Search Results (1,460)

Temperature is a crucial indicator in monitoring industrial operations. Two-wire temperature transmitters, known for their precise measurements, are extensively used in sectors like crude oil extraction, refining, and fine chemicals. These transmitters can handle a maximum input voltage of 36 V and output a current signal up to 20 mA, enhancing resistance to electromagnetic interference and line noise while improving system compatibility and safety. In contrast, traditional low-dropout linear regulators (LDOs) typically have an input voltage below 6 V and suffer from limitations such as low power supply rejection ratio (PSRR), inadequate current driving capability, and significant temperature drift. This paper proposes a wide-input-range LDO with enhanced output accuracy and digital trimming, designed using the 180 nm BCD process. It incorporates dynamic mismatch compensation, digital trimming, and a strong-drive buffer, achieving a broad input voltage range and high PSRR with minimal temperature drift. The input voltage spans 6 V to 60 V, the output voltage is 1.8 V, and the PSRR reaches 124.5 dB. Across a temperature range of −40 °C to 130 °C, the maximum output voltage error is only 0.3%. This makes it highly suitable for high-precision circuit power supplies in industrial process control. Full article

Hybrid PV-TEG systems can harvest both solar electrical and thermoelectric power, but their operating point drifts with irradiance, temperature gradients, partial shading, and load changes—often yielding multi-peak P-V characteristics. Conventional MPPT (e.g., P&O) and fixed-structure integer-order PID struggle to remain fast, stable, and globally optimal in these conditions. To address fast, robust tracking in these conditions, we propose an adaptive fractional-order PID (FOPID) MPPT whose parameters (K_p, K_i, K_d, λ, μ) are auto-tuned by the red-billed blue magpie optimizer (RBBMO). RBBMO is used offline to set the controller’s search ranges and weighting; the adaptive law then refines the gains online from the measured ΔV, ΔI slope error to maximize the hybrid PV-TEG output. The method is validated in MATLAB R2024b/Simulink 2024b, on a boost-converter–interfaced PV-TEG using five testbeds: (i) start-up/search, (ii) stepwise irradiance, (iii) partial shading with multiple local peaks, (iv) load steps, and (v) field-measured irradiance/temperature from Shanxi Province for spring/summer/autumn/winter. Compared with AOS, PSO, MFO, SSA, GHO, RSA, AOA, and P&O, the proposed tracker is consistently the fastest and most energy-efficient: 0.06 s to reach 95% MPP and 0.12 s settling at start-up with 1950 W·s harvested (vs. 1910 W·s AOS, 1880 W·s PSO, 200 W·s P&O). Under stepwise irradiance, it delivers 0.95–0.98 kJ at t = 1 s and under partial shading, 1.95–2.00 kJ, both with ±1% steady ripple. Daily field energies reach 0.88 × 10⁻³, 2.95 × 10⁻³, 2.90 × 10⁻³, 1.55 × 10⁻³ kWh in spring–winter, outperforming the best baselines by 3–10% and P&O by 20–30%. Robustness tests show only 2.74% power derating across 0–40 °C and low variability (Δv^max typically ≤ 1–1.5%), confirming rapid, low-ripple tracking with superior energy yield. Finally, the RBBMO-tuned adaptive FOPID offers a superior efficiency–stability trade-off and robust GMPP tracking across all five cases, with modest computational overhead. Full article

With the continuous advancement of urban construction, inertial sensors are increasingly used in the detection of underground pipelines. However, inertial measurement units (IMUs) are susceptible to a variety of error sources, leading to the accumulation of position estimation errors over time, which severely restricts their positioning accuracy. This paper provides a systematic review of IMU calibration and drift suppression error compensation methods applicable to underground pipeline environments. Building upon this foundation, it innovatively proposes a three-tiered review framework based on “error characteristics–compensation mechanisms–application scenarios”. The framework begins with the characterization of error factors, maps them to corresponding compensation mechanisms, and then applies them to specific operating conditions. It identifies current research limitations in real-time performance, robustness, experimental validation, and standardized evaluation. Future efforts should focus on designing lightweight fusion algorithms, integrating deep learning with sensor fusion, and establishing standardized testing platforms. This paper aims to summarize the current state and development trends of inertial sensor error compensation methods, providing a reference for advancing related technologies while offering beginners a clear and systematic learning path. Full article

The six-degree-of-freedom (6-DoF) interaction forces/torque of the tool-end play an important role in the robotic tool manipulation using a gripper, which are usually indirectly measured by a robot wrist force/torque sensor. However, the real-time decoupling of the tool’s inertial force remains a challenge when different tools and grasping postures are involved. This paper presents a universal tool-end interaction forces estimation approach, which is capable of handling diverse grippers and tools. Firstly, to address uncertainties from varying tools and grasping postures, an online-identifiable tool dynamics model was built based on the Newton–Euler approach for the integrated gripper–tool system. Sensor zero-drift caused by factors such as the tool weight and prolonged operation is incorporated into the dynamic model and identified online in real time, enabling a coarse estimation of the interaction forces. Secondly, a spiking neural network (SNN) is specially employed to compensate for uncertainties caused by the wrist sensor creep effect, since its temporal processing and event-driven characteristics match the time-varying creep effects introduced by tool changes. The proposed method is experimentally validated on a robotic arm with a gripper, and the results show that the root mean square errors of the estimated tool-end interaction forces are below 0.5 N with $x$, $y$, and $z$ axes and 0.03 Nm with ${\tau }_x$, ${\tau }_y$, and ${\tau }_z$ axes, which has a comparable precision with the in situ measurement of the interaction forces at the tool-end. The proposed method is further applied to robotic scraper manipulation with impedance control, achieving the interaction forces feedback during compliant operation precisely and rapidly. Full article

Wellbore liquid loading is a major issue in the later stages of gas well development, particularly for low-permeability gas fields such as shale gas and tight gas, severely affecting the normal production of gas wells. Accurately predicting the onset of wellbore liquid loading and implementing preventive measures are crucial for ensuring the normal production of gas fields. Therefore, based on the gas–liquid-carrying mechanism in gas wellbores and the flow patterns of gas–liquid two-phase flow in inclined wells, the criterion for gas critical liquid-carrying is determined by the shear stress between the liquid film and the pipe wall being zero. By considering the relative velocity between gas and liquid phases, porosity, and the distribution of velocity across the cross-section through the gas–liquid momentum balance equations, a gas critical liquid-carrying velocity model based on the drift model is established. Field data are used to compare the proposed model with four existing liquid-loading prediction models using the misjudgment rate, mean relative percentage error, and mean absolute percentage error as evaluation metrics for model accuracy. The results show that the proposed model outperforms the other models, with a misjudgment rate of 2.99%, mean relative percentage error of 3.83%, and mean absolute percentage error of 4.12%. Full article

Real-time sweat pH monitoring offers a non-invasive window into metabolic status, disease progression, and wound healing. However, current wearable pH sensors struggle to balance high electrochemical sensitivity with mechanical compliance. Here we report a stretchable fiber-integrated pH electrode based on a polyaniline-phytic acid-polyvinyl alcohol (PANI-PA-PVA) hydrogel, which combines mechanical elasticity with enhanced electrochemical performance for continuous sweat sensing. Freeze–thaw crosslinking of the hydrogel forms a porous interpenetrating network, facilitating rapid proton transport and stable coupling with dry-spun elastic gold fibers. This wearable device exhibits an ultra-Nernstian sensitivity of 68.8 mV pH⁻¹, ultra-fast equilibrium (<10 s within the sweat-relevant acidic window), long-term drift of 0.0925 mV h⁻¹, and high mechanical tolerance (gel stretch recovery up to 165%). The sensor maintains consistent pH responses under bending and tensile strains, yielding sweat pH measurements at the skin surface during running that closely match commercial pH meters (sweat pH range measured in test subjects: 4.2–5.0). We further demonstrate real-time wireless readouts by integrating elastic gold and Ag/AgCl fibers into a three-electrode textile structure. This PANI-PA-PVA hydrogel strategy provides a scalable material platform for robust, high-performance wearable ion sensing and skin diagnostics. Full article

A tracking method based on adaptive morphological correlation and neural predictive models is presented. The morphological correlation filters are optimized according to the aggregated binary dissimilarity-to-matching ratio criterion and are adapted online to appearance variations of the target across frames. Morphological correlation filtering enables reliable detection and accurate localization of the target in the scene. Furthermore, trained neural models predict the target’s expected location in subsequent frames and estimate its bounding box from the correlation response. Effective stages for drift correction and tracker reinitialization are also proposed. Performance evaluation results for the proposed tracking method on four image datasets are presented and discussed using objective measures of detection rate (DR), location accuracy in terms of normalized location error (NLE), and region-of-support estimation in terms of intersection over union (IoU). The results indicate a maximum average performance of 90.1% in DR, 0.754 in IoU, and 0.004 in NLE on a single dataset, and 83.9%, 0.694, and 0.015, respectively, across all four datasets. In addition, the results obtained with the proposed tracking method are compared with those of five widely used correlation filter-based trackers. The results show that the suggested morphological-correlation filtering, combined with trained neural models, generalizes well across diverse tracking conditions. Full article

This study proposes and empirically assesses a bio-inspired conceptual framework, termed Digital DNA, for modeling Industry 5.0 transformation as a complementary extension of established Industry 4.0 principles with an explicit focus on human-centricity, sustainability, and resilience. Rather than positing a new industrial revolution, our positioning follows the European Commission’s view that Industry 5.0 complements Industry 4.0 by emphasizing stakeholder value and human-technology symbiosis. We encode organizational capabilities (genotype) into four gene groups, Adaptability, Technology, Governance, and Culture, and link them to five human-centric outcomes (phenotype). Twenty capability genes and ten outcome measures were scored, normalized (0–100 scale), and analyzed using correlations, K-means clustering, and mutation/drift tracking to capture both static maturity levels and dynamic change patterns. Results show that high Industry 5.0 readiness is consistently associated with elevated Governance and Culture scores. Three transformation archetypes were identified: Alpha, representing holistic socio-technical integration; Beta, with strong technical capacity but weaker cultural alignment; and Gamma, with fragmented capabilities and elevated vulnerability. The Digital DNA framework offers a replicable diagnostic tool for linking socio-technical capabilities to human-centric outcomes, enabling readiness assessment and guiding adaptive, ethical manufacturing strategies. Full article

Objectives: This paper introduces an adaptive learning framework for handling concept drift in data by dynamically adjusting model updates based on the severity of detected drift. Methods: The proposed method combines multiple statistical measures to quantify distributional changes between recent and historical data windows. The resulting severity score drives a three-tier adaptation policy: minor drift is ignored, moderate drift triggers incremental model updates, and severe drift initiates full model retraining. Results: This approach balances stability and adaptability, reducing unnecessary computation while preserving model accuracy. The framework is applicable to both single-model and ensemble-based systems, offering a flexible and efficient solution for real-time drift management. Also, different transformation methods were reviewed, and quantile transformation was tested. By applying a quantile transformation, the Kolmogorov–Smirnov (KS) statistic decreased from 0.0559 to 0.0072, demonstrating effective drift adaptation. Full article

We present the ML-CALMO framework, which integrates machine learning with queueing theory for last-mile delivery optimization under dynamic conditions. The system combines Long Short-Term Memory (LSTM) demand forecasting, Convolutional Neural Network (CNN) traffic prediction, and Deep Q-Network (DQN)-based routing with theoretical stability guarantees. Evaluation on modern benchmarks, including the 2022 Multi-Depot Dynamic VRP with Stochastic Road Capacity (MDDVRPSRC) dataset and real-world compatible data from OSMnx-based spatial extraction, demonstrates measurable improvements: 18.5% reduction in delivery time and +8.9 pp (≈12.2% relative) gain in service efficiency compared to current state-of-the-art methods, with statistical significance (p < 0.01). Critical limitations include (1) computational requirements that necessitate mid-range GPU hardware, (2) performance degradation under rapid parameter changes (drift rate > 0.5/min), and (3) validation limited to simulation environments. The framework provides a foundation for integrating predictive machine learning with operational guarantees, though field deployment requires addressing identified scalability and robustness constraints. All code, data, and experimental configurations are publicly available for reproducibility. Full article

The current paper presents a comparative analysis between a high-fidelity simulation tool and computational fluid dynamics (CFD) in evaluating the behavior of a fully coupled floating offshore wind turbine (FOWT) system subjected to three distinct design load cases, with a particular emphasis on extreme weather scenarios. While both approaches yielded comparable results under standard operational conditions, noticeable discrepancies emerged in surge drift and mooring line tension during typhoon conditions. The present work highlighted a significant limitation of standard calibration methods based on free-deck motion that are not reflective of the unique features of extreme environmental responses. To address this limitation, a novel calibration methodology is suggested that uses drag coefficients derived from direct measurement of extreme load cases. The prediction accuracy of the high-fidelity simulation model was significantly improved by refining the transverse component of the drag coefficients of major structural components, decreasing prediction accuracy of surge and mooring tension responses from almost 30% error to about 5%. Further, despite increasing the fidelity of calibration under extreme environmental conditions, it is primarily contingent on high-fidelity measurements corresponding to the use of the most conventional calibration approach under normal environmental conditions. Ultimately, the results demonstrate the need for accurate calibration approaches to provide reliable performance predictions of FOWT systems under varying extreme environmental conditions. Full article

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 proposes a local sliding mode control (SMC) strategy for frequency regulation and active power sharing in islanded microgrids (MGs). Unlike advanced strategies, either droop-based or droop-free, that rely on inter-inverter communication, the proposed method operates in a fully decentralized manner, using only measurements available at each inverter. In addition, it adopts a minimalist structure that avoids adaptive laws and consensus mechanisms, which simplifies implementation. A discontinuous control law is derived to enforce sliding dynamics on a frequency-based surface, ensuring robust behavior in the face of disturbances, such as clock drifts, sudden load variations, and topological reconfigurations. A formal Lyapunov-based analysis is conducted to establish the stability of the closed-loop system under the proposed control law. The method guarantees that steady-state frequency deviations remain bounded and predictable as a function of the controller parameters. Simulation results demonstrate that the proposed controller achieves rapid frequency convergence, equitable active power sharing, and sustained stability. Owing to its communication-free design, the proposed strategy is particularly well-suited for MGs operating in rural, isolated, or resource-constrained environments. A comparative evaluation against both conventional droop and communication-based droop-free SMC approaches further highlights the method’s strengths in terms of resilience, implementation simplicity, and practical deployability. Full article

Efficient pesticide application remains a critical resource-management challenge in modern agriculture, where limited spray penetration reduces treatment efficacy, wastes chemical inputs, and increases environmental losses. This study quantified the effect of air-assisted spraying (AAS) on droplet deposition in two contrasting field crops, oilseed rape and wheat. Field trials were conducted using a sprayer equipped with an adjustable airflow module, and spray coverage was measured with water-sensitive papers at multiple canopy heights and orientations. In oilseed rape, AAS improved deposition on front-facing and top surfaces in the lower canopy, for example, increasing top-surface coverage at 90 cm from 53.4% to 65.5% at 6 km∙h⁻¹, indicating more uniform distribution and enhanced penetration. In wheat, which typically exhibits a more open canopy structure compared to oilseed rape, AAS effects were smaller and less consistent, with the greatest gain on front-facing lower surfaces (from 13.3% to 21.9% at 7 km∙h⁻¹). Although drift was not measured in this experiment, previous studies using the same sprayer prototype demonstrated measurable reductions, supporting the environmental relevance of improved deposition. These results highlight the role of canopy architecture in determining AAS performance and underscore the technology’s potential to reduce pesticide inputs, minimize off-target losses, and improve the resource efficiency of crop protection in line with EU Farm to Fork objectives. Full article

Frequency-modulated continuous-wave (FMCW) LiDAR systems are extensively utilized in industrial metrology, autonomous navigation, and geospatial sensing due to their high precision and resilience to interference. However, the intrinsic nonlinear dynamics of laser systems introduce significant distortion, adversely affecting measurement accuracy. Although conventional iterative pre-distortion correction methods can effectively mitigate nonlinearities, their long-term reliability is compromised by factors such as temperature-induced drift and component aging, necessitating periodic recalibration. In light of recent advances in artificial intelligence, deep reinforcement learning (DRL) has emerged as a promising approach to adaptive nonlinear compensation. By continuously interacting with the environment, DRL agents can dynamically modify correction strategies to accommodate evolving system behaviors. Nonetheless, existing DRL-based methods often exhibit limited adaptability in rapidly changing nonlinear contexts and are constrained by inefficient uniform experience replay mechanisms that fail to emphasize critical learning samples. To address these limitations, this study proposes an enhanced Soft Actor-Critic (SAC) algorithm incorporating a hybrid prioritized experience replay framework. The prioritization mechanism integrates modulation frequency (MF) error and temporal difference (TD) error, enabling the algorithm to dynamically reconcile short-term nonlinear perturbations with long-term optimization goals. Furthermore, a time-varying delayed experience (TDE) injection strategy is introduced, which adaptively modulates data storage intervals based on the rate of change in modulation frequency error, thereby improving data relevance, enhancing sample diversity, and increasing training efficiency. Experimental validation demonstrates that the proposed method achieves superior convergence speed and stability in nonlinear correction tasks for FMCW LiDAR systems. The residual nonlinearity of the upward and downward frequency sweeps was reduced to $1.869\times {10}^{-5}$ and $1.9411\times {10}^{-5}$, respectively, with a spatial resolution of 0.0203m. These results underscore the effectiveness of the proposed approach in advancing intelligent calibration methodologies for LiDAR systems and highlight its potential for broad application in high-precision measurement domains. Full article