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International Conference on Intelligent Transportation

作者： Gaël P. Atheupe Didier Martinez Bruno Monsuez Renault Technical centre RENAULT Group & ENSTA Paris Institute Polytechnique de Paris (IP Paris) Paris France dept. Chassis control & ADAS systems Renault Technical centre RENAULT Group Guyancourt France dept. Computer science & systems engineering ENSTA Paris Institute Polytechnique de Paris (IP Paris) Paris France

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The transition to vehicle electrification introduces new demands on chassis dynamics, paving the way for advances in driving dynamics, safety, and efficiency. A key consideration arises: how are driving torque impulses converted in electric vehicles with multiple power sources? This study introduces a novel approach to address these demands through the integration of state-of-the-art yaw control, using concurrent front/rear axle and front right/front left torque distribution based on the wheel speed balance control concept. Our aim is not necessarily to demonstrate the supremacy of our approach, but rather to present a fresh philosophical perspective on the problem of yaw control in electric vehicles. Our method embodies dynamic load transfer and axle dynamics into the yaw control problem formulation, setting it apart from current methodologies. Through experimental results, we illustrate the effectiveness of our proposed approach regardless of road surface conditions.

关键词： Torque Systematics Roads Wheels Electric vehicles Stability analysis Trajectory Safety Vehicle dynamics Axles

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Accelerating Fingerprint-based Person Identification through Computation Offloading in Edge and Cloud Environments

Accelerating Fingerprint-based Person Identification through...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Renan A. Barbosa João P. B. Andrade Mauro R. C. Da Silva Fernando A. M. Trinta Paulo A. L. Rego Group of Computer Networks Software Engineering and Systems (GREat) Federal University of Ceará (UFC) Fortaleza Ceará Brazil Institute of Computing University of Campinas (Unicamp) São Paulo Brazil

The increasing popularity of smartphones has driven the development of advanced features, including access to users’ biometric data and a wide range of services via wireless connections. This provides developers with opportunities to create increasingly complex applications. However, the limited resources on mobile devices pose challenges when executing operations such as fingerprint-based person identification, especially with large datasets. Given these limitations, we propose and evaluate a software architecture to enable fingerprint identification on mobile devices. Our approach leverages computation offloading techniques to migrate application components to cloud and edge/cloudlet servers. Through our evaluation, we have observed that offloading the task to cloudlet or cloud servers significantly accelerates the identification process, even when multiple devices simultaneously offload to the remote environment. These findings highlight the potential benefits of leveraging offloading to enhance the performance of fingerprint identification on mobile devices.

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Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research

arXiv

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arXiv 2024年

作者： Shukla, Vandita Morelli, Luca Trybala, Pawel Remondino, Fabio Gan, Wentian Yu, Yifei Wang, Xin Trento Italy Computer Vision and Machine Learning Systems Group Institute for Geoinformatics University of Muenster Germany Dept. of Civil Environmental and Mechanical Engineering University of Trento Italy School of Geodesy and Geomatics Wuhan University China

UAV-based biodiversity conservation applications have exhibited many data acquisition advantages for researchers. UAV platforms with embedded data processing hardware can support conservation challenges through 3D habitat mapping, surveillance and monitoring solutions. High-quality real-time scene reconstruction as well as real-time UAV localization can optimize the exploration vs exploitation balance of single or collaborative mission. In this work, we explore the potential of two collaborative frameworks - Visual Simultaneous Localization and Mapping (V-SLAM) and Structure-from-Motion (SfM) for 3D mapping purposes and compare results with standard offline approaches. © 2024, CC BY-NC-SA.

关键词： Unmanned aerial vehicles (UAV)

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Impact of Techno-Economic Parameters on Optimal Sizing of EV Fast Charging Stations

Impact of Techno-Economic Parameters on Optimal Sizing of EV...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Anindita Golder Sheldon S. Williamson Department of Electrical and Computer Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech-Automotive Center of Excellence (ACE) Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

ISBN: (数字)9798350317664

ISBN: (纸本)9798350317671

As the adoption for electric vehicles (EV) increases, the need for proper charging infrastructure also increases. As such, it is necessary to ensure proper charging infrastructure to meet the charging demand. Hence, studies concerning the planning of EV charging stations which include the sizing of distributed energy resources (DERs) in the station to reduce the peak demand on the grid. In this paper, EV charging stations were designed keeping in mind factors such as the EV charging load profile (on demand charging versus deferred charging), uncertainties in the load profile as well as the tariff rates. A sensitivity analysis was also carried out in order to examine the impact of several techno-economic parameters on the overall cost of the system.

关键词： Vehicle-to-grid Costs Uncertainty Sensitivity analysis Tariffs Vehicular ad hoc networks Transportation

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Towards Time-Variant-Aware Link Prediction in Dynamic Graph Through Self-supervised Learning 9th

Towards Time-Variant-Aware Link Prediction in Dynamic Graph...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wen, Guangqi Cao, Peng Jin, Zhiyong Song, Ruoxian Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Dynamic graph link prediction is a challenging problem because the graph topology and node attributes vary at different times. A purely supervised learning scheme for the dynamic graph data usually leads to poor generalization due to insufficient supervision. As a promising solution, self-supervised learning can be introduced to dynamic graph analysis tasks. However, the self-supervised learning paradigm for dynamic graph learning has not been sufficiently investigated due to the complicated properties of the evolving graphs. We assume that the dynamic graphs consist of three independent types of key factors, i.e., graph time-variant information, time-invariant information and noise. Based on this assumption, we propose the Self-supervised Decoupling for Dynamic Graph (SDDG) framework for explicitly decoupling the representation which characterizes these three factors, thus enhancing the interpretability of the learned representation and link prediction performance. More specifically, we design an encoder-decoder architecture to sufficiently exploit the dynamic graph itself with multiple regularizations, so that the time-variant embedding of dynamic graph data can be effectively decoupled from the perspectives of node and structure for time-variant-aware link prediction. Experiments on five benchmark link prediction tasks demonstrate the significant improvement of our SDDG over the state-of-the-art methods. For example, SDDG achieves 98.2% and 97.3% Top-1 AUC on Reddit-B and Reddit-T, outperforming the DDGCL model, by 1.9% and 1.8%, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Supervised learning

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On the Difficulty of Designing Processor Arrays for Deep Neural Networks 2nd

On the Difficulty of Designing Processor Arrays for Deep Neu...

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2nd International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and 1st International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020

作者： Stehle, Kevin Schindler, Günther Fröning, Holger Computing Systems Group Institute of Computer Engineering Heidelberg University Heidelberg Germany

ISBN: (纸本)9783030667696

Systolic arrays are a promising computing concept which is in particular inline with CMOS technology trends and linear algebra operations found in the processing of artificial neural networks. The recent success of such deep learning methods in a wide set of applications has led to a variety of models, which albeit conceptual similar as based on convolutions and fully-connected layers, in detail show a huge diversity in operations due to a large design space: An operand’s dimension varies substantially since it depends on design principles such as receptive field size, number of features, striding, dilating and grouping of features. Last, recent networks extent previously plain feedforward models by various connectivity, such as in ResNet or DenseNet. The problem of choosing an optimal systolic array configuration cannot be solved analytically, thus instead methods and tools are required that facilitate a fast and accurate reasoning about optimality in terms of total cycles, utilization, and amount of data movements. In this work we introduce Camuy, a lightweight model of a weight-stationary systolic array for linear algebra operations that allows quick explorations of different configurations, such as systolic array dimensions and input/output bitwidths. Camuy aids accelerator designers in either finding optimal configurations for a particular network architecture or for robust performance across a variety of network architectures. It offers simple integration into existing machine learning tool stacks (e.g TensorFlow) through custom operators. We present an analysis of popular DNN models to illustrate how it can estimate required cycles, data movement costs, as well as systolic array utilization, and show how the progress in network architecture design impacts the efficiency of inference on accelerators based on systolic arrays. © 2020, Springer Nature Switzerland AG.

关键词： Systolic arrays

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Optimization of Uniform Amplitude Periodic Linear Phased Arrays for Grating Lobe Reduction

Optimization of Uniform Amplitude Periodic Linear Phased Arr...

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European Conference on Antennas and Propagation, EuCAP

作者： Yanki Aslan Nehir Berk Onat Faculty of Electrical Engineering Mathematics and Computer Science Microwave Sensing Signals and Systems Group Delft University of Technology Delft The Netherlands

The optimization of mode excitation coefficients in linear periodic arrays of multi-mode antenna elements is studied for grating lobe reduction. A novel beamforming architecture is proposed with a new optimization problem based on equi-amplitude element excitations for optimal power efficiency. The capabilities of the proposed synthesis approach on suppressing the grating lobe for wide scan angles, and on maintaining the peak gain at the steering angle are analyzed. A 16-element 0.7-wavelength spaced array of dual-mode circular patch antenna elements is used for demonstration purposes. It is shown that a good performance trade-off is achieved when the excitation amplitude of the high order mode is restricted to a sufficiently large value. The ratio of the peak gain outside the main lobe to the gain at the angle of steering is reduced up to about −15 dB and −14 dB for scanning towards 30 and 45 degrees off-broadside, respectively.

关键词： Phased arrays Array signal processing Patch antennas Insertion loss Propagation losses Linear antenna arrays Gratings

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A Positioning and Navigation System for Railway Technical Station Operators 4

A Positioning and Navigation System for Railway Technical St...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Yang, Fan Wang, Zhixue Xiong, Gang Hu, Xiangyu Xu, Kejie Chen, Shichao Shandong Jiaotong University State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Rail Transportation Jinan China Shandong Jiaotong University School of Rail Transportation Jinan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Beijing Technol. and Bus. Univ. State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Computer and Artificial Intelligence Beijing China China Mobile Communication Group Sichuan Co. Ltd Chengdu China Chinese Academy of Sciences Beijing Huairou Academy of Parallel Sensing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China

ISBN: (纸本)9798350349252

As an important node in the dismantling and grouping of freight trains, the operational tasks of the railway technical station are increasingly busy. Various business activities involve a large number of field personnel, and ensuring the personal safety and operational efficiency of these personnel is one of these issues that railway operations need to *** ensure the operational safety and standardization at the railway technical station, high-precision positioning system for the personnel is required. However, typical operational scenarios at railway technical stations often involve problems such as the carriage obstruction and the electromagnetic interference. Only rely on GNSS for positioning is often insufficient to meet practical requirements. Addressing the need of the high-precision positioning for the operational personnel at railway technical stations, this paper constructs a integrated navigation system based on RTK and IMU. It utilizes a Discrete Error State Kalman Filtering algorithm to fuse the solution data from RTK and IMU, thereby obtaining high-precision pedestrian positioning data. Compared to relying solely on GNSS positioning system, the integrated navigation system significantly improves its resistance to the interference and positioning accuracy. Experimental results demonstrate that this positioning system can achieve centimeter-level positioning in obstructed scenarios typical of railway technical stations, meeting the positioning requirements of the operational personnel at railway technical station work sites. © 2024 IEEE.

关键词： Kalman filters

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High-Level Metrics for Service Level Objective-aware Autoscaling in Polaris: a Performance Evaluation

High-Level Metrics for Service Level Objective-aware Autosca...

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IEEE International Conference on Fog and Edge Computing (ICFEC)

作者： Nicolò Bartelucci Paolo Bellavista Thomas Pusztai Andrea Morichetta Schahram Dustdar Department of Computer Science and Engineering University of Bologna Bologna Italy Distributed Systems Group Vienna University of Technology Vienna Austria

With the increasing complexity, requirements, and variability of cloud services, it is not always easy to find the right static/dynamic thresholds for the optimal configuration of low-level metrics for autoscaling resource management decisions. A Service Level Objective (SLO) is a high-level commitment to maintaining a specific state of a service in a given period, within a Service Level Agreement (SLA): the goal is to respect a given metric, like uptime or response time within given time or accuracy constraints. In this paper, we show the advantages and present the progress of an original SLO-aware autoscaler for the Polaris framework. In addition, the paper contributes to the literature in the field by proposing novel experimental results comparing the Polaris autoscaling performance, based on highlevel latency SLO, and the performance of a low-level average CPU-based SLO, implemented by the Kubernetes Horizontal Pod Autoscaler.

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Enhanced Detection of Demagnetization in Direct-Drive Wind Turbine Generators Using eMIST and Machine Learning

Enhanced Detection of Demagnetization in Direct-Drive Wind T...

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IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Qiuyi Chen Panagiotis Tsilifis Jaime Renedo Anglada Department of Mechanical Engineering University of Maryland College Park MD USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Vernova Advanced Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

The rapid growth of offshore wind energy needs robust condition monitoring to ensure the reliability and efficiency of direct-drive permanent magnet synchronous generators (PMSGs). Demagnetization, a critical fault in these generators, can severely impact performance and increase operational costs, especially in harsh marine environments. Traditional diagnostic techniques often struggle to detect subtle magnetic property changes under varying load conditions. This paper presents an innovative framework for demagnetization detection in PMSGs, leveraging the Electrical Multi-phase Imbalance Separation Technique (eMIST) combined with machine learning models. Using a dataset from offshore wind turbines, including simulated faulty conditions, the proposed methodology enhances anomaly detection by isolating imbalance signatures and correlating them with key operational parameters. Gaussian Process Regression (GPR) is employed to predict anomalies, offering high accuracy and confidence in fault classification. Results demonstrate the approach's ability to identify early-stage demagnetization, enabling predictive maintenance, reducing downtime, and improving the economic viability of offshore wind farms.

关键词： Costs Accuracy Wind energy Machine learning Gaussian processes Wind farms Generators Wind turbines Demagnetization Anomaly detection

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