Search Results (728)

This paper proposes an integrated finite-time relative-threshold event-triggered control (FTRTETC) framework for unmanned surface vehicle (USV) formations under input saturation and unknown time-varying external disturbances. Firstly, a scheme of USV formation control based on signed graph theory is proposed. Next, a Gaussian error function is used to handle input saturation and simplify the backstepping design. Then, a finite-time formation controller is developed based on the active disturbance rejection control (ADRC) method with extended state observers (ESOs) and tracking differentiators (TDs). Also, a relative-threshold event-triggered mechanism is designed to reduce the frequency of control execution and communication load. By Lyapunov’s stability theory, the proposed controller is proven to achieve finite-time convergence, ensuring all closed-loop signals achieve global uniform ultimate boundedness (GUUB) and the system is without Zeno behaviour. Finally, numerical simulation examples are presented to validate the effectiveness and robustness of the proposed controller. Full article

The cement industry is central to sustainable manufacturing due to its high energy demand and associated CO₂ emissions. In cement production, a substantial share of electrical energy is consumed in the clinker grinding circuit, where Blaine fineness (specific surface area, cm²/g), a key quality output, affects both cement performance and specific energy consumption. However, laboratory Blaine measurements are typically available with a 30–60 min delay, which limits timely process interventions and may promote conservative operating practices (e.g., precautionary over-grinding) to secure quality. This study develops machine-learning models to predict the finished-product Blaine fineness (Blaine-F) from routinely recorded industrial quality-control inputs, including XRF-based oxide composition, derived chemical moduli (lime saturation factor, LSF; silica modulus, SM; alumina modulus, AM), laser-diffraction particle-size distribution descriptors (Q10/Q50/Q90 corresponding to D10/D50/D90 percentile diameters; and R3 residual fractions at selected cut sizes), and intermediate in-process fineness (Blaine-P). The models were trained on over 200 finished-product samples obtained from the quality-control laboratory information management system (LIMS) of Seza Cement Factory (SYCS Group, Turkey). Ridge regression, Random Forest, XGBoost, LightGBM, and CatBoost were tuned using RandomizedSearchCV with five-fold cross-validation and evaluated on a held-out test set using MAE, RMSE, and R². The results show that the linear baseline provides limited explanatory power (Ridge: R² ≈ 0.50), consistent with the strongly non-linear behavior of the grinding–separation system, whereas tree-based ensemble methods achieve higher predictive accuracy. XGBoost yields the best overall performance (R² = 0.754; RMSE = 76.9 cm²/g), while Random Forest attains R² = 0.744 with the lowest MAE (61.7 cm²/g). Explainability analyses indicate that Blaine-F is primarily influenced by the fine-tail PSD descriptor Q10 (D10 particle size) and the intermediate fineness Blaine-P, whereas chemistry-related variables (e.g., LSF and SiO₂, and particularly SM) provide secondary yet meaningful contributions. These findings support the use of the proposed model as a virtual sensor to reduce decision latency associated with delayed laboratory Blaine measurements and to enable tighter fineness targeting. Potential energy and CO₂ implications should be quantified using site-specific, plant-calibrated relationships between kWh/t and Blaine fineness, rather than inferred as measured outcomes within the present study. Full article

To clarify the hydrocarbon generation potential of dark mudstones and the source of crude oil in the seventh member of the Yanchang Formation (Chang 7 Member) in the Pingbei area of the Ordos Basin, and to support tight oil exploration in this region, this study focuses on the source rocks and crude oils from the Chang 7 Member. Comprehensive analyses including total organic carbon (TOC), rock pyrolysis, vitrinite reflectance (Ro), and saturated hydrocarbon gas chromatography–mass spectrometry (GC-MS) were conducted to systematically investigate the characteristics of source rocks and the geochemical properties of crude oils, and to perform oil–source correlation. The results indicate that the dark mudstones in the Chang 7 Member of the Pingbei area meet the geological conditions of effective source rocks: they exhibit high organic matter abundance with an average TOC content of 1.69% and strong heterogeneity, among which the Chang 7³ sub-member has an average organic carbon content of 2.8%, conforming to the standard of high-quality source rocks; the organic matter type is dominated by Type II₁, mixed with a small amount of Type II₂ and Type III, characterized by both aquatic biological and terrestrial organic matter inputs; the Ro values range from 0.76% to 0.87%, indicating a mature stage corresponding to the peak period of liquid hydrocarbon generation. The crude oils in the study area can be classified into two types (Type A and Type B): Type A crude oil is distributed in deep reservoirs of the Chang 7 Member, while Type B crude oil is present in both shallow and deep layers. Oil–source correlation shows that Type A crude oil is highly consistent with the dark mudstones of the Chang 7 Member in terms of Pr/Ph ratio, rearranged hopane enrichment degree, and pentacyclic triterpane distribution pattern, clearly indicating that the Chang 7 Member dark mudstones are the main source rocks for Type A crude oil. In contrast, Type B crude oil is geochemically consistent with crude oils from the sixth member of the Yanchang Formation (Chang 6 Member) in oilfields surrounding the Pingbei area; it is derived from the Chang 6 Member source rocks in the peripheral regions. Full article

To address dynamic positioning (DP) challenges encountered by ships navigating amid unknown model parameters, environmental disturbances, and input saturation, this study proposes a nonlinear gains recursive sliding mode (RSM) DP control law. Within this control framework, an RSM strategy is devised, leveraging variable-gain technology to enhance DP system control performance. A variable-gain adaptive radial basis function (RBF) neural network is employed for real-time online training to approximate the unknown ship model. Simultaneously, an auxiliary dynamic system is incorporated to deal with input saturation. Furthermore, a robust control item is implemented to counteract the influence of RBF neural network approximation errors and external disturbances on the DP system. By constructing an appropriate Lyapunov function, it is proven that all signals in the DP closed-loop control system are uniformly ultimately bounded. Finally, simulation results demonstrate the ship DP system’s rapid response and high accuracy under the proposed control law, along with an enhanced ability to reject environmental disturbances. Full article

In this paper, frequency control of shipboard microgrids is achieved in the presence of measurement noise, dynamic uncertainty, and actuator faults. Measurement noise arises from incorrect signal processing, electromagnetic interference, converter switching dynamics, mechanical vibrations from propulsion and generators, and transients caused by sudden changes in load or generation. Actuator faults are caused by intense mechanical vibrations, temperature-induced stress, degradation of power electronic devices, communication latency, and wear or saturation in fuel injection and governor components. To regulate the frequency deviation under these challenges, a cross-entropy-based fault-tolerant model predictive control method, utilizing a discrete-time linear Kalman filter, is developed. Firstly, the discrete-time linear Kalman filter ensures that uncertain states of the shipboard microgrids are measurable in a noisy environment. Afterward, the model predictive control scheme is employed to obtain an optimal control input based on the measurable states. This controller ensures the frequency regulation of shipboard microgrids in the presence of measurement noise. Furthermore, a fault-tolerant control technique that utilizes the concept of cross-entropy is extended to provide a robust controller that verifies the frequency regulation of shipboard microgrids with actuator faults. To demonstrate the stability of the closed-loop system of the shipboard microgrids based on the proposed controller, considering the effects of measurement noise, state uncertainty, and actuator faults, the Lyapunov stability concept is employed. Finally, simulation results in MATLAB/Simulink R2025b are provided to show that the proposed control method for frequency regulation in renewable shipboard microgrids is both effective and practicable. Full article

Using satellite data products across 41 years, this study explores the combined effect of summer (15 May–15 September) ambient temperatures and relative humidity on the exposure to excessive heat of people living in northern Israel. Specifically, we fused 126 Landsat satellite image data collected between 1984 and 2024 with surface meteorological observations, topographical, and land-use data. The ambient temperature (T_a) was estimated by a Random Forest (RF) regression model, with the Landsat land surface temperature (LST) as its main input. A complete spatial cover of the ambient relative humidity (RH) was obtained by a hybrid model based on Bolton’s equations. Namely, we used (i) T_a estimates of the RF model to obtain the saturation vapor pressure; (ii) a spatial interpolation of dew point temperature measurements by certified meteorological stations across the whole study area; (iii) estimates of the surface air pressure based on a digital elevation model; and (iv) thermodynamic equations for calculating the ambient vapor pressure. Initially, we calculated T_a and RH at noontime in summer at all the grid cells of each Landsat image, accounting for all the qualified Landsat images in each summer. Next, we evaluated the summer-average estimates against corresponding in situ Israel Meteorological Service (IMS) stations’ average summer measurements. Finally, we studied long-term trends over the whole study area, revealing significant summer noontime long-term trends in T_a, RH and the heat index (HI) over the study area (T_a: 0.03–0.14 °C/summer; RH: 0.05–0.18%/summer; HI: 0.08–0.82 °C/summer), as well as changes in their spatial patterns. Full article

In this paper, we study the leader-following time-varying formation (TVF) tracking control of general linear multi-agent systems (MASs) with nonzero control input of the leader, and the followers which have magnitude and rate saturation (MRS) and unknown disturbances. Under the assumption that only the followers connecting to the leader have access to the leader’s input and state, an output feedback controller incorporating a distributed extended state observer (ESO) is developed to ensure the asymptotic convergence of the formation errors without input saturation. Then, a saturation model is inserted to each follower’s dynamics to constrain the magnitude and rate of the control input, with consideration of MRS. Anti-windup protection loops are applied to compensate for the saturated signals to improve the closed-loop performance. Finally, the theoretical findings are demonstrated via a series of numerical simulations. Full article

Recent advances in deep learning (DL) have enabled the integration of diverse biomedical data for disease prediction and risk stratification. Building on this progress, the overall objective of this study was to develop and evaluate a multimodal DL framework for robust multi-label classification (MLC) of major comorbidities in patients with obstructive sleep apnea (OSA) using physiological time series signals and clinical data. This study proposes a robust framework for multi-label classification (MLC) of comorbidities in patients with OSA using diverse physiological and clinical data sources. We conducted a retrospective observational study including a convenience sample of 144 patients referred for overnight polysomnography at the Sleep Medicine Center (SleepLab Split), University Hospital Centre Split (KBC Split), Split, Croatia. Patients were selected based on predefined inclusion criteria and data availability. A one-dimensional Convolutional Neural Network (1D-CNN) was developed to process and fuse time series signals, oxygen saturation ($Sp{O}_2$), derived $Sp{O}_2$ features, and nasal airflow (FP0), with demographic and physiological parameters, enabling the identification of key comorbidities such as arterial hypertension, diabetes mellitus, and asthma/COPD. The instruments included polysomnography-derived signals (SpO₂ and FP0 airflow) and structured demographic/physiological parameters. Signals were preprocessed and used as inputs to the proposed fusion model. The proposed model was trained and fine-tuned using the Optuna hyperparameter optimization framework, addressing class imbalance through weighted loss adjustments. Its performance was comprehensively assessed using multi-label evaluation metrics, including macro/micro F1-score, AUC-ROC, AUC-PR, subset and partial accuracy, Hamming loss, and multi-label confusion matrix (MLCM). The study protocol was approved by the Ethics Committee of the School of Medicine, University of Split (Approval No. 003-08/23-03/0015, Date: 17 October 2023). The 1D-CNN achieved superior predictive performance compared to traditional machine learning (ML) classifiers with macro AUC-ROC = 0.731 and AUC-PR = 0.750. The model demonstrated consistent behavior across age, gender, and BMI groups, indicating strong generalization and minimal demographic bias. In conclusion, the results confirm that $Sp{O}_2$ and airflow signals inherently encode comorbidity-specific physiological patterns, enabling efficient and scalable screening of OSA-related comorbidities without the need for full polysomnography. Although the study is limited by data set size, it provides a methodological basis for the application of multi-label DL models in clinical decision support systems. Future research should focus on the expansion of multi-center datasets, thereby improving model interpretability and potential clinical adoption. Full article

The lack of reliable sensory input from prosthetic limbs limits transfemoral amputees’ ability to perceive limb movement without visual monitoring. This study evaluated design parameters of a proposed forearm-based vibrotactile system in a pre-clinical, design-level perceptual evaluation, conveying prosthetic joint positions through patterned vibrations to provide non-invasive proprioceptive feedback. Healthy participants completed two experiments assessing detection of tactile cues from dual-actuator bands on the wrist and elbow representing assumed ankle and knee positions. The effects of temporal structuring (sequential vs. simultaneous stimulation), actuator configuration, amplitude and frequency settings, and signal duration on response accuracy were examined. Sequential vibrations produced significantly higher recognition accuracy than simultaneous presentation (72.4% vs. 42.7%, p < 0.001) in a variety of vibration signal parameter values. Actuator placement also influenced performance: simultaneous stimulation on opposite forearm sides yielded significantly lower accuracy (p < 0.001) than same-side configurations, whereas this directional effect was not significant for sequential presentation. Accuracy did not differ significantly between equal and unequal amplitude or frequency levels across actuators. Longer stimulus durations improved accuracy, increasing from 82.3% at 60 ms to 92.5% at 240 ms, though the results indicated a saturation point, suggesting an optimal temporal window. These findings inform the design of forearm-based sensory feedback systems for improved prosthetic limb control. Full article

Edge AI is redefining the deployment of computer vision systems by enabling real-time inference directly on resource-constrained edge devices. This shift offers significant advantages in terms of reduced latency, data privacy, and operational autonomy in bandwidth-limited computing environments. In this paper, we present a systematic performance benchmarking of multiple variants of YOLOv8 on the NVIDIA Jetson Orin NX platform, focusing on object detection tasks. We evaluate inference latency, frame throughput, and computational resource usage across varying input sizes and model complexities. Furthermore, we validate the deployment effectiveness through practical use cases, such as vehicle and package detection. Our findings show that the TensorRT model outperforms PyTorch by 17.7% at a batch size of 2, although PyTorch presents greater stability at larger batch sizes (e.g., 8), where TensorRT encounters resource constraints. In terms of memory usage, it increases linearly with batch size: 69% from batch 1 to 4, with TensorRT requiring 429.20 MB at batch size 2 compared to PyTorch’s 451.24 MB. Furthermore, the processing time per image decreases by 42% when scaling from batch size 1 to 4, highlighting a critical saturation point for edge resources. In summary, the results provide insight into the trade-offs between model size and speed, offering guidance for selecting detection architectures tailored to real-time edge applications. Full article

Shape memory alloy (SMA) actuators possess unique advantages for aerospace applications, including significant deformation, a high work-to-weight ratio, and structural simplicity. However, SMA actuators exhibit inherently strongly saturated and asymmetric hysteresis characteristics, which cause significant hysteresis in the output response. These hysteresis nonlinearities, compounded by process and measurement noise, severely degrade control precision. To overcome these issues, this study proposes a Smoothed Generalized Prandtl–Ishlinskii (SGPI) model to characterize such hysteresis behavior. Based on the SGPI model, we developed a state-space representation for the SMA actuator. Furthermore, an Extended Kalman Filter (EKF) is employed to estimate unmeasurable internal hysteresis states, and these estimates are subsequently utilized as input states for Model Predictive Control (MPC). The simulation results demonstrate that the proposed EKF-MPC approach achieves both rapid dynamic response and high-precision tracking control, effectively compensating for hysteresis nonlinearity while rejecting noise disturbances. Full article

This study investigates the relationship between apparent electrical resistivity (ER) and key material parameters governing moisture and pore-structure characteristics of concrete. An experimental program was conducted using six concrete mix designs, where ER was continuously measured under controlled wetting and drying cycles to characterize its dependence on the degree of saturation (DS). Results confirmed that ER decreases exponentially with increasing DS across all mixtures, with R² values between 0.896 and 0.997, establishing DS as the dominant factor affecting electrical conduction. To incorporate additional pore-structure parameters, eight input combinations consisting of DS, porosity (P), water–cement ratio (WCR), and compressive strength (f′c) were evaluated using five machine learning models. Gaussian Process Regression and Neural Networks achieved the highest accuracy, particularly when all parameters were included. SHAP analysis revealed that DS accounts for the majority of predictive influence, while porosity and WCR provide secondary but meaningful contributions to ER behavior. Guided by these insights, nonlinear multivariate regression models were formulated, with the exponential model yielding the strongest predictive capability (R² = 0.96). The integrated experimental–computational approach demonstrates that ER is governed by moisture dynamics and pore-structure refinement, offering a physically interpretable and statistically robust framework for nondestructive durability assessment of concrete. Full article

This study evaluates the performance of single concrete-filled frictional Fibre-Reinforced Polymer (FRP) piles embedded in saturated liquefiable sand and subjected to seismic loading using a shaking table. A unidirectional shaking table equipped with a 1000 mm × 1000 mm × 1000 mm laminar shear box with 27 lamina rings was utilized in the study. FRP tubes manufactured from epoxy-saturated Carbon Fibre-Reinforced Polymer (CFRP) and Glass Fibre-Reinforced Polymer (GFRP) fabrics were filled with 35 MPa concrete and allowed to cure for 28 days, serving as model piles for the experimental programme, with cylindrical concrete prisms employed to represent the behaviour of traditional piles. Pile dimensions and properties based on scaling relationships were selected to account for the nonlinear nature of soil–pile systems under seismic loading. Scaled versions of ground motions from the 2010 Val-des-Bois and 1995 Hyogo-Ken Nambu earthquakes were implemented as input motions in the tests. The results show limited variation in the inertial and kinematic responses of the piles, especially before liquefaction. Head rocking displacements were within 5% of each other during liquefaction. Post liquefaction, the concrete-filled FRP piles showed lower response compared to the traditional concrete pile. The results suggests that concrete-filled FRP piles, especially those made from carbon fibre, provide practical alternatives for use. Full article

The article discusses the control of nonlinear processes with first-order dominant dynamics, focusing on implementation using modern hardware available in various programmable devices and embedded systems. The first two approaches rely on linearization with an ultra-local process model, considering small changes of the process input and output around a fixed operating point, which can be adjusted through gain scheduling with the setpoint variable. This model is used to configure either the historically established automatic reset controller (ARC) or a stabilizing proportional (P) controller enhanced by an inversion-based disturbance observer (DOB). This solution can be interpreted as an application of modern control theory (MCT), as DOB-based control (DOBC) or as advanced disturbance rejection control (ADRC). Alternatively, they can be viewed as a special case of automatic offset control (AOC) based on two types of linear process models. In the third design method, setpoint tracking by exact linearization (EL) is extended with a nonlinear DOB designed using the inverse of the nonlinear process dynamics (EEL). The fourth approach augments EL-based tracking with a DOB derived from the transfer functions of nonlinear processes (NTF). An illustrative example involving the control of a liquid reservoir with a variable cross-section clarifies motivation for the definition of (linear) local and ultra-local process models as well as their advantages in designing robust control that accounts for process uncertainties. Thus, the speed, homogeneity, and shape of transient responses, the ability to reconstruct disturbances, control signal saturation, and measurement noise attenuation are evaluated according to the assumptions specified in the controller design. The novelty of the paper lies in presenting a unifying perspective on several seemingly different control options under the impact of measurement noise. By explaining their essence, advantages, and disadvantages, it provides a foundation for controlling more complex time-delayed systems. The paper emphasizes that certain aspects of controller design, often overlooked in traditional linearization procedures, can significantly improve closed-loop properties. Full article

This work presents a direct conversion transmitter (DCT) for 5G new radio (NR) that eliminates the RF driver by directly feeding a single stage cascode PA through a baseband buffer amplifier and passive up-conversion mixer. The baseband interface uses Class-AB buffers to hold the output capacitor voltage, enabling accurate sampling at the PA input. A mixer switch is selected for minimal on-resistance variation over the required baseband swing. The PA is designed with separate I and Q voltage inputs and a current summing structure. The PA operates at 2.5 V; other blocks use 1.2 V. Post-layout two-tone simulations at 5 GHz indicate 21 dBm output saturation power and −36.1 dBc of IMD3 at 9 dB PBO power while removing the driver to inter stage matching network of a two-stage design. The results validate a compact, driverless architecture for integrated transmitters. Full article