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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Anusha, G. Arora, Krishan Sharma, Himanshu Thota, Surya Prakash Verma, Chaman Raboaca, Maria Simona Filote, Constantin Lovely Professional University Dept. of Eee Punjab India Gnits Dept. of Eee Hyderabad India Eötvös Loránd University Faculty of Informatics Dept. of Media and Educational Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava Romania

ISBN: (纸本)9798350321388

Multilevel inverters (MLI) have gained a significant emphasis in the area of renewable energy applications in recent years. The popularity of MLIs is due to their ability to efficiently convert the sustainable sources of energy, such as wind and solar power, into usable electrical energy. One key advantage of using reduced multilevel inverter configurations is that it can help to reduce the system overall cost by decreasing number of components required. The reduction of components in the inverter circuit results in simplification of design and lower expenditure. This paper proposes a fifteen-level modified multilevel inverter with cascaded h-bridge with a minimized switch count. The simulation results of the proposed inverter demonstrate that it is able to effectively attenuate harmonics and with reduced number of components required while still providing good performance. The suggested inverter design is also evaluated on its harmonic reduction characteristics, providing a cleaner output waveform and less distortion. © 2023 IEEE.

关键词： Solar energy

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A Highly Interpretable Deep Equilibrium Network for Hyperspectral Image Deconvolution

A Highly Interpretable Deep Equilibrium Network for Hyperspe...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Alexandros Gkillas Dimitris Ampeliotis Kostas Berberidis Dept. of Computer Engineering & Informatics University of Patras Greece Dept. of Digital Media and Communication Ionian University Greece

In this paper, a novel technique for the hyperspectral image deconvolution problem is developed. First, considering the highly ill-posed nature of the examined problem, it is imperative to incorporate proper priors (regularizers) to capture the strong spectral and spatial dependencies of the hyperspectral images. Then, in light of this, a novel optimization problem is proposed by employing a convolutional neural network to act as a regularizer, which is learnt to reflect the properties of the signals of interest. To solve the proposed optimization problem, we use the half quadratic splitting methodology, thus designing an efficient iterative solver (iteration map). Based on the Deep Equilibrium (DEQ) modeling, which aims to express the proposed iterative solver as an equilibrium (fixed-point) computation, a highly interpretable deep learning-based network is derived, which can be trained endto-end. Extensive numerical results using two publicly available datasets illustrate that the proposed method markedly outperforms other state-of-the-art approaches.

关键词： Deep learning Deconvolution Signal processing algorithms Signal processing Numerical models Iterative methods Convolutional neural networks

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Observing Generator Performance by Finite Element Analysis Considering Coil Numbers 5

Observing Generator Performance by Finite Element Analysis C...

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5th International Conference on Technology and Policy in Energy and Electric Power, ICT-PEP 2023

作者： Habibi, Muhammad Afnan Ali Irfani, Mochamad Farhan Gumilar, Langlang Kusumawardana, Arya Aripriharta Baskoro, Angger Universitas Negeri Malang Study Program of Power Plant Engineering Technology Malang Indonesia Universitas Negeri Malang Department of Electrical Engineering and Informatics Malang Indonesia National Yang Ming Chiao Tung University Dept. Electrical Engineering and Computer Sciences Hsinchu Taiwan

ISBN: (纸本)9798350369717

This paper conducts a comprehensive analysis of electrical generator performance using Finite Element Analysis (FEA), with a specific emphasis on the role of coil numbers in influencing generator efficiency and functionality. In the context of modern energy systems, where efficient power generation is paramount, this research aims to elucidate the relationship between the number of coils within a generator and its overall performance, including power output and electromagnetic behavior. Through systematic FEA simulations that vary coil numbers while keeping other parameters constant, this study provides valuable insights into the trade-offs associated with increased coil numbers and enhanced efficiency. These findings have significant implications for optimizing generator designs across various applications, from renewable energy systems to industrial power generation, ultimately advancing our understanding of generator dynamics and contributing to more sustainable and efficient power generation technologies. © 2023 IEEE.

关键词： Finite element method

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Deep Equilibrium Models Meet Federated Learning

arXiv

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arXiv 2023年

作者： Gkillas, Alexandros Ampeliotis, Dimitris Berberidis, Kostas Dept. of Computer Engineering and Informatics University of Patras Greece Dept. of Digital Media and Communication Ionian University Greece

In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL, such as the communication overhead due to the sharing large models and the ability to incorporate heterogeneous edge devices with significantly different computation capabilities. Additionally, a weighted average fusion rule is proposed at the server-side of the FL framework to account for the different qualities of models from heterogeneous edge devices. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning, contributing to the development of an efficient and effective FL framework. Finally, promising initial experimental results are presented, demonstrating the potential of this approach in addressing challenges of FL. © 2023, CC BY.

关键词： Deep learning

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Enhancing Cabbage Plant Protection: CNN Algorithm for Insect Identification

Enhancing Cabbage Plant Protection: CNN Algorithm for Insect...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Remi Senjaya Purnawarman Musa Puji Sularsih Novrina Novrina Witari Aryunani Suhartini Suhartini dept. of Informatics Engineering Gunadarma University Depok Indonesia dept. of Electrical Engineering Gunadarma University Depok Indonesia dept. of Information Technology Gunadarma University Depok Indonesia dept. of Computer Science Gunadarma University Depok Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

People frequently find it straightforward to identify insects that feed on cabbage plants based on the insects’ characteristics. However, challenges arise when employing a computer for precise bug categorization and identification. The implementation of Deep Learning (DL) utilizes the Convolutional Neural Network (CNN) technique, renowned for its effectiveness in categorizing and recognizing objects in digital photo data. The CNN algorithm delivers notable results in photo identification, simulating the human visual brain’s image recognition through artificial neural networks in the Python programming language. To enrich the user experience, frameworks like Keras and TensorFlow are employed in neural network development. Our research will use the CNN algorithm to determine insect species in cabbage images. Achieving high accuracy involves coupling the CNN algorithm with the VGGNet architectural model. Notably, utilizing an epoch value of 150 resulted in a 97.75% accuracy rate during the training model technique; however, the validation accuracy was slightly lower at 94.25%. The corresponding loss and validation rates were 0.0613% and 0.2945%, respectively. Moreover, based on the model (150 epochs), the testing outcomes in the study, involving 48 images beyond the dataset and incorporating additional information, resulted in an accuracy rate of 91.66%.

关键词： Training Visualization Technological innovation Accuracy Insects Prediction algorithms User experience Classification algorithms Convolutional neural networks Testing

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Sentiment Analysis Twitter (X) Users Towards the Boycott of Pro-Israel Brand Using Support Vector Machine Method with SMOTE Approach

Sentiment Analysis Twitter (X) Users Towards the Boycott of ...

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Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE International Conference on

作者： Hana Patricia Mawarnie Widodo Murien Nugraheni Dept. of Informatics and Computer Engineering Education Universitas Negeri Jakarta Jakarta Indonesia Dept. of Information System and Technology Universitas Negeri Jakarta Jakarta Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

The Israel-Palestine conflict that occurred on October 7, 2023, resulted in a boycott of brands known to be pro-Israel. Twitter or X, as one of the largest social media platforms in the world, became a platform for people to voice their opinions regarding the boycott. This study aims to analyze the sentiment of Twitter or X users towards the boycott of pro-Israel brands using the Support Vector Machine (SVM) method with the SMOTE (Synthetic Minority Oversampling Technique) approach. The Support Vector Machine (SVM) method is a machine learning method used for classification and regression. SMOTE is a technique used to overcome data imbalance. The data used in this study is Indonesian language tweet data from October 7, 2023, to April 7, 2024. The data was collected using data crawling techniques. The results of the study from the 80:20 data split, show that the Support Vector Machine (SVM) polynomial kernel model with the SMOTE technique has better performance in analyzing the sentiment of Twitter or X users towards the boycott of pro-Israel brands, with an accuracy value of 98,47%.

关键词： Support vector machines Sentiment analysis Accuracy Social networking (online) Blogs Machine learning Polynomials Internet of Things Kernel Robots

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Emotion Recognition from Video Frame Sequence using Face Mesh and Pre-Trained Models of Convolutional Neural Network

Emotion Recognition from Video Frame Sequence using Face Mes...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Derry Pramono Adi Eko Mulyanto Yuniarno Diah Puspito Wulandari Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Emotions are a collection of subjective cognitive experiences and psychological and physiological characteristics that express a wide range of feelings, thoughts, and behaviors in human interaction. Emotions can be represented through several means, such as facial expressions, tone of voice, and behavior. Deep Learning (DL) research has focused on incorporating facial expressions. Images with facial expressions are commonly used as data input for the DL model. Unfortunately, most DL models in Facial Emotion Recognition (FER) use static images. This method does not take into consideration all conceivable facial expressions. The static image of facial expressions is insufficient for recognizing emotions, but a sequential image from a video is required. In this study, we extract MediaPipe’s face mesh feature, the state-of-the-art multidimensional expression key points embedded in the video image sequence. Furthermore, we feed sequence image data into the pre-trained Convolutional Neural Network (CNN) model. The data we used is from The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) with the emotion classes of “Anger,” “Fearful,” “Happy,” and “Sad.” For this specific FER task, we found that the best pre-trained CNN model achieved 92.8% accuracy (using the VGG-19 model), with the fastest runtime of $\sim2.3$ seconds (achieved using the SqueezeNet model).

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SMART TRANSPORT FOR SMART CITIES: A FUTURISTIC SCENARIO AND A REALISTIC PROJECT 7

SMART TRANSPORT FOR SMART CITIES: A FUTURISTIC SCENARIO AND ...

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7th International Conference on Smart Data and Smart Cities, SDSC 2022

作者： Pantazis, D.N. Argyrakis, P. Moussas, V. Geroulis, G. Daverona, A.C. Stergiou, C. Voyiatzis, I. Aposporis, P. SOCRATES LAB Dept. of Surveying and Geoinformatics Engineering University of West Attica Egaleo Athens Greece Dept. of Mechanical Engineering University of West Attica Aigaleo Athens Greece Dept. of Civil Engineering University of West Attica Aigaleo Athens Greece Dept. of Informatics and Computer Engineering University of West Attica Aigaleo Athens Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece /NTUA Greece

Smart transport for smart cities: our article presents an innovative and original idea in the field of urban air transport with a new type of manned drone. This scenario is analysed starting from some initial futuristic ideas up to the reality of the construction of such a vehicle. After a brief presentation of a list of manned aerial vehicles (manned drones) that reflects the efforts that have been realized until now, a number of parameters and specifications related to such aerial urban transport, are also pointed out. In addition, various emerging issues comprising legislation matters, infrastructure for landing and take-off in cities, navigation methods and other possible impacts on the urban transport, have also been presented shortly. © Author(s) 2022.

关键词： Drones

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Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons

arXiv

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arXiv 2024年

作者： Afentaki, Florentia Duarte, Paula Carolina Lozano Zervakis, Georgios Tahoori, Mehdi B. Computer Engineering & Informatics Dept. University of Patras Patras26504 Greece Department of Computer Science Karlsruhe Institute of Technology Karlsruhe76131 Germany

Printed electronics technology offers a cost-effective and fully-customizable solution to computational needs beyond the capabilities of traditional silicon technologies, offering advantages such as on-demand manufacturing and conformal, low-cost hardware. However, the low-resolution fabrication of printed electronics, which results in large feature sizes, poses a challenge for integrating complex designs like those of machine learning (ML) classification systems. Current literature optimizes only the Multilayer Perceptron (MLP) circuit within the classification system, while the cost of analog-to-digital converters (ADCs) is overlooked. Printed applications frequently require on-sensor processing, yet while the digital classifier has been extensively optimized, the analog-to-digital interfacing, specifically the ADCs, dominates the total area and energy consumption. In this work, we target digital printed MLP classifiers and we propose the design of customized ADCs per MLP’s input which involves minimizing the distinct represented numbers for each input, simplifying thus the ADC’s circuitry. Incorporating this ADC optimization in the MLP training, enables eliminating ADC levels and the respective comparators, while still maintaining high classification accuracy. Our approach achieves 11.2x lower ADC area for less than 5% accuracy drop across varying MLPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Comparator circuits

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ML-Inspired Multiple Relay Selection in UAV-Enabled Untrusted Networks

ML-Inspired Multiple Relay Selection in UAV-Enabled Untruste...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Tsipi, Lefteris Michailidis, Emmanouel T. Maliatsos, Konstantinos Vouyioukas, Demosthenes University of the Aegean Dept. of Information and Communication Systems Engineering Samos83200 Greece University of West Attica Dept. of Informatics and Computer Engineering Egaleo12243 Greece University of Piraeus Dept. of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350368369

In this paper, multiple relay selection (MRS) schemes for untrusted unmanned aerial vehicle (UAV)-enabled networks are proposed. In this context, various machine learning (ML) models are employed to improve secrecy performance by optimally selecting multiple aerial relays from the available ones, all of which are regarded as untrusted. Notably, these ML models can cope with the quickly changing and random positioning of the aerial relays, effectively decoupling the intricate coupling relationship between the secrecy rate, channel coefficients, and inter-node distances. The results indicate that the ML-based MRS schemes obtain sufficient accuracy and better decoupling than a respective exhaustive searching (ES) approach, while also maintaining a lower computational complexity. © 2025 IEEE.

关键词： Drones

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