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Enhancing DC microgrids cluster performance with distributed event-triggered consensus protocols

e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 10卷

作者： Ghanimi, Hanan Mohammed Jawad Ghoreishy, Hoda Mirimani, Seyyed Mehdi Electrical & Computer Engineering Faculty Babol Noshirvani University of Technology Iran

Microgrid (MG) clusters offer solutions to the challenges faced by individual MGs in extreme conditions but require precise coordination to manage generation-load imbalances effectively. This paper presents a hierarchical control strategy that combines time-based and event-based consensus protocols (ETC) in the overarching control layer. Traditional time-based protocols require constant sampling, leading to unnecessary communication and computational burdens. To address this, we introduce a mixed time-state dependent distributed event-triggered consensus protocol (MDETC), which reduces communication needs while maintaining MG performance. A set-time consensus algorithm is used at the secondary control level to synchronize currents and voltages quickly. Additionally, the integration of Particle Swarm Optimization (PSO) in primary control ensures efficient current distribution and voltage regulation among neighboring MGs. The study also explores automatic reconfiguration within the cluster to support Plug-N-Play functionality, ensuring seamless MG operation. The effectiveness of this control approach is demonstrated through MATLAB simulations of a four-DC MG cluster, showing improved efficiency in reducing triggering instances and achieving faster current and voltage stabilization under various conditions. © 2024 The Author(s)

关键词： Clustered microgrids Dc microgrids Distributed control strategies Droop control strategy Event triggered

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AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations 18

AdaptiveCloset: Reinforcement Learning in Personalized Cloth...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Majeed, Omar Kashif Ul Abideen, Zain Arshad, Usama Ali, Raja Hashim Habib, Abdullah Mustafa, Rafay BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, AI's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. In this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty. © 2023 IEEE.

关键词： Reinforcement learning

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Network Anomaly Detection with Stacked Sparse Shrink Autoencoders and Improved XGBoost

Network Anomaly Detection with Stacked Sparse Shrink Autoenc...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Guan, Ziyue Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms. © 2023 IEEE.

关键词： autoencoders feature extraction Network anomaly detection particle swarm optimization XGBoost

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Improved Alzheimer's Disease Early Prediction using Machine Learning Method : A Comprehensive Diagnostic Approach with Random Forest Classifier

Improved Alzheimer's Disease Early Prediction using Machine ...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Madiha Munawar Monika Singh T Kishor Kumar Reddy C Marlia Mohd Hanafiah Department of Computer Science and Engineering Stanley College of Engineering and Technology for Women Hyderabad India Faculty of Science and Technology Universiti Kebangsaan Malaysia Malaysia

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Alzheimer's Disease (AD) often affects the elder persons and is the prevalent kind of Dementia. AD has a huge expense, especially when it comes to the treatment. AD is a main reason the older generations die whereas when Alzheimer Disease is detected early there will be minimal deaths and accurate diagnosing likely occurs in most cases. Using anonymized Oasis longitudinal MRI data, the study trains several machine learning algorithms to identify AD patients. Analyzing the test result includes utilizing different learning strategies or algorithms e.g., Ensemble Random Forest (RF), Hierarchical Decision Tree (DT), Logistic Regression (LR), Convolutional Neural Network (CNN), Deep Neural Network binary classifier (DNN), soft voting classifier (SVC), transfer learning approaches (TLA), Support Vector Machine (SVM) and boosting AdaBoost in reference of their Accuracy, Error Rate, AUC (area under the curve), Time Taken and Recall for each method. The obtained results concluded that the proposed Random Forest classifier achieves higher accuracy (92.84%), Error Rate (7.16%), AUC (95%) (Area Under Curve) and Recall (89%). The Random Forest classifier also achieves higher Time Taken that is 483.654 sec.

关键词： Support vector machines Deep learning Machine learning algorithms Accuracy Error analysis Magnetic resonance imaging Prediction algorithms Classification algorithms Alzheimer's disease Random forests

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Advanced Deployment Strategies for Elastic Load Balancing in AWS: A Comprehensive Study on Multi-Tier Architecture Optimization

Advanced Deployment Strategies for Elastic Load Balancing in...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Sonam Sharma Rajendra Prasad Mahapatra Dambarudhar Seth Manoj Kapil Department of Computer Science Engineering SRM Institute of Science and Technology Ghaziabad India Faculty of Engineering and Technology Swami Vivekananda Subharti University India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Load Balancing is the concept of distributing the load evenly to multiple servers in the cloud environment. It basically deals with the concept of forwarding the traffic reaching out to the servers in a particular on-premises centre so that each server can handle it with proper management and efficiency. Here, in this paper, I would be focusing on deployment of load balancer in the cloud provider AWS which will further forward the traffic to multiple instances in Auto Scaling group. Along with the distribution of the traffic, we need to keep this in focus that Load Balancer not only distributes the traffic but also check the health checks of a particular instance in AWS. This is actually been done so that if any server is facing down time or any technical issue or is not working properly could not be serving any sort of data or traffic coming from internet.

关键词： Cloud computing Terminology Focusing Logic gates Load management Virtual machines Telecommunication computing Servers Low latency communication Optimization

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Binary Tumbleweed Algorithm for Application of Feature Selection 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Yuan, Xu Pan, Jeng-Shyang Chu, Shu-Chuan Snášel, Václav College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava708 00 Czech Republic College of Science and Engineering Flinders University 1284 South Road TonsleySA5042 Australia

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

The tumbleweed algorithm (TA) is a new swarm intelligence optimization algorithm based on tumbleweed seedling growth and seed propagation. In this paper, a binary tumbleweed algorithm (BTA) is proposed to solve the binary optimization problem. The continuous to binary conversion is critical, the binary tumbleweed algorithm uses transfer function to implement the continuous to binary conversion. To verify the effectiveness of the proposed algorithm, this paper uses 23 benchmark functions to test. The proposed BTA has strong competitiveness on 23 benchmark functions. Finally, the algorithm was successfully used for feature selection in the intrusion detection dataset and achieved satisfactory results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Transfer functions

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Optimizing Energy Efficiency in 5G Networks: Integrating Multi-Packet Reception with Reinforcement Learning and Genetic Algorithms 8

Optimizing Energy Efficiency in 5G Networks: Integrating Mul...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Arun, V. Gowtham, A. Arul, U. Angeljulie, J. School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India Madanapalle Andhra Pradesh Annamayya India Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India Karpagam Academy of Higher Education Department of Cse Tamilnadu Coimbatore India

ISBN: (纸本)9798331505462

The rapidly expanding data demand and the explosion of connected devices require energy-efficient answers in 5G networks. This study proposes a three-pronged solution that integrates Multi-Packet Reception (MPR), Reinforcement Learning (RL), and Genetic Algorithms (GA) to improve energy efficiency in 5G networks. MPR allows base stations to process incoming data more efficiently by receiving multiple packets at once. This not only helps the base station 'talk' more efficiently with the devices that are supposed to be connected to it at any moment (which obviously is more inefficient under 5G, the way that MPR can help be more efficient, makes it the prime candidate for meeting the challenge at hand) but also allows the incoming data to be processed at the same rate it could under the old conditions of processing one packet at a time. We use Genetic Algorithms to directly tackle what we consider the core problems of the 5G system: how to efficiently (jointly) serve users and how to efficiently allocate power among the serving base stations to minimize power consumption without sacrificing service quality (QoS). We developed an integrated method that demonstrates how to simultaneously achieve three goals: (1) significant energy savings;(2) improved network performance;and (3) enhanced sustainability under 5G conditions.... We assume a base station [BS] model close to real-world. Based on this model, we simulate our method in several scenarios. In these scenarios, we validate the network performance gains, energy savings, and enhanced sustainability that we design our method to achieve. © 2024 IEEE.

关键词： Base stations

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques 18

Deciphering Faces: Enhancing Emotion Detection with Machine ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ishaq, Muhammad Haseeb Mustafa, Rafay Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim Habib, Abdullah Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies. © 2023 IEEE.

关键词： Emotion Recognition

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Fast Simple Image Encryption Technique Based on Chaos Based System

Fast Simple Image Encryption Technique Based on Chaos Based ...

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2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Ddiab, Tamer O. Sobky, Wageda I.Al Serageldin, Salwa M. Mahmoud, Ahmed Khairy Zaky, Hany Nasry Hogo, Mofreh A. Banha University Benha Faculty of Engineering Electrical Engineering Department Banha13511 Egypt Banha University Faculty of Engineering Basic Engineering Sciences Department Banha13511 Egypt Taif University College of Computers and Information Technology Department of Computer Engineering Saudi Arabia Tanta University Faculty of Engineering Department of Electronics and Electrical Communications Engineering Egypt Alexandria University Air Defense College Alexandria Egypt Military Technical College Department of Mathematics Egypt

ISBN: (纸本)9798350326062

In this paper, a novel image encryption technique based on chaotic maps is described. Encrypting an image with chaotic maps ensures that the safe cipher characteristics of confusion and diffusion are met. Moreover, chaos-based image cryptosystems have many features compared to the classical cryptographic techniques such as fast, effective security, minimum computing overheads, extremely sensitive to the control parameters and initial settings. These all features make effective encryption techniques with the best performance. Four chaotic maps are used in the proposed scheme to generate the encryption key, initial vector, and ciphering encryption S-Boxes. The maps used are the Chebyshev map, 1D improved quadratic map, 1D Improved Logistic Map and Altered Sine-Logistic based Tent map ASLT. © 2023 IEEE.

关键词： Cryptography

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Retraction Note: A blockchain-based electronic medical records management framework in smart healthcare infrastructure(Multimedia Tools and Applications, (2023), 82, (35419–35437), 10.1007/s11042-023-15152-z)

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Multimedia Tools and Applications 2025年第15期84卷 15625-15625页

作者： Zakzouk, Amaal El-Sayed, Ayman Hemdan, Ezz El-Din Alexandria Higher Institute of Engineering and Technology Alexandria Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menoufia Egypt

The Editor-in-Chief has retracted this article because it contains significant textual overlap with two prior publications with no common authors [1, 2]. The Publisher was not able to confirm a current contact email for Ayman El-Sayed. The remaining authors did not respond to correspondence from the Publisher about this retraction. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

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