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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Lauren Bramblett Nicola Bezzo Departments of Systems and Information Engineering and Electrical and Computer Engineering University of Virginia Charlottesville VA USA

In applications such as search and rescue or disaster relief, heterogeneous multi-robot systems (MRS) can provide significant advantages for complex objectives that require a suite of capabilities. However, within these application spaces, communication is often unreliable, causing inefficiencies or outright failures to arise in most MRS algorithms. Many researchers tackle this problem by requiring all robots to either maintain communication using proximity constraints or assuming that all robots will execute a predetermined plan over long periods of disconnection. The latter method allows for higher levels of efficiency in a MRS, but failures and environmental uncertainties can have cascading effects across the system, especially when a mission objective is complex or time-sensitive. To solve this, we propose an epistemic planning framework that allows robots to reason about the system state, leverage heterogeneous system makeups, and optimize information dissemination to disconnected neighbors. Dynamic epistemic logic formalizes the propagation of belief states, and epistemic task allocation and gossip is accomplished via a mixed integer program using the belief states for utility predictions and planning. The proposed framework is validated using simulations and experiments with heterogeneous vehicles.

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Characterization Procedure for Unsymmetrical Single-Phase Capacitor-Start Induction Machines

IEEE Open Access Journal of Power and Energy

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IEEE Open Access Journal of Power and Energy 2021年 8卷 2-10页

作者： Cale, J. Lute, C. Ross, G. Othee, A. Systems Engineering and Electrical and Computer Engineering Departments Colorado State University Fort CollinsCO United States Systems Engineering Department Colorado State University Fort CollinsCO United States Electrical and Computer Engineering Department Colorado State University Fort CollinsCO United States

This paper describes an experimental and numerical optimization procedure for off-line extraction of parameters for unsymmetrical, single-phase induction machines with capacitor-start operation. In addition to permitting asymmetry between phase winding parameters, the approach requires only average and rms measurements as inputs;electrical phase angle measurements are not required. The method is validated on an experimental motor with transient and steady-state simulations, using fitted parameters obtained using the proposed approach. © 2020 IEEE.

关键词： Optimization

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BatteryCDE: A Transferable Future Capacity Estimation Method for Battery Degradation with Irregular Sampling and Missing Data

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IEEE Transactions on Transportation Electrification 2025年

作者： Wang, Tianjing Ren, Chao Xiong, Houbo Song, Qun Dong, ZhaoYang City University of Hong Kong Department of Electrical Engineering Hong Kong KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Zhejiang University School of Electrical Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

To overcome critical limitations in existing methods for battery capacity estimation, including long-term estimation, handling of irregular sampling and missing data, and transferability across different battery types, this study proposes a novel estimation method, termed batteryCDE, by integrating neural controlled differential equations (CDEs) with attention mechanisms. It first constructs a continuous data path using cubic spline interpolation, followed by neural CDEs that generate feature-wise and cycle-wise attention features to capture essential battery characteristics. These attention-weighted features are processed by another neural CDE to model differential relationships, leading to precise capacity estimations. This study also provides a theoretical analysis of the advantages of batteryCDE over conventional discrete models in terms of long-term estimation, handling of irregular sampling and missing data, and transferability. Extensive experiments evaluate batteryCDE’s performance, utilizing three scenarios: varying estimation horizons, missing data, and cross-battery transfer. Results show that batteryCDE outperforms traditional models like LSTM, Transformer and neural CDE networks. Even with 50% missing data, an estimation horizon of 100 cycles, and application to different batteries, batteryCDE maintains an estimation error below 0.05. Compared to other methods, batteryCDE reduces estimation errors by 6.59% for long-term predictions, 15.09% for handling missing data, and 17.01% for cross-battery transferability. © 2025 IEEE. All rights reserved.

关键词： Battery degradation capacity irregular and missing data neural controlled differential equations transferability

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Structure and Control of Biology-inspired Networks

arXiv

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arXiv 2024年

作者： Sun, Zexin Baillieul, John Division of Systems Engineering Boston University MA02215 United States Departments of Mechanical Engineering Electrical and Computer Engineering Division of Systems Engineering Boston University MA02215 United States

There is increasing interest in developing the theoretical foundations of networked control systems that illuminate how brain networks function so as to enable sensory perception, control of movement, memory and all the operations that are needed for animals to survive. The present paper proposes a biologically inspired network model featuring dynamic connections regulated by Hebbian learning. Drawing on the machinery of graph theory and classical control we show that our novel nonlinear model exhibits such biologically plausible features as bounded evolution, stability, resilience, and a kind of structural stability-meaning that perturbations of the model parameters leave the essential properties of the model in tact. The proposed network model involves generalized cactus graphs with multiple control input nodes, and it is shown that the properties of the network are resilient to various changes in network topology provided these changes preserve the generalized cactus structure. A particular example described in what follows is an idealized network model of the visual system of a macaque monkey. The model displays resilience to network disruptions such as might occur in a living organism due to disease or injury. A different model of the same type provides an example of a system that can perform data classification. © 2024, CC BY-NC-SA.

关键词： Sensory perception

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Self-Supervised Learning Using Nonlinear Dependence

arXiv

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arXiv 2025年

作者： Sepanj, M. Hadi Ghojogh, Benyamin Fieguth, Paul Vision and Image Processing Group Systems Design Engineering University of Waterloo ON Canada Department of Electrical and Computer Engineering University of Waterloo ON Canada

Self-supervised learning has gained significant attention in contemporary applications, particularly due to the scarcity of labeled data. While existing SSL methodologies primarily address feature variance and linear correlations, they often neglect the intricate relations between samples and the nonlinear dependencies inherent in complex data. In this paper, we introduce Correlation-Dependence Self-Supervised Learning (CDSSL), a novel framework that unifies and extends existing SSL paradigms by integrating both linear correlations and nonlinear dependencies, encapsulating sample-wise and feature-wise interactions. Our approach incorporates the Hilbert-Schmidt Independence Criterion (HSIC) to robustly capture nonlinear dependencies within a Reproducing Kernel Hilbert Space, enriching representation learning. Experimental evaluations on diverse benchmarks demonstrate the efficacy of CDSSL in improving representation quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Progress Toward Production Grade Infrared LED Scene Projector systems

Progress Toward Production Grade Infrared LED Scene Projecto...

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2024 IEEE Research and Applications of Photonics in Defense Conference, RAPID 2024

作者： Kiamilev, F. Ahmed, H. Mcgee, R. Deputy, A. Singh, J. Browning, T. Steenkamer, B. Lassitter, T. Greenlee, M. Joyce, M. Kiamilev, L. University of Delaware Department of Electrical and Computer Engineering 139 The Green NewarkDE19716 United States Chip Design Systems Inc 256 Chapman Rd. Suite 104 NewarkDE19720 United States

ISBN: (纸本)9798350373097

关键词： Optical projectors

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Awareness and Assistance: General Drivers' Cyber Threat Identification and the Role of an In-Vehicle Console Display

Awareness and Assistance: General Drivers' Cyber Threat Iden...

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International Conference on Intelligent Transportation

作者： Fan He Juliet Kern Sebastian Fischmeister Catherine M. Burns Department of Systems Design Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

This online survey-based study investigated human drivers' awareness of cyber threats to automated vehicles from a human-centric perspective. Findings show drivers more readily identify explicit threats, particularly affecting vehicle motion, but struggle to grasp their origins. The study also assesses the effectiveness of Tesla Model 3's console display, as an example of a popular modern vehicle dashboard interface, in aiding threat identification, with drivers viewing it as moderately helpful. However, the driver's performance in identifying threat details is limited. This work's results highlight the challenges drivers face in responding to cyber threats and their need for information displayed for cyber threat diagnostics, actionable guidance, and a potential dedicated operation center for timely, systematic cyber threat management in modern driving systems.

关键词： Systematics Focusing Vectors Intelligent transportation systems Faces Vehicles

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Adapting to Human Preferences to Lead or Follow in Human-Robot Collaboration: A System Evaluation

arXiv

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arXiv 2023年

作者： Noormohammadi-Asl, Ali Ayub, Ali Smith, Stephen L. Dautenhahn, Kerstin The Department of Systems Design Engineering The Department of Electrical and Computer Engineering The University of Waterloo WaterlooON Canada

With the introduction of collaborative robots, humans and robots can now work together in close proximity and share the same workspace. However, this collaboration presents various challenges that need to be addressed to ensure seamless cooperation between the agents. This paper focuses on task planning for human-robot collaboration, taking into account the human’s performance and their preference for following or leading. Unlike conventional task allocation methods, the proposed system allows both the robot and human to select and assign tasks to each other. Our previous studies evaluated the proposed framework in a computer simulation environment. This paper extends the research by implementing the algorithm in a real scenario where a human collaborates with a Fetch mobile manipulator robot. We briefly describe the experimental setup, procedure and implementation of the planned user study. As a first step, in this paper, we report on a system evaluation study where the experimenter enacted different possible behaviours in terms of leader/follower preferences that can occur in a user study. Results show that the robot can adapt and respond appropriately to different human agent behaviours, enacted by the experimenter. A future user study will evaluate the system with human participants. © 2023, CC BY-NC-ND.

关键词： Manipulators

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Adapting to Human Preferences to Lead or Follow in Human-Robot Collaboration: A System Evaluation

Adapting to Human Preferences to Lead or Follow in Human-Rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Ali Noormohammadi-Asl Ali Ayub Stephen L. Smith Kerstin Dautenhahn Department of Systems Design Engineering and the Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada

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Accelerating OTA Circuit design: Transistor Sizing Based on a Transformer Model and Precomputed Lookup Tables

Accelerating OTA Circuit Design: Transistor Sizing Based on ...

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design, Automation and Test in Europe Conference and Exhibition

作者： Subhadip Ghosh Endalk Y. Gebru Chandramouli V. Kashyap Ramesh Harjani Sachin S. Sapatnekar Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA Cadence Design Systems Portland OR USA

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Device sizing is crucial for meeting performance specifications in operational transconductance amplifiers (OTAs), and this work proposes an automated sizing framework based on a transformer model. The approach first leverages the driving-point signal flow graph (DP-SFG) to map an OTA circuit and its specifications into transformer-friendly sequential data. A specialized tokenization approach is applied to the sequential data to expedite the training of the transformer on a diverse range of OTA topologies, under multiple specifications. Under specific performance constraints, the trained transformer model is used to accurately predict DP-SFG parameters in the inference phase. The predicted DP-SFG parameters are then translated to transistor sizes using a precomputed look-up table-based approach inspired by the 9m / I d methodology. In contrast to previous conventional or machine-learning-based methods, the proposed framework achieves significant improvements in both speed and computational efficiency by reducing the need for expensive SPICE simulations within the optimization loop; instead, almost all SPICE simulations are confined to the one-time training phase. The method is validated on a variety of unseen specifications, and the sizing solution demonstrates over 90% success in meeting specifications with just one SPICE simulation for validation, and 100% success with 3–5 additional SPICE simulations.

关键词： Training Computational modeling Transformers SPICE Tokenization Topology Computational efficiency Transistors Integrated circuit modeling Transconductance

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