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Enhancing Photovoltaic Cell Parameters Extraction through Grey Wolf Optimizer

IFAC-PapersOnLine 2024年第13期58卷 624-629页

作者： Charaf Chermite Moulay Rachid Douiri Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University 112 Bd Abdelkrim Al Khattabi Marrakech 40000 Morocco

Efficient and precise parameter extraction from solar Photovoltaic (PV) models is paramount for the comprehensive simulation, assessment, and management of PV systems. Despite the proliferation of analytical, numerical, and metaheuristic algorithms aimed at this task in recent years, the extraction of parameters remains a formidable obstacle. This study employs the Grey Wolf Optimizer (GWO) to extract the five key parameters of the RTC France solar cell. The GWO’s performance is systematically compared with metaheuristic algorithms such as Enhanced Chaotic JAYA (CJAYA) and Performance-Guided JAYA (PGJAYA). The study showcases the prowess of GWO in optimizing PV parameters, marking a significant stride forward in the realm of optimization techniques for PV cell modeling. Through meticulous analysis using MATLAB-SIMULINK, the research unveils the profound effectiveness of GWO in navigating the intricate landscape of parameter extraction within PV systems.

关键词： Photovoltaic cell Parameters extraction Metaheuristics algorithm Grey Wolf Optimizer

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High-Accuracy Crop Yield Estimation Through IoT and Remote Sensing

High-Accuracy Crop Yield Estimation Through IoT and Remote S...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mohammed, Raja Salih Malhotra, Rohit Shamout, Mohamed Dawood Pithadiya, Bhavesh Patil, Shrishailappa Sangve, Sunil M Computer Science Institute of Medical Technology Middle Technical University Al-Mansour Baghdad Iraq Electronics and Instrumentation University Karnataka Udupi India College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates Madhuben & Bhanubhai Patel Institute of Technology Applied Science and Humanities Gujarat New Vidhyanagar India Computer Engineering Vishwakarma Institute of Technology Pune*** India Ai & Ds Vit Pune*** India Sppu India

ISBN: (纸本)9798350329773

This paper examines the ability to use present-day technologies with the Internet of Factors (IoT) and far-flung sensing for excessive-accuracy crop yield estimation. Mainly using, soil moisture and temperature sensors, NDVI measurements, plant growth models, and machine learning algorithms are tested to evaluate crop overall performance. Records accrued through the sensors are used to develop crop yield estimation fashions and classifications, which can, as should be, expect consequences. The results of this study show that combining an array of IoT and far-off sensing methods into an included gadget can improve the accuracy and reliability of crop yield estimations. This research can offer a treasured perception of how farmers and other stakeholders come across and extract valuable information from their plants to increase crop productivity. Performance paper presents a high-accuracy crop yield estimation system that mixes the Internet of Things (IoT) and remote Sensing generation. This machine utilizes floor-based totally IoT sensors to gather crop yield information, inclusive of soil moisture readings, temperature, humidity, solar radiation, and extra. This information is mixed with photographs taken from UAVs or satellites to provide a complete view of the yield. The gadget then uses synthetic intelligence and gadget-gaining knowledge of strategies to estimate the crop yield with excessive accuracy. The effects can be compared with historic manufacturing records to enhance destiny yield prediction fashions. This technique can contribute to more green and accurate farm control practices and help improve crop yields.. © 2023 IEEE.

关键词： Internet of things

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Optimizing Virtual Machine Downtime in Live Migration using the Least Frequently Used Service-based Pre-copy Algorithm

Optimizing Virtual Machine Downtime in Live Migration using ...

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International Conference on computer Communication and Informatics (ICCCI)

作者： C. P. Shabariram N. Shanthi P. Priya Ponnusamy R. Prema R. S. Sowmiya Priya Department of Computer Science and Engg PSG Institute of Technology and Applied Research Coimbatore India Department of Computer Science and Engg Kongu Engineering College Erode India

Cloud computing has been considered the most reliable and safe technology among many cloud users. Virtual machine migration is a crucial component of cloud computing technology. A Virtual machine is transferred from one host machine to another during the migration process, which is accomplished by repeatedly copying the memory pages available in the virtual disk. The time required for the movement of secondary storage, main memory, process states, and network connectivity, as well as the maintenance of their accuracy, must be considered for the efficient migration of virtual machines from one physical host to another in the cloud service provider. The most robust mechanism for system maintenance, resilience system, balancing the load of the service provider, and power-saving is the live migration of virtual machines between the nodes in the data center. So many services are running in the virtual machines during live migration; thus, it needs to take place with the least amount of downtime possible to prevent service degradation. The memory pages in the virtual storage must be copied to the other host using the least frequently used service-based pre-copy algorithm to optimize the downtime. The effectiveness of the suggested solution was evaluated in comparison to OpenNebula and OpenStack, two cloud computing platforms. The least frequently used service-based pre-copy algorithm reduces the amount of virtual machine downtime during live migration.

关键词： Degradation Cloud computing Data centers Memory management Maintenance engineering Virtual machining Reliability

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Detection Systems for Distributed Denial-of-Service (DDoS) Attack Based on Time Series: A Review

Detection Systems for Distributed Denial-of-Service (DDoS) A...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Ahmed Adil Nafea Mustafa Maad Hamdi Baraa saad Abdulhakeem Ahmed Thair Shakir Mustafa S. Ibrahim Alsumaidaie Ali Muwafaq Shaban dept. Artificail inteligence College of Computer Science and Information Technology University of Anbar Ramadi Iraq dept. of Computer Science College of Computer Science and IT University of Anbar Ramadi Iraq dept. of Information System College of Computer Science and IT University of Anbar Ramadi Iraq dept. of Biophysics College of Applied Sciences University of Anbar Ramadi Iraq dept. Information System College of Computer Science and Information Technology University of Anbar Ramadi Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

The Distributed Denial-of-Service (DDoS) attacks are one of the most critical threats to the stability and security of the Internet. With the increasing number of devices connected to the Internet, the frequency and severity of DDoS attacks are also increasing. To mitigate the impact of DDoS attacks, intelligent detection systems are becoming increasingly important. This paper reviews the recent literature on intelligent techniques, including machine learning (ML), Deep Learning (DL), and artificial intelligence (AI), for detecting DDoS attacks. We will provide an overview of the existing research in the field and analyse the trends in using time series data analysis for DDoS attack detection. A taxonomy and conceptual framework for DDoS mitigation are presented. This study highlights the use of several intelligent techniques for detecting DDoS attacks and evaluates the performance utilizing real datasets and also discusses future research directions in this field.

关键词： Data analysis Reviews Time series analysis Taxonomy Reinforcement learning Denial-of-service attack Stability analysis

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment

IoT-Enabled Remote-Controlled Raft for Enhanced Water Qualit...

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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Thattapon Surasak Kotcharat Kitchat Nattagit Jiteurtragool Dept. of Computer and Information Science Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Dept. of Computer Science and Information Engineering National Central University Taoyuan Taiwan

ISBN: (数字)9798350382310

ISBN: (纸本)9798350382327

Water is vital for all life, serving as the foundation for plants and aquatic creatures and promoting abundance among all living beings. This study introduces an innovative approach by combining the Internet of Things (IoT) with advanced sensing techniques to assess water quality. The IoT-powered water quality testing raft has been developed to significantly enhance the monitoring capabilities. Equipped with sensors measuring key parameters such as turbidity, pH, and temperature, the raft provides a comprehensive understanding of water quality. Its GPS module enables real-time tracking and maintains location history, ensuring precise sampling. The collected data undergoes robust processing by a Raspberry Pi, ensuring integrity before being showcased on the Thingsboard. The system’s uniqueness lies in its capacity to promptly issue alerts via LINE Notify upon detecting water quality abnormalities, ensuring timely intervention. This IoT-powered raft represents a paradigm shift in water quality assessment, characterised by its precision, remote capabilities, and user-focused approach. Practical applications have been demonstrated through real-world testing in a private lake in Cha-choengsao province, Thailand. The seamless integration of IoT into environmental monitoring marks a significant advancement in ensuring water quality and ecosystem health.

关键词： Temperature sensors Temperature measurement Water quality Turbidity Real-time systems Sensor systems Sensors

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Human Activity Recognition for People with Knee Abnormality Using Surface Electromyography and Knee Angle Sensors 8

Human Activity Recognition for People with Knee Abnormality ...

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Joint 8th International Conference on Digital Arts, Media and technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Faculty of Engineering Department of Computer Engineering Nakorn Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350396546

With the onset of technological advancements, biosensors are being effectively used in a variety of contexts, including the diagnosis of diseases, the promotion of their prevention and rehabilitation, the monitoring of patient health, and human activity recognition (HAR). Recently, HAR research results have been obtained and applied in many commercial applications such as fall detection using smart watch sensors, activity classification using smart home sensors. However, previous HAR models utilizing wearable sensors primarily utilized data from healthy individuals, and such models are frequently inaccurate when applied to individuals with medical mobility impairments. In this work, surface electromyography and goniometer sensors were used to categorize various types of rehabilitation activities based on HAR models developed for individuals with knee abnormalities. To achieve the goal of our study, a deep residual network called ResNeXt was proposed to detect human activities with high performance. To investigate the classification performance of deep learning models, a 5-fold cross-validation technique was applied for both training and testing. Based on the results of our study, the combination of features extracted from the goniometer and electromyograph signals resulted in the highest F1-score (93.31%) and the best accuracy (93.52%). © 2023 IEEE.

关键词： Deep learning

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Leveraging Semantic Parsing using Text Embeddings and Reinforcement Learning

Leveraging Semantic Parsing using Text Embeddings and Reinfo...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Jessenia Piza-Londoño Edgar J. Andrade-Lotero Applied Mathematics and Computer Science Universidad del Rosario Bogotá Colombia School of Engineering Science and Technology Universidad del Rosario Bogotá Colombia

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Representing natural language information is a key challenge in Artificial Intelligence and Cognitive science, requiring the transformation of unstructured data into formats suitable for computational tasks. While logical formalisms offer robust methods for information representation, their complexity often limits widespread adoption. Conversely, transformer architectures provide strong generalization capabilities but struggle with logical inference tasks. To address both the need for generalization and reliable logical inference, we propose a novel approach using deep reinforcement learning, enabling agents to autonomously learn the rules of semantic parsing. Our preliminary results indicate successful generation of appropriate representations for simple queries. Future work will extend the environment to handle a wider range of real-world sentences.

关键词： Translation Semantics computer architecture Information representation Linguistics Transformers Stability analysis Complexity theory Reliability Thermal stability

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PDMA: Probabilistic service migration approach for delay-aware and mobility-aware mobile edge computing

PDMA: Probabilistic service migration approach for delay-awa...

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作者： Xu, Minxian Zhou, Qiheng Wu, Huaming Lin, Weiwei Ye, Kejiang Xu, Chengzhong Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Center for Applied Mathematics Tianjin University Tianjin China School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Lab of IOTSC Department of Computer Science University of Macau China

As a key technology in the 5G era, mobile edge computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an extension of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the user's dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service. Therefore, when a mobile user moves in the MEC environment, certain approaches are needed to schedule services deployed on the edge server to ensure the user experience. In this article, we model service scheduling in MEC scenarios and propose a delay-aware and mobility-aware service management approach based on concise probabilistic methods. This approach has low computational complexity and can effectively reduce service delay and migration costs. Furthermore, we conduct experiments by utilizing multiple realistic datasets and use iFogSim to evaluate the performance of the algorithm. The results show that our proposed approach can optimize the performance on service delay, with 8%–20% improvement and reduce the migration cost by more than 75% compared with baselines during the rush hours. © 2021 John Wiley & Sons Ltd.

关键词： Mobile edge computing

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5G Dual Polarized Sectoral Antenna its Design with MIMO Channel 6

5G Dual Polarized Sectoral Antenna its Design with MIMO Chan...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Khatkar, Monika Vigenesh, M. Kumar Verma, Sanjeev Premananthan, G. Sharma, Sachin Mathurkar, Piyush K. R. Mangalam University Department of Computer Science & Engineering Haryana Gurugram122103 India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Invertis University Department of Applied Sciences Uttar Pradesh Bareilly243123 India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore India School of Engineering and Computer Science Dev Bhoomi Uttarakhand University Uttarakhand 248007 India E&TC Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350304480

Huge networking of items (IoT) with 5G is an exciting future innovation with many benefits. It poses some difficulties because many devices require a strong connection. In this situation, picture-input photo-output (MIMO) devices provide great ability to communicate and volume. With MIMO, there are a problem with antenna coherence. In order to lessen association, this article suggests a photo-sector MIMO home location antenna using 5G-New Media (5G-NR) spectrum N77 using a dual polarization structure. The suggested antenna exhibits an association of less than 0.01 with an aggregate connection of -16.90 dB. Its BER, erroneous bit picture (BER) rate, efficiency attained is remarkably similar to that of picture-correlated antennae having a 1.87 GHz range. It indicates how the recommended antenna had been thoughtfully created. © 2023 IEEE.

关键词： 5G Antenna picture illustration

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Blockchain Based Secure Interoperable Framework for the Internet of Medical Things 16th

Blockchain Based Secure Interoperable Framework for the Inte...

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16th International Conference on Information technology and Applications, ICITA 2022

作者： Rafique, Wajid Shah, Babar Hakak, Saqib Khan, Maqbool Anwar, Sajid Department of Computer Science and Operations Research University of Montreal Quebec Canada Center of Excellence in IT Institute of Management Sciences Peshawar Pakistan Faculty of Computer Science Canadian Institute for Cybersecurity University of New Brunswick Fredericton Canada Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Pakistan Software Competence Center Hagenberg Vienna Austria College of Information Technology Zayed University Academic United Arab Emirates

ISBN: (纸本)9789811993305

Internet of Medical Things (IoMT) has revolutionized the way medical infrastructure has been managed in the past. Multiple platforms in IoMT have disparate communication standards, data format requirements, and access policies, which produce immense overhead during data transfer among these platforms. In order to provide seamless healthcare services using IoMT, interoperability concerns of heterogeneous devices need to be addressed. Smart contracts using blockchain provide a secure communication for distributed objects to interact in a secure way. We propose a Blockchain-based Secure Interoperable Framework (BSIIoMT) using smart contracts for secure communication in IoMT. We present components, workflow, and design considerations of the BSIIoMT framework to show the feasibility of using edge-enabled blockchain for secure interoperability in IoMT. The BSIIoMT framework is an ongoing project where we present the framework and its components in this research where further results and evaluation will be presented in future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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