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9th IEEE International Conference on Photonics, ICP 2022

作者： Zin, Z.H.M. Daniyal, W.M.E.M.M. Fen, W.Y. Noor, A.S.M. Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Systems Engineering Serdang Selangor43400 Malaysia Universiti Putra Malaysia Faculty of Science Serdang Selangor43400 Malaysia Universiti Putra Malaysia Faculty of Engineering Wireless and Photonics Research Centre of Excellence Serdang Selangor43400 Malaysia

ISBN: (数字)9781665427661

ISBN: (纸本)9781665427661

A gold-based surface plasmon resonance (SPR) biosensor for detection of uric acid (UA) is reported. The unique electronic and optical properties of black phosphorous like direct band gap, smaller work function, high charge carrier mobility, and better binding of molecules on sensor surface are predicted to have a better bonding with analytes thus better sensitivity towards UA detection. The thickness of gold layer is optimized at 633 nm wavelength to achieve best possible sensing performance parameter. Various UA concentration varying from 1-9 mg/dl (similar to saliva UA concentration) are prepared and tested with the developed biosensor. It is observed that the developed biosensor capable of differentiating UA in saliva from 1-9mg/dl © 2022 IEEE.

关键词： Surface plasmon resonance

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Performance Evaluation of YOLO Models in Varying Conditions: A Study on Object Detection and Tracking

Performance Evaluation of YOLO Models in Varying Conditions:...

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International Conference on computer and Applications (ICCA)

作者： Ahmed S. Mekled Slim Abdennadher Omar M. Shehata Multi-Robot Systems (MRS) Research Group Cairo Egypt Faculty of Engineering and Materials Science German University Cairo Egypt Faculty of Engineering German University Cairo Egypt Computer Science Faculty of Informatics German International University Egypt Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Object detection and tracking are essential components in autonomous systems, enabling accurate environmental perception and real-time decision-making. Despite the widespread use of advanced frameworks such as YOLO, RCNN, SSD, FPN, and RetinaNet in applications like autonomous driving and surveillance, these systems often experience degraded performance in images with poor lighting conditions caused by adverse weather and other external factors. Recent developments have introduced various deep-learning and image-processing techniques to address these challenges. This paper provides an extensive evaluation of three state-of-the-art object detection models-YOLOv5, YOLOv8, and YOLOv9 under diverse experimental conditions, including varying confidence thresholds (50%, 60%, 70%, and 80%), different object poses, lighting conditions (day and night), and camera configurations (static and dynamic). The evaluation is conducted using key performance metrics such as mean Average Precision (µAP), Intersection over Union (IoU), Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), Identity F1 score (IDF1), precision, and recall. The study offers a comprehensive analysis of the models' performance and suitability for different real-world applications, delivering practical insights for model selection and deployment. Additionally, it highlights potential areas for future research aimed at enhancing model robustness and effectiveness in challenging environments.

关键词： YOLO Performance evaluation Analytical models Technological innovation Accuracy Surveillance Lighting Real-time systems Robustness Object tracking

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Leveraging Digital Twins and Demand Side Recommender Chatbot for Optimizing Smart Grid Energy Efficiency

Leveraging Digital Twins and Demand Side Recommender Chatbot...

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2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Tallinn University of Technology Department of Software Science Akadeemia tee 15a Tallinn12618 Estonia Tallinn University of Technology Department of Computer Systems Akadeemia tee 15a Tallinn12618 Estonia Technion - Israel Institute of Technology Faculty of Electrical & Computer Engineering Haifa3200003 Israel

ISBN: (纸本)9798350327748

Electricity consumers often face the challenge of selecting an optimal plan for saving energy. Strategic energy management and monitoring plays a key role in overcoming these challenges. Developments around Industry 5.0 powered smart grid proffers adequate solutions which allows end-consumers to monitor their energy performance towards effecting demand side recommendation services. Specific problems where end-users are likely to ignore recommended advice exists, thereby contributing to widening 'knowledge-action gap'. An ensemble of hybrid digital twins (DT) asset modelling based on ordinary differential equation (ODE) physics engine and data driven recurrent neural network (RNN) prediction approach alongside PageRank based asset behavior scoring algorithm deployed for demand side recommender and generative pre-trained transformers (GPT) based conversational chatbot technology show effectiveness in engaging and extending end-consumers interests in recommended advice. The novelty of the study lies in extending current scope of demand side recommender scheme via conversational chatbot interface for DT of electricity grid assets that better engage and monitors end-user's energy behavior while offering appropriate energy efficiency advice towards achieving energy conservation goals of smart grid consumers. Extensive experiments, including evaluation of end-user studies, revealed the effectiveness of proposed approach in terms of improved recommendation quality and user engagement towards net electricity demand reduction. © 2023 IEEE.

关键词： Energy efficiency

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StixelNExT: Toward Monocular Low-Weight Perception for Object Segmentation and Free Space Detection

arXiv

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arXiv 2024年

作者： Vosshans, Marcel Ait-Aider, Omar Mezouar, Youcef Enzweiler, Markus The Institute for Intelligent Systems The Faculty of Computer Science and Engineering University of Applied Sciences Esslingen Germany Universite Clermont Auvergne INP CNRS France

In this work, we present a novel approach for general object segmentation from a monocular image, eliminating the need for manually labeled training data and enabling rapid, straightforward training and adaptation with minimal data. Our model initially learns from LiDAR during the training process, which is subsequently removed from the system, allowing it to function solely on monocular imagery. This study leverages the concept of the Stixel-World to recognize a medium level representation of its surroundings. Our network directly predicts a 2D multi-layer Stixel-World and is capable of recognizing and locating multiple, superimposed objects within an image. Due to the scarcity of comparable works, we have divided the capabilities into modules and present a free space detection in our experiments section. Furthermore, we introduce an improved method for generating Stixels from LiDAR data, which we use as ground truth for our network. © 2024, CC BY.

关键词： Image segmentation

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Electromagnetic-Thermal Performance Trade-Offs in Phased Arrays with Shaped Elevation Patterns

Electromagnetic-Thermal Performance Trade-Offs in Phased Arr...

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Feza Turgay Celik Changxu Zhao Alexander Yarovoy Yanki Aslan Department of Microelectronics Microwave Sensing Signals and Systems Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

Both thermal and electromagnetic performance of substrate-integrated waveguide (SIW) and microstrip line-fed shaped-beam arrays with slot and patch radiating elements are conducted. Three array types operating at 26 GHz band, namely SIW slot array, SIW array with patches, and proximity coupled patch array, are considered. The array performances regarding shaped radiation pattern stability with frequency and maximal temperature at the power amplifier chips are discussed. The study highlights intriguing trade-offs between radiation pattern performance and cooling ability in phased arrays.

关键词： Phased arrays Microstrip antenna arrays Time-frequency analysis Electromagnetic waveguides Thermal conductivity Thermal analysis Microstrip

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Applications of Digital Twins for Demand Side Recommendation Scheme with Consumer Comfort Constraints

Applications of Digital Twins for Demand Side Recommendation...

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

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel

ISBN: (纸本)9798350396782

The next evolution of traditional energy systems towards smart grid will require end-consumers to actively participate and make informed decisions regarding their energy usage. Industry 4.0 facilitates such progress by allowing more advanced analytics and creating means for end-consumers and distributed grid assets to be modelled as their Digital twins (DT) equivalents, paving the way for asset-level analytics. Note-worthily, consumers’ comfort is crucial towards promotion of easy adoption of such models from consumers’ perspectives. This study presents the application of hybrid DT and multiagent reinforcement learning models for real-time estimation of end-consumers future energy behaviors while generating actionable recommendation feedback for improving their energy efficiency and enhancing end-user comfort. © 2023 IEEE European Union.

关键词： Consumer comfort Demand side recommender system Distributed power systems Hybrid digital twins Industry 5.0

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Emerging Role of Industry 5.0 Digital Twins in Demand Response Electricity Market and Applications

Emerging Role of Industry 5.0 Digital Twins in Demand Respon...

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

ISBN: (纸本)9798350396782

Traditional power system is facing challenges demanding new operational requirements to meet targets of Net Zero Emissions by 2050. Aggregators are playing progressively important role in the demand response (DR) electricity market but are often riddled with deep level of market monopoly and lack of transparency/secrecy. Emerging real-time information technology (IT) applications and novel modelling of digital twins (DT) of individual electricity assets are challenging this position by promising improvements and openness that allows TSO direct access to available demand side flexibility. Additionally, DT technologies are facilitating how demand response services are delivered to end-users by allowing individual assets participation at the atomic level. In this study, the application potentials of assets’ DT in participating in the demand response electricity market was examined. Again, an overview of DT applications for DR was conducted. The novelty of this study is highlighted in development of new approach that facilitates individual end-user assets’ contribution to demand response efforts. The research identifies key useful questions that might serve as inspiration for stakeholders and policy-makers to further close existing gaps in the field of DT, smart-grid and demand response. © 2023 European Union.

关键词： Aggregators demand response digital twins Industry 5.0 smart-grid

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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Sunflower Array of Infinitesimal Dipoles for Constrained Antenna Modeling

Sunflower Array of Infinitesimal Dipoles for Constrained Ant...

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European Conference on Antennas and Propagation, EuCAP

作者： Nehir Berk Onat Alexander Yarovoy Yanki Aslan Department of Microelectronics Faculty of Electrical Engineering Microwave Sensing Signals and Systems Group Mathematics and Computer Science Delft University of Technology Delft The Netherlands

The sunflower array topology concept is introduced, for the first time, to the constrained infinitesimal dipole modeling (IDM) technique to increase the computational efficiency and reduce the modeling errors. The concept is applied to embedded element pattern predictions via matrix inversion. A novel study on the impact of the type and orientation of the dipoles on the IDM performance in pattern mean square error (MSE) and stability against noise (linked to the matrix condition number) is conducted. A 5 by 5 patch antenna array modeled with 81 dipoles is used for demonstration. It is shown that using magnetic dipoles (oriented in the direction of a radiating edge of the patch) in IDM yields the optimal performance. Besides, the sunflower topology significantly lowers the MSE (by 5 dB, on average), while reducing the condition number by a factor of 10.

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System for Predicting Heart Disease using Sophisticated Machine Learning Techniques

System for Predicting Heart Disease using Sophisticated Mach...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Pandey, Trilok Nath Bhusan Dash, Bibhuti Barik, Lalbihari De, Utpal Chandra Tripathy, Abinash Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Kiit Deemed to Be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350308167

Cardiovascular illness stands as a significant global health issue, and timely identification of this condition holds paramount importance to ensure effective intervention. In this paper, we introduce a fused model for anticipating heart ailments. This amalgamation incorporates four distinct algorithms: LightGBM, XGBoost, Multilayer Perceptron, and AdaBoost. Through an assessment on the Cleveland dataset, AdaBoost emerged as the most accurate with a precision of 0.985. Subsequently, XGBoost followed closely with an accuracy of 0.980, succeeded by LightGBM at 0.956, and Multilayer Perceptron at 0.936. To harness the collective power of these models, we employed a weighted bagging technique that accords weights to predictions based on their individual accuracy rates. The resultant composite model attained an accuracy of 0.96. This outcome is slightly lower than the model with the highest accuracy, as it involves a weighted mean calculation across model predictions. This study indicates the potential utility of the proposed methodology in cardiac disease prediction, leveraging the ensemble approach to potentially outperform standalone models. © 2023 IEEE.

关键词： AdaBoost Ensemble model Heart Disease LightGBM Machine Learning Multilayer Perceptron XGBoost

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