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Real-Time Sensor Fault Tolerant Control of DC-DC Converters in DC Microgrids Using a Switching Unknown Input Observer

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IEEE Access 2025年 13卷 95837-95850页

作者： Ouahabi, Mohammed Said Benyounes, Abdelhafid Barkat, Said Ihammouchen, Syphax Rekioua, Toufik Rabehi, Abdelaziz El-Kenawy, El-Sayed M. Alharbi, Amal H. University of M’sila Electrical Engineering Laboratory Faculty of Technology M’sila28000 Algeria University of Béjaïa Laboratoire de Technologie Industrielle et de l’Information Béjaïa06000 Algeria Faculty of Science and Technology Djelfa17000 Algeria Isa Town11937 Bahrain Applied Science Private University Applied Science Research Center Amman11931 Jordan Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia

DC Microgrids are rapidly developing, driven by the increasing integration of renewable sources and dynamic loads. Their growing adoption in critical applications raises the need for operational safety and reliability, particularly under fault conditions. This paper presents a sensor fault-tolerant control (FTC) for DC-DC power converters in DC Microgrids designed to maintain resilient and reliable operation. The proposed FTC leverages a single Switching Unknown Input Observer (SW-UIO) at the bottom control level to estimate arbitrary sensor faults with a high accuracy (0.3% fault estimation error), allowing a real-time identification and precise control reconfiguration to accommodate sensor faults with minimal computational burden. The approach is validated under diverse conditions, including time-varying loads, complete sensor failure, noise abnormality, and simultaneous faults/failure, achieving a consistent fault accommodation of 1 μs from fault occurrence to compensation. Real-time simulation has been performed in the OPAL-RT environment to assess the efficacy and superiority of the proposed sensor fault tolerant strategy in comparison to reference FTC strategies. © 2013 IEEE.

关键词： Identification (control systems)

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Energy Management System for a Smart Green Nanogrid feeding a Research laboratory with Autonomous Mobile Robots

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IFAC-PapersOnLine 2024年第2期58卷 156-161页

作者： Matteo Fresia Lorenzo De Simone Ilaria Traverso Stefano Bracco Silvia Siri Department of Electrical Electronic Telecommunication Engineering and Naval Architecture University of Genoa 16145 Genova Italy Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genoa 16145 Genova Italy

This paper proposes a mixed-integer linear programming optimization model used to define an energy management system tailored for nanogrids in buildings, integrating renewable energy sources, battery energy storage systems and task-executing autonomous mobile robots. Focused on a nanogrid to be realised at the Savona Campus of the University of Genoa, the energy management system optimizes power flows and robot task scheduling in order to minimize the operating costs, the curtailment of the photovoltaic source and the number of unperformed tasks. Its novelty lies in combining energy and task planning constraints, offering significant potential for sustainable building energy management.

关键词： Energy management system Optimal operation Nanogrids Prosumer building Autonomous mobile robots Renewable energy sources Battery energy storage system

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Effective Spline-GMPPT Approach Performance for Photovoltaic Systems under Uniform Irradiance and Partial Shading Conditions 2

Effective Spline-GMPPT Approach Performance for Photovoltaic...

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2nd International Conference on Energy Transition and Security, ICETS 2023

作者： Ahmed, Saidi Touhami, Abdelouahed Abdelghani, Draoui Amine, Meziane Mohamed Abdelkader, Harrouz Ahmed Draia University Laboratory of Sustainable Development and Computer Science Department of Electrical Engineering Adrar01000 Algeria University of Tahri Mohamed Laboratory of Smart Grid and Renewable Energy Department of Electrical Engineering Bechar08000 Algeria Tindouf37000 Algeria Naama University Center Naama45000 Algeria

ISBN: (纸本)9798350372182

In the literature, there are several criteria techniques classified to achieve the point of maximum power tracking (MPPT) in photovoltaic (PV) power systems, such as accuracy, speed, and simplicity. These criteria are often trade-offs;in this case, the higher accuracy generally achieved at the expense of two other criteria: speed and *** contribution proposes a new technique based on the Spline-Global-MPPT approach to provide its reliability in multiple criteria as an accurate, fast, and simple technique to find the MPP of PV power generation systems under two cases, such as uniform irradiance and partial shading conditions (PSCs), in the total distorted characteristics of the PV string. A cubic spline interpolation approach has proposed in this method, which defines from a few simple points an approximate *** localize the GMPP under standard (uniform) irradiance conditions, there are a huge number of interpolation-based approaches proposed in the literature. Unfortunately, this interpolation approach became incapable of detecting the point of the global maximum power (GMPP) under PSCs. The MPP of the system predicted by this new Spline-GMPPT method using a limited sample set of current and voltage, and it maintains this position as long as the external conditions stay unchanged. In the last part, the simulation results prove the reliability and robustness of the proposed approach. © 2023 IEEE.

关键词： Maximum power point trackers

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An Enhanced Hybrid Cryptography Model for Online Banking Authentication and Security 2nd

An Enhanced Hybrid Cryptography Model for Online Banking Aut...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Awotunde, Joseph Bamidele Brahma, Biswajit Adeniyi, Abidemi Emmanuel Lauretta Nkonyeasua, Edogbo Imoize, Agbotiname Lucky Mejdoub, Youssef Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria McKesson Corporation 32559 Lake Bridgeport Street FremontCA94555 United States Department of Computer Science Bowen University Iwo Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

In the digital age, protecting the security and privacy of sensitive data has become of the utmost importance, particularly while conducting online financial transactions. This paper intends to build an advanced hybrid cryptography model, combining the strengths of the Rivest-Shamir-Adleman (RSA) and advanced Encryption Standard (AES) algorithms, to improve the authentication and security procedures of online banking systems. To provide a strong and effective encryption framework, the suggested model makes use of RSA’s asymmetric key advantages and AES’s symmetric key capabilities. Because AES is quick and efficient at encrypting large amounts of data, it is used to optimize performance while encrypting the actual data being transmitted. Meanwhile, RSA is used to solve the problem of key distribution, secure the exchange of symmetric keys, and enable secure communication. The goal of the paper is to create an extensive system that incorporates this improved hybrid cryptography model into the online banking authentication procedure. User authentication, transaction verification, and data secrecy are the system’s three main building blocks. To ensure the viability and feasibility of the suggested paradigm in real-world online banking scenarios, implementation difficulties such as key management, algorithm integration, and performance optimization are addressed. This research advances cryptographic methods of contemporary banking security by utilizing the advantages of AES and RSA in a hybrid approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Efficient Hardware Implementation of Artificial Neural Networks on FPGA

Efficient Hardware Implementation of Artificial Neural Netwo...

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IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Kasem Khalil Tamador Mohaidat Mahmoud Darwich Ashok Kumar Magdy Bayoumi Department of Electrical and Computer Engineering University of Mississippi Mississippi USA Department of Electrical Engineering Assiut University Egypt Department of Mathematics and Computer Science University of Mount Union Alliance OH USA The Center for Advanced Computer Studies University of Louisiana at Lafayette LA USA Department of Electrical and Computer Engineering University of Louisiana at Lafayette LA USA

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

A neural network finds widespread applications across various domains, with the primary challenge being the design of a network characterized by low area and power consumption. This paper introduces an efficient design strategy for a cost-effective neural network architecture. It leverages a shared multiplier and accumulator arrangement between neighboring nodes, halving the number of these units and subsequently reducing the overall area of the neural network. A configuration mechanism is employed to synchronize the operation between the two common nodes. The proposed method is implemented and tested using VHDL on an Altera 10 GX FPGA, and evaluations include testing with two datasets: MNIST and CIFAR-10. The results demonstrate that the proposed method maintains classification accuracy while achieving a 76% reduction in area compared to traditional methods, with a power consumption of 82 mW. These findings underscore the efficiency and suitability of the proposed method for applications prioritizing both area and energy efficiency.

关键词： Power demand VHDL Accuracy Circuits and systems Hardware Energy efficiency Synchronization

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NEARLY MINIMAX OPTIMAL OFFLINE REINFORCEMENT LEARNING WITH LINEAR FUNCTION APPROXIMATION: SINGLE-AGENT MDP AND MARKOV GAME 11

NEARLY MINIMAX OPTIMAL OFFLINE REINFORCEMENT LEARNING WITH L...

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11th International Conference on Learning Representations, ICLR 2023

作者： Xiong, Wei Zhong, Han Shi, Chengshuai Shen, Cong Wang, Liwei Zhang, Tong Department of Mathematics The Hong Kong University of Science and Technology Hong Kong Center for Data Science Peking University China Department of Electrical and Computer Engineering University of Virginia United States National Key Laboratory of General Artificial Intelligence Peking University China School of Intelligence Science and Technology Peking University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms have been proposed for offline RL in the previous literature, the minimax optimality has only been (nearly) established for tabular Markov decision processes (MDPs). In this paper, we focus on offline RL with linear function approximation and propose a new pessimism-based algorithm for offline linear MDP. At the core of our algorithm is the uncertainty decomposition via a reference function, which is new in the literature of offline RL under linear function approximation. Theoretical analysis demonstrates that our algorithm can match the performance lower bound up to logarithmic factors. We also extend our techniques to the two-player zero-sum Markov games (MGs), and establish a new performance lower bound for MGs, which tightens the existing result, and verifies the nearly minimax optimality of the proposed algorithm. To the best of our knowledge, these are the first computationally efficient and nearly minimax optimal algorithms for offline single-agent MDPs and MGs with linear function approximation. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Reinforcement learning

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Enhancement of Co-located Shared VR Experiences: Representing Non-HMD Observers on Both HMD and 2D Screens 23

Enhancement of Co-located Shared VR Experiences: Representin...

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23rd IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2024

作者： Guo, Zixuan Xu, Wenge Wang, Hongyu Wan, Tingjie Baghaei, Nilufar Lo, Cheng-Hung Liang, Hai-Ning Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou China The University of Liverpool Department of Computer Science Liverpool United Kingdom Birmingham City University College of Computing Birmingham United Kingdom The University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia Xi'an Jiaotong-Liverpool University Department of Industrial Design Suzhou China Computational Media and Arts Thrust Guangzhou China

ISBN: (纸本)9798331516475

Virtual reality (VR) not only allows head-mounted display (HMD) users to immerse themselves in virtual worlds but also to share them with others. When designed correctly, this shared experience can be enjoyable. However, in typical scenarios, HMD users are isolated by their devices, and non-HMD observers lack connection with the virtual world. To address this, our research investigates visually representing observers on both HMD and 2D screens to enhance shared experiences. The study, including five representation conditions, reveals that incorporating observer representation positively impacts both HMD users and observers. For how to design and represent them, our work shows that HMD users prefer methods displaying real-world visuals, while observers exhibit diverse preferences regarding being represented with real or virtual images. We provide design guidelines tailored to both displays, offering valuable insights to enhance co-located shared VR experiences for HMD users and non-HMD observers. © 2024 IEEE.

关键词： Virtual environments

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Time-Synchronized Safety-Critical Attitude Control for Spacecraft with Hardware-in-Loop Validation

Time-Synchronized Safety-Critical Attitude Control for Space...

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2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Tong, Shang Yang, Haoyang Zhang, Rongchuan Hu, Qinglei Sam Ge, Shuzhi Li, Dongyu Tianmushan Laboratory Hangzhou China School of Cyber Science and Technology Beihang University Beijing China School of Engineering University of Warwick Coventry United Kingdom Institute Beihang University Hangzhou International Innovation Hangzhou China School of Automation Science and Electrical Engineering Beihang University Beijing China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798331504755

A time-synchronized controller is proposed to solve the time-varying constrained reorientation problem. Time-Synchronized Stability (TSS) that considers the interrelationships among different state errors plays an important role in our method, enabling synchronized convergence of individual error components while ensuring compliance with state constraints. Compared to traditional sliding mode controllers, the efficiency of our safety-critical attitude controller is imporoved without adding computational burden, as evidenced by improved control energy consumption and reduced convergence time. A hardware-in-the-loop (HiL) experiment confirms its practical deployment capabilities. © 2024 IEEE.

关键词： Sliding mode control

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Towards a Longitudinal Comparison Between Different Strategies for Android Malware Detection 3

Towards a Longitudinal Comparison Between Different Strategi...

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3rd International Conference on Theoretical and Applicative Aspects of computer science, ICTAACS 2023

作者： Mesbah, Abdelhak Baddari, Ibtihel Riahla, Mohamed Amine University m'Hamed Bougara of Boumerdes Faculty of Sciences Limose Laboratory Department of Computer Science Boumerdes Algeria University m'Hamed Bougara of Boumerdes Faculty of Technology Limose Laboratory Department of Electrical Systems Engineering Boumerdes Algeria

ISBN: (纸本)9798350385854

The growing popularity of the Android platform makes it a target of malware authors. The effective identification of such malware is an ongoing challenge. Several methods using machine learning have been proposed to prevent this threat. These methods are usually conventionally evaluated without considering the extent of performance over time. Given the evolving nature of both malware and benign apps, conventional evaluation may lack information. To imitate reality, this study compares the longitudinal performance of different machine learning models, using different strategies that combine permissions and API calls as features extracted through static analysis. Thus, to determine which strategy of features on which classifier are most effective to characterize malware for building a robust malware detector. To achieve this goal, on the one hand, we use a large real-world app set consisting of 100K (50k benign, 50k malware) apps date-labeled, collected across ten years, first seen between 2013 and 2022. On the other hand, each feature's strategy is fed into five classifiers (i.e., SVM, RF, LR, DT, and ANN), using old apps for the training and new apps for the evaluation. Among the assessed machine learning models, the SVM achieves the most promising results over time by employing the combination strategy of the high difference usage of API calls and permissions. © 2023 IEEE.

关键词： Static analysis

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