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Impact of the Increasing Integration of Photovoltaic Systems and Charging Stations for Electric Vehicles with Vehicle-To-Grid Concept on Voltage Stability of the Distribution Grid 20

Impact of the Increasing Integration of Photovoltaic Systems...

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20th International Conference on Smart Technologies, EUROCON 2023

作者： Kljajic, Ruzica Misljenovic, Nemanja Maric, Predrag Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9781665463973

The increasing integration of renewable energy sources (RES) and variable loads can stress the operation of the distribution grid during certain times of the day. In another way, electric vehicles, which are assumed to take an increasing share in the overall transportation structure, are considered as variable loads with the ability to store energy and thus support stability of the distribution grid. This paper focuses on a model of a prosumer-rich distribution grid with increasing integration of photovoltaic (PV) systems, electric vehicle charging stations (EVCS) as well as vehicle-to-grid (V2G) in the foreseeable future. The impact of integration on voltage stability was analyzed for the different operating scenarios considering voltage profiles and characteristic P-V curves. © 2023 IEEE.

关键词： Vehicle-to-grid

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An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility 2

An Empirical Investigation on Employing Machine Learning for...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Khatri, Anshu Mathi, Senthilkumar Ashiq Sheriff, A. Deepthika, R. Naveen Raaghavendran, G. Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods. © 2024 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Wireless Community Networks from Routing Perspective: A Systematic Review

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第7期51卷 852-867页

作者： Matter, Safaa S. Shaikhli, Imad F. Al Hashim, Aisha H. A. Soliman, Amr Mohmed Abdellatief, Walaa A. Ahmed, Abdelmoty M. Khattab, Mahmoud M. Department of Computer Science Applied College King Khalid University Abha61421 Saudi Arabia Faculty of Engineering Duzce University Turkey Department of Electrical and Electronic Engineering International Islamic University Malaysia Kuala Lumpur50728 Malaysia Special Education Dept College of Education King Khalid University Abha Saudi Arabia Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-kom32511 Egypt College of Computer Science Nahda University Beni Suef Egypt Department of Computer Science Kulliyyah of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur50728 Malaysia

Recently, the internet has created a global community by connecting millions of individuals, devices, and organizations. Unfortunately, internet connectivity is still not available to many people around the world. To provide everyone with internet access, network infrastructures must be expanded, and internet connection costs must be reduced, particularly in developing regions. Therefore, Wireless Community Networks (WCNs) have emerged as a crucial alternative for individuals who lack direct access to internet services provided by traditional internet service providers. This research paper aims to investigate the routing protocols that have been widely employed in WCNs over the past decade. Specifically, it focuses on evaluating the strengths and limitations of these protocols to assess their effectiveness and reliability in establishing optimal routing paths for end-to-end message delivery across the network. A set of recommendations and future directions are proposed to facilitate further research into unresolved challenges related to WCN routing protocols. © (2024) International Association of Engineers. All rights reserved.

关键词： Routing protocols

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IoT-Enabled Remote-Controlled Raft for Enhanced Water Quality Assessment 20

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

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

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

ISBN: (纸本)9798350382310

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. © 2024 IEEE.

关键词： Internet of things

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Machine Learning-Based Calibration Approaches for Single-Beam and Multiple-Beam Distance Sensors

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IEEE Sensors Journal 2024年第1期24卷 975-983页

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Biernacki, Piotr Ziebinski, Adam National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice44-100 Poland Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice44-100 Poland

Sensors are the foundation to facilitate smart cities, smart grids, and smart transportation, and distance sensors are especially important for sensing the environment and gathering information. Researchers have developed various methods to calibrate distance sensors;however, they mainly focus on using external information for calibration, which is inefficient and computationally expensive. To alleviate the above problems, we propose machine learning (ML)-based calibration approaches for distance sensors. We develop two calibration mechanisms, where the naive mechanism directly predicts the true distance, while the residual mechanism predicts the residual for calibration. The conventional ML regression algorithms are then adopted to predict the true distance or calibration residual. Experimental results show that the designed residual calibration mechanism outperforms the naive mechanism by reducing the root mean square error (RMSE) by 92.2% and mean absolute error (MAE) by 93.3% in the fitting task. In prediction, the proposed 2-D-residual mechanism achieves an improvement of 78.6% and 65.7% compared to the state-of-the-art method. The experimental results show the effectiveness and generalization of the proposed ML-based calibration method, especially for the 2-D mechanism, which achieves the highest robustness in all fitting and prediction tasks. © 2001-2012 IEEE.

关键词： Calibration

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A Novel Approach for Hashtag Topic Classification with Multimodal Features and VLMs

A Novel Approach for Hashtag Topic Classification with Multi...

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2024 International Conference on Horizons of information technology and engineering, HITE 2024

作者： Zafar, Hafiza Tayyaba Tahir, Bilal Mehmood, Muhammad Amir University of Engineering and Technology Al-Khawarizmi Institute of Computer Science Lahore54890 Pakistan Islamic University of Madinah Associated with Faculty of Computer and Information Systems Madinah42351 Saudi Arabia

ISBN: (纸本)9798331516055

The rapid expansion of Instagram content marked by a large volume of photos and comments demands effective methods for discovering topics. While hashtags facilitate the grouping of posts, categorizing these hashtags into specific topics is challenging task due to the multimodal and noisy nature of content on the platform. In this paper, we introduce a novel approach that leverages multimodal features and Vision-Language Models (VLMs) for the topic classification of hashtags. To achieve this, we develop a dataset InstaHash which includes 464 hashtags and 12,345 posts spanning five topic categories: army, politics, sports, judiciary, and religion. We extract text features using the TF-IDF algorithm and visual features using HSV, SIFT, ResNet50, and VGG16 algorithms. Additionally, we introduce 34 binary features derived from the VLM of LLAVA through prompt engineering. These prompt engineering features examine the six aspects of images such as people, symbols and objects, buildings, events, uniforms, and embedded text. We train four classifiers of Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Convolutional Neural Network (CNN) for topic classification of hashtags. Our analysis reveals that combining text and visual features yields an accuracy of 0.89 for topic classification with the CNN classifier. However, by incorporating additional prompt features into the model, we achieve the highest accuracy of 0.96. Moreover, we notice that prompt features related to aspects of people and symbols are the most important features for the topic classification of hashtags. © 2024 IEEE.

关键词： Visual languages

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The impact of different prosumer configurations on achieving savings due to the increase in electricity prices 4

The impact of different prosumer configurations on achieving...

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4th International Conference on Smart Grid Metrology, SMAGRIMET 2023

作者： Simic, Zvonimir Misljenovic, Nemanja Dubravac, Marina Topic, Danijel Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350323597

This paper investigates the consumption, and the cost of electricity for different configurations of prosumers in Croatia and additionally considers the current increase in electricity prices. In April 2022, electricity prices for households increased by 15%. Furthermore, from October 2022, electricity prices for households with annual electricity consumption above 5000 kWh are 50% higher than for households with annual electricity consumption below 5000 kWh. The load shifting from a period with a higher price to a period with a lower price can reduce electricity costs but cannot reduce the total amount of electricity imported from the distribution grid. Therefore, there is a possibility to install a PV power plant to meet consumers' own needs for electricity and to reduce the total amount of electricity imported from the distribution grid. Prosumers with different configurations regarding a PV power plant, battery energy storage, and optimal load shifting are simulated, and the total amount of electricity imported from the grid and total electricity costs are compared. According to simulation results, a prosumer with a PV power plant can reduce electricity consumption below 5000 kWh. On the other side, a prosumer with a PV power plant, battery energy storage, and optimal load shifting can significantly reduce total electricity costs. © 2023 IEEE.

关键词： Electric batteries

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Target tracking method of Siamese networks based on the broad learning system

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CAAI Transactions on Intelligence technology 2023年第3期8卷 1043-1057页

作者： Dan Zhang C.L.Philip Chen Tieshan Li Yi Zuo Nguyen Quang Duy Navigation College Dalian Maritime UniversityDalianChina Innovation and Entrepreneurship Education College Dalian Minzu UniversityDalianChina Computer Science and Engineering College South China University of TechnologyGuangzhouChina Department of Computer and Information Science Faculty of Science and TechnologyUniversity of MacaoMacaoChina School of Automation Engineering University of Electronic Science and Technology of ChinaChengduChina Faculty of Navigation Vietnam Maritime UniversityHaiphongVietnam

Target tracking has a wide range of applications in intelligent transportation,real‐time monitoring,human‐computer interaction and other ***,in the tracking process,the target is prone to deformation,occlusion,loss,scale variation,background clutter,illumination variation,etc.,which bring great challenges to realize accurate and real‐time *** based on Siamese networks promotes the application of deep learning in the field of target tracking,ensuring both accuracy and real‐time ***,due to its offline training,it is difficult to deal with the fast motion,serious occlusion,loss and deformation of the target during ***,it is very helpful to improve the performance of the Siamese networks by learning new features of the target quickly and updating the target position in time *** broad learning system(BLS)has a simple network structure,high learning efficiency,and strong feature learning *** at the problems of Siamese networks and the characteristics of BLS,a target tracking method based on BLS is *** method combines offline training with fast online learning of new features,which not only adopts the powerful feature representation ability of deep learning,but also skillfully uses the BLS for re‐learning and re‐*** broad re‐learning information is used for re‐detection when the target tracking appears serious occlusion and so on,so as to change the selection of the Siamese networks search area,solve the problem that the search range cannot meet the fast motion of the target,and improve the *** results show that the proposed method achieves good results on three challenging datasets and improves the performance of the basic algorithm in difficult scenarios.

关键词： broad learning system siamese network target tracking

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Deep Forest-Based Fall Detection in Internet of Medical Things Environment

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computer Systems Science & engineering 2023年第6期45卷 2377-2389页

作者： Mohamed Esmail Karar Omar Reyad Hazem Ibrahim Shehata College of Computing and Information Technology Shaqra UniversityP.O.Box 33Shaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohag82524Egypt Department of Computer and Systems Engineering Faculty of EngineeringZagazig UniversityZagazig44519Egypt

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest *** cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural ***,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated *** suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a *** includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and *** results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural *** considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

关键词： Elderly population fall detection wireless sensor networks internet of medical things deep forest

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Fake review detection using transformer-based enhanced LSTM and RoBERTa

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 250-258页

作者： Mohawesh, Rami Bany Salameh, Haythem Jararweh, Yaser Alkhalaileh, Mohannad Maqsood, Sumbal College of Engineering Al Ain University Al Ain United Arab Emirates Hijjawi Faculty of Engineering Technology Yarmouk University Irbid Jordan Department of Computer Science Jordan University of Science and Technology Jordan College of Education Al Ain University Al Ain United Arab Emirates School of Information Technology University of Tasmania TAS Australia

Internet reviews significantly influence consumer purchase decisions across all types of goods and services. However, fake reviews can mislead both customers and businesses. Many machine learning (ML) techniques have been proposed to detect fake reviews, but they often suffer from poor accuracy due to their focus on linguistic features rather than semantic content. This paper presents a novel semantic- and linguistic-aware model for fake review detection that improves accuracy by leveraging advanced transformer architecture. Our model integrates RoBERTa with an LSTM layer, enabling it to capture intricate patterns within fake reviews. Unlike previous methods, our approach enhances the robustness of fake review detection and authentic behavior profiling. Experimental results on semi-real benchmark datasets show that our model significantly outperforms state-of-the-art methods, achieving 96.03 % accuracy on the OpSpam dataset and 93.15 % on the Deception dataset. To further enhance transparency and credibility, we utilize Shapley Additive Explanations (SHAP) and attention techniques to clarify our model's classifications. The empirical findings indicate that our proposed model can offer rational explanations for classifying specific reviews as fake. © 2024

关键词： Fake detection

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