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Towards new realities: Implications of personalized online layers in our daily lives

i-com 2024年第2期23卷 221-229页

作者： Herder, Eelco Stojko, Laura Strecker, Jannis Neumayr, Thomas Yigitbas, Enes Augstein, Mirjam Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Science University of the Bundeswehr Munich Neubiberg Germany Institute of Computer Science University of St. Gallen St. Gallen Switzerland Research Group Personalized Environments and Collaborative Systems University of Applied Sciences Upper Austria Hagenberg Austria Faculty of Electrical Engineering Computer Science and Mathematics Paderborn University Paderborn Germany

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. © 2024 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Human computer interaction

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Exploring the Influence of Digital Media on Secondary School Students' Daily Lives 7

Exploring the Influence of Digital Media on Secondary School...

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Gogh, Elod Kovari, Attila Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Science University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The focus of this study is to investigate how digital media use affects the daily activities, emotional well-being, and general lifestyle of high school students in relation to education amidst concerns about a 'new terrain or venue' for learning in an era moving full swing towards digitization. The study, conducted with students from grades 9-11, seeks to identify overall trends and associations involving digital media interactions: considering both byproducts of using new technology as an educational tool- in addition discursive aspects allowing for different forms of distraction. In summary, the results showed that digital media use is common and often regarded as educationally beneficial or a positive form of relaxation;however, daily activities are it affected by substantial adverse impacts on everyday life and time management. Suggestions focus on supporting healthy digital media habits, including content-wise engagement and time management tools. © 2024 IEEE.

关键词： Daily Activities Digital Media Digital Media Impact

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ChatGPT the Omniscient? A Guide to Effective Prompting 7

ChatGPT the Omniscient? A Guide to Effective Prompting

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7th IEEE International Conference and Workshop Obuda on electrical and Power engineering, CANDO-EPE 2024

作者： Kovari, Attila Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Science University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The invention of artificial intelligence and natural language processing has revolutionised human-machine interaction, and OpenAI's ChatGPT models are at the forefront of this. GPT-3 and GPT-4 models generate human-like text, answer questions and perform all kinds of language-related tasks. ChatGPT relies heavily on the quality of the requests it receives. This paper addresses the issue of efficient prompting in order to maximise its potential. Different prompting techniques are described in the paper: prompt template design, continuous prompts, few-shot learning, chain of thought prompting and metadata expansion. The conclusions show that learning how to write good prompts is crucial for realizing the full potential of ChatGPT in various natural language processing tasks. © 2024 IEEE.

关键词： Zero-shot learning

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Verification of the Usefulness of the Virtual Reality Application for URM Assembly 15

Verification of the Usefulness of the Virtual Reality Applic...

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

作者： Svetlik, Jozef Kovac, Juraj Pollak, Martin Kocisko, Marek Stejskal, Tomas Sasala, Michal Technical University of Košice Faculty of Mechanical Engineering Department of Production Systems and Robotics Košice Slovakia Technical University of Košice Faculty of Mechanical Engineering Department of Industrial and Digital Engineering Košice Slovakia Technical University of Košice Faculty of Manufacturing Technologies Department of Computer Aided Manufacturing Technologies Prešov Slovakia

ISBN: (纸本)9798350396225

The article deals with the use of virtual reality in assembly processes. It defines the basic concepts regarding montage and virtual reality. In the individual chapters of the article, the hardware and software resources necessary to implement the assembly simulation in virtual reality are listed and described. The specific Virtual Reality software Pixyz Review is also described in more detail, where its functions and a specific example of assembling the URM assembly are listed. © 2023 IEEE.

关键词： Virtual reality

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Intelligent Control Based in Neural-Fuzzy of Adaptive Backstepping for The Double Stator IPMSM Powered by a Five-Level NPC Inverter 13

Intelligent Control Based in Neural-Fuzzy of Adaptive Backst...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Benaissa, Rabie Mansouri, Smail Ouledali, Omar University of Adrar Faculty of Sciences and Technology Department of Electrical Engineering Laboratory for Sustainable Development and Computer Science 01000 Algeria University of Adrar Faculty of Sciences and Technology Department of Hydrocarbons and Renewable Energies Laboratory for Energy Environment and Information Systems 01000 Algeria

ISBN: (纸本)9798350356199

This article supplies a proposed approach Neuro Fuzzy Controller (NFC)-Adaptive Backstepping Controller (ABC)-Space Vector Modulation (SVM) for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM). This paper's primary objective is to enhance DSIPMSM's performance by the use of strong control using the proposed approach. Given its benefits - including high torque density, high efficiency, high power density, and minimal maintenance - DSIPMSM, have recently been the focus of much research. Using the input-output linearization approach and Lyapunov stability theory, asymptotically stable trajectory-following dynamics are demonstrated using nonlinear adaptive backstepping control. The use of fuzzy logic and intelligence-based controllers are increasingly being used to enhance traditional control's performance and output. In addition, the fuzzy logic controller has a lot of flaws that are presently being fixed by the extremely effective NFC tool. The proposed method's worth was demonstrated by the simulation results in terms of durability, effective tracking dynamics, precision, and good disturbance rejection with little ripple. © 2024 IEEE.

关键词： Disturbance rejection

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Simple verification improvements of the NETCONF data model in programmable computer Networks 30

Simple verification improvements of the NETCONF data model i...

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30th Telecommunications Forum, TELFOR 2022

作者： Martinovic, Bojana Vuleta, Maksim Popic, Srdjan Todorovic, Branislav M. Faculty of Electrical Engineering Banjaluka Bosnia and Herzegovina RT-RK Institute for Computer Based Systems Novi Sad Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

One of the most important aspects of programmable networks is their correctness. The quality of being correct is not something that is built into any architecture of programmable networks. Since it is decoupled into two planes: the control and data plane, it depends on the level of correctness of both planes and the technologies that implement these planes. In cases when the data plane is implemented with Network Configuration Protocol (NETCONF) together with YANG (yet another new generation) modeling language, most of the validation and verification is implemented within the YANG validation. But there is still a verification gap that makes it possible for developers to make mistakes such as setting up the incorrect IP address or ports in the given configuration. This paper proposes enhancements in the data model verification by comparing the loaded configuration with the expected configuration. XML comparison techniques have implemented the verification improvement. © 2022 IEEE.

关键词： XML

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A Comparison of Various Deep Learning Methods for Household Load Forecasting

A Comparison of Various Deep Learning Methods for Household ...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Deivamani, Karthikeyan Norouzi, Farshid Shekhar, Aditya Bauer, Pavol Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of DC Systems Energy Conversion and Storage Delft University of Technology Delft Netherlands

ISBN: (纸本)9798350396782

Forecasting energy consumption is vital for smart grid operations to manage demand, plan loads, and optimize grid operations. This work aims at reviewing and experimentally evaluating six univariate deep learning architectures to forecast load for a single household using a real-world dataset. Multi-layer perceptron (MLP), Convolutional neural network (CNN) and recurrent neural networks (Simple RNN, Long Short Term Memory (LSTM)) were the neural network methods that were analysed along with robust LSTM architectures like Bidirectional LSTM and CNN-LSTM Hybrid. All the models were tuned using Bayesian optimization and evaluated using root mean squared error (RMSE) as the metric. In addition to neural network models, Seasonal ARIMA (SARIMA) a statistical model is also presented to observe the performance. As a result, Bi-directional LSTM was observed to have achieved the best performance with the smallest value of RMSE;however, it was also observed that differences in performances between other neural network models were quite low, especially between the RNN architectures. Additionally, although machine learning methods performed better than SARIMA the former model was more complex and computationally intensive. © 2023 IEEE.

关键词： deep learning electric load forecasting smart grid time-series forecasting univariate

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A Multimodal Learning-based Approach for Autonomous Landing of UAV

A Multimodal Learning-based Approach for Autonomous Landing ...

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IEEE/ASME International Conference on Mechtronic and Embedded systems and Applications, MESA

作者： Francisco S. Neves Luís M. Branco Maria I. Pereira Rafael M. Claro Andry M. Pinto Department of Electrical and Computer Engineering (DEEC) Faculty of Engineering of University of Porto (FEUP) Institute for Systems and Computer Engineering - Technology and Science (INESC TEC) Porto Portugal

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

In the field of autonomous Unmanned Aerial Vehicles (UAVs) landing, conventional approaches fall short in delivering not only the required precision but also the resilience against environmental disturbances. Yet, learning-based algorithms can offer promising solutions by leveraging their ability to learn the intelligent behaviour from data. On one hand, this paper introduces a novel multimodal transformer-based Deep Learning detector, that can provide reliable positioning for precise autonomous landing. It surpasses standard approaches by addressing individual sensor limitations, achieving high reliability even in diverse weather and sensor failure conditions. It was rigorously validated across varying environments, achieving optimal true positive rates and average precisions of up to 90%. On the other hand, it is proposed a Reinforcement Learning (RL) decision-making model, based on a Deep Q-Network (DQN) rationale. Initially trained in simulation, its adaptive behaviour is successfully transferred and validated in a real outdoor scenario. Furthermore, this approach demonstrates rapid inference times of approximately 5ms, validating its applicability on edge devices.

关键词： Training Wind Image edge detection Decision making Detectors Reinforcement learning Robot sensing systems Autonomous aerial vehicles Transformers Resilience

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Asymmetric Channel Reuse Model for Wi-Fi 6

Asymmetric Channel Reuse Model for Wi-Fi 6

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International Conference on Emerging Technologies, ICET

作者： Aitizaz Uddin Syed Safdar Nawaz Khan Marwat Cyber Security Operations Ministry of Justice Doha Qatar Department of Computer Systems Engineering Faculty of Electrical and Computer Engineering University of Engineering and Technology Peshawar Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Channel reuse is an effective technique to reutilize the limited resource of the frequency radio spectrum to fulfill the ever-increasing demands of wireless communication. Implementing the channel reuse in Wi-Fi 6 by embedding the new enhancements of Channel Bonding, Beamforming, and utilizing reclaimed channels of the 5 GHz spectrum creates an asymmetric behavior. In this paper, we propose and investigate the Asymmetric Channel Reuse Model (ACRM) for 160 MHz communication in the presence and absence of Terminal Doppler Weather Radar (TDWR) channels at 5 GHz spectrum. In both conditions, we observe significant improvement in throughput and SINR compared to known symmetric channel reuse models.

关键词： Wireless communication Meteorological radar Array signal processing IEEE 802.11ax Standard Interference Throughput Doppler radar Bonding Signal to noise ratio Meteorology

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Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

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computers, Materials & Continua 2022年第7期72卷 2141-2158页

作者： Muhammad Irfan Nasir Ayub Faisal Althobiani Zain Ali Muhammad Idrees Saeed Ullah Saifur Rahman Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Fahad Salem Alkahtani Samar Alqhtani Piotr Gas Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science Federal Urdu University of Science and TechnologyIslamabad44000Pakistan Faculty of Maritime Studies King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Engineering University of Engineering and TechnologyNarowal CampusLahore54000Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Electrical and Power Engineering AGH University of Science and Technology Mickiewicza 30 AvenueKrakow30-059Poland

One of the major concerns for the utilities in the Smart Grid(SG)is electricity *** the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously *** this purpose,we have taken historical data of energy thieves and normal *** avoid imbalance observation,biased estimates,we applied the interpolation ***,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further *** proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time *** minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark *** proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,***,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

关键词： Smart grids and meters electricity theft detection machine learning AdaBoost optimization techniques

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