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Optimizing EV Charging Recommendations Using Graph Neural Networks

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Optimizing EV Charging Recommendations Using Graph Neural Ne...

IEEE Conference on Vehicular Technology (VTC)

作者： Lydia Douaidi Sidi-Mohammed Senouci Ines El Korbi Fouzi Harrou DRIVE Lab Université de Bourgogne Nevers France Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Electric vehicles (EVs) offer low carbon emissions; however, drivers frequently encounter difficulties scheduling charging sessions and locating available Charging Stations (CSs). These stations not only need to be conveniently located but also tailored to meet individual charging preferences. Addressing these challenges is crucial for overcoming barriers to the widespread adoption and efficiency of electric mobility, consequently enhancing the overall user experience. This paper introduces an innovative two-stage framework that improves the accessibility of EVCSs by integrating Graph Neural Networks (GNNs) with optimization algorithms. A bipartite graph representing user-station interactions is constructed in the first stage, and a GNN is utilized to leverage this structure. The GNNs efficiently capture complex relational patterns within the graph, enabling the generation of personalized station recommendations. In the subsequent stage, an optimization algorithm is employed to strategically assign users to these recommended stations. This algorithm considers station availability and proximity factors, ensuring optimal user assignments. The proposed recommender framework’s effectiveness is verified using data collected from the Yonne department in France. Experimental evaluations highlight the framework’s efficiency, achieving a high-performance measure of 98%, significantly reducing waiting times and maximizing user satisfaction for drivers compared to baseline approaches.

关键词： Technological innovation Renewable energy sources Smart cities Graph neural networks User experience Resource management Vehicle dynamics Sustainable development Optimization Vehicles

Enhancing Sovereign Allocation of Resources in cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability

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Enhancing Sovereign Allocation of Resources in Cloud Milieus...

Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Chandrasekhar Rao Katru Sathish Srinivasan Mohan Kumar T G Sunita Yadwad G. Satish Ramya Maranan Lead Software Engineer Indian Land SC USA Oracle Cloud Infrastructure Division Machine Learning & AI Development San Francisco Bay Area California USA Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda Gujarat Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Telangana Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance.

关键词： cloud computing Energy consumption Algebra Scalability Quality of service Transformers Real-time systems Resource management Time factors Service level agreements

Irregular Honeycomb Network: Revolutionizing k-Coverage in Spatial Wireless Sensor Networks

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Irregular Honeycomb Network: Revolutionizing k-Coverage in S...

International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Dakshanya Maddala Habib M. Ammari Department of Electrical Engineering and Computer Science Frank H. Dotterweich College of Engineering Wireless Sensor and Mobile Autonomous Networks (WiSeMAN) Research Lab Texas A&M University-Kingsville Kingsville Texas USA

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Sensor deployment, and in particular coverage, is a fundamental component in the design and development of wireless sensor networks. Achieving coverage (or 1-coverage) in three-dimensional (or spatial) wireless sensor networks (WSNs), which requires that every point in a spatial field of interest (FoI), is an open challenging problem. In particular, the problem of k-coverage in spatial WSNs, where every point in a spatial FoI is k-covered (or covered by at least k sensors at the same time), is even more challenging. In this paper, we investigate the problem of connected k-coverage in spatial WSNs, where a spatial FoI is k-covered while all the sensors participating in this k-coverage process are connected to each other. Our study aims at producing an energy-efficient and optimized protocol, which helps achieve connected k-coverage in spatial WSNs with a minimum number of sensors, thus, extending the network lifespan. Precisely, this study is based on convex polyhedral space fillers that help establish an irregular hexagonal prism tessellation referred to as Irregular Honeycomb Network (IHN), which serves as a medium to achieve k-coverage in spatial WSNs. First, we propose a randomly distributed sensor placement strategy, which is designed to guarantee full k-coverage of a spatial FoI. To this end, we tile a spatial FoI with irregular hexagonal prisms, known as space fillers, which are adjacent to each other without any gaps or overlaps. Second, we exploit this tiling strategy to guarantee k-coverage of a spatial FoI. Then, we compute the spatial sensor density (i.e., number of sensors per unit volume) to k-cover a spatial FoI. Third, we determine the necessary relationship between the sensors’ communication and sensing ranges to ensure network connectivity. We corroborate our analysis with various simulation results.

关键词： Wireless communication Wireless sensor networks Sensor placement Protocols Simulation Stochastic processes Energy efficiency

Low Complexity Joint Channel Estimation and Data Detection for AFDM Receivers With Oversampling

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arXiv 2024年

作者： Ranasinghe, Kuranage Roche Rayan Ge, Yao de Abreu, Giuseppe Thadeu Freitas Guan, Yong Liang School of Computer Science and Engineering Constructor University Bremen28759 Germany Continental-NTU Corporate Lab Nanyang Technological University 639798 Singapore School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

In this paper, we propose a novel low complexity time domain (TD) oversampling receiver framework under affine frequency division multiplexing (AFDM) waveforms for joint channel estimation and data detection (JCEDD). Leveraging a generalized doubly-dispersive channel model, we first derive the input-output (I/O) relationship for arbitrary waveforms when oversampled in the TD and present the I/O relationship for AFDM as an example. Subsequently, utilizing the multiple sample streams created via the oversampling procedure, we use the parametric bilinear Gaussian belief propagation (PBiGaBP) technique to conduct JCEDD for decoding the transmitted data and estimating the complex channel coefficients. Simulation results verify significant performance improvements both in terms of data decoding and complex channel coefficient estimation with improved robustness against a varying number of pilots over a conventional Nyquist sampling rate receiver. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

Toward VR in VR: Assessing Engagement and Social Interaction in a Virtual Conference

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Toward VR in VR: Assessing Engagement and Social Interaction...

作者： Moreira, Catarina Simoes, Francisco P. M. Lee, Mark J. W. Zorzal, Ezequiel R. Lindeman, Robert W. Pereira, Joao Madeiras Johnsen, Kyle Jorge, Joaquim Queensland University of Technology School of Information Systems BrisbaneQLD4000 Australia Universidade Federal Rural de Pernambuco Departamento de Computação Recife52171-900 Brazil La Trobe University Computer Science and Information Technology BundooraVIC3086 Australia University of Canterbury HIT Lab NZ Christchurch8041 New Zealand Instituto Superior Técnico Universidade de Lisboa INESC-ID Department of Computer Science and Engineering Lisboa1000-029 Portugal University of Georgia School of Electrical and Computer Engineering AthensGA30602 United States

The pandemic brought about an unprecedented number of virtual conferences, given the heavy restrictions on travel to in-person meetings. Despite all the advances in technology, people still complain about virtual events. There is Zoom fatigue, confinement malaise, and a longing for personal social interactions. This paper discusses our experience organizing the IEEE Virtual Reality Conference (IEEE VR) as a virtual event. IEEE VR was a success with 1200+ registered paying participants, dozens of workshops and tutorials, and hundreds of technical papers. We used (1) a virtual environment platform, together with (2) discussion tools and (3) videoconferencing/broadcast/online tools to further provide effective social interaction and increase engagement. In this paper, we explore the synergies between virtual environments and other online tools and assess user engagement by analyzing the messages exchanged between participants across different genders and geographical regions. To this end, we apply diverse engagement metrics for online conferences. Our analysis shows that these metrics have the potential to highlight engagement, diversity, and inclusion by combining textual messages, participant geographic and gender information, communities of participants, and visitation patterns in a virtual environment. Drawing on our results and experiences, we propose guidelines for organizing technical virtual events to increase diversity and social interaction. © 2013 IEEE.

关键词： Virtual reality

An Efficient Scene Coordinate Encoding and Relocalization Method

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arXiv 2024年

作者： Xu, Kuan Jiang, Zeyu Cao, Haozhi Yuan, Shenghai Wang, Chen Xie, Lihua School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Spatial AI & Robotics Lab Department of Computer Science and Engineering University at Buffalo BuffaloNY14260 United States

Scene Coordinate Regression (SCR) is a visual localization technique that utilizes deep neural networks (DNN) to directly regress 2D-3D correspondences for camera pose estimation. However, current SCR methods often face challenges in handling repetitive textures and meaningless areas due to their reliance on implicit triangulation. In this paper, we propose an efficient scene coordinate encoding and relocalization method. Compared with the existing SCR methods, we design a unified architecture for both scene encoding and salient keypoint detection, enabling our system to focus on encoding informative regions, thereby significantly enhancing efficiency. Additionally, we introduce a mechanism that leverages sequential information during both map encoding and relocalization, which strengthens implicit triangulation, particularly in repetitive texture environments. Comprehensive experiments conducted across indoor and outdoor datasets demonstrate that the proposed system outperforms other state-of-the-art (SOTA) SCR methods. Our single-frame relocalization mode improves the recall rate of our baseline by 6.4% and increases the running speed from 56Hz to 90Hz. Furthermore, our sequence-based mode increases the recall rate by 11% while maintaining the original efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Triangulation

A Learning-based Quadcopter Controller with Extreme Adaptation

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arXiv 2024年

作者： Zhang, Dingqi Loquercio, Antonio Tang, Jerry Wang, Ting-Hao Malik, Jitendra Mueller, Mark W. High Performance Robotics Lab Dept. of Mechanical Engineering UC Berkeley United States The University of Pennsylvania United States Dept. of Electrical Engineering and Computer Science University of California at Berkeley United States

This paper introduces a learning-based low-level controller for quadcopters, which adaptively controls quadcopters with significant variations in mass, size, and actuator capabilities. Our approach leverages a combination of imitation learning and reinforcement learning, creating a fast-adapting and general control framework for quadcopters that eliminates the need for precise model estimation or manual tuning. The controller estimates a latent representation of the vehicle’s system parameters from sensor-action history, enabling it to adapt swiftly to diverse dynamics. Extensive evaluations in simulation demonstrate the controller’s ability to generalize to unseen quadcopter parameters, with an adaptation range up to 16 times broader than the training set. In real-world tests, the controller is successfully deployed on quadcopters with mass differences of 3.7 times and propeller constants varying by more than 100 times, while also showing rapid adaptation to disturbances such as off-center payloads and motor failures. These results highlight the potential of our controller to simplify the design process and enhance the reliability of autonomous drone operations in unpredictable environments. Video and code are at: https://***/muellerlab/xadapt_ctrl © 2024, CC BY.

关键词： Reinforcement learning

An Adaptive Column Compression Family for Self-Driving Databases

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arXiv 2022年

作者： Fehér, Marcell Lucani, Daniel E. Chatzigeorgiou, Ioannis Agile Cloud Lab Department of Electrical and Computer Engineering DIGIT Aarhus University Aarhus Denmark School of Computing and Communications Lancaster University Lancaster United Kingdom

Modern in-memory databases are typically used for high-performance workloads, therefore they have to be optimized for small memory footprint and high query speed at the same time. Data compression has the potential to reduce memory requirements but often reduces query speed too. In this paper we propose a novel, adaptive compressor that offers a new trade-off point of these dimensions, achieving better compression than LZ4 while reaching query speeds close to the fastest existing segment encoders. We evaluate our compressor both with synthetic data in isolation and on the TPC-H and Join Order Benchmarks, integrated into a modern relational column store, Hyrise. Copyright © 2022, The Authors. All rights reserved.

关键词： Economic and social effects

Fast multiplication of random dense matrices with fixed sparse matrices

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arXiv 2023年

作者： Liang, Tianyu Murray, Riley Buluç, Aydın Demmel, James Electrical Engineering and Computer Science Department UC Berkeley United States Computational Research Division Lawrence Berkeley National Lab United States International Computer Science Institute United States Sandia National Laboratories

This work focuses on accelerating the multiplication of a dense random matrix with a (fixed) sparse matrix, which is frequently used in sketching algorithms. We develop a novel scheme that takes advantage of blocking and recomputation (on-the-fly random number generation) to accelerate this operation. The techniques we propose decrease memory movement, thereby increasing the algorithm's parallel scalability in shared memory architectures. On the Intel Frontera architecture, our algorithm can achieve 2x speedups over libraries such as Eigen and Intel MKL on some examples. In addition, with 32 threads, we can obtain a parallel efficiency of up to 45%. We also present a theoretical analysis for the memory movement lower bound of our algorithm, showing that under mild assumptions, it's possible to beat the data movement lower bound of general matrix-matrix multiply (GEMM) by a factor of √M, where M is the cache size. Finally, we incorporate our sketching method into a randomized algorithm for overdetermined least squares with sparse data matrices. Our results are competitive with SuiteSparse for highly overdetermined problems;in some cases, we obtain a speedup of 10x over SuiteSparse. Copyright © 2023, The Authors. All rights reserved.

关键词： Random number generation