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The Impact of Weather Conditions on Renewable Energy Production: A Machine Learning Approach

The Impact of Weather Conditions on Renewable Energy Product...

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IEEE International Conference on Big Data

作者： Recep Özbay Merve Çelebi Uraz Yavanoğlu Dept. Of Computer Forensics Graduate School of Informatics Gazi University Ankara Turkey Dept. Of Computer Education and Instructional Technologies Faculty of Education Hatay Mustafa Kemal University Hatay Turkey Dept. of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In recent decades, obtaining energy from wind, solar, and hydro energy has brought hope for a sustainable future. Machine learning (ML) significantly impacts the field of energy systems. ML techniques have emerged as a powerful tool for analyzing and optimizing energy systems. This approach can potentially reduce the cost of analyzing a device by focusing solely on the data generated during its operation. This paper investigates the application of regression models to predict renewable energy production based on weather conditions, aiming to enhance the reliability and efficiency of renewable energy systems. Experiments are performed using Decision Tree (DT), k-nearest Neighbors (KNN), Multiple Linear (MLR), Polynomial (PR), Random Forest (RF), and Support Vector (SVR) Regression algorithms. The performances of ML models are compared based on the evaluations obtained through the Coefficient of Determination (R2), and error measures namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). RF is the most successful algorithm in predicting renewable energy production depending on weather conditions as it has the highest R2 value and the lowest error measures.

关键词： Radio frequency Renewable energy sources Costs Wind energy Biological system modeling Measurement uncertainty Production Nearest neighbor methods Prediction algorithms Reliability

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Land Valuation Using an Innovative Model Combining Machine Learning and Spatial Context

Land Valuation Using an Innovative Model Combining Machine L...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Feride Tanrikulu Timothy L. Haithcoat Grant J. Scott Matthew Foulkes Ilker Ersoy Institute for Data Science and Informatics University of Missouri Dept. Electrical Engineering and Computer Science University of Missouri Dept. of Geography University of Missouri

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Land valuations are used by buyers, sellers, regulators, and authorities to assess fair value. Urbanization demands a modern and efficient land valuation system. An alternative method that integrates geographic information systems and machine learning can produce more reliable, repeatable, and accurate valuations. The land’s value can vary significantly based on the parcel’s neighborhood and context. Geospatial analytics, remote sensing and vector information were integrated to predict land values in Springfield, Missouri, USA. A point geodatabase was assembled using calculated proximities, accessibility, and various index measures. Supervised machine learning models were trained using government-provided appraised land values as ground truth. The database approach integrated spatial context and socioeconomic data. Slight differences in performance between random forest (98%, 0.13) and gradient boosting (98%, 0.15) algorithms were found (Adj. R 2 , STD).

关键词： Regulators Machine learning algorithms Urban areas Spatial databases Vectors Reliability Socioeconomics

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Real-Time Object Detection with IOT Using a Smart Cart

Real-Time Object Detection with IOT Using a Smart Cart

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Muhammad Omer Sardar Jaffar Ali Syed Muhammad Raza Duc-Tai Le Hyunseung Choo Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Dept. of AI System Engineering Sungkyunkwan University Suwon Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea College of Computing and Informatics Sungkyunkwan University Suwon Korea

With Industry 4.0's technological advancements, the convenience and affordability of Artificial Intelligence (AI) and IoT have increased. However, customers still face long waiting times in payment queues during the smart shopping experience. Current solutions rely on expensive sensor-based RFIDs or simple barcodes, making them impractical and limited in scalability. This paper proposes an economically smart solution with two cameras on NVIDIA JETSON NANO for realtime object monitoring and automated shopping. Communication between modules is facilitated by TCP/IP and HTTP protocols. By utilizing the YOLO v7, real-time monitoring is achieved. This innovative approach revolutionizes the customer experience by providing a seamless interface and time-saving features. Additionally, it offers potential integration with e-commerce businesses and payment gateways, further enhancing the overall customer and vendor experience.

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Deep-Learning-based VCSEL transmitter emulator

Deep-Learning-based VCSEL transmitter emulator

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Deligiannidis, Stavros Argyris, Nikos Dris, Stefanos Kalavrouziotis, Dimitrios Bakopoulos, Paraskevas Mesaritakis, Charis Bogris, Adonis Department of Informatics and Computer Engineering University of West Attica Aghiou Spiridonos Egaleo Athens12243 Greece NVIDIA Ermou 56 Athens10563 Greece University of the Aegean Dept. Information and Communication Systems Engineering Palama 2 str. Samos Karlovasi83200 Greece

ISBN: (纸本)9798350345995

The design of photonic/opto-electronic structures is a crucial field of research due to the wide range of applications in which these devices are utilized, such as communications, sensing and imaging. The traditional approach to design, which involves numerical simulation and iterative adjustments, is both time-consuming and computationally intensive. The ability to design-emulate photonic structures quickly and efficiently could bring new technologies to market at a faster pace and can also facilitate the process of extracting critical performance metrics from complex fabricated devices. The Vertical Cavity Surface Emitting Laser (VCSEL) and its use as a transmitter broadly used in data center interconnects is an example of a rather complex nonlinear system also exhibiting bandwidth limitation mechanisms attributed to its frequency response and parasitic effects, and thus its numerical simulation based on conventional rate equations can only partially approximate its behavior. When it comes to modelling fabricated devices, such physical models are notoriously difficult to parameterize in order to fit to observed laboratory data;in contrast, the emulation of the experimentally recorded behavior with the use of nonlinear models based on neural networks provides a much more efficient path to modelling, and with potentially higher prediction accuracy [1]. In this work, two types of bidirectional recurrent neural networks (RNN), namely Long Short-Term Memory (bi-LSTM) and Vanilla (bi-VRNN) were used to simulate the dynamic behavior of an experimentally characterized VCSEL-based, PAM-4 transmitter, exhibiting a high prediction accuracy approaching 100%. © 2023 IEEE.

关键词： Frequency response

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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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Design of Unipolar Inverter Simulation of On Grid Mode With Current Control Method

Design of Unipolar Inverter Simulation of On Grid Mode With ...

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2022 International Conference on Electrical engineering and informatics, ICELTICs 2022

作者： Suriadi Nada, Katrin Ramdhan Halid, S. Syukri, Mahdi Marwan Yanis, Muhammad Affan, Muzailin Nazaruddin Universitas Syiah Kuala Dept. Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Informatics Fmipa Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Geophisic Engineering Banda Aceh Indonesia

ISBN: (数字)9781665487665

ISBN: (纸本)9781665487665

This study explains the simulation design and working principle of unipolar inverters on grid using the current control method using PSIM software. The inverter circuit used is a full bridge inverter circuit using a current control method. The series of filters used is a low pass (LC) filter using an L value of 4.06 mH and a C value of 6.23 μ F. The control circuit used is an SPWM (Sinusoidal Pulse Width Modulation) circuit. The power supplied by the inverter to the grid is affected by the magnitude of the angle, voltage, and the same frequency. The power supplied by the inverter to the grid based on the reference of the control system will run if the angle phase between the inverter and the grid is the same. The grid will accommodate the excess power generated by the inverter and will also supply power if the inverter is under-powered from the solar power plant source. This study also aims to regulate the power required at the load, hence the power supplied to the optimal load, with the main supply coming from the inverter and the lack of power supply can be seconded by the grid from PT. PLN (State Electricity Company). This is shown when the reference current is set at 4 A, the current required load is 5.65 A, where the inverter supplies current to the load by 3.44 A, and the lack in supply from the grid is 2.207 A. The frequency value on the system is constant at 50Hz. The current control method aims to make the current supplied to the load obtained by setting the priority scale of the current supply, where the inverter current is the main current source and the grid as the secondary supply. © 2022 IEEE.

关键词： Solar energy

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Detection of Macrolepiota Procera Mushrooms Using Machine Learning 5

Detection of Macrolepiota Procera Mushrooms Using Machine Le...

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5th World Symposium on Communication engineering, WSCE 2022

作者： Chaschatzis, Christos Karaiskou, Chrysoula Goudos, Sotirios K. Psannis, Konstantinos E. Sarigiannidis, Panagiotis University of Western Macedonia Dept. Electrical and Computer Engineering Kozani Greece Aristotle University of Thessaloniki Dept. Physics Thessaloniki Greece University of Macedonia Dept. Applied Informatics Thessaloniki Greece

ISBN: (数字)9781665450577

ISBN: (纸本)9781665450577

The overpopulation of the latest centuries and recent technological advancements in the primary sector increased demand for improved productivity and product quality in agricultural activities. Modern customers value eco-friendly activities and sustainable consumption. Thus they choose foods with high concentrations of nutritious ingredients. Machine learning algorithms enable the implementation of numerous potential solutions in precision agriculture in conjunction with new potent mechanisms. One algorithm that offers high-precision real-time object identifications is the YOLOv5 (You Only Look Once). Furthermore, to improve the resilience of precision agriculture, this work presents experimental findings from the machine learning algorithm (Yolov5) on a unique dataset based on mushroom crops, such as Macrolepiota Procera species. © 2022 IEEE.

关键词： Precision agriculture

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

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2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Quijano, Andrew Halkidis, Spyros T. Gallagher, Kevin Akkaya, Kemal Samaras, Nikolaos New York University Dept. of Computer Science and Engineering New YorkNY United States University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Florida International University Advanced Wireless and Security Lab MiamiFL United States

ISBN: (纸本)9798350367942

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site."Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks. © 2024 IEEE.

关键词： Differential privacy

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Lung Segmentation using MultiResUNet CNN based on Computed Tomography Image 23

Lung Segmentation using MultiResUNet CNN based on Computed T...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： Ferdinandus, Fx Setiawan, Esther Irawati Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Surabaya Indonesia Institut Sains dan Teknologi Terpadu Surabaya Dept. of Informatics Surabaya Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya Indonesia Surabaya Indonesia

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

Lung segmentation is the first step in medical image processing to determine various lung diseases. Currently, the image segmentation process will be more optimal by using deep learning through the convolution process. Various Convolution Neural Network (CNN) based architectures for image segmentation were created by many researchers, however U-Net is the current state of the art for medical image segmentation. Nevertheless, the modification of U-Net continues, and MultiResUNet is one of the new architectures claimed to be better. In this study, we use MultiResUNet for lung segmentation on Computed Tomography (CT) images as the first step to Covid-19 infection segmentation, and the results will be compared using the U-Net architecture. Based on the results of the segmentation experiment, we got satisfactory results. Using the Mean-IoU evaluation metric, it was concluded that the MultiResUNet score was slightly better than the U-Net score for patient lung segmentation, where there was an increase in the score of 1.33% (MultiResUNet=93.05%, U-Net=91.83%) in the dataset which we use. © 2022 IEEE.

关键词： Semantic Segmentation

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Increasing the RES hosting capacity of the Cyprus distribution system

Increasing the RES hosting capacity of the Cyprus distributi...

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CIRED 2024 Vienna Workshop

作者： Phivos Therapontos Rafail Konstantinou Charalambos A. Charalambous Petros Aristidou Distribution System Operator Electricity Authority of Cyprus Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9781837241224

The Renewable Energy Sources (RES) penetration in the power system of Cyprus has dramatically increased over the last years. As a result, the system is already facing significant challenges limiting the RES hosting capacity of the Distribution Network (DN). The major limitation factors are network congestion and voltage security. In this paper, alternative solutions for increasing the RES hosting capacity in DNs are reviewed, and a methodology is introduced to evaluate their effectiveness. More specifically, different inverter settings and an advanced centralised voltage control from power transformers are used to mitigate voltage-related issues, while network reinforcements and upgrading the operating voltage are considered for further increase in hosting capacity. The solutions and evaluation methodology are tested using a real MV network of the Cyprus distribution system and the strategic plan of the Cyprus Distribution System Operator (DSO) for maximizing RES hosting capacity is outlined.

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