Search Results (157)

In a PV array, mismatch among modules changes the shape of the P–V curve and may create several power peaks. This makes maximum power point tracking (MPPT) more difficult, especially when the tracker needs to distinguish the global maximum power point (GMPP) from local peaks. This paper studies this problem with SVM-ILEA, a hybrid MPPT method that combines support vector machine (SVM) regression and an improved love evolution algorithm (ILEA). The SVM model takes module irradiance and temperature as inputs and predicts a voltage close to the GMPP. ILEA uses this voltage as the search center and avoids scanning the full voltage range. The modified convergence factor and adaptive distance factor further adjust the voltage movement during iteration, giving wider search steps at the early stage and smaller corrections near the optimum to reduce steady-state power oscillations. The simulation setup in MATLAB/Simulink R2019b includes standard test conditions (STC) and static partial shading with non-uniform irradiance and temperature distributions, as well as dynamic operating conditions. Across the four static conditions, SVM-ILEA achieves mean tracking times of 0.0233–0.0303 s and mean steady-state power fluctuations of 0.0111–0.0500 W. Across the three dynamic tests, the mean MPPT efficiency ranges from 97.9057% to 98.2991%. The results obtained demonstrate fast GMPP tracking, small power fluctuation, and stable re-tracking under complex PV operating conditions. Full article

The growth of two strains of Pseudochloris wilhelmii, Adriatic strain SAG 55.87 and Mangrove strain SAG 1.80, is compared under different salinities, nitrogen sources and concentrations by a fast 96-well screening method. Cultures were grown in BG11 medium with modifications of nitrogen source (ammonium and nitrate) and nitrogen concentrations (0.3 to 19.2 mM) across salinities ranging from 2 to 24 PSU. Growth was measured by optical density at 690 nm, and specific growth rates were analyzed using a Bayesian generalized additive mixed model to identify optimal conditions. Both strains tolerate a broad range of salinities, but cell density was generally higher with nitrate as a nitrogen source than with ammonium nitrogen. The Adriatic strain showed better growth performance when grown on nitrate, with a mean observed growth rate of 0.143 d⁻¹ and a 1.96-fold increase in OD₆₉₀. The Mangrove strain showed narrower response and lower mean growth rates on nitrate (0.104 d⁻¹). Overall, both strains showed similar growth performance on ammonium, with comparable mean growth rates of 0.0304 for the Adriatic strain and 0.0358 for the Mangrove strain. The results show high intraspecific variation in tolerance to salinity and nitrogen for P. wilhelmii. This 96-well screening method for microalgae cultivation is a powerful and rapid tool for narrowing down optimal growth conditions, as well as to be used as a guide for a larger-scale setup. Full article

Loading fluctuations and fatigue-related structural demand under turbulent wind conditions are important factors that limit the reliability of small wind turbines. This study investigates the separate effects of turbulence intensity and pitch angle on a 5 kW distributed variable-pitch wind turbine prototype using an OpenFAST-based aeroelastic model validated against field measurements. Under the adopted simulation setup and selected operating conditions, increasing turbulence intensity from 5% to 20% leads to a pronounced increase in the extreme blade-root flapwise bending moment and a substantial reduction in the estimated comparative fatigue life. The analysis also reveals a clear trade-off between aerodynamic efficiency and structural durability: among the tested pitch settings, the 6° case yields the highest power output, but also exhibits the largest load fluctuations and the shortest estimated comparative fatigue life. Adjusting the pitch angle to 0° or 12°, while reducing power to some extent, alleviates fatigue-related structural demand and increases the estimated comparative fatigue life. Overall, the results provide a validated prototype-level comparative assessment of how turbulence intensity and pitch angle influence aerodynamic performance, structural response, and fatigue-related demand in the studied turbine. Because the present work focuses on one prototype and does not include cross-turbine comparison or a full stochastic convergence study, the reported quantitative results should not be interpreted as directly generalizable to other turbine configurations. These findings may nevertheless provide a useful basis for future studies on load-aware pitch regulation under turbulent inflow. Full article

With the rapid increase in electric vehicle (EV) ownership, the strategic planning and layout of charging infrastructure have become essential to encourage EV adoption. This study introduces a comprehensive multi-objective optimization method for selecting locations and designing layouts for high-power and extreme fast charging stations. By thoroughly accounting for user charging demands, economic expenses, and traffic conditions, a multi-objective optimization mathematical model is created aiming to minimize user time and costs while maximizing service capacity and user satisfaction. The model combines queuing theory, network topology analysis, and genetic algorithms to simultaneously handle discrete variables related to station placement, continuous variables for charging pile setup, and complex constraints. Using Panyu District in Guangzhou as a case study, a simulation model with 20,000 electric vehicles and 20 high-power and extreme fast charging stations is developed, focusing on the optimal arrangement of 120 kW, 240 kW, and 480 kW charging piles. The simulation results demonstrate that the optimized charging station layout scheme (13 units of 120 kW, 6 units of 240 kW, and 1 unit of 480 kW) lowers overall costs by 6.74%, reduces user charging waiting time from 1.54 h to 0.65 h, improves user satisfaction by 8.1%, and cuts the peak-to-valley difference in charging load from 900 kW to 450 kW. This work offers both theoretical insights and practical recommendations for the effective planning of electric vehicle charging infrastructure. Full article

In the framework of Agriculture 4.0, the modernization and predictive maintenance of legacy heavy machinery are essential for ensuring food security and operational efficiency. This study presents a non-invasive automated diagnostic system for classifying the operational status of mechanical diesel injection pumps in agricultural tractors through vibration analysis and machine learning. A rigorous experimental setup was conducted on an International 523 tractor to acquire vibration signals under controlled fuel pressure conditions ranging from 1 to 4 bar, with 2 bar established as the optimal nominal pressure. The signal processing methodology employed a hybrid feature extraction approach, integrating spectral components from the Fast Fourier Transform (FFT) with time-domain statistical variables. After evaluating 33 classification algorithms, a Support Vector Machine (SVM) model demonstrated superior performance, achieving a training accuracy of 96.7% and Area Under the Curve (AUC) values exceeding 0.90 across all classes. Notably, the model achieved perfect identification (AUC = 1.0) of critical low-pressure faults (1 bar), which significantly compromise engine start-up and combustion efficiency. Validation with an independent dataset confirmed the robustness of the system, maintaining a 95% accuracy rate. These findings validate the proposed approach as a reliable, low-cost solution for condition monitoring, facilitating the integration of conventional tractors into digital maintenance ecosystems. Full article

Bayesian operational modal analysis (OMA) provides a probabilistic framework for identifying modal parameters of structures under ambient excitation while quantifying identification uncertainty. By casting modal identification as a Bayesian inference problem, it enables systematic incorporation of modeling assumptions, measurement noise, and data limitations, thereby addressing fundamental shortcomings of conventional OMA methods. This paper presents a comprehensive review of Bayesian OMA, covering its theoretical foundations, representative identification algorithms, uncertainty quantification and management, and practical applications. Emphasis is placed on frequency domain Bayesian formulations, fast Bayesian FFT-based identification algorithms, treatment of multi-setup and asynchronous data, closely spaced modes, and recent advances in both computational acceleration and capturing environmental variations. Developments on uncertainty laws are synthesized to elucidate the fundamental limits of achievable identification precision and their implications for uncertainty management and test design. A range of applications is reviewed to demonstrate how Bayesian OMA methods support robust modal identification and long-term structural health monitoring under operational and environmental variations. Finally, key challenges and future research directions are discussed to facilitate further methodological development and engineering adoption of Bayesian OMA. Full article

Modern-day urban warehouses face exploding large inventory and tight spaces requiring fast, accurate, and safe stocktaking in a narrow aisle in a GPS-denied environment. This paper proposes a complete UAV-enabled framework performing real-time QR code scanning with inventory synchronization through a safety-aware trajectory generation for obtaining collision-free motion. A novel hybrid workflow integrating MATLAB/Simulink R2024b and Unreal Engine is used for dynamics and photorealistic rendering, alongside a real-time warehouse setup using drone cameras and 3D LiDAR coupled with a ground control station and live dashboard. The system in this paper was evaluated by testing with single and multi-UAV models across high-fidelity simulations and experiments. Results demonstrate simulated QR accuracy of approximately 95 to 96%, with experimental validation achieving between 86 and 90.5% due to real-world environmental factors. In experimental and simulation analysis, mean end-to-end latency remained under half a second, trajectory error range between 8 and 10 cm, and safety margins were consistently maintained throughout the test. It was further observed that multi-UAV coordination halved mission time compared to single-drone tests while keeping duplicate reads negligible, indicating a scalable and safe pipeline for industry application. Full article

Polarisation encoding is widely used in fibre-based Quantum Key Distribution (QKD), but random birefringence in optical fibres causes the transmitted states to drift, requiring active compensation at the receiver. Electronic Polarisation Controllers (EPCs) are commonly used for this purpose, yet the relationship between their control voltages and the resulting polarisation transformation is highly nonlinear and difficult to model. While optimisation algorithms are frequently employed to align and stabilise polarisation states, their comparative performance has not been systematically studied in realistic QKD settings. In this work, we benchmark four optimisation algorithms for electronic polarisation control, using both a numerical model and a 50 km fibre-based experimental setup. We evaluate each algorithm in terms of convergence time, failure rate, and stability, under both initial alignment and continuous drift compensation scenarios. Coordinate Descent achieved the fastest average alignment time (2.1 ms in simulation; 34.6 s experimentally), while Simulated Annealing delivered perfect reliability. We further propose a hybrid control strategy that combines fast initial alignment with high-reliability realignment. This approach was validated over a continuous 2 h QKD simulation with real fibre drift, demonstrating robust polarisation control without manual intervention. Our results provide guidance for algorithm selection in practical QKD deployments and suggest a pathway to resilient, autonomous polarisation tracking in long-distance quantum networks. Full article

This paper suggests a novel approach based on machine learning (ML) and deep learning (DL) for medical image classification in a fast and accurate manner. The proposed method merges the strengths of the convolutional neural network (CNN) using the VGG19 model for feature extraction with an artificial neural network (ANN) classifier for medical dataset classification. The suggested model is improved by applying the slime mold algorithm (SMA) to the task of feature selection and the particle swarm optimization (PSO) approach to optimize the ANN classifier. PSO is a crucial component in neural network design to optimize the ANN setup and hyperparameters. Through adjustments to the bias and weight parameters, the PSO approach enhances the ANN method’s ability to classify medical images. The experiments were conducted on the LC25000 histopathological dataset, which comprises 25,000 histopathological images of lung and colon cancer tissue, partitioned into five classes, each with 5000 images: lung benign tissue, lung adenocarcinoma, lung squamous cell carcinoma, colon adenocarcinoma, and colon benign tissue. The results demonstrated that the suggested model (CNN-PSO-ANN) does better at illness detection than ANN alone. The proposed model is evaluated utilizing several metrics, like accuracy, RMSE, and MAE. The accuracy rate is 94.1% when ANN is utilized independently, while the percentage increases to 98.8% when PSO is employed with the ANN. Additionally, the proposed model is compared with other medical data classification systems that utilize PSO and neural networks. The proposed model (CNN-PSO-ANN) performed better than the other models. With the suggested CNN-PSO-ANN model, diseases, especially cancer, can be found and treated earlier and better. Full article

Permanent magnet synchronous motors (PMSM) are widely favored in industry for their high efficiency, compact size, and robust performance. This study employs a model-based PID control approach for speed regulation of PMSM. In contrast to traditional PID approaches, this method addresses the inherent nonlinearity of PMSM systems and tunes PID coefficients dynamically for fast multi-input and multi-output (MIMO) operations. Traditional PID controllers typically assume linear motor dynamics and determine a single set of coefficients, often through trial and error. However, the nonlinear dynamics of motor drives and variations in motor parameters often lead to instability, limiting the effectiveness of conventional PID controllers. The proposed auto-tuning PID controller adjusts its coefficients in real-time based on the system’s operational state. This method has been implemented in both simulation and experimental setups, with real-time execution facilitated by dSPACE DS1104. A comparative analysis with conventional PI control demonstrates the enhanced stability and adaptability of the proposed approach. Full article

Heavy metals release in the environment represents a growing threat to human health and nature, particularly due to industrial activities contributing to soil and water contamination. In this study, Ganoderma lucidum heteropolysaccharides (GLHP) were evaluated as a biosorbent for cadmium removal. The biomass was acquired following the production of Ganoderma lucidum fruiting bodies and consisted of remnants from the fungus and cultivation substrate. Cd(II) and elemental analysis were carried out by atomic adsorption spectrometry (AAS) and inductively coupled plasma mass spectroscopy (ICP-MS), respectively. The biosorption efficiency was critically evaluated, optimizing physical adsorption parameters for batch, column, and percolation configuration, as well as application in real environmental water. Utilizing a simple pre-rinsing step, completely omitting any chemical pretreatment, the Cd(II) removal efficiency was improved from 41.2% to 78.4% in a batch system and up to 98.4% in a fixed-bed column, making it suitable not only for wastewater treatment but also for drinking water purification. The adsorption kinetics were described by a pseudo-second-order (PSO) model and further analyzed using a revised PSO (rPSO) model, which explicitly accounts for adsorbate and adsorbent concentrations. A global fit to the PSO model demonstrated that the rate constant was independent of the adsorbent concentration, supporting its application as a robust descriptor of the adsorption process. GLHP showed good adsorption performance, following the Sips adsorption isotherm and Thomas model for batch and column setup, respectively, demonstrating the potential as a scalable, low-cost biosorbent for fast and efficient Cd(II) removal from contaminated waters. Full article

This study presents the design and development, fabrication, and experimental testing of a four-circular-coil system capable of generating controlled, very low-frequency magnetic fields for biomedical applications. The system is tailored for use with a bioreactor cultivating mesenchymal stem cells, ensuring highly uniform magnetic fields within the area of interest (AOI). An asymptotic approach—the Multiple-Turn Thin-Wire Approximation (MTTWA)—was employed for fast calculations and modeling of multi-turn coil systems with massive windings. The MTTWA-calculated magnetic field distribution of the four-multi-turn coil system was verified with Ansys Maxwell simulations, showing good agreement. The coils and coil system were designed and fabricated, along with a prototype of the exposure system to validate both numerical modeling and simulation results, achieving magnetic field uniformity of at least 97% within the AOI. In the fabricated four-coil exposure setup, symmetric coils are connected in parallel with two separate amplifier-controlled outputs, enabling precise adjustment of field strength, uniformity, and intentional inhomogeneity for specialized experiments. An automated measurement system has been designed and fabricated to measure the magnetic field within the AOI volume with a spatial resolution of 1 mm. Full article

This paper investigates the design of end-to-end (E2E) autoencoders within AI-enhanced communication systems. It emphasizes the advantages of transitioning from Fast Fourier Transform (FFT)-based Orthogonal Frequency Division Multiplexing (OFDM) to a modulation technique based on the Walsh–Hadamard transform (WHT). This study underscores the WHT’s use of aperiodic basis functions, in contrast with the periodic bases of Fourier transforms. The proposed E2E autoencoder model integrates neural networks in both the transmitter and receiver for signal processing. The model is trained to adapt the bit rate according to the measured channel signal-to-noise ratio (SNR) using the same neural network, enabling operation at low SNR levels (down to −10 dB). Additionally, the model was experimentally validated in a laboratory setting using a software-defined radio (SDR)-based system setup. Full article

In road cycling, developing bike handling skills can prevent crashes and falls. Nevertheless, bike handling remains largely unexplored in the world of road cycling. The goal of this research was to develop a methodology to assess bike handling during races and training by estimating the rider–bicycle roll angle and road-plane accelerations from global positioning system (GPS) data only. A multi-dimensional bike-rider mathematical model was included in an optimal control framework to follow a reference trajectory generated from GPS data points. Estimated variables and experimental data collected with a cost-effective setup showed good agreement, i.e., root mean square error (RMSE) of 12° and 0.1 g for roll angle and both longitudinal and lateral accelerations, respectively, in the worst-case scenarios. This methodology might allow for the estimation of key bike handling variables during fast segments with cost-effective instrumentation. It can therefore constitute a tool for objectively assessing bike handling in road cycling training and racing. Full article

In this article, we present LaserCAD, an open-source, script-based software toolkit for the design and visualization of optical setups based on parametric ray tracing. Unlike conventional commercial tools, which focus on complex lens optimization and offer dense GUIs with extensive parameters, LaserCAD is tailored for fast, intuitive modeling of laser beam paths and opto-mechanical assemblies with minimal setup overhead. Written in Python, it allows users to describe optical systems in a language close to geometrical optics, using simple commands with sensible defaults for most parameters. Optical elements can be automatically positioned including the required mounts. As a graphical backend, FreeCAD renders 3D models of all components for interactive visualization and post-processing. LaserCAD supports integration with other simulation tools and can automate the creation of alignment aids for 3D printing. This makes it especially suitable for rapid prototyping and lab-ready designs. Full article