Dynamic Security Assessment (DSA) is critical for maintaining stability in large-scale smart grids, especially with the growing integration of renewable energy sources and the inherent uncertainties. Traditional model-based analytical methods are increasingly inadequate under these complex conditions. To address these challenges, we propose a pioneering Quantum Federated Learning-based DSA (QFLDSA) method by combining hybrid quantum-classical machine learning and federated learning. QFLDSA offers an effective way to deal with high-dimensional data and uncertainties inherent in the grid. Moreover, QFLDSA leverages the unique capabilities of quantum computing to enhance the processing of differential-algebraic equations that underpin grid stability. This paper demonstrates through extensive simulations that QFLDSA significantly outperforms traditional methods, achieving the highest average F1-score performance at 97.94%, while maintaining 97.67 ± 0.17% prediction accuracy on both classical and quantum computing devices only with fewer transmitted model parameters (reducing up to ∼1000X). These enhancements enable more reliable and rapid deployment of preventive stability control measures across smart grids. Our results underscore QFLDSA's potential as a robust solution for the dynamic security challenges of modern smart grids, paving the way for future innovations in grid management technology. Note to Practitioners-In the rapidly evolving world of smart cyber-physical grids, ensuring the stability of electric power systems is paramount. Failures in these systems can lead to catastrophic blackouts, affecting countless homes and businesses. Traditional DSA methods to assess and ensure this stability, while effective, are becoming increasingly complex and vulnerable to single points of failure or cyberattacks. Enter the QFLDSA

Recently, the application of stochastic gradient descent (SGD) with Polyak stepsizes has gained attention and exhibited promising performance for machine learning problems. However, when the interpolation condition (where each loss attains a minimum at a globally optimal solution) is not satisfied, SGD with Polyak stepsizes encounters a primary limitation that impedes its overall effectiveness: a lack of knowledge of the optimal loss. In this study, we introduce a non-diminishing accelerated stochastic Polyak stepsize (AccSPS) with level adjustment coupled with approximate variance-reduced gradient (AVRG) descent to overcome this limitation. Our approach incorporates a decision-based level adjustment method to obtain an accurate estimation of the optimal loss. To significantly reduce memory requirements, we adopt a variance-reduced method that keeps a snapshot of average gradients after specific iterations. Theoretical analyses establish the convergence rate to the exact minimum in the non-interpolated setting. Numerical studies demonstrate the superior performance of AccSPS, showcasing a significantly lower loss when compared to state-of-the-art algorithms like DecSPS, AdaGrad, Adam, and AMSGrad, up to several orders of magnitude.

Accurate hand-eye and robot-world calibration remains crucial for vision-guided robotic applications. Existing methods predominantly focus on deriving closed-form or nonlinear optimization solutions for hand-eye equation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AX=XB</i> or hand-eye-robot-world calibration equation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AX=YB</i> under various rotation parameterizations, while the critical role of input data refinement on calibration accuracy has been largely overlooked. This paper addresses this gap by proposing a pose graph optimization model that strategically enhances the precision of key pose chains in robotic vision systems, particularly under significant discrepancies between robotic positioning and visual measurement accuracies. Building upon the optimized pose chains associated with the hand-eye and robot-world parameters, a pair of dual equations for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y</i> is then constructed and solved via our proposed Kronecker product-based least squares algorithm, allowing simultaneous and accurate estimation on rotation matrices and translation components of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y</i>. Simulation and real-world experimental results have validated the superiority of the proposed pose graph optimization model, under significant robot-vision precision disparities. Moreover, even when the error magnitudes between the robot and visual measurement system are comparable, the proposed method achieves superior accuracy for both the hand-eye and robot-world parameters compared to nine state-of-the-art approaches.

Magnetic levitation (Maglev) technology is widely adopted for transportation, photolithography, clean room, and material-handling applications, owing to its vibration- and friction-free, untethered motion. The magnetic movers are actuated using the stator coil array, which functions as mobile maglev robots (MLRs) with precision position and orientation motion control. Therefore, this article studies the MLRs for intelligent manufacturing, such as material handling and collaboration with manufacturing components, by integrating them with deep reinforcement learning (DRL) policy controllers. Linear DRL policies are developed and compared between Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) for the horizontal motion control, which takes 455 μs to compute the actuating forces for real-time operations. During policy training, random magnitude horizontal step responses are ticked every 500 ms to explore and exploit the dynamic capacity of the MLRs. The PPO policies are finalized based on the discovered deterministic nature of the maglev environment. Furthermore, the symmetry of the MLR configuration is utilized for policy training, with a threshold boundary included to enable MLRs to translate smoothly across the horizontal operating range. The trained policies exhibit aggressive oscillations and persistent offsets relative to references, respectively. To alleviate the fitting problem, distance-based soft state machines are introduced to integrate the DRL policy with the basic controller. The state machine parameters are empirically tuned using various location step responses. This feature enables the dynamic responses of the DRL policy to be combined with the precision responses at references. The soft state machines are also trained as auxiliary policy actions and evaluated using full operating-range trajectory tracking and long-stroke step responses alongside the policy and the basic controller. The DRL policy controllers outperform the basic controllers, with improved rise and settling times in step responses, while showing enhanced performance in tracking trajectories. Finally, the policy robustness is empirically evaluated.

Tabular data is the most widely used data form in real-world applications, and tree-based models are suitable for it due to their model structures. In practice, it is crucial to quantify predictive uncertainty, and several uncertainty estimation methods have been developed for tree-based models. However, some of them compromise point estimation accuracy or incur high computational overhead. To address this limitation, we propose a variance-based method for predictive uncertainty estimation of tabular data using gradient boosting decision tree (VarBoost). VarBoost estimates variance by analytically calculating statistical characteristics based on gradient boosting strategy, ensuring efficient and accurate variance estimation while maintaining point estimation performance. To avoid overfitting or underfitting, hyperparameters are determined via cross-validation using proper scoring rules that balance calibration and sharpness. The effectiveness of VarBoost is validated by comparisons with several state-of-the-art uncertainty estimation methods on a collection of UCI tabular datasets. Experimental results demonstrate that VarBoost achieves superior performance in both point estimation and uncertainty estimation.

In many animal species, courtship-related social behavior is essential for mating success. Given the diversity and complexity of animal courtship-related social behavior, it is challenging to automatically recognize courtship-related social behavior. Zebrafish has emerged as an important animal model for studying courtship behavior. However, to the best of our knowledge, no research has been conducted on the automated recognition of zebrafish courtship-related social behavior. Therefore, this work builds a benchmark dataset of zebrafish courtship-related social behavior and proposes a zebrafish courtship-related social behavior automated recognition (ZCSBAR) framework. The ZCSBAR framework consists of three functional modules, including pose estimation, long-time identity tracking and social behavior recognition. The experimental results show that the ZCSBAR achieves high performance in zebrafish social behavior recognition, specifically the identity recognition index IDF1 reaches 0.902 and the average behavior recognition accuracy reaches 0.859. These findings highlight the potential of ZCSBAR for quantifying the courtship-related social behavior in the fields of animal behavior and reproduction.