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11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Krpan, Matej Erceg, Igor Kuzle, Igor Pandžić, Hrvoje University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia Department of Electric Machines Drives and Automation Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

ISBN: (纸本)9798350399660

This paper derives a small-signal model of a three-phase synchronous reference frame phase-locked loop for use in system frequency response studies assuming a system of balanced three-phase voltages. The small-signal model is described by a second-order transfer function which relates the actual to the estimated grid frequency. The small-signal model behaves identically to the large-signal model for small disturbances. Simulation results demonstrate that phase-locked loop dynamics can be neglected in the high-inertia scenarios, while in the low-inertia scenarios they have a significant impact on the grid frequency and should be included in models. © 2022 IEEE.

关键词： Phase locked loops

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Analytical Characterization of Epileptic Dynamics in a Bistable System 63

Analytical Characterization of Epileptic Dynamics in a Bista...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Qin, Yuzhen El-Gazzar, Ahmed Bassett, Danielle S. Pasqualetti, Fabio Van Gerven, Marcel Donders Institute for Brain Cognition and Behaviour Radboud University Department of Machine Learning and Neural Computing Nijmegen Netherlands University of Pennsylvania The Santa Fe Institute Department of Bioengineering The Department of Electrical & Systems Engineering The Department of Physics & Astronomy The Department of Psychiatry The Department of Neurology United States University of California at Riverside Department of Mechanical Engineering United States

ISBN: (纸本)9798350316339

Epilepsy is one of the most common neurological disorders globally, affecting millions of individuals. Despite significant advancements, the precise mechanisms underlying this condition remain largely unknown, making accurately predicting and preventing epileptic seizures challenging. In this paper, we employ a bistable model, where a stable equilibrium and a stable limit cycle coexist, to describe epileptic dynamics. The equilibrium captures normal steady-state neural activity, while the stable limit cycle signifies seizure-like oscillations. The noise-driven switch from the equilibrium to the limit cycle characterizes the onset of seizures. The differences in the regions of attraction of these two stable states distinguish epileptic brain dynamics from healthy ones. We analytically construct the regions of attraction for both states. Further, using the notion of input-to-state stability with respect to small inputs, we analytically show how the regions of attraction influence the robustness of the system subject to external perturbations. Generalizing the bistable system into coupled networks, we also find the role of network parameters in shaping the regions of attraction. Our findings shed light on the intricate interplay between brain networks and epileptic activity, offering mechanistic insights into potential avenues for more predictable treatments. © 2024 IEEE.

关键词： Neurons

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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Computer-Aided Potato Disease Detection by Using Deep Learning Techniques

Computer-Aided Potato Disease Detection by Using Deep Learni...

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International Conference on Green Energy, computing, and Intelligent Technology, GEn-CITy 2023

作者： Razaq, Fareeha Bilal, Muhammad Ramzan, Muhammad Naveed, Muhammad Razzaq, Samreen Department of Computer Science University of Sargodha Sargodha Pakistan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (纸本)9789819998326

Potato is the most widely grown and consumed food throughout the world. There are a number of potato crop diseases that affect production, and these diseases differ in symptoms, circumstances, and controls. Early detection and recognition of disease information can aid in disease prevention and production. This paper presents deep learning models using proposed CNN and pre-trained models for potato disease detection and classification. The proposed model is more efficient and accurate at detection and classification. To perform classification, we used two datasets: PLD and PlantVillage. The Xception CNN model serves as the foundation for our proposed model. It achieved 1.00 accuracy, 0.99 precision, 1.00 recall, and 0.99 F1-score on the PLD dataset. On the PlantVillage dataset, it achieved 1.00 accuracy, 1.00 precision, 1.00 recall, and 1.00 F1-score. We also compared the results of Inception-ResNet-V2, MobileNet-V2, VGG-19, Inception-V3, and Xception models with the performance of our proposed model. For three classes of potato leaves, the proposed CNN model produced more accurate results than other pre-trained models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

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Computers, Materials & Continua 2022年第5期71卷 4151-4166页

作者： Maha Farouk S.Sabir Irfan Mehmood Wafaa Adnan Alsaggaf Enas Fawai Khairullah Samar Alhuraiji Ahmed S.Alghamdi Ahmed A.Abd El-Latif Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversitySaudi Arabia Centre for Visual Computing Faculty of Engineering and InformaticsUniversity of BradfordBradfordU.K Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahP.O.Box 23713Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoufia University32511Egypt

Today,due to the pandemic of COVID-19 the entire world is facing a serious health *** to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of *** governmental bodies of different countries imposed that wearing a face mask is compulsory in public ***,it is very difficult to manually monitor people in overcrowded *** research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of *** paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or *** proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked ***,this work applies to real-time and can be implemented in any public service area.

关键词： COIVD-19 deep learning faster-RCNN object detection transfer learning face mask

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Enhancing Perception for Autonomous Driving systems with Dual-Vision Input Cycle GAN for Night-To-Day Image Translation 8

Enhancing Perception for Autonomous Driving Systems with Dua...

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8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Fernando, K.J.P. Lakmal, H.K.I.S. Dissanayake, M.B. Kalansooriya, L.P. NSBM Green University Department of Electrical Electronic & Systems Engineering Homagama Sri Lanka NSBM Green University Department of Mechatronic & Industrial Engineering Homagama Sri Lanka University of Peradeniya Department of Electrical and Electronic Engineering Peradeniya Sri Lanka General Sir John Kotelawala Defence University Faculty of Computing Ratmalana Sri Lanka

ISBN: (纸本)9798331509170

Autonomous Driving systems (ADS) and Advanced Driver Assistance systems (ADAS) heavily rely on vision-based data for critical decision-making. However, these systems face significant performance limitations in low-light conditions where conventional RGB vision inputs struggle to capture information due to low light. Current solutions, including high-gain cameras or LiDAR-based approaches, are expensive and increase system complexity. This study presents a novel approach to address these challenges by employing a night-To-day image translation model with modified CycleGAN. The proposed model translates RGB and infrared (IR) inputs into a daytime representation, enhancing overall perception and object detection in low-light scenarios. Unlike existing methods, the model operates in an unsupervised learning framework, avoiding the need for large, paired datasets, which are often difficult to obtain. The study also introduces architectural modifications to the CycleGAN, including a dualinput framework and refined architecture, to optimize performance across varied conditions. The model's performance is demonstrated through reference-less evaluation metrics, achieving average scores of FID 8.04, NIQE 4.92, and BRISQUE 36.3. These results illustrate the potential of the model to enhance the safety and operational stability of autonomous driving systems in low-light environments. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Oil Spill Detection Using 2D Convolution Neural Network and Generative Adversarial Network 4

Oil Spill Detection Using 2D Convolution Neural Network and ...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Madhura, R. Nikkath Bushra, S. Syed Ismail, A. Haleema, P.K. SRM Institute of Science and Technology Department of Computing Technologies KattankulathurTamil Nadu Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Tamil Nadu Chennai India School of Computing and Engineering University of West London RAK Branch Campus United Arab Emirates

ISBN: (纸本)9798350385793

Finding oil spills in the ocean is one of the most crucial tasks in preserving our ecosystem. Using satellite or aerial photographs as input to a deep learning model that makes use of 2D CNN (Convolution Neural Network) and GAN (Generative Adversarial Network). Deep learning assists in automatically detecting oil spills in the ocean. A strong model that may be applied to volumetric data is 2D CNN. To protect the environment and its biodiversity, it is critical to identify and minimize oil spills in delicate maritime environments. Conventional techniques for tracking oil spills in satellite imagery sometimes depend on labor-intensive, human error-prone manual inspection. When deep learning models are taught with a lot of data, they perform well. The Gathering of datasets, however the situation proved to be challenging. As a result, employing Generative Adversarial Networks (GAN) to produce new photos by utilizing preexisting ones. As a result, by giving the model a suitable number of photos to use for training and testing, it improves accuracy. © 2024 IEEE.

关键词： Satellite imagery

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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Computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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Plant Disease Detection and Classification using Emperor Penguin Optimizer (EPO) based Region Convolutional Neural Network (RCNN) 2

Plant Disease Detection and Classification using Emperor Pen...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Shanbhog, Manjula Bhatia, Neha Singh, A Swathi Pavithra, M. School of Sciences Department of Computer Science Delhi NCR India Panipat Institute of Engineering and Technology Department of Information Technology Haryana Panipat India MIT-ADT School of Computing Department of Information and Technology Maharashtra Pune India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350384277

Agriculture stands as India's most crucial industry, despite grappling with a 35% annual loss in crop yield attributed to plant diseases. Traditionally, the detection of plant diseases has been a laborious process, hampered by insufficient laboratory infrastructure and expert knowledge. Plant disease detection methods that are automated provide a useful way to expedite the labor-intensive process of keeping an eye on large-scale agricultural fields and recognizing disease symptoms as soon as they appear on plant leaves. Current developments in deep learning (DL) and computer vision have highlighted the benefits of creating autonomous models for plant disease identification based on visible symptoms on leaves. In this study, we propose a novel method for detecting and classifying plant diseases by combining the Emperor Penguin Optimizer (EPO) with a Region Convolutional Neural Network (RCNN). The suggested methodology uses EPO to improve the discriminative power of features extracted from plant pictures, allowing for a more robust and accurate classification procedure. The Classification Region Convolutional Neural Network (RCNN) is used to leverage spatial correlations within the image, allowing for exact disease region localization. The goal of this integration is to increase the overall efficiency and dependability of plant disease detection systems. The investigations made use of the well-known PlantVillage dataset, which comprises 54,305 data of different plant disease types in 38 categories. Furthermore, an analysis was carried out in comparison with similar advanced investigations. According to the experiment results, RCNN-EPO outperformed in terms of classification accuracy, achieving 94.552%. © 2024 IEEE.

关键词： Convolutional neural networks

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