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Grid to vehicle and vehicle to grid systems for large-scale penetration of renewable generation

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Grid to vehicle and vehicle to grid systems for large-scale ...

2019 ECEEE Summer Study on Energy Efficiency: Is Efficient Sufficient?

作者： Moura, Pedro S. Pires, António Delgado, Joaquim de Almeida, Aníbal T. Institute of Systems and Robotics Dep. of Electrical and Computer Engineering University of Coimbra Polo II Coimbra3030-290 Portugal Institute of Systems and Robotics Department of Electrical Engineering Escola Superior de Tecnologia de Viseu Campus Politecnico de Repeses Viseu3504-510 Portugal

ISBN: (纸本)9789198387841

The electric power system is quickly changing due to the growing penetration of intermittent and non-dispatchable renewable energy sources. Simultaneously, the demand should ideally be able to be adapted to the renewable generation availability, directly with demand response or indirectly using energy storage technologies. The transport sector with electric vehicles (EVs) is increasingly an important consumer of electricity, and as fleets increase, EVs can be used as controllable loads, charging in periods of high renewable generation or low prices, using the Grid to Vehicle (G2V) system. With adequate technology, in addition to absorbing power from the grid, EVs can also use some of their storage capacity to inject energy into the grid, in order to ensure the balance between the generation and demand, using the Vehicle to Grid (V2G) system. However, the additional charging cycles due to V2G will accelerate the degradation of batteries and therefore its associated cost must be taken into account. This paper discusses the technologies and methodologies for the implementation of Grid to Vehicle and Vehicle to Grid systems, as well as their potential benefits for the grid in a scenario with large-scale penetration of renewable generation. To assess such impact a case study for the Portuguese grid is presented, considering three scenarios of penetration of EVs in the Portuguese vehicle fleet (in which EVs constitute 10 %, 25 % or 50 % of the fleet). The obtained data allowed to assess the potential of V2G to transfer the generation surplus between hours of low and high demand. Additionally, the economic benefits from the grid and user point-of-views were assessed considering the degradation of the batteries (evaluated through a model developed in Simulink environment), associated with the use of V2G, and their replacement cost. The results show a high impact on the compensation of renewable generation surplus, as well as economic benefits from the grid point-of-view. Ev

关键词： Electric storage Electric vehicles Renewable energy Vehicle charging

State-Based Transcription of Components of Carnatic Music

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State-Based Transcription of Components of Carnatic Music

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Venkata Subramanian Viraraghavan Arpan Pal Hema Murthy R Aravind Embedded Systems and Robotics TCS Research and Innovation Tata Consultancy Services Department of Computer Science and Engineering Indian Institute of Technology Madras Department of Electrical Engineering Indian Institute of Technology Madras

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Automatic Carnatic Music (CM) transcription is an open problem in need of a standardized descriptive notation. The level of detail needed in a descriptive transcription makes it tedious to obtain ground truth by manual means. In this paper, we propose a novel state-based representation of the pitch curve motivated by CM components called constant-pitch notes and stationary points. We also propose a novel transcription technique that uses the Viterbi algorithm to estimate the states and quantized pitch-values. The proposed technique adheres best to raga-notes compared to the existing critical-points technique and uniform quantization. In a listening test, clips synthesized from the proposed notation were rated significantly better (324 ratings, p <;0.001) than those from critical-points. Further, speed-halving based on state information best matches the actual, observed CM component-duration ratios without losing raga-characteristics. Thus, the proposed transcription can be corrected manually to obtain ground truth and can enhance learning tools.

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A Multimodal Dataset Addressing Motor Function in Autism

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Scientific data 2025年第1期12卷 959页

作者： João Ruivo Paulo Teresa Sousa João Perdiz Lara Pereira Mariette Vasen Susana Mouga Gabriel Pires Miguel Castelo-Branco Institute of Systems and Robotics University of Coimbra Coimbra Portugal. jpaulo@isr.uc.pt. Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT) Institute of Nuclear Sciences Applied to Health (ICNAS) University of Coimbra Coimbra Portugal. Intelligent Systems Associate Laboratory (LASI) Guimarães Portugal. Institute of Physiology Faculty of Medicine University of Coimbra Coimbra Portugal. Institute of Systems and Robotics University of Coimbra Coimbra Portugal. Physics Department NOVA School of Science and Technology NOVA University of Lisbon Lisbon Portugal. Faculty of Mathematics and Computer Science Friedrich-Schiller-University Jena Jena Germany. Department of Informatics Karlsruhe Institute of Technology (KIT) Karlsruhe Germany. Engineering Department Polytechnic Institute of Tomar Tomar Portugal.

Autism has primarily been characterized at a social-cognitive level, with evidence suggesting impairments in action-perception and motor function. However, there is a lack of publicly available datasets that specifically address the neural and behavioral mechanisms linking these functions in autism. The Move4AS dataset aims to fill this gap, having been designed to facilitate the study of the underlying mechanisms of motor function in the autism spectrum. It combines multiple data modalities, including electroencephalography (EEG) and 3D motion data, collected during motor imitation tasks - dancing and walking - designed to recruit motor function in emotional and social contexts. It comprises a control group of 20 participants and a clinical group of 14 participants. EEG was recorded through a 16-channel wireless EEG cap, and 3D motion was captured using marker-based motion capture suits tracked by a 10-camera setup. Additionally, the dataset includes neuropsychological characterization of the participants (IQ and autism score).

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Learning hybrid control barrier functions from data

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arXiv 2020年

作者： Lindemann, Lars Hu, Haimin Robey, Alexander Zhang, Hanwen Dimarogonas, Dimos V. Tu, Stephen Matni, Nikolai Division of Decision and Control Systems KTH Royal Institute of Technology Sweden Department of Electrical and Systems Engineering University of Pennsylvania United States Google Brain Robotics United States

Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data. In particular, we assume a setting in which the system dynamics are known and in which data exhibiting safe system behavior is available. We propose hybrid control barrier functions for hybrid systems as a means to synthesize safe control inputs. Based on this notion, we present an optimization-based framework to learn such hybrid control barrier functions from data. Importantly, we identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned hybrid control barrier functions, and hence the safety of the system. We illustrate our findings in two simulations studies, including a compass gait walker. Copyright © 2020, The Authors. All rights reserved.

关键词： Hybrid systems

Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines

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arXiv 2020年

作者： Bird, Jordan J. Faria, Diego R. Premebida, Cristiano Ekárt, Anikó Vogiatzis, George School of Engineering and Applied Science Aston University Birmingham United Kingdom Lab Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

The novelty of this study consists in a multi-modality approach to scene classification, where image and audio complement each other in a process of deep late fusion. The approach is demonstrated on a difficult classification problem, consisting of two synchronised and balanced datasets of 16,000 data objects, encompassing 4.4 hours of video of 8 environments with varying degrees of similarity. We first extract video frames and accompanying audio at one second intervals. The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89.27% and 93.72%, respectively. This is followed by late fusion of the two neural networks to enable a higher order function, leading to accuracy of 96.81% in this multi-modality classifier with synchronised video frames and audio clips. The tertiary neural network implemented for late fusion outperforms classical state-of-the-art classifiers by around 3% when the two primary networks are considered as feature generators. We show that situations where a single-modality may be confused by anomalous data points are now corrected through an emerging higher order integration. Prominent examples include a water feature in a city misclassified as a river by the audio classifier alone and a densely crowded street misclassified as a forest by the image classifier alone. Both are examples which are correctly classified by our multi-modality approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Proximity perception in human-centered robotics: A survey on sensing systems and applications

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arXiv 2021年

作者： Navarro, Stefan Escaida Mühlbacher-Karrer, Stephan Alagi, Hosam Zangl, Hubert Koyama, Keisuke Hein, Björn Duriez, Christian Smith, Joshua R. Inria Lille-Nord Europe France JOANNEUM RESEARCH ROBOTICS Institute for Robotics and Mechatronics Klagenfurt Austria Karlsruhe Germany Karlsruhe Institute of Technology IAR - IPR Karlsruhe Germany University of Applied Sciences Karlsruhe Germany Alpen-Adria-Universität Klagenfurt Institute of Smart System Technologies Sensors and Actuators Klagenfurt Austria Osaka University Department of Systems Innovation Graduate School of Engineering Science Osaka Japan University of Washington Allen School of Computer Science and Engineering Department of Electrical Engineering Seattle United States

—Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey paper, we cover the developments of this field from the early days up to the present, with a focus on human-centered robotics. Here, proximity sensors are typically deployed in two scenarios: first, on the exterior of manipulator arms to support safety and interaction functionality, and second, on the inside of grippers or hands to support grasping and exploration. Starting from this observation, we propose a categorization for the approaches found in the literature. To provide a basis for understanding these approaches, we devote effort to present the technologies and different measuring principles that were developed over the years, also providing a summary in form of a table. Then, we show the diversity of applications that have been presented in the literature. Finally, we give an overview of the most important trends that will shape the future of this domain. © 2021, CC BY.

关键词： Surveys

Model of Misalignment Tolerant Inductive Power Transfer System for EV Charging

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Model of Misalignment Tolerant Inductive Power Transfer Syst...

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Mattia Simonazzi Leonardo Sandrolini Luca Zarri Ugo Reggiani José Alberto Department of Electrical Electronic and Information Engineering Alma Mater Studiorum - University of Bologna Bologna Italy Institute of Systems & Robotics University of Coimbra Coimbra Portugal

ISBN: (数字)9781728156354

ISBN: (纸本)9781728156361

This paper presents a study on the behaviour of the currents in a 1-D resonator array of inductive power transfer (IPT) systems that can be used for electric vehicle (EV) charging. The resonator array is used to ease misalignment issues between the receiver and the transmitter and to improve the transmission distance. Besides, IPT systems with resonator arrays may offer the possibility of dynamic charging. The analysis is performed assuming a generic position of the receiver over a 1-D array. A theoretical model - tolerant to the misalignment of the receiver - has been developed and experimentally verified. The model is used to determine the currents in the system. The obtained results highlight significant stresses on the circuit components that must be considered in the design stage of the apparatus, especially when a large power is transferred, such as in the case of EV charging.

关键词： Receivers Impedance Coils Mathematical model Batteries Inverters Inductance

Corrections to “a Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing”

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IEEE robotics and Automation Letters 2024年第10期9卷 8426-8426页

作者： Lorenzo Lamberti Elia Cereda Gabriele Abbate Lorenzo Bellone Victor Javier Kartsch Morinigo Michał Barciś Agata Barciś Alessandro Giusti Francesco Conti Daniele Palossi Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy Dalle Molle Institute for Artificial Intelligence USI-SUPSI Lugano Switzerland Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi UAE Integrated Systems Laboratory ETH Zürich Zürich Switzerland

Presents corrections to the article “a Sim-to-Real Deep Learning-Based Framework for Autonomous Nano-Drone Racing”.

关键词： Robots Technological innovation IEEE Associate Members

Optimal Estimator Design and Properties Analysis for Interconnected systems with Asymmetric Information Structure

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arXiv 2021年

作者： Wang, Yan Xiong, Junlin Yang, Zaiyue Su, Rong School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China School of Electrical and Electronic Engineering Nanyang Technological University Nanyang Avenue 50 Singapore Department of Automation University of Science and Technology of China Hefei230026 China Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China

This paper studies the optimal state estimation problem for interconnected systems. Each subsystem can obtain its own measurement in real time, while, the measurements transmitted between the subsystems suffer from random delay. The optimal estimator is analytically designed for minimizing the conditional error covariance. The boundedness of the expected error covariance (EEC) is analyzed. In particular, a new condition that is easy to verify is established for the boundedness of EEC. Further, the properties of EEC with respect to the delay probability are studied. We found that there exists a critical probability such that the EEC is bounded if the delay probability is below the critical probability. Also, a lower and upper bound of the critical probability is derived. Finally, the proposed results are applied to a power system, and the effectiveness of the designed methods is illustrated by simulations. © 2021, CC BY-SA.

关键词： Errors