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American control Conference (ACC)

作者： Ignacio Rubio Scola Luis Rodolfo Garcia Carrillo Andrew T. Sornborger João P. Hespanha Department of Industrial Products Engineering INTI CONICET and UNR Rosario Argentina Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA Los Alamos National Laboratory Los Alamos NM USA Center for Control Dynamical Systems and Computation University of California Santa Barbara USA

Self-learning control techniques mimicking the functionality of the limbic system in the mammalian brain have shown advantages in terms of superior learning ability and low computational cost. However, accompanying stability analyses and mathematical proofs rely on unrealistic assumptions which limit not only the performance, but also the implementation of such controllers in real-world scenarios. In this work the limbic system inspired control (LISIC) framework is revisited, introducing three contributions that facilitate the implementation of this type of controller in real-time. First, an extension enabling the implementation of LISIC to the domain of SISO affine systems is proposed. Second, a strategy for resetting the controller’s Neural Network (NN) weights is developed, in such a way that now it is possible to deal with piece-wise smooth references and impulsive perturbations. And third, for the case when a nominal model of the system is available, a technique is proposed to compute a set of optimal NN reset weight values by solving a convex constrained optimization problem. Numerical simulations addressing the stabilization of an unmanned aircraft system via the robust LISIC demonstrate the advantages obtained when adopting the extension to SISO systems and the two NN weight reset strategies.

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Minimum structural sensor placement for switched linear time-invariant systems and unknown inputs

arXiv

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arXiv 2021年

作者： Reed, Emily A. Ramos, Guilherme Bogdan, Paul Pequito, Sérgio Ming Hsieh Electrical and Computer Engineering Department University of Southern California United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal University of Southern California Ming Hsieh Electrical and Computer Engineering Department Delft University of Technology Delft Center for Systems and Control

In this paper, we study the structural state and input observability of continuous-time switched linear time- invariant systems and unknown inputs. First, we provide necessary and sufficient conditions for their structural state and input observability that can be efficiently verified in O((m(n + p))2), where n is the number of state variables, p is the number of unknown inputs, and m is the number of modes. Moreover, we address the minimum sensor placement problem for these systems by adopting a feed-forward analysis and by providing an algorithm with a computational complexity of O((m(n + p) + α)2.373), where α is the number of target strongly connected components of the system's digraph representation. Lastly, we explore different assumptions on both the system and unknown inputs (latent space) dynamics that add more structure to the problem, and thereby, enable us to render algorithms with lower computational complexity, which are suitable for implementation in large-scale systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Time varying control systems

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Super Twisting Sliding Mode control Strategy for a Three-Level T-Type Dual MPPT Inverter

Super Twisting Sliding Mode Control Strategy for a Three-Lev...

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Compatibility in Power Electronics (CPE)

作者： Farzaneh Bagheri Naki Guler Marco Rivera Hasan Komurcugil Department of Electrical and Electronic Engineering Antalya Bilim University Antalya Turkey Electrical and Electronic Engineering Technology Faculty Gazi University Ankara Turkey Renewable Energy and Smart Grids Application and Research Center Gazi University Ankara Turkey Power Electronics Machines and Control (PEMC) Research Institute University of Nottingham Nottingham UK Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP) Vicerrectoría Académica Universidad de Talca Curicó Chile Department of Computer Engineering Eastern Mediterranean University (EMU) Famagusta Turkey

ISBN: (数字)9798331515171

ISBN: (纸本)9798331515188

Nowadays Residential and small commercial power systems are taking advantage of the dual maximum power point tracking (MPPT) inverters, optimizing energy harvesting from two individual solar panel arrays to increase performance and energy output. Three-level T-type inverters are also famous due to their lower total harmonic distortion, high efficacy, and lower voltage stress on the switch which are a good option to be integrated into the dual MPPT solar plants. So, this article incorporates the dual MPPT solar plants with a T-Type grid-connected inverter which uses a double super twisting sliding mode control (STSMC) to control the injected currents to the grid in d and q reference frames. The STSMC mitigates chattering while showing robustness under model variations and dynamic response. The efficiency of the proposed control method under different irradiation values is verified through simulation results.

关键词： Maximum power point trackers Total harmonic distortion Switches Inverters Robustness Real-time systems Solar panels Time factors Voltage control Sliding mode control

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Extracting Stress-Related EEG Patterns from Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2024年 32卷 1817-1827页

作者： Su, Cheng-Hua Ko, Li-Wei Jung, Tzyy-Ping Onton, Julie Tzou, Shey-Cherng Juang, Jia-Chi Hsu, Chung-Yao Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Department of Biomedical Science and Environmental Biology Kaohsiung807 Taiwan University of California San Diego Swartz Center for Computational Neuroscience La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan University of California San Diego Institute for Neural Computation La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Biological Science and Technology Hsinchu300 Taiwan Kaohsiung Medical University Hospital Sleep Disorders Center Department of Neurology Kaohsiung807 Taiwan Kaohsiung Medical University College of Medicine Department of Neurology Kaohsiung807 Taiwan

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution. © 2001-2011 IEEE.

关键词： Entropy

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Unified Temporal Erasable Itemset Mining with a Lower-Bound Strategy

Unified Temporal Erasable Itemset Mining with a Lower-Bound ...

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IEEE International Conference on Big Data

作者： Tzung-Pei Hong Jia-Xiang Li Yu-Chuan Tsai Wei-Ming Huang Department of Computer Science and Information Engineering National University of Kaohsiung Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan Department of Electrical and Control China Steel Inc Kaohsiung Taiwan

ISBN: (纸本)9781665480468

Erasable itemset mining has been a valuable mining problem for manufacturers. It can extract less profitable materials from a product dataset and provide managers with good decision-making and a trade-off between cost and profit. However, the traditional erasable itemset mining methods seldom consider the time factor. For time-sensitive industries such as agro-processors, the time range is important in determining which materials are less profitable. Hong et al. first proposed the concept of temporal erasable itemset mining and seven lifespan options. They also proposed a unified temporal erasable (UTE) mining algorithm for getting incomplete temporal erasable itemsets. Howerever, the UTE algorithm does not satisfy the property of downward closure. In this work, we propose an improved algorithm to improve the performance of the UTE algorithm based on a lower-bound strategy and satisfying the property of downward closure. The proposed algorithm uses a hash table to store information that will be reused during the mining process to avoid scanning a dataset multiple times. The designed lower-bound strategy can preserve the downward closure property, narrowing the search space of candidate itemsets. In numerical experiments, we compare the performance using several metrics between the proposed method and the previous work. From the results of experiments, our proposed method outperforms the existing method on various metrics, such as execution time and the number of candidate erasable itemsets.

关键词： Measurement Industries Costs Itemsets Databases Decision making Big Data

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Multivariate Mutual Information Measures Functional Connectivity Accurately

Multivariate Mutual Information Measures Functional Connecti...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Mahnaz Ashrafi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665409391

Most studies use linear correlation as an estimator of functional connectivity. This measure does not detect the nonlinear dependence between two variables. During resting state, there are nonlinear relations among time series discarded by common functional connectivity measures such as Pearson correlation. Another limitation of linear correlation is the inability of calculating the association between two multivariate variables. Typically, a dimension reduction such as averaging is applied to each region time series. This reduction leads to a loss of spatial information across voxels within the region. Here, we propose to use a new information-theoretic measure as an interaction estimator between brain regions. Using simulated data, we show that this measure, multivariate mutual information (MVMI), overcomes the above mentioned limitations.

关键词： Dimensionality reduction Correlation Time series analysis Signal processing Time measurement Intelligent systems Mutual information

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Incremental Fuzzy Utility Mining with Tree Structure

Incremental Fuzzy Utility Mining with Tree Structure

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IEEE International Conference on Big Data

作者： Tzung-Pei Hong Wei-Teng Hung Wei-Ming Huang Yu-Chuan Tsai Department of Computer Science and Information Engineering National University of Kaohsiung Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Electrical and Control China Steel Inc Kaohsiung Taiwan Library and Information Center National University of Kaohsiung Kaohsiung Taiwan

ISBN: (纸本)9781665480468

High-utility-itemset mining, extended from frequent-itemset mining, considers external utilities of items in quantitative databases to obtain the itemsets with high utility. To easily understand those patterns with high utility values, fuzzy utility mining adopts fuzzy sets to increase the readability of the derived utility itemsets. However, real-world databases are usually dynamic. New transactions may be intermittently added, and the corresponding mined patterns must be updated to keep correct knowledge. In this paper, we propose an incremental method based on our previously proposed batch-processing fuzzy utility tree-based mining algorithm. The proposed approach adopts the fast-update (FUP) strategy. It considers newly coming data to readjust the head table and the tree structure, which generate desired itemsets in two phases. The experimental results reveal that the proposed algorithm outperforms the tree-based batch mining method.

关键词： Fuzzy sets Databases Itemsets Heuristic algorithms Big Data Data mining

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Urban Digital Twins for Intelligent Road Inspection

Urban Digital Twins for Intelligent Road Inspection

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Fan, Rui Zhang, Yikang Guo, Sicen Li, Jiahang Feng, Yi Su, Shuai Zhang, Yanting Wang, Wenshuo Jiang, Yu Junaid Bocus, Mohammud Zhu, Xingyi Chen, Qijun Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems State Key Laboratory of Intelligent Autonomous Systems Shanghai201804 China Donghua University Department of Computer Science & Technology Shanghai201620 China McGill University Department of Civil Engineering MontrealH3A 0C3 Canada Cto Office at ClearMotion BillericaMA01821 United States University of Bristol Department of Electrical & Electronic Engineering BristolBS8 1UB United Kingdom Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Shanghai200092 China

ISBN: (纸本)9781665480451

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022. © 2022 IEEE.

关键词： Data acquisition

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Disturbance Observers for Robust Backup control Barrier Functions

arXiv

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arXiv 2025年

作者： van Wijk, David E.J. Daş, Ersin Alan, Anil Coogan, Samuel Molnar, Tamas G. Burdick, Joel W. Majji, Manoranjan Hobbs, Kerianne L. Aerospace Engineering Texas A&M University College StationTX77845 United States Mechanical and Civil Engineering California Institute of Technology PasadenaCA91125 United States Delft Center for Systems and Control Delft University of Technology Delft Netherlands Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Mechanical Engineering Wichita State University WichitaKS67260 United States Safe Autonomy Lead Air Force Research Laboratory Wright-Patterson Air Force BaseOH45433 United States

Designing safe controllers is crucial and notoriously challenging for input-constrained safety-critical control systems. Backup control barrier functions offer an approach for the construction of safe controllers online by considering the flow of the system under a backup controller. However, in the presence of model uncertainties, the flow cannot be accurately computed, making this method insufficient for safety assurance. To tackle this shortcoming, we integrate backup control barrier functions with a disturbance observer and estimate the flow under a reconstruction of the disturbance while refining this estimate over time. We prove that the controllers resulting from the proposed Disturbance Observer Backup control Barrier Function (DO-bCBF) approach guarantee safety, are robust to unknown disturbances, and satisfy input constraints. © 2025, CC BY.

关键词： Robust control

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