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Fabrication of Laser-Induced Graphene (LIG) Diffractive Lenses on Flexible Coloeless Polyimide (CPI) Substrates for Aerospace Applications
No full textMulti-Sensor Fusion for Quadruped Robot State Estimation Using Invariant Filtering and Smoothing
No full textThis letter introduces two multi-sensor state estimation frameworks for quadruped robots, built on the Invariant Extended Kalman Filter (InEKF) and Invariant Smoother (IS). The proposed methods, named E-InEKF and E-IS, fuse kinematics, IMU, LiDAR, and GPS data to mitigate position drift, particularly along the z-axis, a common issue in proprioceptive-based approaches. We derived observation models that satisfy group-affine properties to integrate LiDAR odometry and GPS into InEKF and IS. LiDAR odometry is incorporated using Iterative Closest Point (ICP) registration on a parallel thread, preserving the computational efficiency of proprioceptive-based state estimation. We evaluate E-InEKF and E-IS with and without exteroceptive sensors, benchmarking them against LiDAR-based odometry methods in indoor and outdoor experiments using the KAIST HOUND2 robot. Our methods achieve lower Relative Position Errors (RPE) and significantly reduce Absolute Trajectory Error (ATE), with improvements of up to 28% indoors and 40% outdoors compared to LIO-SAM and FAST-LIO2. Additionally, we compare E-InEKF and E-IS in terms of computational efficiency and accuracy.
An investigation of flow-induced vibrations (FIV) during cold testing on a helical tube steam generator for a high-temperature gas-cooled reactor (HTGR)
No full textFlow-induced vibration (FIV) is a critical phenomenon where the interaction between flowing fluid and structural elements, such as pipes or tubes, results in vibrations, which may compromise system integrity. This research aims to model and simulate the behavior of a helical tube steam generator designed for HighTemperature Gas Reactors (HTGRs), employing the latest ANSYS software licensed by BRIN. The study addresses the problem of understanding fluid flow on steam generators' vibration characteristics to improve their stability and reliability. This work analyzes the system's pressure distribution, mass flow, and vibration frequencies under cold testing and turbulent conditions. The findings of this research lie in its specific focus on identifying risks of flow-induced vibrations under non-thermal conditions, an area that is rarely studied but essential for evaluating mechanical reliability during cold testing scenarios. This approach fosters a deeper understanding of the pure impact of fluid dynamics on tube structures without the complications of thermal phenomena. The one-way FSI method with external load was chosen over system coupling because it directly maps fluid-induced forces to the structural domain without requiring repeated bidirectional convergence. This approach is computationally efficient and has been extensively validated for scenarios where fluid-to-structure interactions dominate. Results indicated a pressure drop from 16 MPa at the inlet to 15.3 MPa at the outlet. Modal analysis revealed a range of natural frequencies from 0.32 Hz to 28.35 Hz. Additionally, the calculated flow-induced shedding frequency was 39.7 Hz, while the natural frequency of the helical tube stood at 16.25 Hz. The Reynolds number (Re) and Dean number (De) were determined to be 48,921 and 50,383, respectively, confirming the presence of turbulent flow. The study highlights the risk of resonance when the natural frequency approaches the shedding frequency, which could lead to excessive vibrations and potential structural damage. These findings provide valuable insights for developing safer and more efficient steam generators.
Enhanced UAV Detection and Classification With Birds Using NLFM Pulse-Doppler Radar
No full textDetecting UAVs in clutter environments and classification with birds is a difficult and important problem. The high sidelobe levels of a typical linear frequency modulation waveform limit the ability to detect UAVs without applying windowing methods with significant SNR reduction. In this paper, nonlinear frequency modulation (NLFM) waveforms are proposed to enhance the detection probability of UAVs. The comparisons between LFM and NLFM show that NLFM provides improved performance in UAV signal detection. Furthermore, a simple clutter classification algorithm is proposed to classify signals containing micro-Doppler components, such as UAVs or birds, from clutter. The NLFM waveform has low clutter sidelobes and produces high power spectral density in the main lobe, which preserves and enhances micro-Doppler capabilities more effectively than those of LFM. Taking advantage of these features, a convolutional neural network (CNN) based classification of UAVs and birds using the Doppler spectrum is demonstrated. The performance of the NLFM waveform was validated using the proposed lightweight CNN model. The Doppler spectrum features derived from NLFM have achieved improved detection and high performance.
Constructing an expeditious and durable composite as an air electrode of solid oxide cells through synergistic phase transformation and phase segregation engineering
No full textThe sluggish catalytic activity of iron-rich perovskite-based air electrodes at low temperatures (<650 degrees C) is a common problem faced by solid oxide cells (SOCs). Herein, an expeditious and durable iron-rich, multifunctional, composite material is reported as an outstanding air electrode for SOCs. Such a composite consists of a dominant cubic single perovskite (SP) phase, SrFe1-x(Ta,Nb)(x)O3-delta, and a minor oxygen vacancy-rich double perovskite (DP) phase, Sr2FeNbO6-delta. The incorporation of pentavalent Ta and Nb effectively inhibits the formation of tetragonal SP and induces phase transformation to a cubic SP with high symmetry, while the in-situ separated DP phase synergistically boosts the performance of oxygen activation. Such multiple benefits result in the generation of an oxygen-ion conductor-based solid oxide fuel cell (O-SOFC) with the developed composite electrode that yields a superb maximum power density (P-max) of 1259 mW cm(-2) at 600 degrees C, similar to 2.1 times that of an O-SOFC with SrFeO3-delta parent electrode (595 mW cm(-2)). A reversible protonic ceramic cell (R-PCC) with such composite air electrode delivers a remarkable electrochemical performance, e.g., a P-max of 844 mW cm(-2) and an electrolysis current density of -957 mA cm(-2) @ 1.3 V at 650 degrees C. More attractively, the resulting cell exhibits an outstanding operating endurance of 500 h in fuel cell mode and 210 h in cycle mode (i.e., alternating between fuel cell and electrolysis cell modes).
In-Context Vision-Pattern-Language Model for Enhancing Vessel Activity Explanation
No full textIllegal vessel activities pose significant threats to marine resources and ecosystems worldwide, necessitating effective monitoring and detection methods. Current vessel monitoring systems struggle to accurately interpret vessel behaviors at a detailed level due to limitations in utilizing multi-modal data and regulatory frameworks. To overcome these challenges, we propose a new Vision-Pattern-Language (VPL) model designed to enhance the explanation and detection of illegal, unreported, and unregulated (IUU) vessel activities. To handle AIS-off in boundary waters, proposed model integrates satellite imagery with Automatic Identification System (AIS) trajectory data using a probabilistic fusion approach based on Maximum A Posteriori (MAP) estimation with Monte Carlo dropout. Additionally, the VPL model employs a CLIP-based zero-shot classifier to accurately identify vessel behaviors. To support law enforcement, the VPL model with in-context learning also generates faithful and contextually reasonable explanations grounded in the fused data and a legal-text database. Extensive experimental evaluations on the AIS and satellite imagery dataset demonstrate that the VPL model significantly improves trajectory prediction accuracy and classification performance than baselines. Moreover, VPL attains higher faithfulness and reasoning scores compared to Llama-3.3, highlighting its potential for robust and reliable maritime surveillance and contributing meaningfully to the detection and regulation of IUU vessel activities.
Comprehensive human locomotion and electromyography dataset: Gait120
No full textUnderstanding human locomotion patterns and their variations requires comprehensive data across different age groups and movement tasks, given the complexity of the human musculoskeletal system. This study presents a dataset of human locomotion during daily activities, collected from 120 healthy male participants (age range: 20-59 years). The experimental protocol included seven distinct tasks: level walking, stair ascent/descent, slope walking (ascent/descent), and sit-to-stand/stand-to-sit movements. Data were collected using an optical motion capture system, force plates, and surface electromyography sensors on the right lower limb. The final dataset includes 6,882 movement cycles across all tasks, including full-body joint kinematics and muscle activity patterns. This comprehensive dataset will contribute to understanding the variations in movement patterns and muscle activation during common daily activities across a broad adult male population.
Dual Stimuli-Responsive Actuation of Oriented MXene/Polymer Composite
No full textSoft actuators capable of programmable deformation and multimodal responsiveness are increasingly in demand for advanced platforms requiring precise motion control, environmental sensing, and remote actuation. While bilayer structures are commonly used to achieve asymmetric actuation, their discrete interfaces often lead to delamination, which can be avoided by employing a structurally continuous monolayer system. We present a scalable method for fabricating monolithic gradient Ti3C2T x MXene/polymer composite films with directional bending actuation. An in-plane alternating current (AC) electric field applied during photopolymerization not only induces vertical nanosheet alignment but also promotes their sedimentation near the substrate. Aligned films exhibit enhanced bending performance at identical filler concentrations and controlled bending direction compared to random ones. Through structural analysis and condition-dependent tests, the dual-mode actuation mechanism was elucidated. Our actuator maintains excellent reversibility and durability under repeated infrared light and humidity stimuli. This work offers a robust platform for MXene-based soft actuators with defined anisotropy, applicable to adaptive robotics, wearable humidity sensors, and thermally responsive systems.
Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification
No full textWe study continual learning on multiple linear classification tasks by sequentially running gradient descent (GD) for a fixed budget of iterations per each given task. When all tasks are jointly linearly separable and are presented in a cyclic/random order, we show the directional convergence of the trained linear classifier to the joint (offline) max-margin solution. This is surprising because GD training on a single task is implicitly biased towards the individual max-margin solution for the task, and the direction of the joint max-margin solution can be largely different from these individual solutions. Additionally, when tasks are given in a cyclic order, we present a non-asymptotic analysis on cycle-averaged forgetting, revealing that (1) alignment between tasks is indeed closely tied to catastrophic forgetting and backward knowledge transfer and (2) the amount of forgetting vanishes to zero as the cycle repeats. Lastly, we analyze the case where the tasks are no longer jointly separable and show that the model trained in a cyclic order converges to the unique minimum of the joint loss function
