Search Results (1,073)

Non-contact monitoring of human vital signs using microwave radar has attracted increasing attention due to its capability to operate unobtrusively and through clothing or light obstacles. In vector network analyzer (VNA)-based radar systems, vital signs can be extracted from phase variations in the forward transmission coefficient $S_{21}$, whose sensitivity strongly depends on the electromagnetic performance of the antenna system. This work presents the design, optimization, fabrication, and experimental validation of a high-gain 12 GHz 4 × 4 microstrip patch antenna array specifically developed for phase-based vital signs monitoring. The antenna array was progressively optimized through coaxial feeding, slot-based impedance control, stepped transmission line matching, and mitered bends, achieving a simulated gain of 17.8 dBi, a measured gain of 17.06 dBi, a reflection coefficient of −26 dB at 12 GHz, and a total efficiency close to 74%. The antenna performance was experimentally validated in an anechoic chamber and subsequently integrated into a continuous-wave VNA-based radar system. Comparative measurements were conducted against a commercial biconical antenna, a single patch radiator, and an MIMO antenna under identical conditions. Results demonstrate that while respiration can be detected with moderate-gain antennas, reliable heartbeat detection requires high-gain, narrow-beam antennas to enhance phase sensitivity and suppress environmental clutter. The proposed array significantly improves pulse detectability in the (1–1.5) Hz band without relying on advanced signal processing. These findings highlight the critical role of antenna design in $S_{21}$-based biomedical radar systems and provide practical design guidelines for high-sensitivity non-contact vital signs monitoring. Full article

This article introduces an integrated control scheme combining an Adaptive Extended Kalman Filter (AEKF) with a Passivity-Based Control (PBC) approach to stabilize a DC-DC boost converter feeding both constant voltage and constant power loads (CPLs) in DC microgrids. Unlike conventional observers, the AEKF adapts its covariance matrices in real time to accurately estimate both system states and the unknown load dynamics introduced by CPLs, thereby eliminating the need for additional sensors and enhancing estimation convergence. Coupled with the PBC, the estimated disturbances are compensated via a feedforward path, significantly improving the system’s resilience to input voltage fluctuations and load variations. Through a Lyapunov-based stability analysis, the combined strategy is proven to ensure large-signal stability while maintaining a rapid transient recovery profile, even under significant parametric uncertainties. The simulation of this algorithm was implemented using PLECS, thoroughly validating the effectiveness and robustness of the proposed method. Full article

To compensate for hysteresis, low damping vibration, and their coupling effects, this paper proposes a gated recurrent unit and convolutional neural network (GRU-CNN) model as a feedforward control model that maps desired displacement trajectories to driving voltages. The GRU-CNN integrates a gated recurrent unit (GRU) layer to capture long-term temporal dependencies, a multi-layer convolutional neural network (CNN) to extract local data features, and residual connections to mitigate information distortion. The GRU-CNN is then combined with transfer learning (TL) for feedforward control of cross-batch and cross-type piezoelectric actuators (PEAs), so as to reduce reliance on training datasets. The analysis focuses on the impacts of target PEA data volume and source-target similarity on transfer learning strategies. The GRU-CNN trained on PEA #1 achieves high control accuracy, with a mean absolute error (MAE) of 0.077, a root mean square error (RMSE) of 0.129, and a coefficient of determination (R²) of 0.997. When transferred to cross-batch PEA #2 and cross-type PEA #3, the GRU-CNN feedforward controller still delivers favorable performance; R² values all exceed 0.98, representing at least a 27% improvement compared to training from scratch. These results indicate that the proposed transfer learning-based feedforward control method can effectively reduce retraining effort, suggesting its potential applicability to batch production scenarios. Full article

In cryogenic air liquefaction systems, a major share of the mechanical energy consumption is associated with exergy destruction caused by heat transfer in recuperative heat exchangers. This study investigated the exergetic optimization of cryogenic gas separation systems by focusing on the Claude–Heylandt cycle as an advanced structural modification of the classical Linde–Hampson scheme. An exergy-based analysis demonstrates that minimizing mechanical energy consumption requires a progressive reduction in the temperature difference between the hot forward stream and the cold returning stream toward the cold end of the heat exchanger. This condition was achieved by extracting a fraction of the high-pressure stream and expanding it in a parallel expander, thereby creating a controlled imbalance in the heat capacities between the two streams. The proposed configuration reduces the share of exergy destruction associated with heat transfer in the recuperative heat exchanger from 14% to 3.5%, leading to a fourfold increase in the exergetic efficiency, together with a 3.6-fold increase in the liquefied air fraction compared with the Linde–Hampson cycle operating under identical conditions. The effects of key decision parameters, including the compression pressure, imposed temperature differences, and expander inlet temperature, were systematically analyzed. The study was further extended by integrating an air separation column into the Claude–Heylandt cycle and optimizing its configuration based on entropy generation minimization. The optimal liquid-air feeding height and threshold number of rectification trays were identified, beyond which further structural complexity yielded no thermodynamic benefit. The results highlight the effectiveness of exergy-based optimization as a unified design criterion for both cryogenic liquefaction and separation processes. Full article

This study contributes to the advancement of circular economy practices in polymer manufacturing by applying machine learning algorithms (MLA) to predict the tensile strength of recycled low-density polyethylene (LDPE) building films. As the construction and packaging industries increasingly seek eco-efficient and low-carbon materials, recycled LDPE offers a valuable route toward sustainable resource management. However, ensuring consistent mechanical performance remains a challenge when reusing polymer waste streams. To address this, tensile tests were conducted on LDPE films produced from recycled granules, measuring tensile strength, strain, mass per unit area, thickness, and surface roughness. Three established machine learning algorithms—feed-forward Neural Network (NN), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were implemented, trained, and optimized using the experimental dataset using R statistical software (version 4.4.3). The models achieved high predictive accuracy, with XGBoost providing the most robust performance and the highest level of explainability. Feature importance analysis revealed that mass per unit area and surface roughness have a significant influence on film durability and performance. These insights enable more efficient production planning, reduced raw material usage, and improved quality control, key pillars of sustainable technological innovation. The integration of data-driven methods into polymer recycling workflows demonstrates the potential of artificial intelligence to accelerate circular economy objectives by enhancing process optimization, material performance, and resource efficiency in the plastics sector. Full article

Identified-hadron production (p, ${\pi }^{\pm }$, $K^{\pm }$) in ${\pi }^{+}+\mathrm{Be}$ at $p_{\mathrm{lab}}=60\mathrm{GeV}/c$ ($\sqrt{s}\simeq 10.6\mathrm{GeV}$) is investigated using Pythia 8.315 (Monash tune) with the Angantyr extension. Differential multiplicities $d^{2}n/\left(dpd\theta \right)$ are confronted with NA61/SHINE measurements across standard $\theta$ bins. Within the fluctuating-radii Double-Strikman (DS) scheme, two unsuppressed opacity mappings are compared to quantify systematics. In addition, a minimal extension is introduced: a flat, post-classification, channel-wise acceptance applied after ND/SD/DD/EL tagging. It acts on primary and secondary $\pi N$ pairs, keeps hadronization fixed (Lund string), and leaves the internal event generation of each admitted subcollision unchanged. Opacity-mapping variations alone induce only percent-level differences and do not resolve the soft/forward tensions. By contrast, the flat acceptance—interpretable as a reduced effective ND weight—improves agreement across species and angles. It hardens the forward ${\pi }^{+}$ spectra and lowers large-$\theta$ yields, produces milder charge-asymmetric changes for ${\pi }^{-}$ consistent with the weaker leading feed, suppresses proton yields at all angles (with a residual $\sim 30\%$ forward high-p deficit), and improves $K^{\pm }$, with a stronger effect for $K^{+}$ than $K^{-}$. These results show that a geometry-blind reweighting of the subcollision mixture suffices to capture the main NA61/SHINE trends for ${\pi }^{+}+\mathrm{Be}$ at SPS energies without modifying hadronization. The approach provides a controlled baseline for subsequent, channel-balanced refinements and broader ${\pi }^{+}A$ tuning. Full article

Reliable and low-latency seizure detection from electroencephalography (EEG) is critical for continuous clinical monitoring and emerging wearable health technologies. Spiking neural networks (SNNs) provide an event-driven computational paradigm that is well suited to real-time signal processing, yet achieving competitive seizure detection performance with constrained model complexity remains challenging. This work introduces a hybrid spike encoding scheme that combines Delta–Sigma (change-based) and stochastic rate representations, together with two spiking architectures designed for real-time EEG analysis: a compact feed-forward HybridSNN and a convolution-enhanced ConvSNN incorporating depthwise-separable convolutions and temporal self-attention. The architectures are intentionally designed to operate on short EEG segments and to balance detection performance with computational practicality for continuous inference. Experiments on the CHB–MIT dataset show that the HybridSNN attains 91.8% accuracy with an F1-score of 0.834 for seizure detection, while the ConvSNN further improves detection performance to 94.7% accuracy and an F1-score of 0.893. Event-level evaluation on continuous EEG recordings yields false-alarm rates of 0.82 and 0.62 per day for the HybridSNN and ConvSNN, respectively. Both models exhibit inference latencies of approximately 1.2 ms per 0.5 s window on standard CPU hardware, supporting continuous real-time operation. These results demonstrate that hybrid spike encoding enables spiking architectures with controlled complexity to achieve seizure detection performance comparable to larger deep learning models reported in the literature, while maintaining low latency and suitability for real-time clinical and wearable EEG monitoring. Full article

Driven by global initiatives to mitigate climate change, the offshore wind power industry is experiencing rapid growth. Personnel transfer between service operation vessels (SOVs) and offshore wind turbines under complex sea conditions remains a critical factor governing the safety and efficiency of operation and maintenance (O&M) activities. This study establishes a fully coupled dynamic response and control simulation framework for an SOV equipped with an active motion-compensated gangway. A numerical model of the SOV is first developed using potential flow theory and frequency-domain multi-body hydrodynamics to predict realistic vessel motions, which serve as excitation inputs to a co-simulation environment (MATLAB/Simulink coupled with MSC Adams) representing the Stewart platform-based gangway. To address system nonlinearity and coupling, a composite control strategy integrating velocity and dynamic feedforward with three-loop PID feedback is proposed. Simulation results demonstrate that the composite strategy achieves an average disturbance isolation degree of 21.81 dB, significantly outperforming traditional PID control. Validation is conducted using a ship motion simulation platform and a combined wind–wave basin with a 1:10 scaled prototype. Experimental results confirm high compensation accuracy, with heave variation maintained within 1.6 cm and a relative error between simulation and experiment of approximately 18.2%. These findings demonstrate the framework’s capability to ensure safe personnel transfer by effectively isolating complex vessel motions and validate the reliability of the coupled dynamic model for offshore operational forecasting. Full article

The increasing penetration of wind energy has led to reduced system inertia and heightened sensitivity to dynamic disturbances in modern power systems. This paper proposes a Newton–Raphson-Based Optimizer (NRBO) for tuning proportional, integral, and feedforward gains of a grid-forming converter applied to a wind energy conversion system operating in a low-inertia environment. The study considers an aggregated wind farm modeled as a single equivalent DFIG-based wind turbine connected to an infinite bus, with detailed dynamic representations of the converter control loops, synchronous generator dynamics, and network interactions formulated in the dq reference frame. The grid-forming converter operates in a grid-connected mode, regulating voltage and active–reactive power exchange. The NRBO algorithm is employed to optimize a composite objective function defined in terms of voltage deviation and active–reactive power mismatches. Performance is evaluated under two representative scenarios: small-signal disturbances induced by wind torque variations and short-duration symmetrical voltage disturbances of 20 ms. Comparative results demonstrate that NRBO achieves lower objective values, faster transient recovery, and reduced oscillatory behavior compared with Differential Evolution, Particle Swarm Optimization, Philosophical Proposition Optimizer, and Exponential Distribution Optimization. Statistical analyses over multiple independent runs confirm the robustness and consistency of NRBO through significantly reduced performance dispersion. The findings indicate that the proposed optimization framework provides an effective simulation-based approach for enhancing the transient performance of grid-forming wind energy converters in low-inertia systems, with potential relevance for supporting stable operation under increased renewable penetration. Improving the reliability and controllability of wind-dominated power grids enhances the delivery of cost-effective, cleaner, and more resilient energy systems, aiding in expanding sustainable electricity access in alignment with SDG7. Full article

In this paper, a hierarchical adaptive control strategy is proposed to enhance the handling stability of distributed drive electric vehicles. In this strategy, the upper-level fuzzy controller calculates the additional yaw moment and rear wheel angle by utilizing the error between the actual and the target yaw velocity, as well as the error between the actual and the target sideslip angle. The quadratic programming algorithm is adopted to achieve the optimal torque distribution scheme through the lower-level controller, and the electronic stability control system (ESC) is utilized to generate the braking force required for each wheel. The four-wheel steering controller optimizes the rear wheel angle by using proportional feedforward combined with fuzzy feedback or Akerman steering based on the steering wheel angle and vehicle speed, through actuators such as active front-wheel steering (AFS) and active rear-wheel steering (ARS), which generate the steering angle of each wheel. This approach is validated through simulations under serpentine and double-lane-change conditions. Compared to uncontrolled and single-control strategies, the actuators are decoupled, the actual sideslip angle and yaw velocity of the vehicle can effectively track the target value, the actual response is highly consistent with the expected response, the goodness of fit exceeds 90%, peak-to-peak deviation with a small tracking error. Full article

Hand exoskeletons are increasingly used to support post-stroke reach-to-grasp, yet most intention-detection strategies trigger assistance from local hand events without considering the synergy between proximal arm transport and distal hand shaping. We evaluated whether proximal arm kinematics, alone or fused with EMG, can predict flexor and extensor digitorum activity for synergy-aligned hand assistance. We trained nine models per participant: linear regression (LINEAR), feedforward neural network (NONLINEAR), and LSTM, each under EMG-only, kinematics-only (KIN), and EMG+KIN inputs. Performance was assessed by RMSE on test trials and by a synergy-retention analysis, comparing synergy weights from original EMG versus a hybrid EMG in which extensor and flexor digitorum measure signals were replaced by model predictions. Results have shown that kinematic information can predict muscle activity even with a simple linear model (average RMSE around 30% of signal amplitude peak during go-to-grasp contractions), and synergy analysis indicated high cosine similarity between original and hybrid synergy weights (on average 0.87 for the LINEAR model). Furthermore, the LINEAR model with kinematics input has been tested in a real-time go-to-grasp motion, developing a high-level control strategy for a hand exoskeleton, to better simulate post-stroke rehabilitation scenarios. These results suggest the intrinsic synergistic motion of go-to-grasp actions, offering a practical path, in hand rehabilitation contexts, for timing hand assistance in synergy with arm transport and with minimal setup burden. Full article

To enhance desalination efficiency and reduce experimental costs, the development of advanced mathematical models for EMS is essential. In this study, we propose a novel hybrid approach that integrates neural networks with high-accuracy numerical simulations of electroconvection. Based on dimensionless similarity criteria (Reynolds, Péclet numbers, etc.), we establish functional relationships between critical parameters, such as the dimensionless electroconvective vortex diameter and the plateau length of current–voltage curves. Training datasets were generated through extensive numerical experiments using our in-house developed mathematical model, while multilayer feedforward neural networks with backpropagation optimization were employed for regression tasks. The resulting AI (artificial intelligence)-driven hybrid models enable rapid prediction and optimization of EMS design and operating parameters, reducing computational and experimental costs. This research is situated at the intersection of membrane science, artificial intelligence, and computational modeling, forming part of a broader foresight agenda aimed at developing next-generation intelligent membranes and adaptive control strategies for sustainable water treatment. The methodology provides a scalable framework for integrating physically based modeling and machine learning into the design of high-performance electromembrane systems. Full article

When offshore wind power is connected to a grid via Modular multilevel converter-based High Voltage Direct Current (MMC-HVDC), the sending-end alternating current (AC) system is susceptible to asymmetrical faults. These faults lead to overcurrent surges, voltage drops, and second harmonic circulating currents, which seriously threaten the safe operation of the system. To quickly suppress fault current surges, achieve precise control of system variables, and improve fault ride-through capability, this study proposes a collaborative control strategy. This strategy integrates generalized virtual impedance current limiting, positive- and negative-sequence collaborative feedforward control, and model-predictive control-based suppression of arm energy and circulating currents. The positive- and negative-sequence components of the voltage and current are quickly separated by extending and decoupling the decoupled double synchronous reference frame phase-locked loop (DDSRF-PLL). A generalized virtual impedance with low positive-sequence impedance and high negative-sequence impedance was designed to achieve rapid current limiting. Simultaneously, negative-sequence current feedforward compensation and positive-sequence voltage adaptive support are introduced to suppress dynamic fluctuations. Finally, an arm energy and circulating current prediction model based on model predictive control (MPC) is established, and the second harmonic circulating currents are precisely suppressed through rolling optimization. Simulation results based on PSCAD/EMTDC show that the proposed control strategy can effectively suppress the negative-sequence current, significantly improve voltage stability, and greatly reduce the peak fault current. It significantly enhances the fault ride-through capability and operational reliability of offshore wind power MMC-HVDC-connected systems and holds significant potential for engineering applications. Full article

In this article, the problem of control of the kinematic model of an omnidirectional robot with time delay in the control input is tackled through an Active Disturbance Rejection Control (ADRC) with a disturbance predictor-based scheme, which consists in predicting the generalized forward disturbance input in order to cancel it and then using a feedforward linearization approach to control the system in trajectory tracking tasks. The novelties of the scheme are to demonstrate that using the proposed extended state disturbance estimation leads to a forward estimation following the Taylor series approximation, and, to avoid using additional pose predictions, a feedforward input as an exact linearization approach is used, in which the remaining dynamics can be lumped into the generalized disturbance input. Thus, the use of extended states in prediction improves the robustness of the predictor while increasing the prediction horizon for larger time delays. The stability of the proposal is demonstrated using the second method of Lyapunov, which shows the closed-loop estimation/tracking ultimate bound behavior. Additionally, numerical simulations and experimental tests validate the robustness of the approach in trajectory-tracking tasks. Full article

Transfer learning enables the leveraging of knowledge acquired from other piezoelectric actuators (PEAs) to facilitate the positioning control of a target PEA. However, blind knowledge transfer from datasets irrelevant to the target PEA often leads to degraded displacement control performance. To address this challenge, this study proposes a transfer learning method, termed selective multi-source ensemble transfer learning (SMETL). The SMETL adopts a multi-source transfer learning framework integrated with Proxy A-distance (PAD)-based multi-source domain selection and a greedy ensemble transfer learning strategy. Only when the performance on the target domain validation is improved, fine-tuned GRU-CNN feedforward control models are screened into the ensemble. The outputs of the retained ensemble models are averaged to generate the final prediction. Comparative experiment results demonstrate that SMETL achieves superior control performance across all evaluation metrics. This confirms SMETL’s capability to effectively leverage multi-source domain knowledge and mitigate the risk of introducing irrelevant data. Full article