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检索条件"机构=Dept. of Elec. Engineering and Computer Science"

共 948 条记录，以下是1-10 订阅

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Hybrid Reinforcement Learning for Data Stream Freshness in Autonomous Vehicle Networks

Hybrid Reinforcement Learning for Data Stream Freshness in A...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ebrahimi, Dariush Ghosh, Pronab Alzhouri, Fadi De Oliveira, Thiago E. Alves Wilfrid Laurier University Dept. of Computer Science WaterlooON Canada Lakehead University Dept. of Computer Science Thunder BayON Canada Concordia University Dept. of Elec. & Computer Eng. MontrealQC Canada

ISBN: (纸本)9798350351255

Autonomous vehicles (AVs) are poised to become integral components of intelligent transportation systems, particularly within the framework of future smart cities. Traditional performance metrics such as throughput and latency fall short in adequately addressing the temporal relevance and freshness of data in critical applications such as autonomous driving and accident prevention. Consequently, this paper delves into the challenge of reducing the Age of Information (AoI) for disseminating data streams within AV-assisted vehicular networks. Given the dynamic nature of the environment, the problem is formulated as a Markov decision process and tackled using Q-learning and DDQN, both prominent reinforcement learning (RL) algorithms. Additionally, a heuristic approach is introduced to augment the performance of the RL algorithms, expediting environmental learning convergence. The numerical findings underscore the effectiveness of the proposed methodologies in minimizing the aggregate AoI across all data streams. © 2024 IEEE.

关键词： Adversarial machine learning

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Voltage and Current Feedforward Terms Impact in the Stability of Grid-Forming Inverter Systems

Voltage and Current Feedforward Terms Impact in the Stabilit...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Benfatto, Oriana Tresca, Giulia Formentini, Andrea Anglani, Norma Cossu, Simone Zanchetta, Pericle University of Pavia Electrical Computer and Biomedical Engineering Dept. Pavia Italy University of Genoa El. Elec. and Telec. Engineering and Naval Architecture Dept. Genoa Italy

ISBN: (纸本)9798350376067

This paper explores the issues related to the stability of a grid-connected three-phase grid-forming inverter embedded in a microgrid. The core analysis emphasizes the optimization of system controls and the influence of feed-forward terms on system stability. Through a comprehensive simulation and pole analysis, this paper demonstrates that feed-forward terms significantly affect the stability of the system. © 2024 IEEE.

关键词： Poles

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Deep Learning for Cyberthreats: Performance Analysis and Application of Malware Classification in Edge Computing 2

Deep Learning for Cyberthreats: Performance Analysis and App...

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, elec.rical and elec.ronics, ICIITCEE 2024

作者： Akhil, M.R. Sharma, Adithya Krishna V. Nadig, Abhijith Pavan, A. Shetty, Ashray Sumathi, A. Pes University Dept. of Computer Science Bengaluru India Red Hat Bengaluru India Jss Academy of Technology Dept. of Elec. and Comm. Bengaluru India BNM Institute of Technology Dept. of Ece Bengaluru India

ISBN: (纸本)9798350306415

In the context of the escalating malware threat landscape and the ongoing challenge of identifying modern malware, this research investigates the utility of Deep Neural Networks (DNNs) for malware classification. Various DNN architectures are explored, revealing their ability to accurately classify malware across different types with consistently high accuracy rates. The study also examines the feasibility of deploying DNN models on edge devices for real-time classification in resource-constrained settings, emphasizing the importance of efficient performance optimization. This research contributes to advancing malware detection techniques and highlights the significance of early malware detection for preventing adverse consequences. It additionally addresses the distribution of security tasks to edge devices to maintain the integrity and availability of large-scale IoT systems, fostering a more resilient and secure digital ecosystem. © 2024 IEEE.

关键词： Cybersecurity Data Protection Deep Neural Networks IoT Security Malware Classification Performance Evaluation

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Dynamic Stability of Large-Scale Photovoltaic Based Generation Integration into Power Systems 5

Dynamic Stability of Large-Scale Photovoltaic Based Generati...

引用

5th International Conference on Power engineering and Renewable Energy, ICPERE 2022

作者： Tayyab, Muhammad A. Muljadi, Eduard Auburn University Dept. of Elec. and Computer Engineering AuburnAL United States

ISBN: (纸本)9781665455480

The recent drought trends and decrease in hydropower generation are alarming to the point where we must plan for an alternate power source to utilize the available transmission line capacity. Interconnection of solar Photo-Voltaic (PV) as a viable elec.ric power supply has been substantially increasing in recent years, making it the most compelling alternate power source to compensate for the loss of hydropower. This paper aims to discuss the dynamic stability of a power system when a large-scale PV-based generation is interconnected to the power system to counteract an increase in load and compensate for drought-impacted hydro generation. For demonstration, a real-world power system is used for this study with permission from Western elec.ricity Coordinating Council (WECC). The system is simulated for five scenarios, (I) an existing power system, (II) a system with a 500 MW increase in system load and 500 MW additional PV generation, (III)-(V) a system with 500 MW, 720 MW, and 1440 MW PV generation interconnection to compensate for different levels of decrease in the hydro generation to test different PV penetration levels. The system is simulated with GE PSLF software for various contingencies, and dynamic stability results are reported with fictitious bus names without any Critical elec.ricity Infrastructure Information (CEIl). © 2022 IEEE.

关键词： Drought

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HGDL: Heterogeneous Graph Label Distribution Learning 38

HGDL: Heterogeneous Graph Label Distribution Learning

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Yufei Lian, Heng He, Yi Zhu, Xingquan Dept. of Elec. Eng. & Computer Sci. Florida Atlantic University Boca RatonFL33431 United States Dept. of Data Science William & Mary WilliamsburgVA23185 United States

Label Distribution Learning (LDL) has been extensively studied in IID data applications such as computer vision, thanks to its more generic setting over single-label and multi-label classification. This paper advances LDL into graph domains and aims to tackle a novel and fundamental heterogeneous graph label distribution learning (HGDL) problem. We argue that the graph heterogeneity reflected on node types, node attributes, and neighborhood structures can impose significant challenges for generalizing LDL onto graphs. To address the challenges, we propose a new learning framework with two key components: 1) proactive graph topology homogenization, and 2) topology and content consistency-aware graph transformer. Specifically, the former learns optimal information aggregation between meta-paths, so that the node heterogeneity can be proactively addressed prior to the succeeding embedding learning;the latter leverages an attention mechanism to learn consistency between meta-path and node attributes, allowing network topology and nodal attributes to be equally emphasized during the label distribution learning. By using KL-divergence and additional constraints, HGDL delivers an end-to-end solution for learning and predicting label distribution for nodes. Both theoretical and empirical studies substantiate the effectiveness of our HGDL approach. Our code and datasets are available at https://***/Listener-Watcher/HGDL. © 2024 Neural information processing systems foundation. All rights reserved.

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Contactless Hand Knuckle Modality for Identity Verification Using Siamese Network 22

Contactless Hand Knuckle Modality for Identity Verification ...

引用

22nd International Conference on Cyberworlds, CW 2023

作者： Hammami, Siwar Jarraya, Islem Hamdani, Tarek M. Alimi, Adel M. Sfax Tunisia Sfax Tunisia Tunisia University of Johannesburg Dept. of Elec. and Electronic Engineering Science Faculty of Engineering and the Built Environment South Africa

ISBN: (纸本)9798350315653

People can be recognized by a lot of unique biometric features either physiological or behavioral. Therefore, a biometric security method for a smartphone is proposed in this paper for easier use and to improve recognition results by using a new knuckle modality: the major knuckle of the thumb finger modality instead of the major knuckle of the middle finger modality which is the most used in related works. In finger knuckle recognition domains, different CNN (Convolutional Neural Network) architectures have been applied, requiring a huge database. To deal with this problem, this paper presents a Siamese network for identity verification using contactless major knuckle of thumb finger modality. Actually, the Siamese network has been well used for one-shot learning that aims at learning and recognizing from little data. The FMK (Finger Major Knuckle) dataset was proposed and constructed for testing and evaluation. In fact, this study achieved good results with accuracy of 82% for the major knuckle of the left thumb finger and 79.63% for the major knuckle of the left middle finger. © 2023 IEEE.

关键词： Convolutional neural networks

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Grid Forming-based Hydrogen System for High-penetration Renewable Energy Microgrid 11

Grid Forming-based Hydrogen System for High-penetration Rene...

引用

11th International Conference on Power and Energy Systems engineering, CPESE 2024

作者： Do-Chi, Kien Nguyen-Hoang, Anh Hoang-Hieu, Long Phan, Long Van Takano, Hirotaka Tran-Thanh, Son Nguyen-Duc, Tuyen Hanoi U.of Science and Tech. Dept. Electrical Engineering Hanoi Viet Nam OWC Vietnam ABL Group Dept. Renewable Energy Ho Chi Minh Viet Nam Gifu University Dept. Elec. Engineering Gifu Japan

ISBN: (纸本)9798350377439

Significant changes are anticipated in how energy is produced and utilized in the future. Many experts see microgrids as pivotal players in this shift. Microgrids offer enhanced reliability and resilience, along with cleaner energy. They are designed to operate in conjunction with distributed energy resources such as solar panels, fuel cells, and battery storage. These DERs are often more dependable and environmentally friendly compared to traditional energy sources. Among available energy storage systems, hydrogen emerges as a suitable solution due to its high power density and potential for bidirectional energy flow. It helps to enhance the elec.rical grid by employing gas-to-power solutions through fuel cells and storing excess elec.rical energy via power-to-gas elec.rolyzers. This paper introduces an islanded microgrid model comprising a controllable fuel cell system connected to an AC load through power elec.ronic devices. The system undergoes operation under varying load conditions to assess the performance of fuel cells as grid-forming units and the microgrid's stability under the designed control structure. The results demonstrate that the proposed fuelcell system can operate autonomously without reliance on the main grid, in various conditions, from no-load to short-term overload. Moreover, they also verified the system performance of the proposed system under unbalanced load scenario. Overall, the results proved that the designed PEMFC distributed generator can remain stable under various working condition and temporary elec.rical faults. © 2024 IEEE.

关键词： Hydrogen storage

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Using Conceptual Blending to Teach Software Design Principles to Undergraduates 54

Using Conceptual Blending to Teach Software Design Principle...

引用

54th IEEE Frontiers in Education Conference, FIE 2024

作者： Gaffar, Ashraf Selim, Mohamed Y. Eulenstein, Oliver Iowa State University Elec. & Comp. Eng. Dept. AmesIA United States Iowa State University Computer Science Department AmesIA United States

ISBN: (纸本)9798350351507

The domain of software design is gaining a long overdue recognition as a vital discipline within software engineering, necessitating innovative approaches to its teaching in undergraduate education. Despite the growing importance of software design, academic programs often treat it as a secondary skill, overshadowed by the strong emphasis on coding. Only a few schools offer a design degree or dedicated design courses. This disparity between industry demands and educational practices underscores the need for novel pedagogical strategies. In our innovative work, we discuss a new way of effectively teaching software design-by-analogy for undergraduates to help them rapidly acquire the essential skills needed to design complex software without getting entangled in complex code generation and management. Software design does not necessarily follow the same clear delineation/separation between modules and components naturally apparent in tangible engineering domains. We employ 'Conceptual Blending' to help students map their everyday experiences onto software design concepts. The process begins with students analyzing a simple two-arm watch to identify its user interface and create a finite state automaton for its interaction design. Success rates decline as the complexity of the watches increases, underscoring the software design challenges. By comparing these exercises to software interfaces, students learn to apply design techniques such as navigation modeling and prototyping, ensuring they can create intuitive, user-friendly software. © 2024 IEEE.

关键词： common sense mapping software design teaching watch

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Use of Ball-based Movement for Omnidirectional Collaborative Carry Robot Systems 17

Use of Ball-based Movement for Omnidirectional Collaborative...

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17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Meyer, Michael Conrad Wang, Yu Eastern Washington University Comp. Sci. and Elec. Eng. Dept. SpokaneWA United States Gonzaga University Computer Science SpokaneWA United States

ISBN: (纸本)9798331530471

Robotics as a field has taken off in recent years, with a significant focus on autonomy. An area that has a great contribution to the autonomous aspect of collaborative robots is the actual movement mechanisms they use. When two robots are used to collaboratively carry an object, their movements are critical in order to maintain stable control of the shared object. There are many factors that can affect the usefulness of one drivetrain, but the key factor for successful collaborative carry is the degrees of freedom and how freely we can control them while other degrees are or are not being controlled. After constructing robots with each of the drivetrains, the testing showed that the proposed ball-based system brought several benefits to collaborative carry robots, with drawbacks in complexity. © 2024 IEEE.

关键词： Collaborative robots

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CDP: Selec.ive Influential Nodes Based on Cliques in the Diffusion Process in Social Networks 9

CDP: Selective Influential Nodes Based on Cliques in the Dif...

引用

9th International Conference on Web Research, ICWR 2023

作者： Yazdanifar, Fatemeh Beni, Hamid Ahmadi Salimi, Nikoo Bouyer, Asgarali Amirkabir University of Technology Dept. of Mathematics and Computer Science Tehran Iran Madani University Dept. Software Engineering Azarbijan Shahid Tabriz Iran University of Genoa Dept. of the Elec. Electronics Electronics and Telecommunication Eng. and Naval Architecture Genoa Italy Azarbaijan Shahid Madani University Dept. of Computer Engineering Tabriz Iran

ISBN: (纸本)9798350399691

Influence maximization techniques emphasize selec.ing a set of influential nodes in order to maximize influence. Because the algorithms presented in this field ignore the topology of cliques for diffusion, there are two major challenges in influence maximization algorithms: optimal diffusion and computational overhead reduction. As a result, the CDP algorithm is presented in this article to address these issues. This algorithm first selec.s suitable cliques for diffusion based on their position and strategy in social networks. Furthermore, for each clique, a score is calculated based on the topological criteria of the clique, and suitable cliques are selec.ed for diffusion by applying a threshold limit. The seed nodes are chosen from the cliques in the second step. To avoid the rich club phenomenon, only a few nodes from each clique are chosen as seed candidate nodes. Finally, the seed nodes are chosen based on the node's topology and the strength of the node's level one neighbor. In the experiment section, the CDP algorithm significantly outperforms the best algorithms presented in recent years in terms of influence spread rate and execution time. © 2023 IEEE.

关键词： Diffusion

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