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Impact of the Increasing Integration of Photovoltaic Systems and Charging Stations for Electric Vehicles with Vehicle-To-Grid Concept on Voltage Stability of the Distribution Grid 20

Impact of the Increasing Integration of Photovoltaic Systems...

引用

20th International Conference on Smart Technologies, EUROCON 2023

作者： Kljajic, Ruzica Misljenovic, Nemanja Maric, Predrag Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9781665463973

The increasing integration of renewable energy sources (RES) and variable loads can stress the operation of the distribution grid during certain times of the day. In another way, electric vehicles, which are assumed to take an increasing share in the overall transportation structure, are considered as variable loads with the ability to store energy and thus support stability of the distribution grid. This paper focuses on a model of a prosumer-rich distribution grid with increasing integration of photovoltaic (PV) systems, electric vehicle charging stations (EVCS) as well as vehicle-to-grid (V2G) in the foreseeable future. The impact of integration on voltage stability was analyzed for the different operating scenarios considering voltage profiles and characteristic P-V curves. © 2023 IEEE.

关键词： Vehicle-to-grid

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Enhanced Neural SLAM with Semantic Segmentation in Dynamic Environments 8

Enhanced Neural SLAM with Semantic Segmentation in Dynamic E...

引用

8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Shen, Zhengcheng Mao, Minzhe Kastner, Linh Lambrecht, Jens Chair Industry Grade Networks and Clouds Berlin Institute of Technology Faculty of Electrical Engineering and Computer Science Berlin Germany

ISBN: (纸本)9798350300178

Mobile robots face a significant challenge when navigating in dynamic environments such as human crowds. Existing research works typically employ separate data or simulators for dynamic object detection and navigation tasks. This paper combines the photo-realistic simulator Habitat with embedded dynamic objects to create a playground for optimizing vision-based navigation algorithms. To validate our system, we implement and train three approaches - zero masks, image memory inpainting, and semantic map filter - using semantically segmented information. We then implement and evaluate an adapted reinforcement-learning-based SLAM algorithm using the validation Gibson dataset. The results indicate that moderate localization bias occurs when the environment is small and the navigation process is short. However, the error will accumulate over time. Additionally, a dynamic environment leads to less accurate localization. All three approaches can reduce the localization error, while the semantic map filter approach shows the best overall performance. © 2023 IEEE.

关键词： Object detection

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Towards Real-Time Motion Planning for Industrial Robots in Collaborative Environments 49

Towards Real-Time Motion Planning for Industrial Robots in C...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Bhuiyan, Teham Kutschank, Benno Pruter, Karim Flach, Huy Kastner, Linh Lambrecht, Jens Berlin Institute of Technology Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Germany

ISBN: (纸本)9798350331820

In collaborative environments, real-time motion planning is crucial for industrial robots to navigate safely and efficiently. Traditional planning algorithms, such as Rapidly-exploring Random Trees (RRT) or Probabilistic Roadmaps (PRM), often face challenges in coping with dynamic environments due to their inherent computational complexity. To address this issue, we propose an approach based on Deep Reinforcement Learning (DRL) for real-time motion planning of industrial robots. Our method leverages the power of machine learning and neural networks to enable robots to make intelligent decisions in real-time, ensuring prompt and adaptive navigation. However, applying DRL to industrial robots poses unique challenges, as vision-based training is difficult and distance sensors commonly used in mobile robots are unavailable. To overcome these challenges, we employ depth cameras to generate distance information and convert the obtained point cloud into voxels using the Open3D library. The obstacles are then loaded into the simulation environment in real-time, allowing the agent to perceive and react to the dynamic environment. To achieve a low simulation-to-real-gap, we propose a hardware-in-the-loop (HIL) approach, where the real robot mimics the movements of the simulated robot. We demonstrate the effectiveness of our system through real-world experiments. Our code is available on GitHub [1]. © 2023 IEEE.

关键词： Industrial robots

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Firefly Algorithm Approach for Recovering the Mathematical Structure of One-Dimensional Chaotic Maps from Time Series Data 3

Firefly Algorithm Approach for Recovering the Mathematical S...

引用

3rd International Conference on computer Technologies, ICCTech 2024

作者： Galvez, Akemi Fister, Iztok Fister, Iztok Iglesias, Andres University of Cantabria Dept. of Applied Mathematics & Computational Sciences Santander Spain University of Maribor Faculty of Electrical Engineering & Computer Science Maribor Slovenia

ISBN: (纸本)9798350395419

Uncovering the mathematical structure of unknown chaotic systems from limited time series data poses a significant challenge in the field of dynamical systems, with broad applications across various domains. In this paper, we propose a swarm intelligence approach to tackle this issue, specifically focusing on one-dimensional discrete maps. Our approach leverages the firefly algorithm, a popular nature-inspired metaheuristic, known for its optimization capabilities. We assess the performance of the method by applying it to two illustrative examples of time series derived from one-dimensional maps: the Hénon map and the Burger map. The results show that this approach is promising for recovering the equations of motion of one-dimensional chaotic maps, even in situations where no additional information is available beyond the provided data points. © 2024 IEEE.

关键词： Time series

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FireBot - An Autonomous Surveillance Robot for Fire Prevention, Early Detection and Extinguishing 15

FireBot - An Autonomous Surveillance Robot for Fire Preventi...

引用

15th International Conference on computer and Automation engineering, ICCAE 2023

作者： Balen, Josip Damjanovic, Davor Maric, Petar Vdovjak, Kresimir Arlovic, Matej Martinovic, Goran Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350396225

Every year, fire is responsible for numerous deaths, as well as huge material losses. Therefore, prevention and early detection of fire have become a priority for society, as well as the main research and development issue for many scientists and various industries. This paper describes our work in the development of FireBot, an autonomous surveillance robot. The Firebot is equipped with modern technologies and state-of-the-art navigational and computer vision methods that enable autonomous navigation, obstacle avoidance, video surveillance, fire prevention and detection, and fire extinguishing. It utilizes both infrared thermal (IRT) and RGB cameras paired with a modern convolutional neural network (CNN) for fault and fire detection, as well as various other sensors for analyzing air composition, processing of surrounding sounds, and detecting irregularities in its environment in general. The best performing CNN was implemented and tested in real-world environments for fire detection purposes, the results of which are presented in this paper. A state-of-the-art SLAM algorithm paired with LiDAR and a depth camera is used for mapping and navigation. The architecture presented in this paper, along with all functionalities planned for future work, represents an innovative autonomous surveillance system that will make a great contribution in the field of fire prevention and detection. © 2023 IEEE.

关键词： Fires

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Optically Transparent 4-Port UWB Antenna for MIMO Applications 2

Optically Transparent 4-Port UWB Antenna for MIMO Applicatio...

引用

2nd International Microwave and Antenna Symposium, IMAS 2024

作者： Yahya, Rabia Yin, Haosen Bertuch, David El-Ghazaly, Amal Nouakchott University Department of Physics Faculty of Science and Technology Nouakchott Mauritania Cornell University Department of Electrical and Computer Engineering IthacaNY United States

ISBN: (纸本)9798350356359

Herein, we present a novel transparent antenna with ultra-wideband (UWB) performance along with polarization diversity through 4 ports allowing MIMO operation. The antenna design can be of a great interest for a vehicular applications and Internet of Things (IOT) where the dual polarization and multiple ports allows as well as medical glasses and vehicular windows where transparency is needed for visibility. The proposed antenna provides compact size with dimensions of 50 mm×50 mm, through the employment of CPW, dual orthogonal-polarization, isolation level of 20 dB, and envelope correlation coefficient (ECC) of 0.0016 as well as MIMO performance. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Optimal Utilization of Electric Vehicles in the Parking Lot of the faculty Building to Increase Social Welfare 6

Optimal Utilization of Electric Vehicles in the Parking Lot ...

引用

6th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2023

作者： Misljenovic, Nemanja Fekete, Kresimir Knezevic, Goran Pleskalt, Martin Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Faculty of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350328752

This paper strives to investigate the impact of integrating charging stations in the parking lot of the faculty building with an installed photovoltaic power plant (PV) and battery energy storage system (BESS) to maximize social welfare. The integration of charging stations in the parking lot of business buildings would enable savings for owners of electric vehicles (EVs) who live in residential buildings and have to charge their EVs at public charging stations. On the other hand, by integrating charging stations into the parking lot, business buildings could reduce their electricity costs because they use EVs as BESS. Accordingly, the optimization model was developed in the GAMS environment by applying MILP formulation. The research was conducted according to the current market conditions and for a newly proposed market model for charging and discharging batteries of EVs in the parking lot of business buildings. The results show the need to find the optimal prices for charging and discharging batteries of EVs by applying multi-level optimization. © 2023 IEEE.

关键词： Energy management systems

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Image-based Criticality-Aware Fire Detection

Image-based Criticality-Aware Fire Detection

引用

2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Arshad, Alina Shamsi, Jawwad Ahmed Khan, Muhammad Burhan Bawany, Narmeen Zakaria Department of Software Engineering National University of Computer and Emerging Sciences Karachi Pakistan Department of Computer Science National University of Computer and Emerging Sciences Karachi Pakistan Department of Electrical Engineering National University of Computer and Emerging Sciences Karachi Pakistan Faculty of Science Jinnah University for Women Karachi Pakistan

ISBN: (纸本)9798331510503

Context-aware fire detection is a significant task in the era of new urban monitoring. Severe damage might result from fire events. To minimize the occurrence of these events, timely detection of fire accidents is necessary. Various machine-learning and deep learning methods are used for fire detection. Most of them do not focus on the extent of the fire or the damage it causes. All these methods need the detection of the criticality of the fire event. This work proposes an identification system based on Large Vision Models (LVMs) for fire detection. We proposed an intelligent criticality-aware fire detection system that can detect fire and its varying scales from small to large and generate alarming alerts accordingly. The model integrates advanced computer vision techniques with LVMs to explain the hidden context in a textual format. The system is tested on real-time data collected from surveillance cameras and achieves an accuracy of 86.67% in correctly identifying the context-based criticality of the fire events depicted in the input data. This proposed framework can revolutionize surveillance applications by enhancing security and responding to crucial fire incidents in real-time. The system provides an efficient method for allocating resources and facilitates rapid response to incidents to prioritize activities intelligently. Using this approach, we identified and prioritized fire incidents in the urban environment. © 2024 IEEE.

关键词： Fire detectors

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A Multi-agent Reinforcement Learning based Offloading and Resource Allocation for Vehicle Edge Computing 16

A Multi-agent Reinforcement Learning based Offloading and Re...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Yang, Jiazhi Yang, Xinjie Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China Ningbo Arrowdeep Internet Technology Co. Ltd. Ningbo China

ISBN: (纸本)9798331506322

This paper investigates the the challenges of resource allocation optimization for computation offloading in vehicular edge networks. It is assumed that multiple moving vehicles select Mobile Edge Computing (MEC) servers on Road Side Units (RSUs) to offload their computation tasks, for which, MEC servers allocate appropriate resources. To achieve the best balance between minimizing latency and resource consumption to ensure system performance, this study utilizes a Multi-Agent Reinforcement Learning (MARL) framework, where each vehicle and MEC server acts as an agent and learns optimal strategies through interactions with the environment. To improve performance, a new Global Heterogeneous MARL (GHMARL) framework is purposely designed based on existing MARL approaches. The proposed framework trains two Critic networks, namely, a global Critic network to ensure service quality and a local Critic network to ensure resource utilization efficiency. Extensive simulation results demonstrate the performance superiority of this approach over benchmark algorithms. © 2024 IEEE.

关键词： Mobile Edge Computing Multi-Agent Reinforcement Learning Resource allocation Vehicular edge networks

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Validation of Machine Learning-Based Lane Lines Detection Methods Using Different Datasets

Validation of Machine Learning-Based Lane Lines Detection Me...

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2023 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2023

作者： Jukic, Grgur Vranjes, Mario Vajak, Denis Vranjes, Denis TTTech Auto CEE Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350347722

Advanced Driver Assistance System (ADAS) uses various sensors that exist in the vehicle to collect information about the vehicle and its surroundings. One of the most commonly used ADASs is the one designed to recognize driving lane lines. In this paper, analysis, and validation of freely available state-of-The-Art deep learning-based lane lines detection (LLD) methods are done using three different well-known datasets: CULane, TuSimple, and LLAMAS. To perform the validation, as part of this research, two converters of lane line label formats were made. Converters allow testing models that have been previously trained on a dataset that uses different lane line label formats so that their performances can be verified on other datasets with different lane line format types. The experimental results show that the adaptability of the model to the change of the dataset depends on the architecture used as the basis of the model for the LLD, and the similarity of the training set of the initially used dataset and the new, *** dataset. All tested models showed shortcomings when tested on images from datasets on which they were not trained, which confirms that there is still no model that we can confidently say achieves high performance in all possible scenarios. © 2023 IEEE.

关键词： Advanced driver assistance systems

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