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Revisiting the LQR Problem of Singular systems

IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2236-2252页

作者： Komeil Nosrati Juri Belikov Aleksei Tepljakov Eduard Petlenkov IEEE the Department of Computer Systems Tallinn University of Technology the Department of Software Science Tallinn University of Technology

In the development of linear quadratic regulator(LQR) algorithms, the Riccati equation approach offers two important characteristics——it is recursive and readily meets the existence condition. However, these attributes are applicable only to transformed singular systems, and the efficiency of the regulator may be undermined if constraints are violated in nonsingular versions. To address this gap, we introduce a direct approach to the LQR problem for linear singular systems, avoiding the need for any transformations and eliminating the need for regularity assumptions. To achieve this goal, we begin by formulating a quadratic cost function to derive the LQR algorithm through a penalized and weighted regression framework and then connect it to a constrained minimization problem using the Bellman's criterion. Then, we employ a dynamic programming strategy in a backward approach within a finite horizon to develop an LQR algorithm for the original system. To accomplish this, we address the stability and convergence analysis under the reachability and observability assumptions of a hypothetical system constructed by the pencil of augmented matrices and connected using the Hamiltonian diagonalization technique.

关键词： DC motor optimal control penalized weighted regression power system quadratic regulator singular system

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Power synchronization: taming massive diversified serverless functions under power constraints

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science China(Information sciences) 2025年第3期68卷 47-65页

作者： Du LIU Lu ZHANG Yechen XU Xinkai WANG Lingyu SUN Yifei PU Xiaofeng HOU Chao LI Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong University AI Chip Center for Emerging Smart Systems The Hong Kong University of Science and Technology

Carbon neutrality has become an important design objective worldwide. However, the on-going shift to cloud-naive era does not necessarily mean energy efficiency. From the perspective of power management, co-hosted serverless functions are difficult to tame. They are lightweight, short-lived applications sensitive to power capping activities. In addition, they exhibit great individual and temporal variability, presenting idiosyncratic performance/power scaling goals that are often at odds with one another. To date, very few proposals exist in terms of tailored power management for serverless platforms. In this work, we introduce power synchronization, a novel yet generic mechanism for managing serverless functions in a power-efficient way. Our insight with power synchronization is that uniform application power behavior enables consistent and uncompromised function operation on shared host machines. We also propose PowerSync, a synchronization-based power management framework that ensures optimal efficiency based on a clear understanding of functions. Our evaluation shows that PowerSync can improve the energy efficiency of functions by up to 16% without performance loss compared to conventional power management strategies.

关键词： serverless computing variability energy efficiency power management power synchronization

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Biometric-Based Secure Encryption Key Generation Using Convolutional Neural Networks and Particle Swarm Optimization

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第16期49卷 213-234页

作者： Almola, Sahera A.S. Khudeyer, Raidah S. Younis, Hameed Abdulkareem Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq

With the rapid expansion of computer networks and information technology, ensuring secure data transmission is increasingly vital—especially for image data, which often contains sensitive information. This research presents a biometric-based encryption system that uses fingerprint recognition and deep learning to generate strong, random encryption keys. Two convolutional neural networks (CNNs) are employed: one to verify identity based on a user’s ID and another to extract fingerprint features for key generation. These keys are optimized using Particle Swarm Optimization (PSO), enhancing their randomness and resistance to brute-force attacks. The system generates keys in real-time, eliminating the need for storage and minimizing the risk of theft or leakage. To further improve security, encryption keys are automatically updated after every ten messages, with different keys generated from multiple fingerprints of the same individual. Testing with the SOCOFing dataset (6,000 original and 49,270 synthetic images) achieved 99.75% identity verification and 99.83% classification accuracy. Performance metrics—entropy of 7.89, correlation factor of 0.00628, and zero repetition—demonstrate high robustness. This approach offers a secure, adaptive, and personalized encryption method ideal for sensitive domains like finance and healthcare. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Classification and study of music genres with multimodal Spectro-Lyrical Embeddings for Music (SLEM)

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Multimedia Tools and Applications 2025年第7期84卷 3701-3721页

作者： Mehra, Ashman Mehra, Aryan Narang, Pratik Department of Computer Science and Information Systems Birla Institute of Technology and Science Goa Pilani India Computer Science Department Carnegie Mellon University PittsburghPA United States Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Rajasthan Pilani India

The essence of music is inherently multi-modal – with audio and lyrics going hand in hand. However, there is very less research done to study the intricacies of the multi-modal nature of music, and its relation with genres. Our work uses this multi-modality to present spectro-lyrical embeddings for music representation (SLEM), leveraging the power of open-sourced, lightweight, and state-of-the-art deep learning vision and language models to encode songs. This work summarises extensive experimentation with over 20 deep learning-based music embeddings of a self-curated and hand-labeled multi-lingual dataset of 226 recent songs spread over 5 genres. Our aim is to study the effects of varying the weight of lyrics and spectrograms in the embeddings on the multi-class genre classification. The purpose of this study is to prove that a simple linear combination of both modalities is better than either modality alone. Our methods achieve an accuracy ranging between 81.08% to 98.60% for different genres, by using the K-nearest neighbors algorithm on the multimodal embeddings. We successfully study the intricacies of genres in this representational space, including their misclassification, visual clustering with EM-GMM, and the domain-specific meaning of the multi-modal weight for each genre with respect to ’instrumentalness’ and ’energy’ metadata. SLEM presents one of the first works on an end-to-end method that uses spectro-lyrical embeddings without hand-engineered features. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Music

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iSSL-AL: a deep active learning framework based on self-supervised learning for image classification

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Neural Computing and Applications 2024年第28期36卷 17699-17713页

作者： Agha, Rand Mustafa, Ahmad M. Abuein, Qusai Department of Computer Information Systems Jordan University of Science and Technology Irbid22110 Jordan

Deep neural networks have demonstrated exceptional performance across numerous applications. However, DNNs require large amounts of labeled data to avoid overfitting. Unfortunately, the labeled data may not be available;annotating large amounts of data is time-consuming, laborious, and requires human expertise, making it unfeasible to rely on manpower for annotation. One of the solutions to address this limitation is active learning (AL), a technique that utilizes unlabeled data while maintaining high performance. AL reduces the annotation cost of large datasets and enhances the training of models with fewer annotations. Uncertainty sampling has been proven to be one of the most effective strategies in AL;however, it lacks diversity. This research proposes iSSL-AL, a novel active learning framework that utilizes self-supervised learning (SSL) to ensure informative yet diverse samples. Three main aspects categorize the novelty of our work. The first is extending the margin uncertainty sampling by incorporating SSL to select informative and diverse points. The second is employing incremental learning for efficient training of the AL base classifier, where the model is trained incrementally in each AL cycle. The third is addressing the cold start problem, as our framework achieved high results in the early stages of training. Experiments show that iSSL-AL outperforms other state-of-the-art algorithms on the MNIST, FashionMNIST, and CIFAR-10 datasets, with accuracy scores of 99%, 98.9%, and 93.5%, respectively, effectively selecting diverse and informative samples. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Supervised learning

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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Social Media-Based Surveillance systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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computer Modeling in Engineering & sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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Data-driven output regulation control for constrained linear systems

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science China(Information sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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A multi-modal deep learning system for Arabic emotion recognition

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International Journal of Speech technology 2023年第1期26卷 123-139页

作者： Abu Shaqra, Ftoon Duwairi, Rehab Al-Ayyoub, Mahmoud Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan

Emotion analysis is divided into emotion detection, where the system detects if there is an emotional state, and emotion recognition where the system identifies the label of the emotion. In this paper, we provide a multimodal system for emotion detection and recognition using Arabic dataset. We evaluated the performance of both audio and visual data as a unimodal system, then, we exposed the impact of integrating the information sources into one model. We examined the effect of gender identification on the performance. Our results show that identifying speaker’s gender beforehand increases the performance of emotion recognition especially for the models that rely on audio data. Comparing the audio-based system with the visual-based system demonstrates that each model performs better for a specific emotional label. 70% of the angry labels were predicted correctly using the audio model while this percentage was less using the visual model (63%). The accuracy obtained for the surprise class was (40.6%) using the audio model while it was (56.2%) using the visual model. The combination of both modalities improves accuracy. Our final result for the multimodal system was (75%) for the emotion detection task and (60.11%) for emotion recognition task and these results are among the top results achieved in this field and the first which focus on Arabic content. Specifically, the novelty of this work is expressed by exploiting deep learning and multimodal models in emotion analysis and applying it on a natural audio and video dataset for Arabic speaking persons. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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Q-Learning-Assisted Meta-Heuristics for Scheduling Distributed Hybrid Flow Shop Problems

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computers, Materials & Continua 2024年第9期80卷 3573-3589页

作者： Qianyao Zhu Kaizhou Gao Wuze Huang Zhenfang Ma Adam Slowik Institute of Systems Engineering Macao University of Science and TechnologyMacao99078China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin75-453Poland

The flow shop scheduling problem is important for the manufacturing *** flow shop scheduling can bring great benefits to the ***,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted *** work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned ***,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are *** to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local *** of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during ***,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed *** experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random *** verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving *** Friedman test is executed on the results by five *** is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.

关键词： Distributed scheduling hybrid flow shop meta-heuristics local search Q-learning

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