Search Results (2,054)

As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This study presents a multi-objective Whale–Particle Swarm Optimization Algorithm (MOWPSO) for tuning the control parameters of a HESS composed of a lithium-ion battery and a supercapacitor. The proposed full-active configuration with dual bidirectional DC converters enables precise current sharing and independent regulation of energy and power flow. The optimization framework minimizes four objectives: mean battery current amplitude, cumulative aging index, final state-of-charge deviation, and an auxiliary penalty term promoting consistent battery–supercapacitor cooperation. The algorithm operates offline to identify Pareto-optimal controller settings under the Federal Test Procedure 75 cycle, while the selected compromise solution governs real-time current distribution. Robustness is assessed through multi-seed hypervolume analysis, and results demonstrate over 20% reduction in battery aging and approximately 25% increase in effective cycle life compared to battery-only, rule-based and metaheuristic algorithm strategies control. Cross-cycle validation under highway and worldwide driving profiles confirms the controller’s adaptability and stable current-sharing performance without re-tuning. Full article

This work presents a novel microwave sensor system for volatile gas detection, integrating sensing elements based on rectangular cavity resonators (RCR) and multiport demodulation circuitry. Initially, a pump-through gas sensing element utilizing an RCR was developed, and its core sensing functionality was experimentally validated. Subsequently, a rat-race coupler was employed to seamlessly integrate two such rectangular cavity resonator elements—serving as reference and sensing branches—within the multiport demodulation network. This configuration enabled an in-depth investigation of the network’s operating principle, elucidating the critical relationship between the reference and sensing arms. The demodulation network translates the critical output phase shift into corresponding power readings. The quantitative relationship linking phase shift to power output was rigorously characterized and utilized as the basis for estimating volatile gas concentration. Finally, a dedicated LabVIEW-based platform was developed for real-time, quantitative volatile gas monitoring. This integrated measurement system demonstrates excellent detection limits (300 ppm for acetone, 200 ppm for ethanol) and exhibits robust mitigation of measurement artifacts caused by ambient temperature and humidity fluctuations. Comprehensive theoretical analysis and experimental results jointly validate the efficacy of the proposed multiport network and RCR volatile gas sensing architecture. Full article

This paper analyzes a surface plasmon resonance (SPR) sensor utilizing silver (Ag) and Zirconium Nitride (ZrN) for glucose concentration detection in urine samples by the transfer matrix method (TMM). For effective SP excitation, a high-RI BAF10 prism is thought to be used as the coupling layer in the suggested theoretical design. The performance of the proposed SPR biosensor is theoretically evaluated using the wavelength interrogation technique by analyzing wavelength sensitivity (WS), detection accuracy (DA), figure of merit (FoM), and penetration depth (PD) parameters. Glucose in urine samples serves as the sensing medium (SM) in this biosensor configuration. The sensor achieves a maximum wavelength sensitivity of 6416.66 nm/RIU with a penetration depth of 297.53 nm. The ZrN structure incorporated in the biosensor demonstrates enhanced wavelength sensitivity through its molecular recognition sites that provide strong binding with glucose molecules. The improved wavelength sensitivity is attributed to the greater resonance wavelength shift produced by ZrN, resulting in significant performance enhancement of the biosensor for glucose detection. Benefits of the proposed SPR biosensor include very small urine sample concentration requirements (usually 0 mg/dL to 10 g/dL), compatibility with compact prism-based configurations that support the development of portable and affordable point-of-care devices, and quick detection within a few seconds due to real-time plasmonic response. These features make the sensor ideal for rapid, minimally invasive, and field-deployable glucose monitoring in both home and clinical relevance. Full article

Aiming at prominent voltage quality problems in AC/DC hybrid distribution networks with a high proportion of distributed energy and diversified loads, this paper proposes a bi-level energy storage system (ESS) optimization model. The upper level optimizes the ESS configuration with the goal of minimizing the cost, and the lower level optimizes the real-time running state of the ESS. Considering multiple constraints, the improved PSO algorithm and the Gurobi solver are used to solve the problem. The test on the modified IEEE-33 node system verified that the model effectively improved voltage quality and reduced power system costs, which provides theoretical and engineering support for the scientific configuration of the ESS. Full article

Reliable recognition of people on water from UAV imagery remains a challenging task due to strong glare, wave-induced distortions, partial submersion, and small visual scale of targets. This study proposes a hybrid method for human detection and position recognition in aquatic environments by integrating the YOLO12 object detector with optical-flow-based motion analysis, Kalman tracking, and BlazePose skeletal estimation. A combined training dataset was formed using four complementary sources, enabling the detector to generalize across heterogeneous maritime and flood-like scenes. YOLO12 demonstrated superior performance compared to earlier You Only Look Once (YOLO) generations, achieving the highest accuracy (mAP@0.5 = 0.95) and the lowest error rates on the test set. The hybrid configuration further improved recognition robustness by reducing false positives and partial detections in conditions of intense reflections and dynamic water motion. Real-time experiments on a Raspberry Pi 5 platform confirmed that the full system operates at 21 FPS, supporting onboard deployment for UAV-based search-and-rescue missions. The presented method improves localization reliability, enhances interpretation of human posture and motion, and facilitates prioritization of rescue actions. These findings highlight the practical applicability of YOLO12-based hybrid pipelines for real-time survivor detection in flood response and maritime safety workflows. Full article

This paper presents the development and cloud deployment of a system for the segmentation of electromyographic (EMG) signals oriented toward muscle fatigue monitoring in the biceps and triceps. A dataset of 30 subjects was used, resulting in 120 EMG and gyroscope files containing between four and six strength exercise series each. After a quality assessment, approximately 80% of the signals (95 files) were classified as level 1 or 2 and considered suitable for segmentation and subsequent analysis. A near real-time segmentation algorithm was designed based on signal envelopes, sliding windows, and quantile thresholds, complemented with discrete wavelet transform (DWT) filtering. Using EMG alone, segmentation accuracy reached 83% for biceps and 54% for triceps; after incorporating DWT preprocessing, accuracy increased to 87.5% and 71%, respectively. By exploiting the gyroscope’s X-axis signal as a low-noise reference, the optimal configuration achieved an overall accuracy of 80%, with 83.3% for biceps and 76.2% for triceps. The prototype was deployed on Amazon Web Services (AWS) using EC2 instances and SQS queues, and its computational cost was evaluated across four server types. On a t2.micro instance, the maximum memory usage was approximately 219 MB with a dedicated CPU and a maximum processing time of 0.98 s per signal, demonstrating the feasibility of near real-time operation under conditions with limited resources. Full article

We demonstrate that pulsed laser annealing induces plasmonic gold nanoparticles in ZnO thin films, monitored in real-time via pulse-by-pulse spectroscopy. Initially embedded gold nanoparticles (smaller than 5 nm) in sputtered ZnO films were annealed using 532 nm pulses from a Q-switched Nd:YAG laser while monitoring transmission spectra in situ. A plasmonic resonance dip emerged after ~100 pulses in the 530–550 nm region, progressively deepening with continued exposure. Remarkably, different incident energies converged to a thermodynamically stable optical state centered near 555 nm, indicating robust nanoparticle configurations. After several hundred laser shots, the process stabilized, producing larger nanoparticles (40–200 nm diameter) with significant surface protrusion. SEM analysis confirmed substantial gold nanoparticle growth. Theoretical modeling supports these observations, correlating spectral evolution with particle size and embedding depth. The protruding gold nanoparticles can be functionalized to detect specific biomolecules, offering significant advantages for biosensing applications. This approach offers superior spatial selectivity and real-time process monitoring compared to conventional thermal annealing, with potential for optimizing uniform nanoparticle distributions with pronounced plasmonic resonances for biosensing applications. Full article

To meet the real-time, multi-task processing demands of spaceborne synthetic aperture radar (SAR) systems under limited onboard resources, this paper presents a configurable field-programmable gate array (FPGA) architecture that supports both water body and oil spill detection. First, an efficient computing engine partitioning method at coarse and fine granularities is proposed. The operations of the water body and oil spill detection algorithms are clustered and analyzed at two levels, and both general-purpose and specialized computing engines are designed to minimize resource usage. Second, a high-reuse storage optimization strategy is introduced. Based on the data buffering cycle, a shared on-chip memory is designed to minimize storage resource consumption. Building upon these foundations, a software and hardware co-programmable efficient processing system is developed, successfully mapping both detection algorithms onto the FPGA. Finally, the effectiveness of the proposed architecture is confirmed through experimentation, and processing performance is analyzed. Processing times for a 16K × 16K water body scene and a 16K × 16K oil spill scene are 15 s and 13 s, respectively, at a clock frequency of 100 MHz, meeting the real-time multi-task processing requirements of on-board operations. Full article

This study introduces a dynamic, multi-output stacking framework for real-time forecasting of HVAC cooling-coil behavior in air-handling units. The dynamic model encodes short-horizon system memory with input/target lags and rolling psychrometric features and enforces leakage-free, time-aware validation. Four base learners—Random Forest, Bagging (DT), XGBoost, and ANN—are each optimized with an Optuna hyperparameter tuner that systematically explores architecture and regularization to identify data-specific, near-optimal configurations. Their out-of-fold predictions are combined through a Ridge-based stacker, yielding state-of-the-art accuracy for supply-air temperature and chilled water leaving temperature (R² up to 0.9995, NRMSE as low as 0.0105), consistently surpassing individual models. Novelty lies in the explicit dynamics encoding aligned with coil heat and mass-transfer behavior, physics-consistent feature prioritization, and a robust multi-target stacking design tailored for HVAC transients. The findings indicate that this hyperparameter-tuned dynamic framework can serve as a high-fidelity surrogate for cooling-coil performance, supporting set-point optimization, supervisory control, and future extensions to virtual sensing or fault-diagnostics workflows in industrial AHUs. Full article

The rapid evolution of modern vehicles into intelligent and interconnected systems presents new complexities in both functional safety and cybersecurity. In this context, ensuring the reliability and integrity of critical sensor data, such as wheel speed inputs for anti-lock brake systems (ABS), is essential. Effective detection of wheel speed sensor faults not only improves functional safety, but also plays a vital role in keeping system resilience against potential cyber–physical threats. Although data-driven approaches have gained popularity for system development due to their ability to extract meaningful patterns from historical data, a major limitation is the lack of diverse and representative faulty datasets. This study proposes a novel dual learning model, based on Temporal Convolutional Networks (TCN), designed to accurately distinguish between normal and faulty wheel speed sensor behavior within a hardware-in-the-loop (HIL) simulation platform implemented on an FPGA. To address dataset limitations, a TruckSim–MATLAB/Simulink co-simulation environment is used to generate realistic datasets under normal operation and eight representative fault scenarios, yielding up to 5000 labeled sequences (balanced between normal and faulty behaviors) at a sampling rate of 60 Hz. Two TCN models are trained independently to learn normal and faulty dynamics, and fault decisions are made by comparing the reconstruction errors (MSE and MAE) of both models, thus avoiding manually tuned thresholds. On a test set of 1000 sequences (500 normal and 500 faulty) from the 5000 sample configuration, the proposed dual TCN framework achieves a detection accuracy of 97.8%, a precision of 96.5%, a recall of 98.2%, and an F1-score of 97.3%, outperforming a single TCN baseline, which achieves 91.4% accuracy and an 88.9% F1-score. The complete dual TCN architecture is implemented on a Xilinx ZCU102 FPGA evaluation kit (AMD, Santa Clara, CA, USA), while supporting real-time inference in the HIL loop. These results demonstrate that the proposed approach provides accurate, low-latency fault detection suitable for safety-critical ABS applications and contributes to improving both functional safety and cyber-resilience of braking systems. Full article

Due to the large mass, high end load, and long action distance of a mill relining manipulator, gravity effects inevitably lead to a reduction in end effector positioning accuracy. To solve this problem, an online calculation method is proposed to realize real-time end effector deformation prediction. First, a manipulator is simplified into two cantilever beams: the upper arm and the forearm. Second, a reaction force and moment transformation model is established based on the coupling relationship between the forearm and upper arm. Third, finite element (FE) static analysis and simulation are carried out to obtain the end deformation. A total of 3528 discrete joint configurations are selected to cover the entire joint space, and their corresponding FE solutions are used to establish the end deformation offline dataset. Finally, an online deformation calculation algorithm based on Kolmogorov–Arnold networks (KANs) is developed to predict end deformation in any working condition. Visualization analysis and validation experiments are conducted and demonstrate the superiority of the proposed method in reducing gravity effects and improving computational efficiency. In summary, the proposed method provides support for end position compensation, especially for heavy-duty manipulators. Full article

Accurate, real-time monitoring of meat freshness is essential for reducing food waste and safeguarding consumer health, yet conventional methods rely on costly, laboratory-grade spectroscopy or destructive analyses. This work presents a low-cost electronic-nose platform that integrates a compact array of metal-oxide gas sensors (Figaro TGS2602, TGS2603, and Sensirion SGP30) with a Gaussian Process Regression (GPR) model to estimate a continuous freshness index under refrigerated storage. The pipeline includes headspace sensing, baseline normalization and smoothing, history-window feature construction, and probabilistic prediction with uncertainty. Using factorial analysis and response-surface optimization, we identify history length and sampling interval as key design variables; longer temporal windows and faster sampling consistently improve accuracy and stability. The optimized configuration (≈143-min history, ≈3-min sampling) reduces mean absolute error from ~0.51 to ~0.05 on the normalized freshness scale and shifts the error distribution within specification limits, with marked gains in process capability and yield. Although it does not match the analytical precision or long-term robustness of spectrometric approaches, the proposed system offers an interpretable and energy-efficient option for short-term, laboratory-scale monitoring under controlled refrigeration conditions. By enabling probabilistic freshness estimation from low-cost sensors, this GPR-driven e-nose demonstrates a proof-of-concept pathway that could, after further validation under realistic cyclic loads and operational disturbances, support more sustainable meat management in future smart refrigeration and cold-chain applications. This study should be regarded as a methodological, laboratory-scale proof-of-concept that does not demonstrate real-world performance or operational deployment. The technical implications described herein are hypothetical and require extensive validation under realistic refrigeration conditions. Full article

This study analyses the operational efficiency of urban loading and unloading zones (LUZs) by applying queuing theory without waiting (Erlang B model) and incorporating weighted occupancy time as a fundamental metric. Six scenarios were evaluated in an urban block in Zaragoza, Spain: three using field data obtained through real world observation and three simulated. The system’s performance was compared under conditions of free access with a model that strictly enforces the municipal ordinance for Urban Goods Distribution, restricting access to authorized vehicles and maximum dwell times. The objective of this study is to evaluate the operational performance of different LUZ configurations, assessing how real versus regulation-compliant usage affects system capacity, estimated loss rates, and the spatial temporal productivity of the zones. The M/M/1/1 model in Kendall notation is suitable for representing this type of queuing-free urban environment, and weighted occupancy time proves to be a robust indicator for evaluating the performance of heterogeneous zones. The scenario assessment confirms that the sizing of these zones is correct if their proper use is guaranteed. The study concludes with recommendations and best practices for city governance in formulating urban policies aimed at developing more efficient and sustainable logistics to control land use in the LUZ. Full article

This study presents a cost-efficient Hardware-in-the-Loop platform for liquid-level process control education, designed to bridge the gap between theoretical learning and real-world industrial practice. The proposed system integrates NI myRIO and NI myDAQ hardware with LabVIEW-based real-time simulation and controller implementation, enabling flexible experimentation across a range of linear and nonlinear tank models. Through real-time controllers, students can design, tune, and validate classical digital controllers while gaining hands-on experience with real-time process dynamics. Experimental results from Model-in-the-Loop and Hardware-in-the-Loop configurations confirm the high accuracy between simulated and hardware responses, with low normalized root mean square error (NRMSE < 0.07) and high normalized cross-correlation (NCC > 0.99) between MIL and HIL responses. Additionally, learning outcomes were assessed using rubrics and student perception surveys aligned with ABET criteria. The platform successfully satisfies ABET student outcomes (SO1, SO2, SO7) by promoting modeling, system identification, and real-time implementation skills. Student surveys reveal high satisfaction mean = 5.44 and a Cronbach’s α of 0.91367, highlighting enhanced engagement, flexibility, and confidence in control system design. This work demonstrates an adaptable, scalable educational solution that strengthens engineering competencies while keeping implementation costs low. Full article

This study presents a comprehensive experimental investigation of a 2.2 kW brushless DC (BLDC) motor integrated with a three-shaft planetary gearbox, focusing on overall energy efficiency, battery state of charge (SOC) estimation, and real-time speed control under variable load conditions. In the first stage, the gearbox transmission ratio was experimentally verified to establish the kinematic relationship between the BLDC motor and the eddy current dynamometer shafts. In the second stage, the motor was operated in open loop mode at fixed reference speeds while variable load torques ranging from 1 to 7 N.m were applied using an AVL dynamometer. Electrical voltage, current, and rotational speed were measured in real time through precision transducers and a data acquisition interface, enabling computation of overall efficiency and SOC via the Coulomb counting method. The open loop results demonstrated that maximum efficiency occurred in the intermediate-to-high-speed region (2000 to 2800 rpm) and at higher load torques (5 to 7 N.m) while locking the third gearbox shaft produced negligible parasitic losses. In the third stage, a proportional–integral–derivative (PID) controller was implemented in closed loop configuration to regulate motor speed under the same variable load scenarios. The closed loop operation improved the overall efficiency by approximately 8–20 percentage points within the effective operating range of 1600–2500 rpm, reduced speed droop, and ensured precise tracking with minimal overshoot and steady-state error. The proposed methodology provides an integrated experimental framework for evaluating the dynamic performance, energy efficiency, and battery utilization of BLDC motor planetary gearbox systems, offering valuable insights for electric vehicle and hybrid electric vehicle (HEV) drive applications. Full article