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Symbiotic Control of Uncertain Dynamical systems: Harnessing Synergy Between Fixed-Gain Control and Adaptive Learning Architectures

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IEEE Transactions on Automatic Control 2025年

作者： Yucelen, Tansel Sarsilmaz, Selahattin Burak Yildirim, Emre University of South Florida Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems TampaFL33620 United States Utah State University Department of Electrical and Computer Engineering LoganUT84322 United States

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty bounds. In contrast, while adaptive learning does not necessarily require such knowledge, it often results in less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that offers the strengths of fixed-gain control and adaptive learning architectures. Specifically, this framework synergistically integrates these architectures to mitigate the effects of uncertainties in a more predictable manner as compared to adaptive learning alone and it does not require any knowledge on such uncertainties. Both parametric and nonparametric uncertainties are considered, where we utilize neural networks to approximate the unknown uncertainty basis for the latter case. Counterintuitively, the proposed framework has the ability to achieve a desired level of closed-loop system behavior even with an insufficient number of neurons (e.g., when the neural network approximation error is large) or in the face of injudiciously selected adaptive learning parameters (e.g., high leakage term parameters). © 1963-2012 IEEE.

关键词： Closed loop control systems

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MEMS-based twisted bilayer flat optics for multidimensional optical modulation and hyperimaging 14

MEMS-based twisted bilayer flat optics for multidimensional ...

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High Contrast Metastructures XIV 2025

作者： Tang, Haoning Lou, Beicheng Du, Fan Gao, Guangqi Zhang, Mingjie Ni, Xueqi Yacoby, Amir Cao, Yuan Fan, Shanhui Mazur, Eric School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Applied Physics Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States

Our MEMS-based twistoptics device enables precise control of interlayer gaps and twist angles in photonic crystals, offering high-accuracy, multidimensional light manipulation. This finding holds potential for applica... 详细信息

ISBN: (纸本)9781510685048

关键词： Nanophotonics

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Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

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IEEE Transactions on Power systems 2025年

作者： Zhou, Yuqi Zhu, Hao National Renewable Energy Laboratory Power Systems Engineering Center GoldenCO80401 United States The University of Texas Chandra Family Department of Electrical & Computer Engineering AustinTX78712 United States

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations. © 1969-2012 IEEE.

关键词： Decentralized control

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GLOW-ENV: A Dual-Data IoE-Based Approach for Integrating Glucose and Environmental Data into a Diabetes Recommendation System

GLOW-ENV: A Dual-Data IoE-Based Approach for Integrating Glu...

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18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Gaitán-Guerrero, Juan F. Martínez-Cruz, Carmen López, José L. Mata-Mata, Francisco Pérez-Cordón, Luis G. Serrano, José-María Morcillo-Martínez, Juana M. Verdejo-Espinosa, Ángeles Cuevas-Martínez, Juan C. Viciana-Abad, Raquel Reche-López, Pedro J. Pérez-Lorenzo, José M. Díaz-Jiménez, David Espinilla, Macarena Department of Computer Science University of Jaén Jaén23071 Spain Department of Languages and Computer Systems University of Granada Granada18071 Spain Psycology Department Faculty of Social Work University of Jaén Jaén23071 Spain Electrical Engineering Department University of Jaén Jaén23071 Spain Telecommunication Engineering Department University of Jaén Jaén23071 Spain

This paper introduces GLOW-ENV, an intelligent Internet of Everything (IoE)-driven mobile application designed with the objective of integrating real-time glucose monitoring data and environmental metrics to enhance diabetes care and management. The proposed IoE ecosystem integrates a continuous glucose monitoring with a personalized Artificial Intelligence model designed to predict glycemic fluctuations in a near-future. Additionally, GLOW-ENV integrates a rule-based recommendation system to dynamically adapt its suggestions based on contextual glucose and environmental data. This framework advances personalized diabetes care, contributing to their progression and well-being offering valuable insights and improving decision-making. © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Electronic health record

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Systematic exploration of fuzzing in IoT: techniques, vulnerabilities, and open challenges

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The Journal of Supercomputing 2025年第8期81卷 1-46页

作者： Touqir, Asma Iradat, Faisal Iqbal, Waseem Rakib, Abdur Taskin, Nazim Jadidbonab, Hesam Haas, Olivier Department of Computer Science School of Mathematics and Computer Science Institute of Business Administration Karachi Pakistan Department of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Institute for Future Transport and Cities Coventry University Coventry UK Department of Management Information Systems Bogazici University Istanbul Turkey

As our dependence on the internet and digital platforms grows, the risk of cyber threats rises, making it essential to implement effective measures to safeguard sensitive information through cybersecurity, ensure system integrity, and prevent unauthorized data access. Fuzz testing, commonly known as fuzzing, is a valuable technique for software testing as it uncovers vulnerabilities and defects in systems by introducing random data inputs, often leading to system crashes. In the Internet of Things (IoT) domain, fuzzing is crucial for identifying vulnerabilities in networks, devices, and applications through automated tools that systematically inject malformed inputs into IoT systems. However, despite its importance, existing research on fuzzing techniques in IoT contexts remains limited by the absence of standardized benchmarks, inefficiencies in re-hosting strategies, and difficulties in detecting complex, condition-dependent vulnerabilities. The primary objective of this study is to comprehensively evaluate current fuzzing practices, emphasizing adaptive techniques designed for IoT systems. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) model, a systematic literature review was conducted across 32 academic articles published between 2020 and 2024. The analysis revealed that although fuzzing enhances IoT security, its effectiveness is hindered by device heterogeneity, limited system resources, and evolving cyber threat landscapes. The findings suggest that to overcome these limitations, future research should focus on AI-driven fuzzing methods, robust multi-architecture support, and the development of standardized evaluation frameworks to strengthen IoT cybersecurity.

关键词： Model checking

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Impact of PHEV operating conditions and demand side management on the economic operation of a community microgrid system 33

Impact of PHEV operating conditions and demand side manageme...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Dey, Bishwajit Fose, Nande Krishnamurthy, Senthil Ratshitanga, Mukovhe Moodley, Prathaban Cape Peninsula University of Technology Centre for Intelligent Systems and Emerging Technologies Department of Electrical Electronics and Computer Engineering P.O. BOX 1906 Bellville7535 South Africa Upper Grayston Office Park Sandton2146 South Africa

ISBN: (纸本)9798331535162

By classifying loads into shiftable and non-shiftable groups and rearranging a distribution system's load demand model, demand-side management (DSM) reduces operating expenses. This is possible by shifting variable loads when utility costs per unit are lower. This work adopts a bi-level optimization technique to minimize the operational costs of a low-voltage microgrid (MG) system that incorporates plug-in hybrid electric vehicles (PHEVs), renewable energy sources, and fossil fuel generators while operating in grid-connected mode. According to the DSM participation level, the load model is reconfigured at the first optimization stage. Then, the restructured load demand models are considered, and recommendations for distributed generator scheduling are developed to reduce the production costs of the second-level microgrid system. The study's optimization technique used the popular differential evolution (DE) method applied to several power system optimization issues. When DSM, various grid participation and pricing schemes, and other factors were considered, the cost of producing active power was reduced. To ascertain their effect on the MG system's generation cost, scenarios involving different PHEV state of charge (SoC) levels and a taxable rate during V2G were also investigated. © 2025 IEEE.

关键词： Demand side management

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Accelerating Deep Neural Networks with a Low-Cost Lossless Compression

Accelerating Deep Neural Networks with a Low-Cost Lossless C...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Seongwook Byeon, Gwangeun Lee, Seungeui Bae, Youngseok Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Advanced R&D Center Hanwha Systems Co. Ltd. Korea Republic of Agency for Defense Development Emerging Science and Technology Directorate Korea Republic of

ISBN: (纸本)9798331510756

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-Delta-Immediate Compression (BDI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement, © 2025 IEEE.

关键词： Data handling

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Immoral post detection using a one-dimensional convolutional neural network-based LSTM network

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Multimedia Tools and Applications 2025年 1-20页

作者： Saqia, Bibi Khan, Khairullah Rahman, Atta Ur Khan, Aurangzeb Jain, Deepak Kumar Department of Computer Science University of Science and Technology Bannu28100 Pakistan Riphah Institute of Systems Engineering Riphah International University Islamabad46000 Pakistan Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune India

In text mining and Natural Language Processing (NLP), extracting emotions from textual data is gaining rapid attraction. The proliferation of online content and the freedom of expression on social media platforms has compelled the advancement of more sophisticated methods for the automatic identification of unwanted content. The One-Dimensional Convolutional Neural Network (1D-CNN) performs best for feature extraction, while the Long Short-Term Memory Network (LSTM) is well-known for extracting sequential features and classification of textual data. This study proposes a novel solution by combining 1D-CNN with LSTM. Utilizing a context-aware model, this study aims to enhance the detection of immoral posts, including hate speech and other forms of immoral content on social media platforms. Various posts are extracted from social media, which are then processed using Glove word embedding models. It depicts the word representations in the text, which yields the form of a real-valued vector determining the words' meaning. The 1D convolution utilizes a filter window over the input time-series data to extract features. Relying on the acquired parameters of the filters, they act as feature extractors. This feature extraction technique allows the model to distinguish detailed patterns and nuances from the textual contents of posts. The sequential processing capabilities of the LSTM network enable an in-depth examination of the text, capturing temporal relationships and context, which is critical for successful content categorization. To validate the proposed model, we utilized a benchmark dataset, called the Twitter dataset. The proposed model achieved the highest accuracy of 95.37% and outperforms state-of-the-art works. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Parameters Extraction of Photovoltaic Cell Using Particle Swarm Optimization 4th

Parameters Extraction of Photovoltaic Cell Using Particle Sw...

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4th International Conference on Electronic engineering and Renewable Energy systems, ICEERE 2024

作者： Chermite, Charaf Douiri, Moulay Rachid Elamri, Oumaymah Control and Computer Science for Intelligent Systems and Green Energy Department of Applied Physics Faculty of Science and Technology Cadi Ayyad University Marrakech Morocco LAMIGEP EMSI of Marrakech Systems and Applications Engineering Laboratory National School of Applied Sciences Cadi Ayyad University Marrakech Marrakesh Morocco

ISBN: (纸本)9789819799749

Extracting parameters accurately and effectively from solar photovoltaic (PV) models is crucial for detailed simulation, evaluation, and management of PV systems. Although there has been an increase in the development of analytical, numerical, and metaheuristic methods targeting this task, accurately determining these parameters continues to be a significant challenge. This study focuses on applying Particle Swarm Optimization (PSO) for the extraction of key parameters of RTC France solar cells, a critical step towards enhancing PV system efficiency and reliability. Additionally, the PSO's performance is evaluated against other metaheuristic algorithms including Enhanced Chaotic JAYA (CJAYA), Performance-Guided JAYA (PGJAYA), and Enhanced Gradient-Based Optimizer (EGBO), underscoring the effectiveness of the proposed PSO-based approach. Utilizing MATLAB-SIMULINK, comprehensive analysis and results are presented, shedding light on the efficacy of PSO in addressing the intricacies of parameter extraction in PV systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Impact of an electric vehicle, solar PV, and battery energy storage system in a distribution system 33

Impact of an electric vehicle, solar PV, and battery energy ...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Gbadamosi, Saheed Lekan Emmanuel Okafor, Chukwuemeka Krishnamurthy, Senthil Ratshitanga, Mukovhe Moodley, Prathaban Cape Peninsula University of Technology Centre for Intelligent Systems and Emerging Technologies Department of Electrical Electronics and Computer Engineering P.O. BOX 1906 Bellville7535 South Africa Upper Grayston Office Park Sandton2146 South Africa

ISBN: (纸本)9798331535162

The modern distribution power system has witnessed a tremendous increase in integrating renewable energy sources (wind and solar photovoltaic), electric vehicles, and battery energy storage systems. This is consequent upon the commitment of many countries (around the world) to reduce global carbon emissions and ensure an acceptable global temperature increase. This research focuses on the impacts of the integration of solar photovoltaic (PV) systems, electric vehicles (EVs), and battery energy storage systems (BESS) on the distribution system while minimizing the voltage deviations and power losses of the considered distribution network. Through a time-based Power System Simulation tool, the study evaluates four configurations to assess the technical impacts on the distribution network. The Simulation results showed that electric vehicle integration leads to increased power losses and a reduction in voltage deviations. Integrating battery energy storage systems into the distribution system increases the power losses (from 0.9222 MW to 1.602 MW) depending on the location of the battery energy storage system in the distribution network. Therefore, the study reveals the need for optimal sizing and placement of battery energy storage systems in the distribution network for optimal system performance. © 2025 IEEE.

关键词： battery energy storage Electric vehicle power loss solar PV voltage deviation

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