Search Results (38)

This paper presents an adaptive Maximum Power Point Tracking (MPPT) strategy for grid-connected photovoltaic (PV) systems that uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Particle Swarm Optimization (PSO) to enhance energy extraction efficiency under diverse environmental conditions. The proposed ANFIS-PSO-based MPPT controller performs dynamic adjustment Pulse Width Modulation (PWM) switching to minimize Total Harmonic Distortion (THD); this will ensure rapid convergence to the maximum power point (MPP). Unlike conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, which struggle with tracking delays and local maxima in partial shading scenarios, the proposed approach efficiently identifies the Global Maximum Power Point (GMPP), improving energy harvesting capabilities. Simulation results in MATLAB/Simulink R2023a demonstrate that under stable irradiance conditions (1000 W/m², 25 °C), the controller was able to achieve an MPPT efficiency of 99.2%, with THD reduced to 2.1%, ensuring grid compliance with IEEE 519 standards. In dynamic irradiance conditions, where sunlight varies linearly between 200 W/m² and 1000 W/m², the controller maintains an MPPT efficiency of 98.7%, with a response time of less than 200 ms, outperforming traditional MPPT algorithms. In the partial shading case, the proposed method effectively avoids local power maxima and successfully tracks the Global Maximum Power Point (GMPP), resulting in a power output of 138 W. In contrast, conventional techniques such as P&O and INC typically fail to escape local maxima under similar conditions, leading to significantly lower power output, often falling well below the true GMPP. This performance disparity underscores the superior tracking capability of the proposed ANFIS-PSO approach in complex irradiance scenarios, where traditional algorithms exhibit substantial energy loss due to their limited global search behavior. The novelty of this work lies in the integration of ANFIS with PSO optimization, enabling an intelligent self-adaptive MPPT strategy that enhances both tracking speed and accuracy while maintaining low computational complexity. This hybrid approach ensures real-time adaptation to environmental fluctuations, making it an optimal solution for grid-connected PV systems requiring high power quality and stability. The proposed controller significantly improves energy harvesting efficiency, minimizes grid disturbances, and enhances overall system robustness, demonstrating its potential for next-generation smart PV systems. Full article

This study presents a pneumatic proportional pressure valve employing a silicon microfluidic chip (SMC) integrated with a V-shaped electrothermal microactuator, aiming to address the limitations of traditional solenoid-based valves in miniaturization and high-precision control. The SMC, fabricated via MEMS technology, leverages the thermal expansion of microactuator ribs to regulate pressure through adjustable orifices. A first-order transfer function between input voltage and displacement of the microactuator was derived through theoretical modeling and validated via COMSOL Multiphysics 5.2a simulations. Key geometric parameters of the actuator ribs—cross-section, number, inclination angle, width, span length and thickness—were analyzed for their influence on lever mechanism displacement, actuator displacement, static gain and time constant. AMESim 16.0-based simulations of single- and dual-chip valve structures revealed that increasing ζ shortens step-response rise time, while reducing τ improves hysteresis. Experimental validation confirmed the valve’s static and dynamic performance, achieving a step-response rise time of <40 ms, linearity within the 30–60% input voltage range, and effective tracking of sinusoidal control signals up to 8 Hz with a maximum pressure deviation of 0.015 MPa. The work underscores the potential of MEMS-based actuators in advancing compact pneumatic systems, offering a viable alternative to conventional solenoids. Key innovations include geometry-driven actuator optimization and dual-chip integration, providing insights into high-precision, low-cost pneumatic control solutions. Full article

Wave Energy Converters (WECs) rely on effective Power Take-Off (PTO) control strategies to maximize energy absorption under dynamic sea conditions. Traditional hydrodynamic modeling techniques may require computationally intensive convolution calculations, making real-time control implementation challenging. This paper presents an alternative approach by leveraging instantaneous frequency estimation to dynamically adjust PTO damping in response to varying wave frequencies. Two real-time frequency estimation methods are explored: the Hilbert Transform (HT) and Phase-Locked Loop (PLL). The Hilbert Transform method provides accurate frequency tracking but introduces a delayed response due to its dependence on causal data. Conversely, the PLL approach demonstrates strong potential in frequency tracking but requires careful gain tuning, particularly in complex sea states. Comparative evaluations across multiple test cases—including sinusoidal variations, amplitude steps, frequency step changes, and real-world JONSWAP spectrum waves—highlight the strengths and limitations of each method. The two different PTO control techniques across the various frequency estimation methods were tested under real-sea states using a state-space model of a point-absorbing Wave Energy Converter. The Capture Width Ratio (CWR) is used as a performance metric, with results showing that the HT achieves a 10.6% improvement, while the PLL estimation yields a 0.9% improvement relative to the fixed parameter control baseline. These results highlight the effectiveness of real-time frequency estimation in improving energy absorption compared to static control parameters. Full article

In pipeline all-position welding processes, laser scanning provides critical geometric data of width-changing bevel morphology for welding torch swing control, yet conventional second-order derivative zero methods often yield pseudo-inflection points in practical applications. To address this, a third-order derivative weighted average threshold algorithm was developed, integrating image denoising, enhancement, and segmentation pre-processing with cubic spline fitting for precise bevel contour reconstruction. Bevel pixel points were captured by the laser sensor as inputs through the extracted second-order derivative eigenvalues to derive third-order derivative features, applying weighted threshold discrimination to accurately identify inflection points. Dual-angle sensors were implemented to synchronize laser-detected bevel geometry with real-time torch swing adjustments. Experimental results demonstrate that the system achieves a steady-state error of only 1.645% at the maximum swing width, a dynamic response time below 50 ms, and torch center trajectory tracking errors strictly constrained within ±0.1 mm. Compared to conventional methods, the proposed algorithm improves dynamic performance by 20.6% and exhibits unique adaptability to narrow-gap V-grooves. The results of these studies confirmed the ability of the method to provide real-time, accurate control for variable-width weld tracking, forming a swing-width adaptive control system. Full article

Laser cladding (LC) is a promising technique for repairing aluminum alloy components, yet challenges like cracks and uneven surfaces persist due to unstable melt flow and thermal stress. This study employs both fluid flow and stress field models to investigate multi-track LC repair mechanisms. Using a finite volume method (FVM), the dynamic evolution of the molten pool was quantified, revealing a maximum flow velocity of 0.2 m/s, a depth of 0.7 mm, and a width of 4 mm under optimized parameters (1600 W laser power, 600 mm/min scan speed). The model also identified that surface flaws between 300 and 900 μm were fully melted and repaired by a current or adjacent track. Finite element analysis (FEA) showed that multi-layer cladding induced a cumulative thermal stress exceeding 1300 MPa at interlayer interfaces, necessitating ≥ 3 s cooling intervals to mitigate cracking risks. These findings provide critical insights into process optimization, demonstrating that adjusting laser power and scan speed can control molten pool stability and reduce residual stress, thus improving repair quality for aluminum alloys. Full article

The reduction of drag for both aircraft and underwater equipment has the potential to reduce their overall energy consumption. Consequently, research into the drag-reducing performance of metal surfaces has significant practical applications. However, there has been more research on the machining of grooves on flat surfaces and inside tubes and less research on the structure of drag-reducing grooves on the outside of circular rods. This paper presents a study in which laser etching technology is employed to machine a range of secondary fractal topologies and V-groove composite structures on the surface of equal-diameter stainless-steel bodies of revolution. The influence of different parameters on the surface properties of stainless-steel materials is analysed through the use of auxiliary positioning tools, adjustments to laser processing parameters and scanning path schemes, as well as the characterisation of the surface morphology of the processed stainless steel using super-depth microscopy, scanning electron microscopy, and other techniques. Subsequently, an underwater drag-reduction tester is employed to assess the drag-reduction efficacy of the optimised secondary fractal composite structure on the surface of the stainless-steel equal-diameter body of revolution. Subsequently, particle image velocity (PIV) tracking technology is employed to assess the surface flow field velocity and overall velocity average of the secondary fractal composite structure. The findings indicate that the secondary fractal composite structure exhibited a drag-reduction effect on the surface of the stainless-steel body of revolution only when the primary main groove had a width of 0.1 mm. Furthermore, an increase in the Reynolds number Re within the range of 4000 to 7000 resulted in a notable enhancement in the drag-reduction efficacy of the secondary fractal composite structure on the surface of the stainless-steel body of revolution. At Re values of 5000, 6000, and 7000, the corresponding drag-reduction rates were observed to be 5.15%, 5.28%, and 5.40%, respectively. Full article

Accurate and efficient estrus detection in sows is crucial in modern agricultural practices to ensure optimal reproductive health and successful breeding outcomes. A non-contact method using computer vision to detect a change in a sow’s vulva size holds great promise for automating and enhancing this critical process. However, achieving precise and reliable results depends heavily on maintaining a consistent camera distance during image capture. Variations in camera distance can lead to erroneous estrus estimations, potentially resulting in missed breeding opportunities or false positives. To address this challenge, we propose a robust six-step methodology, accompanied by three stages of evaluation. First, we carefully annotated masks around the vulva to ensure an accurate pixel perimeter calculation of its shape. Next, we meticulously identified keypoints on the sow’s vulva, which enabled precise tracking and analysis of its features. We then harnessed the power of machine learning to train our model using annotated images, which facilitated keypoint detection and segmentation with the state-of-the-art YOLOv8 algorithm. By identifying the keypoints, we performed precise calculations of the Euclidean distances: first, between each labium (horizontal distance), and second, between the clitoris and the perineum (vertical distance). Additionally, by segmenting the vulva’s size, we gained valuable insights into its shape, which helped with performing precise perimeter measurements. Equally important was our effort to calibrate the camera using monocular depth estimation. This calibration helped establish a functional relationship between the measurements on the image (such as the distances between the labia and from the clitoris to the perineum, and the vulva perimeter) and the depth distance to the camera, which enabled accurate adjustments and calibration for our analysis. Lastly, we present a classification method for distinguishing between estrus and non-estrus states in subjects based on the pixel width, pixel length, and perimeter measurements. The method calculated the Euclidean distances between a new data point and reference points from two datasets: “estrus data” and “not estrus data”. Using custom distance functions, we computed the distances for each measurement dimension and aggregated them to determine the overall similarity. The classification process involved identifying the three nearest neighbors of the datasets and employing a majority voting mechanism to assign a label. A new data point was classified as “estrus” if the majority of the nearest neighbors were labeled as estrus; otherwise, it was classified as “non-estrus”. This method provided a robust approach for automated classification, which aided in more accurate and efficient detection of the estrus states. To validate our approach, we propose three evaluation stages. In the first stage, we calculated the Mean Squared Error (MSE) between the ground truth keypoints of the labia distance and the distance between the predicted keypoints, and we performed the same calculation for the distance between the clitoris and perineum. Then, we provided a quantitative analysis and performance comparison, including a comparison between our previous U-Net model and our new YOLOv8 segmentation model. This comparison focused on each model’s performance in terms of accuracy and speed, which highlighted the advantages of our new approach. Lastly, we evaluated the estrus–not-estrus classification model by defining the confusion matrix. By using this comprehensive approach, we significantly enhanced the accuracy of estrus detection in sows while effectively mitigating human errors and resource wastage. The automation and optimization of this critical process hold the potential to revolutionize estrus detection in agriculture, which will contribute to improved reproductive health management and elevate breeding outcomes to new heights. Through extensive evaluation and experimentation, our research aimed to demonstrate the transformative capabilities of computer vision techniques, paving the way for more advanced and efficient practices in the agricultural domain. Full article

Numerous recent studies address the concept of energy harvesting from natural wind excitation vibration to piezoelectric surfaces, aerodynamic losses, and electromagnetic dampers. All these techniques require a connection to an energy-management circuit. However, the simulation model for energy conversion and management dedicated to this task has not yet been described. This paper presents a model-based simulation for an energy conversion system using piezoelectric energy-harvester system (PEHS) technology. A controlled pulse width modulation (PWM) rectifier, a closed-loop buck-boost converter, and a piezoelectric transducer comprise a dynamic mathematical model of a PEHS. The control blocks of the closed-loop buck-boost converter use the perturbation and observation (P&O) algorithm based on maximum power point tracking (MPPT), which adapts the operational voltage of the piezoelectric source to deliver the maximum power to load. A simulation program is employed to perform mathematical analysis on various wind vibration scenarios, piezoelectric sources without PWM converters, and piezoelectric vibration sources connected to a closed-loop P&O converter. The crucial results of this paper demonstrated that the proposed dynamic PEHS model effectively fed low-power electronic loads by directly adjusting the output voltage level to the set voltage, even under different vibration severity levels. As a result, the proposed PEHS dynamic model serves as a guideline for researchers in the development of self-powered sensors, which contributes to understanding sustainable energy alternatives. Full article

Wheat is one of the most widely grown cereal crops, serving as a key factor in sustaining the nutritional and food balance in numerous countries. The use of non-contact methods for wheat monitoring allows for the rapid diagnosis of vegetation density, crop growth, and the presence of weeds and diseases in the investigated fields. This study aims to assess the potential for differentiating growth patterns in winter wheat cultivars by examining them with two unmanned aerial vehicles (UAVs), the Mavic 2 Pro and Phantom 4 Pro, equipped with a multispectral camera from the MAPIR™ brand. Based on an experimental study conducted in the Southern Dobruja region (Bulgaria), vegetation reflectance indices, such as the Normalized-Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index 2 (EVI2), were generated, and a database was created to track their changing trends. The obtained results showed that the values of the NDVI, EVI2, and SAVI can be used to predict the productive potential of wheat, but only after accounting for the meteorological conditions of the respective growing season. The proposed methodology provides accurate results in small areas, with a resolution of 0.40 cm/pixel when flying at an altitude of 12 m and 2.3 cm/pixel when flying at an altitude of 100 m. The achieved precision in small and ultra-small agricultural areas, at a width of 1.2 m, will help wheat breeders conduct precise diagnostics of individual wheat varieties. Full article

Different agronomic requirements, production conditions, and crop species result in varying row spacings. To address the issue of seedling damage caused by pressure when a fixed track width sprayer operates in different row spacings and enhance the accuracy of track width adjustment, this study designed a track width adjustment system for a sprayer based on the agronomic requirements for field management during the early and mid-stages of corn growth and the entire growth period of wheat in Henan Province, China. The designed track width adjustment system for the sprayer comprised transmission mechanisms, telescopic track width adjustment mechanisms, and an electro-hydraulic control system. The control system achieved a precise track width adjustment by controlling the movement of the hydraulic cylinders through electrical signals, forming a closed-loop adjustment system with the aid of sensors. Four control schemes are proposed: classical PID, integral separated PID, fuzzy adaptive PID, and integral separated fuzzy PID. Simulation experiments were conducted using MATLAB to compare these schemes. The results indicated that the integral separated fuzzy PID exhibited the fastest response and highest steady-state accuracy. The performance of the track width adjustment system was validated through field experiments. The results demonstrate that the stability coefficient of variation for the track width adjustment was 3.04%, which is below the 10% threshold required by agricultural machinery standards. Additionally, the average error of the track width adjustment was 13.42 mm, indicating high precision and effectively reducing seedling compression damage during plant protection operations. Full article

This study aimed to develop and validate an improved sparrow search algorithm (ISSA)-optimized Least Squares Support Vector Machine (LSSVM) model for accurately predicting the tooth profile deviation of rigid gears produced by wire electrical discharge machining (WEDM). The ISSA was obtained by optimizing the sparrow search algorithm (SSA) using Tent chaotic mapping, adaptive adjustment strategy, dynamic inertia weights, and grey wolf hierarchy strategy. The effectiveness of the ISSA was verified using four different classes of benchmark test functions. Four main process parameters (peak current, pulse width, pulse interval, and tracking) were taken as inputs and the tooth profile deviations of rigid gears were considered as outputs to develop an ISSA-LSSVM-based profile deviation prediction model. The prediction performance of the ISSA-LSSVM model was evaluated by comparing it with the LSSVM model optimized by three standard algorithms. The prediction results of the ISSA-LSSVM model were R² = 0.9828, RMSE = 0.0029, and MAPE = 0.0156. The results showed that the established model exhibits high prediction accuracy and can provide reliable theoretical guidance for predicting the tooth profile deviation of rigid gears. Full article

In the selective laser melting (SLM) process, adjusting process parameters contributes to achieving the desired molten pool morphology, thereby enhancing the mechanical properties and dimensional accuracy of manufactured components. The parameter window characterizing the relationship between molten pool morphology and process parameters serves as an effective tool to improve SLM’s forming quality. This work established a mesoscale model of the SLM process for a GH3625 alloy based on the discrete element method (DEM) and computational fluid dynamics (CFD) to simulate the forming process of a single molten track. Subsequently, the formation mechanism and evolution process of the molten pool were revealed. The effects of laser power and scanning speed on the molten pool size and molten track morphology were analyzed. Finally, a parameter window was established from the simulation results. The results indicated that reducing the scanning speed and increasing the laser power would lead to an increase in molten pool depth and width, resulting in the formation of an uneven width in the molten track. Moreover, accelerating the scanning speed and decreasing the laser power cause a reduction in molten pool depth and width, causing narrow and discontinuous molten tracks. The accuracy of the simulation was validated by comparing experimental and simulated molten pool sizes. Full article

Solar photovoltaic (PV) systems stand out as a promising solution for generating clean, carbon-free energy. However, traditional solar panel installations often require extensive land resources, which could become scarce as the population grows. To address this challenge, innovative approaches are needed to maximize solar power generation within limited spaces. One promising concept involves the development of biological tree-like structures housing solar panels. These “solar trees” mimic the arrangement of branches and leaves found in natural trees, following patterns akin to phyllotaxy, which correlates with the Fibonacci sequence and golden ratio. By adopting an alternative 1:3 phyllotaxy pattern, three solar panels can be efficiently arranged along the stem of the solar tree structure, each rotated at a 120-degree displacement. Optimizing the performance of solar trees requires effective maximum power point tracking (MPPT), a crucial process for extracting the maximum available power from solar panels to enhance the overall efficiency. In this study, a novel metaheuristic algorithm called horse herd optimization (HHO) is employed for MPPT in solar tree applications. Moreover, to efficiently manage the generated power, a cascaded buck–boost converter is utilized. This converter is capable of adjusting the DC voltage levels to match the system requirements within a single topology. The algorithm is implemented using MATLAB and embedded within a Raspberry Pi Pico controller, which facilitates the generation of pulse-width modulation (PWM) signals to control the cascaded buck–boost converter. Through extensive validation, this study confirms the effectiveness of the proposed HHO algorithm integrated into the Raspberry Pi Pico controller for optimizing solar trees under various shading conditions. In essence, this research highlights the potential of solar tree structures coupled with advanced MPPT algorithms and power management systems to maximize solar energy utilization, offering a sustainable solution for clean energy generation within limited land resources. Full article

Mobile robots have become increasingly important across various sectors and are now essential in agriculture due to their ability to navigate effectively and precisely in crop fields. Navigation involves the integration of several technologies, including robotics, control theory, computer vision, and artificial intelligence, among others. Challenges in robot navigation, particularly in agriculture, include mapping, localization, path planning, obstacle detection, and guiding control. Accurate mapping, localization, and obstacle detection are crucial for efficient navigation, while guiding the robotic system is essential to execute tasks accurately and for the safety of crops and the robot itself. Therefore, this study introduces a Guiding Manager for autonomous mobile robots specialized for laser-based weeding tools in agriculture. The focus is on the robot’s tracking, which combines a lateral controller, a spiral controller, and a linear speed controller to adjust to the different types of trajectories that are commonly followed in agricultural environments, such as straight lines and curves. The controllers have demonstrated their usefulness in different real work environments at different nominal speeds, validated on a tracked mobile platform with a width of about 1.48 m, in complex and varying field conditions including loose soil, stones, and humidity. The lateral controller presented an average absolute lateral error of approximately 0.076 m and an angular error of about 0.0418 rad, while the spiral controller presented an average absolute lateral error of about 0.12 m and an angular error of about 0.0103 rad, with a horizontal accuracy of about ±0.015 m and an angular accuracy of about ±0.009 rad, demonstrating its effectiveness in real farm tests. Full article

Understanding the spatial ecology of species has important implications for conservation, as it helps identify suitable habitats and minimum requirements for biodiversity monitoring and management. The spiny-tailed lizard Oplurus cyclurus is a widespread endemic iguanid occurring in dry areas of southern and western Madagascar. While the species is known to be mostly arboreal, populations of the Isalo sandstone massif suggest local adaptation to a less forested savannah and a more exposed habitat. We radio-tracked 19 spiny-tailed lizards to investigate the species’ rock-dwelling behaviour and spatial ecology at Isalo National Park. Tracked individuals showed high site and burrow fidelity, and a basking behaviour mostly tied to the accessibility of their burrow, the time of day, and their life stage. Activity peaked during the sunniest hours, while juveniles were more active than adults with unfavourable weather conditions. Despite high burrow fidelity, lizards used shelters non-exclusively, regularly changing (approx. once a week) with neighbouring burrows (average distance between burrows = 13.6 m). However, there was no obvious relation between lizards’ body and/or tail size and the width and depth of selected burrows. Dynamic Brownian Bridge Movement Models estimated frequented areas over 247.8 m² (95% isopleth), where territorial overlap is common. Our results challenge the notion that burrow-site fidelity is the sole driving factor behind space utilization in the studied population. We argue that the apparently unusual saxicolous habits imposed by habitat features (the absence of trees) may lead to local behavioural adjustments influencing antipredatory and foraging strategies, as well as intraspecific interactions. Full article