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A novel real-time aeroelastic hybrid simulation system of section model wind tunnel testing based on adaptive extended Kalman filter arxiv.org/abs/2504.20063 .flu-dyn .SY .SY

A novel real-time aeroelastic hybrid simulation system of section model wind tunnel testing based on adaptive extended Kalman filter

Elastically-supported section model tests are the most basic experimental technique in wind engineering, where helical springs are commonly employed to simulate the two-degree-of-freedom low-order modal motions of flexible structures. However, the traditional technique has intrinsic limitations in accurately modeling nonlinear structural behaviors and accurate adjustments of nonlinear structural damping. This study proposes a novel Real-Time Aeroelastic Hybrid Simulation system for section model wind tunnel tests by integrating an active control algorithm of adaptive Kalman filter. The proposed system enables the simulation of nonlinear heave-transverse-torsion coupled vibrations of a section model under the action of the oncoming wind. The structural properties, i.g. mass, damping and stiffness, are numerically simulated via an active control system, and the aerodynamic forces are physically modelled via the model-wind interaction in the wind tunnel. To validate the feasibility and accuracy of the proposed RTAHS system, a MATLAB/Simulink-FLUENT/UDF co-simulation framework is developed. Numerical verification results indicate that the proposed algorithm effectively estimates the motion responses in both linear and nonlinear scenarios.

arXiv.org

A Novel Parameter-Tying Theorem in Multi-Model Adaptive Systems: Systematic Approach for Efficient Model Selection arxiv.org/abs/2504.20202 .SY .SY

A Novel Parameter-Tying Theorem in Multi-Model Adaptive Systems: Systematic Approach for Efficient Model Selection

This paper presents a novel theoretical framework for reducing the computational complexity of multi-model adaptive control/estimation systems through systematic transformation to controllable canonical form. While traditional multi-model approaches face exponential growth in computational demands with increasing system dimension, we introduce a parameter-tying theorem that enables significant dimension reduction through careful analysis of system characteristics in canonical form. The approach leverages monotonicity properties and coordinated parameter relationships to establish minimal sets of identification models while preserving system stability and performance. We develop rigorous criteria for verifying plant inclusion within the convex hull of identification models and derive weight transformation relationships that maintain system properties across coordinate transformations. The effectiveness of the framework is demonstrated through application to coupled lateral-roll vehicle dynamics, where the dimension reduction enables real-time implementation while maintaining estimation accuracy. The results show that the proposed transformation approach can achieve comparable performance to conventional methods while requiring substantially fewer identification models, enabling practical deployment in high-dimensional systems.

arXiv.org

Fault Detection and Human Intervention in Vehicle Platooning: A Multi-Model Framework arxiv.org/abs/2504.20209 .SY .SY

Fault Detection and Human Intervention in Vehicle Platooning: A Multi-Model Framework

Vehicle platooning has been a promising solution for improving traffic efficiency and throughput. However, a failure in a single vehicle, including communication loss with neighboring vehicles, can significantly disrupt platoon performance and potentially trigger cascading effects. Similar to modern autonomous vehicles, platoon systems require human drivers to take control during failures, leading to scenarios where vehicles are operated by drivers with diverse driving styles. This paper presents a novel multi-model approach for simultaneously identifying signal drop locations and driver attitudes in vehicular platoons using only tail vehicle measurements. The proposed method distinguishes between attentive and distracted driver behaviors by analyzing the propagation patterns of disturbances through the platoon system. Beyond its application in platooning, our methodology for detecting driver behavior using a multi-model approach provides a novel framework for human driver identification. To enhance computational efficiency for real-time applications, we introduce a blending-based identification method utilizing chosen models and weighted interpolation, significantly reducing the number of required models while maintaining detection accuracy. The effectiveness of our approach is validated through high-fidelity CarSim/Simulink environment simulations. Results demonstrate that the proposed method can accurately identify both the location of signal drops and the corresponding driver behavior. This approach minimizes the complexity and cost of fault detection while ensuring accuracy and reliability.

arXiv.org

SCOPE-MRI: Bankart Lesion Detection as a Case Study in Data Curation and Deep Learning for Challenging Diagnoses arxiv.org/abs/2504.20405 .IV .AI .CV .LG

SCOPE-MRI: Bankart Lesion Detection as a Case Study in Data Curation and Deep Learning for Challenging Diagnoses

While deep learning has shown strong performance in musculoskeletal imaging, existing work has largely focused on pathologies where diagnosis is not a clinical challenge, leaving more difficult problems underexplored, such as detecting Bankart lesions (anterior-inferior glenoid labral tears) on standard MRIs. Diagnosing these lesions is challenging due to their subtle imaging features, often leading to reliance on invasive MRI arthrograms (MRAs). This study introduces ScopeMRI, the first publicly available, expert-annotated dataset for shoulder pathologies, and presents a deep learning (DL) framework for detecting Bankart lesions on both standard MRIs and MRAs. ScopeMRI includes 586 shoulder MRIs (335 standard, 251 MRAs) from 558 patients who underwent arthroscopy. Ground truth labels were derived from intraoperative findings, the gold standard for diagnosis. Separate DL models for MRAs and standard MRIs were trained using a combination of CNNs and transformers. Predictions from sagittal, axial, and coronal views were ensembled to optimize performance. The models were evaluated on a 20% hold-out test set (117 MRIs: 46 MRAs, 71 standard MRIs). The models achieved an AUC of 0.91 and 0.93, sensitivity of 83% and 94%, and specificity of 91% and 86% for standard MRIs and MRAs, respectively. Notably, model performance on non-invasive standard MRIs matched or surpassed radiologists interpreting MRAs. External validation demonstrated initial generalizability across imaging protocols. This study demonstrates that DL models can achieve radiologist-level diagnostic performance on standard MRIs, reducing the need for invasive MRAs. By releasing ScopeMRI and a modular codebase for training and evaluating deep learning models on 3D medical imaging data, we aim to accelerate research in musculoskeletal imaging and support the development of new datasets for clinically challenging diagnostic tasks.

arXiv.org

Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation arxiv.org/abs/2504.18539 .AS .LG .MM .SD

Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation

Audio-visual speech recognition (AVSR) incorporates auditory and visual modalities to improve recognition accuracy, particularly in noisy environments where audio-only speech systems are insufficient. While previous research has largely addressed audio disruptions, few studies have dealt with visual corruptions, e.g., lip occlusions or blurred videos, which are also detrimental. To address this real-world challenge, we propose CAV2vec, a novel self-supervised speech representation learning framework particularly designed to handle audio-visual joint corruption. CAV2vec employs a self-distillation approach with a corrupted prediction task, where the student model learns to predict clean targets, generated by the teacher model, with corrupted input frames. Specifically, we suggest a unimodal multi-task learning, which distills cross-modal knowledge and aligns the corrupted modalities, by predicting clean audio targets with corrupted videos, and clean video targets with corrupted audios. This strategy mitigates the dispersion in the representation space caused by corrupted modalities, leading to more reliable and robust audio-visual fusion. Our experiments on robust AVSR benchmarks demonstrate that the corrupted representation learning method significantly enhances recognition accuracy across generalized environments involving various types of corruption.

arXiv.org

A Unified Alternating Optimization Framework for Joint Sensor and Actuator Configuration in LQG Systems arxiv.org/abs/2504.18731 .SY .SY

A Unified Alternating Optimization Framework for Joint Sensor and Actuator Configuration in LQG Systems

This paper fills a gap in the literature by considering a joint sensor and actuator configuration problem under the linear quadratic Gaussian (LQG) performance without assuming a predefined set of candidate components. Different from the existing research, which primarily focuses on selecting or placing sensors and actuators from a fixed group, we consider a more flexible formulation where these components must be designed from scratch, subject to general-form configuration costs and constraints. To address this challenge, we first analytically characterize the gradients of the LQG performance with respect to the sensor and actuator matrices using algebraic Riccati equations. Subsequently, we derive first-order optimality conditions based on the Karush-Kuhn-Tucker (KKT) analysis and develop a unified alternating direction method of multipliers (ADMM)-based alternating optimization framework to address the general-form sensor and actuator configuration problem. Furthermore, we investigate three representative scenarios: sparsity promoting configuration, low-rank promoting configuration, and structure-constrained configuration. For each scenario, we provide in-depth analysis and develop tailored computational schemes. The proposed framework ensures numerical efficiency and adaptability to various design constraints and configuration costs, making it well-suited for integration into numerical solvers.

arXiv.org

Nonconvex Linear System Identification with Minimal State Representation arxiv.org/abs/2504.18791 .SY .SP .ML .LG .SY

Nonconvex Linear System Identification with Minimal State Representation

Low-order linear System IDentification (SysID) addresses the challenge of estimating the parameters of a linear dynamical system from finite samples of observations and control inputs with minimal state representation. Traditional approaches often utilize Hankel-rank minimization, which relies on convex relaxations that can require numerous, costly singular value decompositions (SVDs) to optimize. In this work, we propose two nonconvex reformulations to tackle low-order SysID (i) Burer-Monterio (BM) factorization of the Hankel matrix for efficient nuclear norm minimization, and (ii) optimizing directly over system parameters for real, diagonalizable systems with an atomic norm style decomposition. These reformulations circumvent the need for repeated heavy SVD computations, significantly improving computational efficiency. Moreover, we prove that optimizing directly over the system parameters yields lower statistical error rates, and lower sample complexities that do not scale linearly with trajectory length like in Hankel-nuclear norm minimization. Additionally, while our proposed formulations are nonconvex, we provide theoretical guarantees of achieving global optimality in polynomial time. Finally, we demonstrate algorithms that solve these nonconvex programs and validate our theoretical claims on synthetic data.

arXiv.org

Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis arxiv.org/abs/2504.18802 .IV .CV .LG

Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis

Urban roads and infrastructure, vital to city operations, face growing threats from subsurface anomalies like cracks and cavities. Ground Penetrating Radar (GPR) effectively visualizes underground conditions employing electromagnetic (EM) waves; however, accurate anomaly detection via GPR remains challenging due to limited labeled data, varying subsurface conditions, and indistinct target boundaries. Although visually image-like, GPR data fundamentally represent EM waves, with variations within and between waves critical for identifying anomalies. Addressing these, we propose the Reservoir-enhanced Segment Anything Model (Res-SAM), an innovative framework exploiting both visual discernibility and wave-changing properties of GPR data. Res-SAM initially identifies apparent candidate anomaly regions given minimal prompts, and further refines them by analyzing anomaly-induced changing information within and between EM waves in local GPR data, enabling precise and complete anomaly region extraction and category determination. Real-world experiments demonstrate that Res-SAM achieves high detection accuracy (>85%) and outperforms state-of-the-art. Notably, Res-SAM requires only minimal accessible non-target data, avoids intensive training, and incorporates simple human interaction to enhance reliability. Our research provides a scalable, resource-efficient solution for rapid subsurface anomaly detection across diverse environments, improving urban safety monitoring while reducing manual effort and computational cost.

arXiv.org

DMA Reception for Simultaneous Area-Wide Sensing and Multi-User Uplink Communications arxiv.org/abs/2504.18843 .SP

DMA Reception for Simultaneous Area-Wide Sensing and Multi-User Uplink Communications

The recent surge in deploying extremely large antenna arrays is expected to play a vital role in future sixth generation wireless networks, enabling advanced radar target localization with enhanced angular and range resolution. This paper focuses on the promising technology of Dynamic Metasurface Antennas (DMAs), integrating numerous sub-wavelength-spaced metamaterials within a single aperture, and presents a novel framework for designing its analog reception beamforming weights with the goal to optimize sensing performance within a spatial Area of Interest (AoI), while simultaneously guaranteeing desired multi-user uplink communication performance. We derive the Cramer-Rao Bound (CRB) with DMA-based reception for both passive and active radar targets lying inside the AoI, which is then used as the optimization objective for configuring the discrete tunable phases of the metamaterials. Capitalizing on the DMA partially-connected architecture, we formulate the design problem as convex optimization and present both direct CRB minimization approaches and low complexity alternatives using a lower-bound approximation. Simulation results across various scenarios validate the effectiveness of the proposed framework, showing it consistently outperforms existing state-of-the-art methods.

arXiv.org
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