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Data-driven output regulation control for constrained linear systems

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 Fairness Analysis of Human and AI-Generated Student Reflection Summaries 5

A Fairness Analysis of Human and AI-Generated Student Reflec...

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5th Workshop on Gender Bias in Natural Language Processing, GeBNLP 2024, held in conjunction with the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Baghel, Bhiman Kumar Narayanan, Arun Balajiee Lekshmi Yoder, Michael Miller Department of Computer Science United States Intelligent Systems Program University of Pittsburgh PA United States

ISBN: (纸本)9798891761377

This study examines the fairness of human- and AI-generated summaries of student reflections in university STEM classes, focusing on potential gender biases. Using topic modeling, we first identify topics that are more prevalent in reflections from female students and others that are more common among male students. We then analyze whether human and AI-generated summaries reflect the concerns of students of any particular gender over others. Our analysis reveals that though human-generated and extractive AI summarization techniques do not show a clear bias, abstractive AI-generated summaries exhibit a bias towards male students. Pedagogical themes are overrepresented from male reflections in these summaries, while concept-specific topics are underrepresented from female reflections. This research contributes to a deeper understanding of AI-generated bias in educational contexts, highlighting the need for future work on mitigating these biases. ©2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrieval

Quantifying the Gaps Between Translation and Native Percepti...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Buettner, Kyle Kovashka, Adriana Intelligent Systems Program University of Pittsburgh United States Department of Computer Science University of Pittsburgh United States

ISBN: (纸本)9798891761643

There is a scarcity of multilingual vision-language models that properly account for the perceptual differences that are reflected in image captions across languages and cultures. In this work, through a multimodal, multilingual retrieval case study, we quantify the existing lack of model flexibility. We empirically show performance gaps between training on captions that come from native German perception and captions that have been either machine-translated or human-translated from English into German. To address these gaps, we further propose and evaluate caption augmentation strategies. While we achieve mean recall improvements (+1.3), gaps still remain, indicating an open area of future work for the community. © 2024 Association for Computational Linguistics.

关键词： Machine translation

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Gymir5G: A Simulation Platform to Study Data Transmission over WebRTC in 5G Networks with Deep Learning Assistance 32

Gymir5G: A Simulation Platform to Study Data Transmission ov...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Smirnov, Nikita Tomforde, Sven Kiel University Department of Computer Science Intelligent Systems Kiel Germany

ISBN: (纸本)9789532901382

This paper presents Gymir5G, an open-source simulation platform for running various simulated scenarios in 4/5G and Wi-Fi networks. It is primarily designed to study the optimization of real-time multimedia data transmission in cellular networks using deep learning algorithms, later to apply the resulting trained models on real remote-controlled devices. Based on the INET and Simu5G libraries from the OMNeT++ discrete event simulator ecosystem written in C++, Gymir5G introduces three main features. First, it presents a basic version of the WebRTC protocol with all of its key congestion control, retransmission, and redundancy mechanisms implemented. Second, there is a separate Python project, GymirDRL, which introduces a deep reinforcement learning setup as an example of a deep learning method applied to data optimization, and an inter-process communication bridge to integrate simulation and learning parts under one solution. Gymir5G provides realistic base station parameters, configurable data streams, and different scenarios including non-standalone and standalone 5G networks, as well as multihoming configurations for the parallel use of cellular and wireless stacks. Finally, Gymir5G presents two example simulations, one of which is designed for the real geographic area in the Bay of Kiel, Germany, and the moving ship as a remote-controlled device. © 2024 University of Split, FESB.

关键词： 5G mobile communication systems

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Dynamic Sampling-Based Meta-Learning Using Multilingual Acoustic Data for Under-Resourced Speech Recognition

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IEEE Access 2024年 12卷 106070-106083页

作者： Hsieh, I-Ting Wu, Chung-Hsien Zhao, Zhe-Hong National Cheng Kung University Graduate Program of Multimedia Systems and Intelligent Computing Tainan70101 Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

Under-resourced automatic speech recognition (ASR) has become an active field of research and has experienced significant progress during the past decade. However, the performance of under-resourced ASR trained by existing methods is still far inferior to high-resourced ASR for practical applications. In this paper, speech data from languages that share the most phonemes with the under-resourced language are selected as supplementary resources for meta-training based on the Model-Agnostic Meta-Learning (MAML) strategy. Besides supplementary language selection, this paper proposes a dynamic sampling method instead of the original random sampling method to select support and query sets for each task in MAML to improve meta-training performance. In this study, Taiwanese is selected as the under-resourced language, and the speech corpus of five languages, including Mandarin, English, Japanese, Cantonese, and Thai, are chosen as supplementary training data for acoustic model training. The proposed dynamic sampling approach uses phonemes, pronunciation, and speech recognition models as the basis to determine the proportion of each supplementary language to select helpful utterances for MAML. For evaluation, with the selected utterances from each supplementary language for meta-training, we obtained a Word Error Rate of 20.24% and a Syllable Error Rate of 8.35% for Taiwanese ASR, which were better than the baseline model (26.18% and 13.99%) using only the Taiwanese corpus and other methods. © 2013 IEEE.

关键词： Speech recognition

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Reaching and Grasping with NAO Robot 16th

Reaching and Grasping with NAO Robot

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Borghese, N. Alberto Chitti, Eleonora Laboratory of Applied Intelligent Systems Department of Computer Science University of Milan Milan Italy

ISBN: (纸本)9789819635184

In this paper we propose a robust approach that combines classical computer vision with a smart setup that enables a NAO humanoid robot to play with children. Blocks are adopted as playing objects as they are widely used and are a highly versatile support. This approach addresses the challenges of object reaching and grasping, by allowing the robot to approach the table over which activity is carried out, orient appropriately, plan the reaching movement that is executed partly through direct and partly through inverse kinematics, grasp the block, moving it over the table and releasing it into its target position. By integrating visual information provided by the robot cameras into a closed-loop control system, no additional sensors are required. This approach enables not only children to play with a Humanoid robot in a richer way, but also to be used to treat cognitive / motor deficits inside what is termed Robot Assisted Therapy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anthropomorphic robots

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ARnopoly: exploring strengths and weaknesses of AR experience enhancing board games 3

ARnopoly: exploring strengths and weaknesses of AR experienc...

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3rd Workshop on Artificial Intelligence for Human-Machine Interaction, AIxHMI 2024

作者： Chitti, Eleonora Barbagallo, Andrea Delia, Andrea Borghese, N. Alberto Applied Intelligent Systems Laboratory Department of Computer Science University of Milan Italy

This project aims to explore and apply the best practices of Augmented Reality (AR) design to the board play. We adapted the Monopoly game, developing an AR version that integrates the traditional game with new elements to take advantage of the virtual world. Our goal is to explore which AR game rules and mechanics can be incorporated into board game design to create a more engaging and effective experience for players. With ARnopoly, we specifically examined the strengths of augmented reality and the weaknesses that need to be addressed to seamlessly integrate the physical board play with the digital experience. ARnopoly was tested involving 7 participants, and from the analysis of their feedback it emerged that the integration of the board play with AR increases the immersion in the game and the overall positive feeling. However, in the long run, since the monopoly-like game is very long-lasting, this positivity is dampened by the constraint of holding the device in hand which causes fatigue in the user. © 2024 for this paper by its authors.

关键词： Game design

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Methods for class-imbalanced learning with support vector machines: a review and an empirical evaluation

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Soft Computing 2024年第20期28卷 11873-11894页

作者： Rezvani, Salim Pourpanah, Farhad Lim, Chee Peng Wu, Q. M. Jonathan Department of Computer Science Ryerson University TorontoON Canada Department of Electrical and Computer Engineering University of Windsor WindsorON Canada Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC Australia

This paper presents a review on methods for class-imbalanced learning with the Support Vector Machine (SVM) and its variants. We first explain the structure of SVM and its variants and discuss their inefficiency in learning with class-imbalanced data sets. We introduce a hierarchical categorization of SVM-based models with respect to class-imbalanced learning. Specifically, we categorize SVM-based models into re-sampling, algorithmic, and fusion methods, and discuss the principles of the representative models in each category. In addition, we conduct a series of empirical evaluations to compare the performances of various representative SVM-based models in each category using benchmark imbalanced data sets, ranging from low to high imbalanced ratios. Our findings reveal that while algorithmic methods are less time-consuming owing to no data pre-processing requirements, fusion methods, which combine both re-sampling and algorithmic approaches, generally perform the best, but with a higher computational load. A discussion on research gaps and future research directions is provided. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Support vector machines

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FedRec:Trusted rank-based recommender scheme for service provisioning in federated cloud environment

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Digital Communications and Networks 2023年第1期9卷 33-46页

作者： Ashwin Verma Pronaya Bhattacharya Umesh Bodkhe Deepti Saraswat Sudeep Tanwar Kapal Dev Department of Computer Science and Engineering Institute of TechnologyNirma UniversityAhmedabadGujarat382481India Department of Institute of Intelligent Systems University of JohannesburgPO Box 524South Africa

The emergence of on-demand service provisioning by Federated Cloud Providers(FCPs)to Cloud Users(CU)has fuelled significant innovations in cloud provisioning *** to the massive traffic,massive CU resource requests are sent to FCPs,and appropriate service recommendations are sent by ***,the FourthGeneration(4G)-Long Term Evolution(LTE)network faces bottlenecks that affect end-user throughput and ***,the data is exchanged among heterogeneous stakeholders,and thus trust is a prime *** address these limitations,the paper proposes a Blockchain(BC)-leveraged rank-based recommender scheme,FedRec,to expedite secure and trusted Cloud Service Provisioning(CSP)to the CU through the FCP at the backdrop of base 5G communication *** scheme operates in three *** the first phase,a BCintegrated request-response broker model is formulated between the CU,Cloud Brokers(BR),and the FCP,where a CU service request is forwarded through the BR to different *** service requests,Anything-as-aService(XaaS)is supported by 5G-enhanced Mobile Broadband(eMBB)*** the next phase,a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender(RBR)model based on the CU *** the final phase,based on the matching recommendations between the CU and the FCP,Smart Contracts(SC)are executed,and resource provisioning data is stored in the Interplanetary File systems(IPFS)that expedite the block *** proposed scheme FedRec is compared in terms of SC evaluation and formal *** simulation,FedRec achieves a reduction of 27.55%in chain storage and a transaction throughput of 43.5074 Mbps at 150 *** the IPFS,we have achieved a bandwidth improvement of 17.91%.In the RBR models,the maximum obtained hit ratio is 0.9314 at 200 million CU requests,showing an improvement of 1.2%in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.76

关键词： Blockchain 5G-enhanced mobile broadband Federated clouds Rank-based recommender model Smart contracts

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Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (λ, λ)) Genetic Algorithm

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ACM Transactions on Evolutionary Learning and Optimization 2023年第4期2卷 1–39页

作者： Hevia Fajardo, Mario Alejandro Sudholt, Dirk Department of Computer Science University of Sheffield Sheffield United Kingdom Chair of Algorithms for Intelligent Systems University of Passau Passau Germany

The self-adjusting (1 + (λ, λ)) GA is the best known genetic algorithm for problems with a good fitness-distance correlation as in OneMax. It uses a parameter control mechanism for the parameter λ that governs the mutation strength and the number of offspring. However, on multimodal problems, the parameter control mechanism tends to increase λ *** study this problem for the standard Jumpk benchmark problem class using runtime analysis. The self-adjusting (1 + (λ, λ)) GA behaves like a (1 + n) EA whenever the maximum value for λ is reached. This is ineffective for problems where large jumps are required. Capping λ at smaller values is beneficial for such problems. Finally, resetting λ to 1 allows the parameter to cycle through the parameter space. We show that resets are effective for all Jumpk problems: the self-adjusting (1 + (λ, λ)) GA performs as well as the (1 + 1) EA with the optimal mutation rate and evolutionary algorithms with heavy-tailed mutation, apart from a small polynomial *** the way, we present new general methods for translating existing runtime bounds from the (1 + 1) EA to the self-adjusting (1 + (λ, λ)) GA. We also show that the algorithm presents a bimodal parameter landscape with respect to λ on Jumpk. For appropriate n and k, the landscape features a local optimum in a wide basin of attraction and a global optimum in a narrow basin of attraction. To our knowledge this is the first proof of a bimodal parameter landscape for the runtime of an evolutionary algorithm on a multimodal problem. © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Genetic algorithms

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