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arXiv 2025年

作者： Zhang, Shilin Hu, Zican Wu, Wenhao Xie, Xinyi Tang, Jianxiang Chen, Chunlin Dong, Daoyi Cheng, Yu Sun, Zhenhong Wang, Zhi Department of Control Science and Intelligent Engineering Nanjing University China School of Engineering Australian National University Australia Australian Artificial Intelligence Institute University of Technology Sydney Australia Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

RL systems usually tackle generalization by inferring task beliefs from high-quality samples or warmup explorations. The restricted form limits their generality and usability since these supervision signals are expensive and even infeasible to acquire in advance for unseen tasks. Learning directly from the raw text about decision tasks is a promising alternative to leverage a much broader source of supervision. In the paper, we propose Text-to-Decision Agent (T2DA), a simple and scalable framework that supervises generalist policy learning with natural language. We first introduce a generalized world model to encode multi-task decision data into a dynamics-aware embedding space. Then, inspired by CLIP, we predict which textual description goes with which decision embedding, effectively bridging their semantic gap via contrastive language-decision pre-training and aligning the text embeddings to comprehend the environment dynamics. After training the text-conditioned generalist policy, the agent can directly realize zero-shot text-to-decision generation in response to language instructions. Comprehensive experiments on MuJoCo and Meta-World benchmarks show that T2DA facilitates high-capacity zero-shot generalization and outperforms various types of baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Supervised learning

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Boolean matrix compressed sensing

Boolean matrix compressed sensing

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qiang Liu Mahdi Soleymani Hessam Mahdavifar National Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Halıcıoğlu Data Science Institute University of California San Diego La Jolla CA USA Department of Electrical and Computer Engineering Northeastern University Boston MA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In real-world datasets, leveraging the low-rank and sparsity properties enables developing efficient algorithms across a diverse array of data-related tasks, including compression, compressed sensing, matrix completion, etc. Notably, these two properties often coexist in certain real-world datasets, especially in Boolean datasets and quantized real-valued datasets. To harness the advantages of low-rank and sparsity simultaneously, we adopt a technique inspired by compressed sensing and Boolean matrix completion. Our approach entails compressing a low-rank sparse Boolean matrix by performing inner product operations with a randomly generated Boolean matrix. We then propose a decoding algorithms based on message-passing techniques to recover the original matrix. Our experiments demonstrate superior recovery performance of our proposed algorithms compared to Boolean matrix completion, with equal measurement requirements.

关键词： Image coding Signal processing algorithms Signal processing Acoustics Sensors Decoding Sparse matrices Speech processing Compressed sensing

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Excess Mean Squared Error of Empirical Bayes Estimators

arXiv

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arXiv 2025年

作者： Ju, Yue Wahlberg, Bo Hjalmarsson, Håkan Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Competence Centre for Advanced BioProduction by Continuous Processing AdBIOPRO Sweden

Empirical Bayes estimators are based on minimizing the average risk with the hyper-parameters in the weighting function being estimated from observed data. The performance of an empirical Bayes estimator is typically evaluated by its mean squared error (MSE). However, the explicit expression for its MSE is generally unavailable for finite sample sizes. To address this issue, we define a high-order analytical criterion: the excess MSE. It quantifies the performance difference between the maximum likelihood and empirical Bayes estimators. An explicit expression for the excess MSE of an empirical Bayes estimator employing a general data-dependent hyper-parameter estimator is derived. As specific instances, we provide excess MSE expressions for kernel-based regularized estimators using the scaled empirical Bayes, Stein’s unbiased risk estimation, and generalized cross-validation hyper-parameter estimators. Moreover, we propose a modification to the excess MSE expressions for regularized estimators for moderate sample sizes and show its improvement on accuracy in numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

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Combining machine learning models and rule engines in clinical decision systems: Exploring optimal aggregation methods for vaccine hesitancy prediction

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computers in Biology and Medicine 2025年 188卷 109749-109749页

作者： Kierner, Slawomir Kierner, Piotr Kucharski, Jacek Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Genetics – Blavatnik Institute Sinclair Lab Harvard Medical School D 77 Avenue Louis Pasteur BostonMA02115 United States Faculty of Electrical Electronic Computer and Control Engineering Lodz University of Technology 18/22 Stefanowskiego St. Lodź90-924 Poland

Background: With the increasing application of artificial intelligence (AI) technologies in the healthcare sector and the emergence of new solutions, such as large language models, there is a growing need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML) offering higher prediction accuracy at the expense of decision-making transparency. Purpose: This study investigates the efficacy of various aggregation methods combining the decisions of an AI model and a clinical rule-based (RB) engine in predicting vaccine hesitancy to maximize the effectiveness of patient incentive programs. This is the first study of parallel ensemble of rules and machine learning in clinical context proposing RB confidence-led fusion of ML and RB inference. Methods: A clinical decision system for predicting hesitation to vaccinate is developed based on a differentially private set of longitudinal health records of 974,000 US patients and clinical rules obtained from the present literature. Various approaches based on possibility theory have been explored to maximize classification accuracy, capture and hurdle rates while ensuring trustworthiness in clinical interventions. Results: Our findings reveal that the hybrid approach outperforms the individual models and RB systems when transparency and accuracy are critical. A RB confidence-led approach emerged as the most effective method. The aggregation of mismatched classes relies on RB results when the RB engine has high confidence (expressed as more than the median degree of membership to the vaccination hesitation output function) and on ML predictions when the RB engine exhibits lower confidence. Conclusions: Implementing such an aggregation method preserves the accuracy and capture rates of a clinical decision system, while potentially improving acceptance among healthcare providers. © 2025 The Authors

关键词： Vaccines

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Torque Characteristic Analyses and Multi-Objective Optimisation of Multi-Layer Flux-Barrier Less-Rare-Earth Permanent Magnet Synchronous Machine

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IET Electric Power Applications 2025年第1期19卷

作者： Wang, Weinan Gong, Weize Zhu, Liangkuan Wei, Jian Wang, Mingqiao Liu, Yong Zheng, Ping College of Computer and Control Engineering Northeast Forestry University Harbin China College of Mechanical and Electrical Engineering Northeast Forestry University Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

This paper investigates the torque characteristic and multi-objective optimisation (MOO) of the multi-layer flux-barrier less-rare-earth permanent magnet synchronous machine (MLFB-LRE-PMSM) used for the electric vehicles (EVs). This study explores the variation of torque characteristics with current angle under different winding current conditions, and thoroughly analyses the influence of permanent magnet (PM) structure parameters on reluctance torque, PM torque and the proportion of reluctance torque in the electromagnetic torque. On this basis, the sensitivity analysis method based on Sobol sequence and joint Sobol index is adopted, which not only simplifies the traditional analysis process, but also effectively considers the interaction effect between the optimisation objectives. Finally, a cooperative optimisation strategy of improved whale optimisation algorithm (WOA) and genetic algorithm (GA) is proposed, which is successfully applied to the MOO design of MLFB-LRE-PMSM. The results show that the improved WOA algorithm shows excellent optimisation performance and can be used as a new solution in the field of motor optimisation. At the same time, the engineering practicability of the proposed collaborative optimisation scheme is verified by finite element simulation. © 2025 The Author(s). IET Electric Power Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Synchronous machinery

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Decomposition method-based global Mittag-Leffler synchronization for fractional-order Clifford-valued neural networks with transmission delays and impulses

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Neural Networks 2025年 189卷 107564页

作者： Sriraman, R. Manoj, N. Pratap, Anbalagan Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

This study examines the global Mittag-Leffler synchronization (GMLS) problem for fractional-order Clifford-valued neural networks (FOCLVNNs) including transmission delays and impulses. Firstly, a novel kind of FOCLVNNs is developed that incorporates impulses and transmission delays, which can be useful for better understanding of Clifford-valued neural networks (CLVNNs) dynamics more precisely. Then, because of the non-commutative multiplication feature of the Clifford algebra, the systems of FOCLVNNs can be decomposed into multi-dimensions real-valued neural networks (RVNNs), that avoid the difficulties associated with Clifford number multiplication. By using Lyapunov functions and inequality techniques, several novel solid conditions for the GMLS of FOCLVNNs under the state feedback controller that has been formulated. In addition, this paper presents novel results that are easy to solve and new insights into GMLS of FOCLVNNs. Then, a numerical verification is given along with graphical analysis to validate the applicability of the obtained theories. Based on the considered master–slave FOCLVNNs, an image encryption algorithm for color images is provided. In addition, simulation and performance analysis are stated to prove the validity of the given algorithms. © 2025 Elsevier Ltd

关键词： Lyapunov functions

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On the Connection Between Magnetic-Field Odometry Aided Inertial Navigation and Magnetic-Field SLAM

arXiv

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arXiv 2025年

作者： Skog, Isaac Kok, Manon Hendeby, Gustaf Huang, Chuan Edridge, Thomas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Delft Center for Systems and Control Delft University of Technology Delft Netherlands Dept. of Electrical Engineering Linköping University Linköping Sweden

Magnetic-field simultaneous localization and mapping (SLAM) using consumer-grade inertial and magnetometer sensors offers a scalable, cost-effective solution for indoor localization. However, the rapid error accumulation in the inertial navigation process limits the feasible exploratory phases of these systems. Advances in magnetometer array processing have demonstrated that odometry information, i.e., displacement and rotation information, can be extracted from local magnetic field variations and used to create magnetic-field odometry-aided inertial navigation systems. The error growth rate of these systems is significantly lower than that of standalone inertial navigation systems. This study seeks an answer to whether a magnetic-field SLAM system fed with measurements from a magnetometer array can indirectly extract odometry information — without requiring algorithmic modifications — and thus sustain longer exploratory phases. The theoretical analysis and simulation results show that such a system can extract odometry information and indirectly create a magnetic field odometry-aided inertial navigation system during the exploration phases. However, practical challenges related to map resolution and computational complexity remain significant. © 2025, CC BY.

关键词： Inertial navigation systems

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Data-Driven Learning and control with Event-Triggered Measurements

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IEEE Transactions on Automatic control 2025年

作者： Feng, Shilun Shi, Dawei Chen, Tongwen Shi, Ling Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Kowloon Hong Kong

Event-triggered control has attracted considerable attention for its effectiveness in resource-restricted applications. To make event-triggered control as an end-to-end solution, a key issue is how to effectively learn unknown system dynamics from event-triggered measurements and consequently, develop a learning-based event-triggered controller. Existing works learn system dynamics based on periodic time-triggered measurements, and it is yet to know how to learn a controller with performance guarantee based on event-triggered measurements. To address this issue, we consider the problem of learning an event-triggered state feedback controller for an unknown linear system based on event-triggered state measurements in this work. In particular, we first analyze the event-triggered measurements within a set-membership framework. We prove that the estimation error belongs to a bounded ellipsoid determined by the historical measurements and the event-triggering condition. Subsequently, we demonstrate that all admissible systems compatible with the collected data samples can be explicitly represented in the form of quadratic matrix inequalities using the state estimates. With the acquired set of admissible systems, a co-design problem for the data-driven controller and event-triggering condition is solved using the linear matrix inequality technique, with guaranteed closed-loop stability and L2-gain performance. Finally, numerical examples and comparisons are provided to illustrate the effectiveness of the proposed event-triggered learning and control approach. © 1963-2012 IEEE.

关键词： Networked control systems

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MULSAM: Multidimensional Attention with Hardware Acceleration for Efficient Intrusion Detection on Vehicular CAN Bus

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, He Shi, Xiaokang Liu, Hansheng Wang, Yanwen Lu, Jiwu Zeng, Haibo Li, Renfa Wu, Di Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha410082 China Hunan University College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University Shenzhen Research Institute Shenzhen518000 China Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24061 United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China

controller Area Network (CAN) protocol is an efficient standard enabling communication among Electronic control Units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. In this article, a novel vehicle Intrusion Detection System (IDS) is developed. The challenge is that existing techniques of Intrusion Detection Systems (IDS) rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges Multi-dimensional Long Short-Term Memory (MD-LSTM) and Self-Attention Mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including Stacked LSTM, MD-LSTM, etc. Experiments show that our approach has the best detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high detection accuracy of 98.81% and a low latency of 1.88 ms. © 1982-2012 IEEE.

关键词： Intrusion detection

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Mixed Small Gain and Phase Theorem: A new view using Scale Relative Graphs

arXiv

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arXiv 2025年

作者： Baron-Prada, Eder Anta, Adolfo Padoan, Alberto Dörfler, Florian Austrian Institute of Technology Vienna1210 Austria Automatic Control Laboratory ETH Zurich Zürich8092 Switzerland Austrian Institute of Technology Vienna1210 Austria Department of Electrical & Computer Engineering University of British Columbia Canada Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland

We introduce a novel approach to feedback stability analysis for linear time-invariant (LTI) systems, overcoming the limitations of the sectoriality assumption in the small phase theorem. While phase analysis for single-input single-output (SISO) systems is well-established, multi-input multi-output (MIMO) systems lack a comprehensive phase analysis until recent advances introduced with the small-phase theorem. A limitation of the small-phase theorem is the sectorial condition, which states that an operator’s eigenvalues must lie within a specified angle sector of the complex plane. We propose a framework based on Scaled Relative Graphs (SRGs) to remove this assumption. We derive two main results: a graphical set-based stability condition using SRGs and a small-phase theorem with no sectorial assumption. These results broaden the scope of phase analysis and feedback stability for MIMO systems. © 2025, CC BY.

关键词： MIMO systems

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