Search Results (2,356)

Wire Arc Additive Manufacturing (WAAM) has been recognized as an efficient and cost-effective metal additive manufacturing technique due to its high deposition rate and scalability for large components. However, the quality and repeatability of WAAM parts are highly sensitive to process parameters such as arc voltage, current, wire feed rate, and torch travel speed, requiring advanced monitoring and adaptive control strategies. In this study, a vision-based monitoring system integrated with a reinforcement learning framework was developed to enable intelligent in situ control of WAAM. A custom optical assembly employing mirrors and a bandpass filter allowed simultaneous top and side views of the melt pool, enabling real-time measurement of layer height and width. These geometric features provide feedback to a tabular Q-learning algorithm, which adaptively adjusts voltage and wire feed rate through direct hardware-level control of stepper motors. Experimental validation across multiple builds with varying initial conditions demonstrated that the RL controller stabilized layer geometry, autonomously recovered from process disturbances, and maintained bounded oscillations around target values. While systematic offsets between digital measurements and physical dimensions highlight calibration challenges inherent to vision-based systems, the controller consistently prevented uncontrolled drift and corrected large deviations in deposition quality. The computational efficiency of tabular Q-learning enabled real-time operation on standard hardware without specialized equipment, demonstrating an accessible approach to intelligent process control. These results establish the feasibility of reinforcement learning as a robust, data-efficient control technique for WAAM, capable of real-time adaptation with minimal prior process knowledge. With improved calibration methods and expanded multi-physics sensing, this framework can advance toward precise geometric accuracy and support broader adoption of machine learning-based process monitoring and control in metal additive manufacturing. Full article

This paper presents a lower-limb hip exoskeleton system integrated with an adjustable-stiffness flexible energy-storage module for three-dimensional gait correction. This system features a modular flexible mechanical design and a stiffness-gain scheduled PID control strategy for dynamic, personalized assistance. Based on biomechanical analysis of the hip joint, a 3D gait correction model was constructed targeting impairments in flexion, abduction, and adduction. The control strategy adjusts system stiffness in real-time according to gait phase and user-specific parameters. Experimental results demonstrated that the exoskeleton effectively reduced joint trajectory variability (22% decrease in standard deviation of hip flexion angle) and improved muscle activation patterns (21.4% increase in rectus femoris activity), thereby enhancing gait symmetry and stability. This study offers a feasible mechatronic solution for pathological gait correction with promising clinical applicability. Full article

The detection and characterization of exoplanets are central topics in astronomy, and high-contrast imaging techniques such nulling interferometry, coronagraphs, and extreme adaptive optics (ExAO) are key tools for the direct detection of exoplanets. This review synthesizes the pivotal role of these techniques in astronomical research and critically analyzes their role as key drivers of progress in the field. Nulling interferometry suppresses stellar light through the phase control of multiple telescopes, thereby enhancing the detection of faint planetary signals. This technology has evolved from the initial Bracewell concept to the LIFE (Large Interferometer For Exoplanets) technique, which will achieve a contrast ratio of 10⁻⁷ in the mid-infrared wavelength range in the future. Coronagraphs block starlight to create a “dark region” for direct observation of exoplanets. By leveraging innovative mask designs, theoretical contrast ratios of up to 4 × 10⁻⁹ can be achieved. ExAO systems achieve precise wavefront correction to optimize the high-contrast imaging performance and mitigate atmospheric disturbances. By leveraging wavefront sensing, thousand-element deformable mirrors, and real-time control algorithms, these systems suppress the turbulence correction residuals to 80 nm RMS, enabling ground-based telescopes to achieve a Strehl ratio exceeding 0.9. This work provides a comprehensive analysis of the underlying principles, prevailing challenges, and future application prospects of these technologies in astronomy. Full article

Current drone navigation systems primarily rely on Global Navigation Satellite Systems (GNSSs), but their signals are susceptible to interference, spoofing, or suppression in complex environments, leading to degraded positioning performance or even failure. To enhance the positioning accuracy and robustness of low-altitude drones in satellite-denied environments, this paper investigates an absolute visual localization solution. This method achieves precise localization by matching real-time images with reference images that have absolute position information. To address the issue of insufficient feature generalization capability due to the complex and variable nature of ground scenes, a visual-based image retrieval algorithm is proposed, which utilizes a fusion of shallow spatial features and deep semantic features, combined with generalized average pooling to enhance feature representation capabilities. To tackle the registration errors caused by differences in perspective and scale between images, an image registration algorithm based on cyclic consistency matching is designed, incorporating a reprojection error loss function, a multi-scale feature fusion mechanism, and a structural reparameterization strategy to improve matching accuracy and inference efficiency. Based on the above methods, a hierarchical absolute visual localization system is constructed, achieving coarse localization through image retrieval and fine localization through image registration, while also integrating IMU prior correction and a sliding window update strategy to mitigate the effects of scale and rotation differences. The system is implemented on the ROS platform and experimentally validated in a real-world environment. The results show that the localization success rates for the h, s, v, and w trajectories are 95.02%, 64.50%, 64.84%, and 91.09%, respectively. Compared to similar algorithms, it demonstrates higher accuracy and better adaptability to complex scenarios. These results indicate that the proposed technology can achieve high-precision and robust absolute visual localization without the need for initial conditions, highlighting its potential for application in GNSS-denied environments. Full article

The Hurst parameter H plays a critical role in modeling long-memory behavior in financial time series, particularly within the framework of the fractional Black–Scholes model (fBSM). While the Method of Moments (MOM) provides a fast, closed-form estimator for H, it suffers from increasing negative bias, especially as H grows beyond 0.6. This paper proposes a bias-corrected version of the MOM estimator based on a quadratic regression fit derived from simulation data. The corrected estimator substantially reduces estimation error while retaining computational efficiency. Through extensive simulations, we quantify the impact of MOM bias on option pricing and demonstrate how our correction method leads to more accurate pricing under the fBSM. We apply the methodology to real financial assets—including Natural Gas, Apple, Gold, and Crude Oil—and show that the corrected Hurst estimates reduce option pricing error by up to USD 0.47 per contract relative to the uncorrected estimator, depending on the asset’s volatility structure. These results underscore the importance of accurate Hurst parameter estimation for derivative pricing, particularly in volatile markets such as energy and commodities, while also remaining relevant to equities and precious metals. The corrected estimator thus offers practitioners a simple yet effective tool to improve financial decision-making. Full article

The design and development of a robust and consistent manufacturing process for monoclonal antibodies (mAbs), augmented by advanced process analytics capabilities, is a key current focus area in the pharmaceutical industry. In this work, we describe the development and operationalization of multivariate statistical process monitoring (MSPM), a data-driven modelling approach, to monitor biopharmaceutical manufacturing processes. This approach helps in understanding the correlations between the various variables and is used for the detection of the deviations and anomalies that may indicate abnormalities or changes in the process compared to the historical dataspace. Therefore, MSPM enables early fault detection with a scope for preventative intervention and corrective actions. In this work, we will additionally cover the value of in silico data in the development of MSPM models, principal component analysis (PCA), and batch modelling methods, as well as refining and validating the models in real time. Full article

The demand for reliable and efficient high-power DC-DC converters has driven significant advancements in fault-tolerant topologies, particularly within modular power converters. Failures in these configurations pose critical operational and safety challenges, necessitating robust mechanisms for timely fault detection, diagnosis, and mitigation to uphold system reliability. This paper explores recent techniques in fault-tolerant design for modular DC-DC converters, emphasizing the application of advanced control algorithms for real-time fault detection and correction. The proposed fault-tolerant methodology employs sophisticated control techniques to efficiently identify various faults, including open-circuit and short-circuit switching anomalies. An integrated advanced control system autonomously reconfigures the converter, isolating faults while maintaining continuous operation in a healthy state. This eliminates the need for complete system shutdown during a fault, leveraging additional power modules to ensure uninterrupted functionality. By incorporating reconfigurable interconnections, advanced control strategies, and robust circuit designs, the approach enhances fault resilience, significantly improving system dependability. The introduction of supplementary semiconductor switches facilitates fault isolation, current management, and the seamless integration of new power modules, safeguarding system performance and operational integrity. Simulation results substantiate the efficacy and performance advantages of this high-efficiency fault-tolerant modular converter topology. Full article

Imaging of intracellular messengers, like calcium, is one of the most reliable methods to follow real-time changes in several aspects of cellular activity, like receptor activation. However, the analysis could be influenced and biased by several factors like the location, shape, and size of the regions of interest (ROIs) and by the detection and correction of the movement of the preparation. Programs which are provided by the manufacturers are expensive and cannot be shared by collaborators. Many self-made programs have been implemented lately which have in-built cell recognizer ROI identification functions. These programs focus on the soma of the cells and neglect the processes, because in full tissue preparation finding cells is still challenging. Subcellular imaging experiments are still rare. To the best of our knowledge there is no program which can automatically define ROIs for subcellular imaging experiments even in single indicated cells with complex morphology. We developed and validated a program to address this gap using simple and understandable mathematical methods for ROI determination and simple statistics for movement correction. Validation experiments were conducted on cochlear Deiters’ cells. Deiters’ cells have processed morphology which connects two fluid compartments in the cochlea. Because of the function and the fine morphology of the cell, it could be interesting to examine the subcellular Ca²⁺ handling mechanisms of it. Test impulses were activated by ATP. With some limitations the program successfully fulfilled its purpose. As a free, easily understandable, and open-source program, we hope it will help to analyze and plan subcellular experiments. Full article

Accurate recognition of personnel behavior in industrial environments is essential for asset protection and workplace safety, yet complex environmental conditions pose a significant challenge to its accuracy. This paper presents a novel, lightweight framework to address these issues. We first enhance a YOLOv8n model with Receptive Field Attention Convolution (RFAConv) and Efficient Multi-scale Attention (EMA) mechanisms, achieving a 6.9% increase in AP50 and a 4.2% increase in AP50:95 over the baseline. Continuous motion trajectories are then generated using the BOT-SORT algorithm and geometrically corrected via perspective transformation to produce a high-fidelity bird’s-eye view. Finally, a set of discriminative trajectory features is classified using a Random Forest model, attaining F1-scores exceeding 82% for all behaviors on our proprietary industrial dataset. The proposed framework provides a robust and efficient solution for real-time personnel behavior recognition in challenging industrial settings. Future work will focus on exploring more advanced algorithms and validating the framework’s performance on edge devices. Full article

In predictive maintenance frameworks, risk curves are used as interpretable, real-time indicators of equipment degradation. However, existing approaches generally assume a monotonically increasing trend and neglect the corrective effect of maintenance, resulting in unrealistic or overly conservative risk estimations. This paper addresses this limitation by introducing a novel method that dynamically corrects risk curves through a quantitative measure of maintenance effectiveness. The method adjusts the evolution of risk to reflect the actual impact of preventive and corrective interventions, providing a more realistic and traceable representation of asset condition. The approach is validated with case studies on critical feedwater pumps in a combined-cycle power plant. First, individual maintenance actions are analyzed for a single failure mode to assess their direct effectiveness. Second, the cross-mode impact of a corrective intervention is evaluated, revealing both direct and indirect effects. Third, corrected risk curves are compared across two redundant pumps to benchmark maintenance performance, showing similar behavior until 2023, after which one unit accumulated uncontrolled risk while the other remained stable near zero, reflected in their overall performance indicators (0.67 vs. 0.88). These findings demonstrate that maintenance-corrected risk curves enhance diagnostic accuracy, enable benchmarking between comparable assets, and provide a missing piece for the development of realistic, risk-informed predictive maintenance strategies. Full article

Liquid microlenses and their arrays (LMLAs) have emerged as a transformative platform in adaptive optics, offering superior reconfigurability, compactness, and fast response compared to conventional solid-state lenses. This review summarizes recent progress from an application-oriented perspective, focusing on actuation mechanisms, fabrication strategies, and functional performance. Among actuation mechanisms, electric-field-driven approaches are highlighted, including electrowetting for shape tuning and liquid crystal-based refractive-index tuning techniques. The former excels in tuning range and response speed, whereas the latter enables programmable wavefront control with lower optical aberrations but limited efficiency. Notably, double-emulsion configurations, with fast interfacial actuation and inherent structural stability, demonstrate great potential for highly integrated optical components. Fabrication methodologies—including semiconductor-derived processes, additive manufacturing, and dynamic molding—are evaluated, revealing trade-offs among scalability, structural complexity, and cost. Functionally, advances in focal length tuning, field-of-view expansion, depth-of-field extension, and aberration correction have been achieved, though strong coupling among these parameters still constrains system-level performance. Looking forward, innovations in functional materials, hybrid fabrication, and computational imaging are expected to mitigate these constraints. These developments will accelerate applications in microscopy, endoscopy, AR/VR displays, industrial inspection, and machine vision, while paving the way for intelligent photonic systems that integrate adaptive optics with machine learning for real-time control. Full article

Background and Objectives: To evaluate the effect of a ureteral access sheath (UAS) on the maximal intra-pelvic pressure (IPP max) during retrograde lithotripsy of hard and soft stones in a porcine model. Materials and Methods: A 22 Fr percutaneous tract was established in the upper calyces of the kidneys in three female pigs. A custom-made Foley catheter with a urodynamic catheter was inserted into the pelvicalyceal system and connected to a urodynamic device for real-time IPP measurement. A Pusen Uscope 7.5 Fr single-use ureteroscope (Zhuhai Pusen Medical Technology, Jinhua, China) with manual pump irrigation was used. BegoStone™ powder (Bego, Lincoln, RI, USA) was prepared in two powder-to-water ratios (15:3 and 15:6) to create hard and soft stones, respectively. Stones were positioned in the pelvicalyceal system through the percutaneous tract, and retrograde intrarenal lithotripsy was performed in three settings: without UAS and with a 9.5/11 Fr UAS, with lasing in the center of the pelvis, and during lithotripsy of soft and hard stones. Results: With manual pump irrigation and without a UAS, the IPP max reached 55 cmH₂O during lasing in the pelvis center. During lithotripsy of soft and hard stones, the IPP max increased to 62 and 65 cmH₂O, respectively. Using a UAS, the IPP max was significantly lower: 18 cmH₂O in the center of the pelvis, and 25 and 29 cmH₂O during lithotripsy of soft and hard stones, respectively. Conclusions: Manual pump irrigation without a UAS can elevate IPP max to potentially unsafe levels during retrograde correct flexible lithotripsy, even when using a 7.5 Fr flexible scope. The addition of a UAS helps maintain the IPP max within safer limits. Full article

Differential tracked robots play a crucial role in various modernized work scenarios such as smart industry, agriculture, and transportation. However, these robots frequently encounter substantial challenges in trajectory tracking, attributable to substantial initial errors and dynamic environments, which result in slow convergence rates, cumulative errors, and diminished tracking precision. To address these challenges, this paper proposes a fuzzy adaptive virtual steering coefficient model predictive control (FVSMPC) algorithm. The FVSMPC algorithm introduces a virtual steering coefficient into the robot’s kinematic model, which is adaptively adjusted using fuzzy logic based on real-time positional error and velocity. This approach not only enhances the robot’s ability to quickly correct large errors but also maintains stability during tracking.The nonlinear kinematic model undergoes linearization via a Taylor expansion and is subsequently formulated as a quadratic programming problem to facilitate efficient iterative solutions. To validate the proposed control algorithm, a simulation environment was constructed and deployed on a real prototype for testing. Results demonstrate that compared to the baseline algorithm, the proposed algorithm performs excellently in trajectory tracking tasks, avoids complex parameter tuning, and exhibits high accuracy, fast convergence, and good stability. This work provides a practical and effective solution for improving the trajectory tracking performance of differential tracked robots in complex environments. Full article

The increasing complexity of urban traffic networks has highlighted the potential of Multi-Agent Reinforcement Learning (MARL) for Traffic Signal Control (TSC). However, most existing MARL methods assume homogeneous observation and action spaces among agents, ignoring the inherent heterogeneity of real-world intersections in topology and signal phasing, which limits their practical applicability. To address this gap, we propose HAPS-PPO (Heterogeneity-Aware Policy Sharing Proximal Policy Optimization), a novel MARL framework for coordinated signal control in heterogeneous road networks. HAPS-PPO integrates two key mechanisms: an Observation Padding Wrapper (OPW) that standardizes varying observation dimensions, and a Dynamic Multi-Strategy Grouping Learning (DMSGL) mechanism that trains dedicated policy heads for agent groups with distinct action spaces, enabling adequate knowledge sharing while maintaining structural correctness. Comprehensive experiments in a high-fidelity simulation environment based on a real-world road network demonstrate that HAPS-PPO significantly outperforms Fixed-time control and mainstream MARL baselines (e.g., MADQN, FMA2C), reducing average delay time by up to 44.74% and average waiting time by 59.60%. This work provides a scalable and plug-and-play solution for deploying MARL in realistic, heterogeneous traffic networks. Full article