical diagnosis and treatment options. © 2023 IEEE.

关键词： Decision Tree (DT) Diabetes diabetes diagnosis explainable AI interpretable LIME Pima Indian dataset Random Forest (RF) SHAP Sylhet dataset XGBoost

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Use of a Hybrid Storage System for Frequency Regulation Services from a Photovoltaic Plant: Emulating Synchronous Generator Dynamics 3

Use of a Hybrid Storage System for Frequency Regulation Serv...

引用

3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Papageorgiou, Pavlos G. Oureilidis, Konstantinos O. Christoforidis, Georgios C. University of Western Macedonia Dept. of Electrical & Computer Engineering Kozani Greece

ISBN: (纸本)9798350375923

The gradual decommissioning of grid-forming synchronous generators and the increasing penetration of grid-following renewable energy sources such as photovoltaics (PVs), undermine the reliable operation of the grid. In this sense, even small power imbalances can cause significant frequency deviations. To address such issues, the employment of standalone battery storage systems is usually proposed to alleviate the power imbalances by releasing/absorbing energy when needed. However, a standalone battery suffers from quick lifetime fading and it cannot effectively provide multiple grid services simultaneously. To this end, this study presents a controller for a hybrid storage system that consists of a power-type superconducting magnetic energy storage (SMES) and an energy-type battery. The hybrid storage system is AC-coupled and provides frequency regulation services on behalf of the PV plant without any modification on its inverter. Through proper control, SMES handles the short-term power fluctuations emulating the inertial properties of a synchronous generator, while battery cope with long-term demands imitating the primary frequency response of the generator. Thus, the distinct features of both SMES and battery are effectively exploited, ensuring minimal stress on battery. To evaluate the proposed method during grid frequency events, simulation results are presented. © 2024 IEEE.

关键词： Synchronous generators

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State of the Art and Innovations needed for Direct Radiating Arrays to place Very High Throughput Satellite Communications into Technology Reach 5

State of the Art and Innovations needed for Direct Radiating...

引用

5th IEEE International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Kaifas, Theodoros N. F. Democritus University of Thrace Dept. of Electrical and Computer Engineering Xanthi Greece

ISBN: (纸本)9798350352863

The current work provides the roadmap towards the advent of Very High Throughput Satellite Communications. The key enabler will be the envisioned and presented in detail placement of the Direct Radiating Arrays vision into the reach of the technology. Such an endeavor entails the proper choice of the multi-element electrically large array's geometry as well as the choice on the element radiator. Those choices should support and implement the overlapping concept in hardware. At the same time, they should leave room for advanced array synthesis techniques to unfold their full potential. Those issues are treated in detail, and proper emphasis is given both on the state of the art as well as to the innovations needed to increase the current relative technological readiness level. © 2024 IEEE.

关键词： Satellite communication systems

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Single Object Tracking in Satellite Videos Through Deep Learning and Motion Detection Techniques 6

Single Object Tracking in Satellite Videos Through Deep Lear...

引用

6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Hayder, Israa Alizadeh, Fattah Dept. of Computer Science and Engineering University of Kurdistan Hawlere Erbil Iraq Dept. of computer science Toronto Metropolitan university Toronto Canada

ISBN: (纸本)9798350394634

Single object tracking in satellite videos has recently gained a lot of attention in the field of computer vision. Although the satellite images are very informative, the small size of the objects and limited spatial and temporal resolution might make object tracking difficult and inaccurate. In this paper, we propose a method for accurately tracking small objects in satellite videos. In the initial phase, we applied the Fast Super-Resolution Convolutional Neural Network (FSRCNN), a deep learning-based super-resolution technique, to enhance the resolution of the frames. This procedure serves to sharpen the details and features of the objects, resulting in a clearer representation. The main contribution, however, lies in our incorporation of a robust motion detection technique into our framework, marking a significant step forward in this study. The motion detection approach combines various methods and operations with the primary goal of making moving objects distinctly visible and easily distinguishable from the complex image background. This enhancement significantly contributes to the precision and accuracy of the tracking system. We combined both approaches with Fully Convolutional Siamese Network (SiamFC) due to its robustness in tracking processes across frames in videos, capacity to handle changes in target appearance, and ability to deal with small datasets. The Satellite Video Single Object Tracking (SatSOT) and SV248A10-SOT (SV248S), two satellite video single object tracking datasets, were used to evaluate the proposed approach. When the techniques were applied, the SatSOT dataset showed an 11% precision score increase and a 10% success rate improvement. The SV248S dataset exhibited even more significant improvements, with precision scores increasing by 18% and success scores rising by 9%. © 2024 IEEE.

关键词： Satellites

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Preserving Pre-trained Representation Space: On Effectiveness of Prefix-tuning for Large Multi-modal Models

Preserving Pre-trained Representation Space: On Effectivenes...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kim, Donghoon Lee, Gusang Shim, Kyuhong Shim, Byonghyo Dept. of Electrical and Computer Engineering Seoul National University Korea Republic of

ISBN: (纸本)9798891761681

Recently, we have observed that Large Multi-modal Models (LMMs) are revolutionizing the way machines interact with the world, unlocking new possibilities across various multi-modal applications. To adapt LMMs for downstream tasks, parameter-efficient fine-tuning (PEFT) which only trains additional prefix tokens or modules, has gained popularity. Nevertheless, there has been little analysis of how PEFT works in LMMs. In this paper, we delve into the strengths and weaknesses of each tuning strategy, shifting the focus from the efficiency typically associated with these approaches. We first discover that model parameter tuning methods such as LoRA and Adapters distort the feature representation space learned during pre-training and limit the full utilization of pre-trained knowledge. We also demonstrate that prefix-tuning excels at preserving the representation space, despite its lower performance on downstream tasks. These findings suggest a simple two-step PEFT strategy called Prefix-Tuned PEFT (PT-PEFT), which successively performs prefix-tuning and then PEFT (i.e., Adapter, LoRA), combines the benefits of both. Experimental results show that PT-PEFT not only improves performance in image captioning and visual question answering compared to vanilla PEFT methods but also helps preserve the representation space of the four pre-trained models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Detection of Primary Open Angle Glaucoma Based on Deep CNN Using Fundus Images 26

Detection of Primary Open Angle Glaucoma Based on Deep CNN U...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ratul, Md. Toyaha Rahman Afroge, Shyla Peyal, Hasibul Islam Zafrin, Dia Ahmed, Kazi Fardeen Abdal, Md. Nazmul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Uttara University Dept. of Computer Science & Engineering Bangladesh Khulna University Dept. of Computer Science & Engineering Discipline Bangladesh

ISBN: (纸本)9798350359015

The early and accurate diagnosis of glaucoma, a primary cause of permanent blindness, is critical for efficient treatment and prevention of vision loss. Although the exact causes of glaucoma are not yet fully understood, it is thought to be a result of several factors, including raised pressure inside the eye and decreased blood supply to the optic nerve. We have developed a convolutional neural network model for accurate detection of glaucoma. Methods based on deep learning have been effective at classifying diseases in retinal fundus images, facilitating in the evaluation of the growing number of images. The goal of this work is to create and train a unique deep CNN model that makes use of the connections between related eye-fundus tasks and metrics used to identify glaucoma. We have meticulously selected two distinct datasets to underpin this research endeavor: the ACRIMA dataset and the LAG dataset. Notably, our model attains a remarkable accuracy score of 99.29% on the ACRIMA dataset and an equally commendable accuracy score of 97.22% on the LAG dataset. This performance eclipses that of the majority of contemporary deep CNN models, underscoring the prowess and sophistication of our approach. © 2023 IEEE.

关键词： CNN deep learning fundus image glaucoma image classification

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