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International Conference on Smart Energy Systems and Technologies (SEST)

作者： Carolina Coppini Alessandro Magrini Cesar Diaz-Londono Giambattista Gruosso Michele Motta Diego Pareschi Dep. Statistics Computer Science Applications University of Florence Florence Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy ABB E-mobility S.p.A Italy

ISBN: (数字)9798350386493

ISBN: (纸本)9798350386509

The electric mobility sector has developed significantly in recent years, with a growing commitment to the goal of zero emissions. This article considers the chargers owned by ABB E-Mobility in Germany and Italy, and analyses their spatial distribution and the trend of their demand in the period 2020–2022. After providing a characterization of the e-mobility network in the two countries, we exploit AutoRegressive Integrated Moving Average (ARIMA) models to forecast future energy consumption. In Italy, fast and ultra-rapid recharging stations are distributed in 138 cities, mainly located in industrial areas, with very few of them placed in commercial and residential ones, and Milan hosts the overwhelming majority of them (10% of the total). Instead, the number of rapid and ultra-rapid chargers in Germany is significantly higher, about 13 times higher than in Italy, with a distribution across 1397 different cities, of which Hamburg has the largest number of stations (6% of the total). The results show that the volume of energy delivery in Germany is higher than in Italy, but Italian consumption is expected to grow faster in the future. These findings reflect the fact that Italy experienced a more recent adoption of electric vehicles and, consequently, owns a less mature charging network compared to Germany. On the other hand, thanks to a greater diffusion of electric vehicles and a more developed charging network, the demand for electric energy in Germany is characterized by greater stability. The differences between the e-mobility network of Italy and Germany highlight the importance of adapting charging network dep.oyment strategies to the specific needs and preferences of each country.

关键词： Energy consumption Adaptation models Graphical models Urban areas Predictive models Electric vehicles Market research Stability analysis Forecasting Distribution functions

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Low-Voltage Stress CI-based Quadratic Boost Converter for Distributed Energy Resources

Low-Voltage Stress CI-based Quadratic Boost Converter for Di...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Morteza Dezhbord Milad Babalou Sobhan Mohamadian Carlo Cecati Dep. Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy Dept. Electrical Engineering Shahid Beheshti University Tehran Iran

This research introduces a high step-up DC-DC converter for renewable energy sources (RESs) that has lower voltage stress on its single switch. With one linked coupled inductor (CI), a quadratic boost converter plus a couple of multiplier units are utilized as circuits for primary and secondary, respectively. The energy from the leakage inductance can be recycled, increasing efficiency and reducing voltage stress. The proposed converter has a higher voltage gain and lower switch stress when compared to similar existing quadratic converter topologies. The operation principles and mathematical analysis of the proposed converter are thoroughly studied. Following that, a comparison of several structures is presented. Finally, simulation results of PSCAD software are shown to further verify the theoretical study.

关键词： Renewable energy sources Inductance Simulation Voltage Switches Software Topology

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System for Talent Acquisition: Integrating AI, Automation, and Data Analysis in HR

System for Talent Acquisition: Integrating AI, Automation, a...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Gulnara Abitova Ayanbek Serikov Vladimir Nikulin Mira Rakhimzhanova Gulnur Shuteyeva Korlan Kulniyazova dep. computer engineeering Astana IT University Astana Kazakhstan dept. electical engineering and computer science Binghamton University Binghamton USA dept. system anslysis and managemnt Eurasian National University Astana Kazakhstan

This article presents a novel system designed for human resources (HR) companies, aiming to predict an individual's personality based on resume analysis through the integration of advanced information technology tools. At the core of this system is Airable, serving as the central database, which is seamlessly I integrated with various other tools such as *** for document handling, GitHub for code repository management, and OpenAI for advanced AI algorithms. The integration and automation of these diverse tools are skillfully orchestrated using Make, a no-code solution that streamlines the workflow and enhances efficiency. This innovative approach allows HR companies to leverage technology for more accurate personality predictions, thereby improving their recruitment processes and decision-making strategies.

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CCDF measurement with Multi Impairment of Wi-Fi 6 performance using MATLAB Simulink

CCDF measurement with Multi Impairment of Wi-Fi 6 performanc...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Fatima Faydhe Al-Azzawi Rasha Hassan Abbas Zainab Faydhe Al-Azzawi Kamal Y. Kamal Electronics Technical Department Institute of Technology Middle Technical University MTU Baghdad Iraq Dep. Computer Technology Engineering AL- Mansour university college Iraq

The current 802.11ac WiFi standard is built upon and improved by WiFi 6, commonly referred to as “AX WiFi” or “802.11ax WiFiIn response to the increasing number of gadgets in the globe, WiFi 6 was first developed. If you have numerous smart home devices, VR equipment, or a lot of other gadgets, WiFi 6 employs a 160 MHz Channel to offer a channel that larger and providing by 1024-QAM to more data with one packed, increasing efficiency. In this paper 802.11 ax design and implemented in MATLAB Simulink where CCDF measurement (Complementary Cumulative Distribution Function measure the probability that a signal's instantaneous power may exceed its target level or average power) with multi-impairment performance illustrated using 1024-QAM to offer a packed signal with increasing data and a channel with160 MHz, 1024 QAM examined with AWGN and Rayliegh fading channel in term of bit error rate.

关键词： Power measurement Phase measurement Quadrature amplitude modulation Software packages Bit error rate Measurement uncertainty IEEE 802.11ax Standard

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PUSH: a primal heuristic based on Feasibility PUmp and SHifting

arXiv

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arXiv 2022年

作者： Agasucci, Valerio Coppola, Corrado Grani, Giorgio Sapienza University of Rome Dep. of Computer Science Control and Management Engineering Rome Italy Sapienza University of Rome Dep. of Statistical Sciences Rome Italy

This work describes PUSH, a primal heuristic combining Feasibility Pump and Shifting. The main idea is to replace the rounding phase of the Feasibility Pump with a suitable adaptation of the Shifting and other rounding heuristics. The algorithm presents different strategies, dep.nding on the nature of the partial rounding obtained. In particular, we distinguish when the partial solution is feasible, infeasible with potential candidates and infeasible without candidates. We used a threshold to indicate the percentage of variables to round with our algorithm and which other to round to the nearest integer. Most importantly, our algorithm tackles directly equality constraints without duplicating rows. We select the parameters of our algorithm on the 19 instances provided for the Mip Competition 2022. Finally, we compared our approach to other start heuristics, like Simple Rounding, Rounding, Shifting and Feasibility Pump on the first 800 MIPLIB2017 instances ordered by the number of non *** Codes 90-08 Copyright © 2022, The Authors. All rights reserved.

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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Electronic engineering (ICEEM), International Conference on

作者： Mahmoud Elshabrawy Marwa M. Eid Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Dept. of Communications & Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Dep. of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Engineering and Control Systems Dept. Faculty of Engineering University Mansoura Egypt

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models.

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Location-enabled IoT (LE-IoT): Indoor Localization for IoT Environments using Machine Learning

Location-enabled IoT (LE-IoT): Indoor Localization for IoT E...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Tanveer Ahmad Xue Jun Li Muhammad Ashfaq Michalis Savva Iacovos Ioannou Vasos Vassiliou Department of Computer Science University of Cyprus Nicosia Cyprus Dep of Electrical and Electronic Engineering Auckland University of Technology Auckland New Zealand Department of Electronics Engineering University of Engineering & Technology Taxila Pakistan Department of Computer Science University of Cyprus & CYENS Centre of Excellence Nicosia Cyprus

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In recent years, the concept of the Internet of things (IoT) has become ubiquitous among several applications. However, traditional localization methods face challenges in accurately measuring sensor node positions, particularly in scenarios involving wireless signal instability, energy harvesting, anchor mobility, latency, and dense environmental conditions. However, these problems open the new ways to utilize machine learning (ML) and self-calibration for localization. To defeat these problems, in this paper, we have proposed a new localization technique, which reduces the localization error caused by the iterations in a triangulation method and drastically shortens the computation time for full network coverage despite noisy environments. First, we present a localization technique based on a looped network. The link quality induction (LQI) is used to formulate multiple feature vectors for the localization problem, solved using linear regression. Second, we examine the appropriateness of a few algorithms under different tradeoffs by testing the localization accuracy for different network parameters, including the anchor node density, additive noise, and quality of the wireless channel. The simulation achieves exciting results that the adoption of a support vector machine (SVM) and nonlinear regression model applied with a radial basis function (RBF) kernel leads to an enhanced localization error with Root-Mean Square Error (RMSE) of 0.25m. Furthermore, we gain 48.5% accuracy in the localization error by implementing two models.

关键词： Location awareness Support vector machines Wireless communication Wireless sensor networks Accuracy Machine learning algorithms Vectors

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Reconfigurable Intelligent Surfaces and DF-relay Improved Spectral Efficiency in Cognitive Radio Networks 97

Reconfigurable Intelligent Surfaces and DF-relay Improved Sp...

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97th IEEE Vehicular Technology Conference, VTC 2023-Spring

作者： El Mettiti, Abderahmane Saber, Mohamed Chehri, Abdellah Chaibi, Hasna Badaoui, Abdel Saadane, Rachid Faculty of Sciences University Mohamed v Ehtp LRIT-GSCM Morocco SIRC/LAGeS Ehtp Laboratory Engineering System Casablanca Morocco Royal Military College Dep. Math. and Computer Science Kingston Canada Supmti Genuis Laboratory Rabat Morocco Mohammed v University Laboratory Lastimi Casablanca Morocco

ISBN: (纸本)9798350311143

Cognitive radio (CR) is considered a primary technology for spectrum usage efficiency and dynamic spectrum management. Spectrum sensing is an essential cognitive radio cycle function. Recently, reconfigurable intelligent surfaces (RIS) technology has emerged as a promising new enabler of 6G wireless communication, with the ability to control signal propagation and increase signal coverage and spectrum management. This paper investigates how integrating RISs with CR can improve spectral efficiency. First, we optimize the RIS parameters by identifying the optimal transmit powers and number of RIS antennas. Then, we compare the novel RIS technology with the traditional decode-and-forward (DF) relaying technology. Next, we investigate how adopting RIS can enhance spectral efficiency. Finally, we demonstrate how integrating the novel technology of reconfigurable intelligent surfaces with CR can significantly increase spectrum detection, the most crucial step in the cognitive radio process. Simulation studies indicate that RIS is more energy-efficient than SISO and DF-relay communications in various setups. Moreover, by boosting spectrum sensing capability in CR situations, RISs can significantly enhance the efficiency of frequency spectrum usage. © 2023 IEEE.

关键词： Cognitive radio

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Camera-LiDAR Fusion based Object Segmentation in Adverse Weather Conditions for Autonomous Driving

Camera-LiDAR Fusion based Object Segmentation in Adverse Wea...

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International Baltic electronics Conference, BEC

作者： Junyi Gu Mauro Bellone Artjom Lind Dep. of Mechanical and Industrial Engineering Tallinn University of Technology Tallinn Estonia FinEst Centre for Smart Cities Tallinn University of Technology Tallinn Estonia Institute of Computer Science University of Tartu Tartu Estonia

ISBN: (数字)9798350365115

ISBN: (纸本)9798350365122

This paper presents the results of a series of machine learning experiments that focus on the domain adaption between different training datasets for autonomous driving. The experiments use a neural network model obtained through transfer learning from the Waymo Open Dataset and then tested with our custom dataset, which was recorded at the TalTech campus in different weather conditions. In this work, we developed a set of tools to extract and process the sensory data from the iseAuto shuttle. The camera and LiDAR sensors of the iseAuto shuttle were calibrated, and the point clouds data of the LiDAR sensor was projected onto the camera plane. We present, here, our publicly available iseAuto dataset which was used for a classification task under adverse weather and low illumination conditions. The iseAuto dataset contains 8000 frames of camera and LiDAR data for traffic object detection and segmentation. We provide ground truth annotations of 2400 frames, in which the object contours were manually labeled out. In addition, we provided the manual-labeled ground-truth segmentations of cars and humans in images, which can be used by the community to test the accuracy of the segmentation of their models. An additional focus of this paper is to demonstrate that with a few custom annotated data, using transfer learning and semi-supervised learning, it is possible to obtain reasonable accuracy on noisy real-world data. The current performance in vehicle segmentation ranges from 65% to 85% in intersection over union (IoU) and between 43% and 60% IoU for pedestrian segmentation in challenging scenarios such as nighttime and rainy weather.

关键词： Point cloud compression Laser radar Accuracy Transfer learning Object segmentation Semisupervised learning Cameras Sensors Autonomous vehicles Meteorology

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Energy-efficient Trajectory Planning with Media Transition for a Hybrid Unmanned Aerial-Underwater Vehicle

Energy-efficient Trajectory Planning with Media Transition f...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Pedro M. Pinheiro Armando Alves Neto Douglas G. Macharet Paulo L. J. Drews NAUTEC Center for Computational Science Universidade Federal do Rio Grande - FURG Rio Grande Brazil Dept. of Electronics Engineering Univ. Federal de Minas Gerais Belo Horizonte Brazil Dep. of Computer Science Computer Vision and Robotics Laboratory (VeRLab) Univ. Federal de Minas Gerais Belo Horizonte Brazil

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Vehicles capable of operating in more than one environment have been developed to solve real problems. Among them, the hybrid unmanned aerial-underwater vehicle (HUAUV) is receiving attention from the robotics community, mainly with a quadrotor-like configuration. However, this vehicle presents high energy consumption because of the larger mass required compared to the only aerial vehicle, limiting its autonomy. This work addresses the trajectory planning problem for a HUAUV. The method is based on Rapidly-exploring Random Trees (RRTs), a highly customizable planning technique. In addition, we propose two new heuristics to increase the energy efficiency of the hybrid vehicle. The first consists of biasing the tree expansion towards the environment with the lowest navigation cost, while the second one assigns estimated costs to nodes in the tree and chooses the least expensive trajectories. These techniques are evaluated in physically realistic simulation experiments performed in 135 scenarios. A comparative analysis of their performances is presented relative to the state of the art. We show that using efficient heuristics can significantly contribute to reducing energy consumption and even increase the average velocity in the missions performed by these vehicles.

关键词： Energy consumption Costs Limiting Trajectory planning Navigation Media Energy efficiency Trajectory Planning Intelligent robots

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