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Fractional-order model reference adaptive control of a multi-source renewable energy system with coupled DC/DC converters power compensation

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ENERGY systemS-optimization modeling SIMULATION AND ECONOMIC ASPECTS 2020年第2期11卷 315-355页

作者： Djebbri, Sihem Ladaci, Samir Metatla, Abderezzak Balaska, Hanane Univ 20th August 1955 Elect Engn Dept Lab Genie Mecan & Mat Skikda 21000 Algeria Natl Polytech Sch Constantine EEA Dept Nouvelle ville Ali Mendjeli Constantine 25100 Algeria Univ Mentouri Bros SP Lab Lab Constantine 25000 Algeria Univ 20th August 1955 Mech Engn Dept Lab Genie Mecan & Mat Skikda 21000 Algeria Univ Larbi Ben MHidi Dept Sci & Technol LGEA Lab Oum El Bouaghi 04000 Algeria

We propose a supervision strategy based on a fractional order model reference adaptive control (FO-MRAC) design for the control of electrical energy transfers in a multi-source renewable energy system. We consider a two sources system (photo-voltaic and lead-acid battery bank) with a specific zero voltage switch full-bridge isolated buck DC/DC power converter and the load which is supposed to be resistive. the global energy system involving coupled DC/DC converters on a DC bus is accurately modelized in the state space domain. then, a fractional order MRAC control law is designed in order to stabilize the system's current and voltage. We show by means of numerical simulation tests that the proposed FO-MRAC control is very efficient and robust even in presence of renewable sources fluctuations and load variations.

关键词： Fractional-order adaptive control FOMRAC Fractional order integral Multi-source renewable energy system DC DC power converter

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(POSTER) Advancing Non-Terrestrial Networks for Critical Scenarios with the RAIN4C Framework

(POSTER) Advancing Non-Terrestrial Networks for Critical Sce...

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International conference on Distributed Computing in Sensor systems (DCOSS)

作者： Angelo Trotta Alexandre Heideker Ivan Zyrianoff Giovanni Interdonato Sara Pizzi Department of Computer Science and Engineering University of Bologna Bologna Italy Department of Electrical and Information Engineering University of Cassino and Southern Lazio Cassino Italy Department of Information Infrastructure and Sustainable Energy Engineering (DIIES) University Mediterranea of Reggio Calabria Reggio Calabria Italy

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

the transition to sixth-generation (6G) wireless networks underscores the importance of reliable network services in critical scenarios, such as post-disaster and rural areas. Space-Air-Ground Integrated Networks (SAGINs), which include satellite systems, aerial networks, and terrestrial communication, are crucial for global coverage in these contexts. Despite their potential, satellite network latencies and the dynamic nature of Unmanned Aerial systems (UAS) present challenges that diverge from 6G requirements. this paper introduces RAIN4C, a multi-layer framework that integrates hybrid communication and computation solutions across satellite, aerial, and ground segments. RAIN4C focuses on reliable connectivity, energy-efficient computation, and effective caching to address the key issues of deploying SAGIN systems. It proposes methods for optimizing these functionalities and evaluates them through simulations and a test-bed involving satellite links and UAVs. the goal is to enable swift deployment of SAGIN systems for on-demand services in critical environments, aligning with the needs of future wireless networks.

关键词： 6G mobile communication Satellites Wireless networks Computational modeling systems operation system performance Computer architecture

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A combined PV-wind energy system for an energy saving greenhouse

A combined PV-wind energy system for an energy saving greenh...

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20th IEEE International conference on Environment and Electrical Engineering (EEEIC) / 4th IEEE Industrial and Commercial Power systems Europe conference (I and CPS Europe)

作者： Riahi, Jamel Vergura, Silvano Mezghani, Dhafer Mami, Abdelkader Univ Tunis Fac Sci Tunis UR LAPER Tunis Tunisia Polytech Univ Bari Dept Elect & Informat Engn Bari Italy

ISBN: (纸本)9781728174556

this paper presents the optimization and simulation of a hybrid power system composed of two renewable energy sources to supply the actuators of an agricultural greenhouse. the first system is a PhotoVoltaic (PV) generator connected to a DC/AC inverter and the second system is a wind turbine connected to a Permanent Magnet Synchronous Generator (PMSG). A Maximum Power Point Tracking (MPPT) control based on fuzzy logic is used to drive a SEPIC converter to feed the maximum power to the agricultural greenhouse. the simulation and validation of the system is carried out in Matlab/Simulink environment. the results show that the proposed system provides an efficient solution to control the microclimate of the agricultural greenhouse in different periods of the year.

关键词： Hybrid systems PV system Wind plant Converter SEPIC Greenhouse Fuzzy logic controller Matlab Simulink

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20th IFIP TC 14 International conference on Entertainment Computing, ICEC 2021

20th IFIP TC 14 International Conference on Entertainment Co...

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20th IFIP TC 14 International conference on Entertainment Computing, ICEC 2021

ISBN: (纸本)9783030893934

the proceedings contain 49 papers. the special focus in this conference is on Entertainment Computing. the topics include: Social Gaming Patterns During a Pandemic Crisis: A Cross-cultural Survey;a Real-Time Drum-Wise Volume Visualization system for Learning Volume-Balanced Drum Performance;speech Recognition Game Interface to Increase Intimacy with Characters;game Development as a Serious Game with Live-Programming and Time-Travel Mechanics;AI Game Agents Based on Evolutionary Search and (Deep) Reinforcement Learning: A Practical Analysis with Flappy Bird;instruction Pictograms for Interactive Entertainment;how Awe Affects Players’ Entertainment Experiences Over Six Weeks of Playing;the Gilmorehill Mystery: A Location-Based Game for Campus Exploration;***: A Music Recommendation system Depending on Listener’s Preference by Creating a Branching Playlist;CS:Show – An Interactive Visual Analysis Tool for First-Person Shooter eSports Match Data;towards Suitable Free-to-Play Games for Children;provenance in Gamification Business systems;computational Narrative Blending Based on Planning;linkages Between Gameplay Preferences and Fondness for Game Music;that Sound’s Juicy! Exploring Juicy Audio Effects in Video Games;what Is a Game Mechanic?;a Method for Supporting Verbalization to Facilitate Observation in Illustration Copy-Drawing;optimization of First-Person Shooter Game Control Using Heart Rate Sensor;virtual Guia Fortress: A 3D-printed eXtended Reality Playset;recreating a TransMedia Architectural Location In-Game via Modular Environment Assets;Fun to Enhance Learning, Motivation, Self-efficacy, and Intention to Play in DGBL;interaction Toolkit for Programming Interactions with Marker-Based Tangibles in Virtual Reality;sketch Recognition for Interactive Game Experiences Using Neural Networks;safety Risks in Location-Based Augmented Reality Games.

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Genetic Algorithm-Based Joint Task Offloading and Resource Allocation in UAV-Enabled Vehicular Edge Computing

Genetic Algorithm-Based Joint Task Offloading and Resource A...

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International conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Ke Xiao Aofei Dong Zhixin Mei Kuiyuan Feng Department of Computer and Information Science Chongqing Normal University Chongqing China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

Integrating Unmanned Aerial Vehicles (UAVs) into Vehicular Edge Computing (VEC) establishes UAV-enabled VEC, effectively addressing potential degradation of offloading per-formance caused by overloaded edge servers in urban aggregation areas. However, the limited dimensions of UAV s impose constraints on their onboard energy and computational capa-bilities, thereby presenting significant challenges in achieving efficient task offloading. Motivated by this, this work studies task offloading and resource allocation in UAV-enabled VEC for enhancing the offloading performance. Specifically, we propose a task offloading framework in UAV-enabled VEC that per-forms task offloading for vehicles. Based on this framework, we formulate a novel problem called Task Offloading and Resource Allocation (TORA), aiming to minimize the weighted sum of completion time and energy consumption for processing computational tasks. Furthermore, due to the non-convex nature of the TORA problem, we propose a genetic algorithm-based joint task offloading and resource allocation scheme to effectively solve the TORA problem. this scheme comprises a chromosome representation for encoding solutions and a set of reproduction operators (i.e., selection, crossover, and mutation) for solution evolution. Finally, the proposed scheme is evaluated through a constructed simulation model, and the experimental results substantiate its effectiveness.

关键词： Energy consumption Computational modeling system performance Autonomous aerial vehicles Computational efficiency Sensors Resource management Servers Genetic algorithms Edge computing

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A Smart Public Service optimization Model Using Linear Programming to Reduce the Number of Deaths

A Smart Public Service Optimization Model Using Linear Progr...

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International conference of Computer Science and Information Technology

作者： A M H Pardede Muammar Khadapi Y Maulita L A N Kadim STMIK Kaputama Binjai North Sumatra Indonesia

In everyday life, it is possible for people to need public services. What is meant by public services in this research are health, security, protection, and assistance services that threaten the safety of people's lives. Public services are needed by the community in every normal or emergency incident that must receive life-saving services as soon as possible, and every service that will be provided is not necessarily the same for all communities. this is because the incident, rescue needs, and location of the victim may vary, such as the occurrence of health problems, traffic accidents, crime incidents, and fires. this research uses the concept of “Smart Health and Safe City” which aims to optimize the smart public service model using linear programming to reduce the number of deaths by utilizing hospital agencies' limited resources to provide treatment as soon as possible, police agencies, fire agencies and Basarnas agencies, where when going to carry out public services, medical (m), police (p), fire brigade (f), and Basarnas (b) depart from the hospital location (ih), police location (ip), fire extinguisher location (if), and Basarnas location (ib) to location of victim (j) to provide public services in order for the modeling's output to address the issue of delivering intelligent services as early as possible which can reduce the number of deaths, minimize costs incurred, minimize service time and maximize resource utilization. possible, as well as obtaining the maximum value of the model's objective function. the simulation results, the maximum value of the objective function was obtained at 164,00000 at the 20th iteration step and produced a model that minimized the arrival time at the location of the service recipient.

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A Computational Approach to Recognizing Poetry Genres in Uzbek Texts

A Computational Approach to Recognizing Poetry Genres in Uzb...

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IEEE Region 8 International conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Davlatyor B. Mengliev Vladimir B. Barakhnin Bobur R. Saidov Mukhammadjon Atakhanov Mukhriddin O. Eshkulov Bahodir B. Ibragimov Novosibirsk State University Novosibirsk Russia Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Urgench Uzbekistan Federal Research Center for Information and Computational Technologies Novosibirsk Russia Novosibirsk State Universitety Novosibirsk Russia Jizzakh polytechnic institute Jizzakh Uzbekistan Urgench State University Urgench Uzbekistan

ISBN: (数字)9798331532024

ISBN: (纸本)9798331532031

In the research paper, the authors propose an algorithm for detecting poetic elements, words, and phrases in Uzbek texts. In addition, during analyzing the algorithm classifies poetic tokens in of the of 2 poetic genres (comedy, drama). In particular, the researchers trained a language model of artificial intelligence with an architectural combination of a convolutional neural network and long short-term memory. A custom language corpus was used as training data. this corpus was formed from more than 3,600 literary sentences, which in turn were selected from U zbek poetic works of the 20th century. Words and phrases in the corpus sentences were tagged according to the BIO-scheme, which the authors also talk about in the corresponding section of the article. Moreover, the authors also included information about the morphology of the U zbek language so that the context of this study was more understandable to all readers. In addition, the authors also conducted a comparative analysis of existing alternative works, where they provide the features of each similar work.

关键词： Training Humanities Analytical models Text recognition Biological system modeling Training data Classification algorithms Convolutional neural networks Long short term memory Testing

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Optimal Scheduling for the Performance optimization of SpMV Computation using Machine Learning Techniques

Optimal Scheduling for the Performance Optimization of SpMV ...

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International conference on Information and Computer Technologies (ICICT)

作者： Fida Ullah Muhammad Ahmed Muhammad Tayyab Zamir Muhammad Arif E Felipe-Riverón Alexander Gelbukh Instituto Politécnico Nacional (IPN) Centro de Investigación en Computación (CIC) Mexico City Mexico

ISBN: (数字)9798350385625

ISBN: (纸本)9798350385632

the Sparse Matrix-Vector Multiplication (SpMV) serves as a critical computational kernel within sparse linear system solvers, playing a vital role in diverse scientific, engineering, and application domains such as industrial engineering, robotics, finance, networking, transportation, medicine, and weather forecasting. this study introduces a classification-based predictive model for shared memory, aiming to optimize scheduling policies in terms of execution time. Utilizing a dataset comprised of over 1000 matrices from 25 application domains, stored in block CSR format, the model is trained on 67% of the data using decision tree, random forest, gradient boosting, and artificial neural network algorithms. Results indicate that random forest exhibits superior accuracy with 20%, precision 4%, and recall 23% compared to other algorithms. Moreover, the runtime scheduling policy consistently demonstrates the best execution time across all matrices and features, outperforming static, dynamic, and guided policies. the study provides valuable insights into optimizing SpMV performance under various conditions, enhancing efficiency in solving sparse linear equation systems.

关键词： Machine learning algorithms Processor scheduling Computational modeling Software algorithms Weather forecasting Transportation Optimal scheduling

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Design and Realization of Intelligent Airborne Information Processing Concept Simulation system

Design and Realization of Intelligent Airborne Information P...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Jianchun Xie Linting Bai Qing Luo Aviation Key Laboratory of Science And Technology On Airborne And Missile-borne Computer AVIC Xi'an Aeronautics Computing Technique Research Institute Xi'an China College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanning China Shenyang Aircraft Design&Reserch Institute Shenyang China

Facing the development trend of future distributed cloud operations, intelligent operations, and the technical requirements of multi-machine collaborative operations in the aviation field, this paper further studies the unified data transmission and management of multiple platforms based on the research of the unified cloud intelligent combat system architecture. Distributed data storage and management, multi-platform open information processing resource management and other key technologies, on the basis of the principle prototype designed and implemented by ourselves, a set of conceptual simulation platforms have been constructed for the simulation and technical verification of the entire airborne information processing system.

关键词： Technical requirements Atmospheric modeling systems architecture Collaboration Prototypes Information processing Market research

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Using Machine Learning to Support Prostate Cancer Detection Without a Dedicated Biomarker

Using Machine Learning to Support Prostate Cancer Detection ...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Hadrien Salem Sarah Ben Othman Marc Broucqsault Slim Hammadi CRIStAL CNRS UMR 9189 Nord France Altao Lille Nord France

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Early disease detection through the analysis of laboratory tests is critical for the successful treatment of certain diseases. However, current medical knowledge relies on the use of certain biomarkers in order to diagnose associated diseases. this means that some diseases cannot be detected unless the corresponding biomarker is actually *** this paper, we will explore the possibility of using machine learning to detect abnormal values in biomarkers, even when they are not directly measured. this stems from the fact that, as an ensemble of features, even a set of common biomarkers contains sufficient information to suspect anomalies in a patient’s *** present a complete pipeline for processing medical laboratory data, taking into account the many challenges offered by this use case, such as data cleanliness, the abundance of missing values, and strong class imbalance. Using this framework, we perform a case study on prostate cancer and its associated biomarker, Prostate-Specific Antigen (PSA). Our implementation, based on an imbalance-aware ensemble prediction model, is tested on a real laboratory dataset of more than 8 million records. We find that basing the ensemble on Random Forest models provides the best results, with a ROC-AUC of 75.86%, indicating that it is indeed possible to detect abnormal PSA values without measuring it directly. We also show that a simple baseline of 24 commonly-measured biomarkers provides near-equivalent results to a full set of features. Our work offers new perspectives for routine disease screening.

关键词： Antigens Machine learning algorithms systematics Sensitivity Biological system modeling Pipelines Biomarkers Prostate cancer Medical diagnostic imaging Diseases

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