Search Results (2,997)

The development of NVH (Noise, Vibration, and Harshness) characteristics in vehicles is facing new challenges with the widespread utilization of electric drivetrains. This shift introduces new requirements in several areas, such as reduced noise and vibration levels, the need for advanced nonlinear characterization methods, and tuning/masking the typically more prominent tonal noise components. More accurate simulation and measurement techniques are essential to meet these demands. This study focuses on the experimental frequency response function (FRF) testing of electric drivetrain components, specifically on potential phase errors caused by inappropriate measurement settings. The influencing parameters and their quantitative effects are analyzed theoretically and demonstrated using real measurement data. A novel numerical approach, termed Maximum Phase Error Analysis (MPEA), is introduced to systematically quantify the largest potential phase errors due to arbitrary alignment between resonance frequencies and discrete spectral lines. MPEA enhances the robustness of phase accuracy assessment, especially critical for lightly damped systems and closely spaced resonance peaks. Based on the findings, optimal testing parameters are proposed to ensure phase errors remain within a predefined limit. The results can be applied in various dynamic testing scenarios, including durability testing and rattling analysis. Full article

External-cavity GaN semiconductor lasers at blue wavelengths enable narrow-linewidth and high-power output but is difficult at >500 nm green wavelengths due to the so-called ‘green gap’. In this Letter, we demonstrate a watt-level, narrow-linewidth, tunable green semiconductor laser based on external-cavity synchronous-locking technique. The laser consists of two green edge-emitting laser diodes (LDs), beam-shaping devices and a visible-wavelength diffraction grating. Because the two green (∼518 nm) LDs have similar spectral and lasing characteristics and are adjacently parallel in spatial mode, synchronous locking of both beam can readily generated with the help of diffraction grating. Namely, the two green LDs are locked at the same wavelength and the 3dB–linewidth is sharply narrowed from 4 nm to 0.06 nm. The locked wavelength can be tuned from 512.2 to 520.2 nm. The maximum output power reaches 1.53 W at 518 nm with a 3dB–linewidth of 0.15 nm. This is, for the first time, to the best of our knowledge, an external-cavity synchronous-locking green semiconductor laser with watt-level output power. Full article

Accurate characterization of liquid manure properties, such as dry matter (DM), total nitrogen (TN), ammonium nitrogen (NH₄-N), and total phosphorus (TP), is essential for effective nutrient management in agriculture. This study investigates the use of near-infrared spectroscopy (NIRS) within the 941–1671 nm range, combined with advanced pre-processing and machine learning techniques to accurately predict the liquid manure properties. The predictive accuracy of NIRS was assessed by comparison with nuclear magnetic resonance (NMR) spectroscopy as a benchmark method. A number of 51 liquid manure samples were analyzed in the laboratory for the reference manure properties and scanned with NIRS and NMR. The NIR data underwent spectral pre-processing, which included two- and three-band index transformations and feature selection. Partial least squares regression (PLSR) and LASSO regression were employed to develop calibration models. According to the results, using cohort-tuned models, NIRS showed fair predictive accuracy for DM (R² = 0.78, RPD = 2.15) compared to factory-calibrated NMR (R² = 0.68, RPD = 0.81). Factory-calibrated NMR outperformed for chemical properties, with R² (RPD) of 0.89 (1.74) for TN, 0.97 (5.70) for NH₄-N, and 0.95 (2.64) for TP, versus NIRS’s 0.66 (1.68), 0.84 (2.45), and 0.84 (2.51), respectively. In this study with 51 samples, two- and three-band indices significantly enhanced NIRS performance compared to raw data, with R² increases of 34%, 57%, 25%, and 33% for DM, TN, NH₄-N, and TP, respectively. Feature selection efficiently reduced NIR spectral dimensionality without compromising the prediction accuracy. This study highlights NIRS’s potential as a portable tool for on-site manure characterization, with NMR providing superior laboratory validation, offering complementary approaches for nutrient management. Full article

In recent years, unconventional machine techniques have made the machining process simpler than it was under conventional machining methods. EDM is recognized as one of the leading methods in unconventional machining processes. The need for materials with improved mechanical properties continues to rise due to constant advancements in the mechanical industry. Cryogenic treatment is used for property enhancement and can be useful in an extensive range of metals. This research investigates the performance of a cryogenically treated copper electrode during EDM of Monel 400. The EDM parameters varied during the research are pulse on time, pulse off time, gap voltage, and discharge current. The experiments were designed using Taguchi’s design of experiment. The constraints of the process are fine-tuned for both MRR and surface smoothness, with their proportion impacts assessed through the ANOVA technique. Regression analysis is accomplished, creating an experimental correlation between both MRR and surface smoothness, examined using RSM technique. This comprehensive study demonstrates that cryogenic treatment of electrode provides better MRR and SR. Full article

Turbulent flow separation over lifting surfaces impacts high-lift systems such as aircraft, wind turbines, and turbomachinery, and contributes to noise, lift loss, and vibrations. Accurate detection of flow separation is therefore essential to enable active control strategies and to mitigate its adverse effects. Several machine learning models are compared for detecting flow separation from surface pressure fluctuations. The models were trained on experimental data covering various airfoils, angles of attack (0°–23°), and Reynolds numbers, with ${\mathrm{Re}}_c=0.8–4.5\times {10}^6$. For supervised learning, the ground-truth binary labels (attached or separated flow) were derived from static pressure distributions, lift coefficients, and the power spectral densities of surface pressure fluctuations. Three machine learning techniques (multilayer perceptron, support vector machine, logistic regression) were utilized with fine-tuned hyperparameters. Promising results are obtained, with the support vector machine achieving the highest performance (accuracy 0.985, Matthews correlation coefficient 0.975), comparable to other models, with advantages in runtime and model size. However, most misclassifications occur near separation onset due to gradual transition, suggesting areas for model refinement. Sensitivity to database parameters is discussed alongside flow physics and data quality. Full article

The large-scale adoption of renewable energy sources, while environmentally beneficial, introduces significant frequency fluctuations due to the inherent variability of wind and solar output. Electric vehicle (EV) integration with substantial battery storage and bidirectional charging capabilities offers potential mitigation for these fluctuations. This study addresses load frequency regulation in multi-area interconnected power systems incorporating diverse generation resources: renewables (solar/wind), conventional plants (thermal/gas/hydro), and EV units. A hybrid controller combining the proportional–integral–derivative with second derivative (PIDD²) and tilted derivative (TD) structures is proposed, with parameters tuned using an innovative optimization method called the Tianji’s Horse Racing Optimization (THRO) technique. The THRO-optimized PIDD²-TD controller is evaluated under realistic conditions including system nonlinearities (generation rate constraints and governor deadband). Performance is benchmarked against various combination structures discussed in earlier research, such as PID-TID and PIDD²-PD. THRO’s superiority in optimization has also been proven against several recently published optimization approaches, such as the Dhole Optimization Algorithm (DOA) and Water Uptake and Transport in Plants (WUTPs). The simulation results show that the proposed controller delivers markedly better dynamic performance across load disturbances, system uncertainties, operational constraints, and high-renewable-penetration scenarios. The THRO-based PIDD²-TD controller achieves optimal overshoot, undershoot, and settling time metrics, reducing overshoot by 76%, undershoot by 34%, and settling time by 26% relative to other controllers, highlighting its robustness and effectiveness for modern hybrid grids. Full article

Remarkable advances in Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) made it one of the most effective gas-sensing techniques in terms of sensitivity and selectivity. Consequently, its range of possible applications is continuously expanding, but in some cases is still limited by the cost and/or size of the equipment needed to im-plement a complete QEPAS sensor. In particular, bulky and expensive lab instruments are often used to realize the electronic building blocks required by this technique, which prevents, for instance, integration of the system on board a drone. This work addresses this issue by presenting the development of compact electronic modules for a QEPAS sensor. A very low-noise, fully differential preamplifier for the quartz tuning fork, with digital output and programmable gain, has been designed and realized. A compact FPGA board hosts both an accurate function generation module, which synthesizes the signals needed to modulate the laser source, and an innovative lock-in amplifier based on the CORDIC algorithm. QEPAS sensors based on the designed electronics have been used for the detection of H₂O and CO₂ in ambient air, proving the full functionality of all the blocks. These results highlight the potential of compact electronics to promote portable and cost-effective QEPAS applications. Full article

Given the critical importance of accurate energy demand and production forecasting in managing power grids and integrating renewable energy sources, this study explores the application of advanced machine learning techniques to forecast electricity load and wind generation data in Austria, Germany, and the Netherlands at different sampling frequencies: 15 min and 60 min. Specifically, we assess the performance of the convolutional neural networks (CNNs), temporal CNN (TCNN), Long Short-Term Memory (LSTM), bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), bidirectional GRU (BiGRU), and the deep neural network (DNN). In addition, the standard machine learning models, namely the k-nearest neighbors (kNN) algorithm and decision trees (DTs), are adopted as baseline predictive models. Bayesian optimization is applied for hyperparameter tuning across multiple models. In total, 54 experimental tasks were performed. For the electricity load at 15 min intervals, the DT shows exceptional performance, while for the electricity load at 60 min intervals, DNN performs the best, in general. For wind generation at 15 min intervals, DT is the best performer, while for wind generation at 60 min intervals, both DT and TCNN provide good results, in general. The insights derived from this study not only advance the field of energy forecasting but also offer practical implications for energy policymakers and stakeholders in optimizing grid performance and renewable energy integration. Full article

The increasing adoption of high-performance DC motor control in embedded systems has driven the development of cost-effective solutions that extend beyond traditional software-based optimization techniques. This work presents a refined hardware-centric approach implementing real-time particle swarm optimization (PSO) directly executed on STM32 microcontroller for DC motor speed control, departing from conventional simulation-based parameter-tuning methods. Novel hardware-optimized composition of an interval type-2 fuzzy logic controller (FLC) and a PID controller is developed, designed for resource-constrained embedded systems and accounting for processing delays, memory limitations, and real-time execution constraints typically overlooked in non-experimental studies. The hardware-in-the-loop implementation enables real-time parameter optimization while managing actual system uncertainties in controlling DC micro-motors. Comprehensive experimental validation against conventional PI, PID, and PIDF controllers, all optimized using the same embedded PSO methodology, reveals that the proposed FT2-PID controller achieves superior performance with 28.3% and 56.7% faster settling times compared to PIDF and PI controllers, respectively, with significantly lower overshoot at higher reference speeds. The proposed hardware-oriented methodology bridges the critical gap between theoretical controller design and practical embedded implementation, providing detailed analysis of hardware–software co-design trade-offs through experimental testing that uncovers constraints of the low-cost microcontroller platform. Full article

Noise is one of the main pollutants in urban contexts, even if it is not perceived as severe as other pollutants. Transportation, specifically road traffic, accounts for most of the urban environmental noise, and its monitoring is very important and sometimes compelled by law. To do this, two different approaches are possible: a direct measurement campaign or a simulation approach. The so-called Road Traffic Noise Models (RTNMs) are used for this second scope. In recent years, noise assessment has also been experimented with through Machine Learning (ML) techniques: ML is very interesting mainly because it is usable in unusual road traffic conditions, like in the presence of roundabouts and/or stops and traffic lights, or more generally when the free flow aspect is not verified, and the classic RTNMs fail. In this contribution, a large and comprehensive study on four different ML regressors is presented. After careful hyperparameter tuning, regressors have been calibrated by using two different approaches: a classic train/test split on real road traffic data, and by using a computed dataset. Results show a quantitative and qualitative description of the outputs of the ML regressors functioning, and how their calibration by using computed data instead of real data can give good output simulations. Full article

A parametric machine learning tool with many applications is the radial basis function (RBF) network, which has been incorporated into various classification and regression problems. A key component of these networks is their radial functions. These networks acquire adaptive capabilities through a technique that consists of two stages. The centers and variances are computed in the first stage, and in the second stage, which involves solving a linear system of equations, the external weights for the radial functions are adjusted. Nevertheless, in numerous instances, this training approach has led to decreased performance, either because of instability in arithmetic computations or due to the method’s difficulty in escaping local minima of the error function. In this manuscript, a three-stage method is suggested to address the above problems. In the first phase, an initial estimation of the value ranges for the machine learning model parameters is performed. During the second phase, the network parameters are fine-tuned within the intervals determined in the first phase. Finally, in the third phase of the proposed method, a local optimization technique is applied to achieve the final adjustment of the network parameters. The proposed method was evaluated on several machine learning models from the related literature, as well as compared with the original RBF training approach. This methodhas been successfully applied to a wide range of related problems reported in recent studies. Also, a comparison was made in terms of classification and regression error. It should be noted that although the proposed methodology had very good results in the above measurements, it requires significant computational execution time due to the use of three phases of processing and adaptation of the network parameters. Full article

Weeds continue to pose a serious challenge to agriculture, reducing both the productivity and quality of crops. In this paper, we explore how modern deep learning, specifically Vision Transformers (ViTs), can help address this issue through fast and accurate weed classification. We developed a transformer-based model trained on the DeepWeeds dataset, which contains images of nine different weed species collected under various environmental conditions, such as changes in lighting and weather. By leveraging the ViT architecture, the model is able to capture complex patterns and spatial details in high-resolution images, leading to improved prediction accuracy. We also examined the effects of model optimization techniques, including fine-tuning and the use of pre-trained weights, along with different strategies for handling class imbalance. While traditional oversampling actually hurt performance, dropping accuracy to 94%, using class weights alongside strong data augmentation boosted accuracy to 96.9%. Overall, our ViT model outperformed standard Convolutional Neural Networks, achieving 96.9% accuracy on the held-out test set. Attention-based saliency maps were inspected to confirm that predictions were driven by weed regions, and model consistency under location shift and capture perturbations was assessed using the diverse acquisition sites in DeepWeeds. These findings show that with the right combination of model architecture and training strategies, Vision Transformers can offer a powerful solution for smarter weed detection and more efficient farming practices. Full article

This paper presents an innovative approach to the modeling and dynamic analysis of DC–DC converters in photovoltaic applications. Departing from traditional studies that focus on the transfer function from duty cycle to output voltage, this work investigates the duty cycle to input voltage transfer function, which is critical for accurate dynamic representation of photovoltaic systems. A notable contribution of this study is the integration of the PV panel behavior in the small-signal representation, considering a model-derived differential resistance for various operating points. This technique enhances the model’s accuracy across different operating regions. The paper also validates the effectiveness of this linearization method through small-signal analysis. A comprehensive comparison is conducted among several non-isolated converter topologies such as Boost, Buck–Boost, Ćuk, and SEPIC under both open-loop and closed-loop conditions. To ensure fairness, all converters are designed using a consistent set of constraints, and controllers are tuned to maintain similar phase margins and crossover frequencies across topologies. In addition, a gain-scheduling control strategy is implemented for the Boost converter, where the PI gains are dynamically adapted as a function of the PV operating point. This approach demonstrates superior closed-loop performance compared to a fixed controller tuned only at the maximum power point, further highlighting the benefits of the proposed modeling and control framework. This systematic study therefore provides an objective evaluation of dynamic performance and offers valuable insights into optimal converter architectures and advanced control strategies for photovoltaic systems. Full article

This paper presents a method to design and optimize a mimetic, multi-band antenna for direction-finding applications based on multiple IoT mobile nodes for protecting sensitive areas. A set of 84 antenna configurations were selected based on possible resonant paths and simulated using a Method of Moments (MoM)-based tool to compute resonant frequencies, VSWR, and gain across three frequency bands centered on 433 MHz, 877.5 MHz, and 2.4 GHz. Compared to a brute-force approach requiring 8¹⁴ full-wave simulations, our technique dramatically reduces computing time by performing only 84 simulations, followed by a fine-tuning procedure targeting the antenna segments with the highest contribution to the error figure. The final design provides good gain and VSWR figures over almost all the frequency ranges of interest. Full article

Reliable schedule-risk estimation in hybrid software development lifecycles is strategically important for organizations adopting AI in software engineering. This study addresses that need by transforming routine process telemetry (CI/CD, SAST, traceability) into explainable, quantitative predictions of completion time and rework. This paper introduces an integrated probabilistic model of the hybrid software development lifecycle that combines Generalized Evaluation and Review Technique (GERT) network semantics with I-AND synchronization, explicit artificial-intelligence (AI) interventions, and a fuzzy treatment of epistemic uncertainty. The model embeds two controllable AI nodes–an AI Requirements Assistant and AI-augmented static code analysis, directly into the process topology and applies an analytical reduction to a W-function to obtain iteration-time distributions and release-success probabilities without resorting solely to simulation. Epistemic uncertainty on critical arcs is represented by fuzzy intervals and propagated via Zadeh’s extension principle, while aleatory variability is captured through stochastic branching. Parameter calibration relies on process telemetry (requirements traceability, static-analysis signals, continuous integration/continuous delivery, CI/CD, and history). A validation case (“system design → UX prototyping → implementation → quality assurance → deployment”) demonstrates practical use: large samples of process trajectories are generated under identical initial conditions and fixed random seeds, and kernel density estimation with Silverman’s bandwidth is applied to normalized histograms of continuous outcomes. Results indicate earlier defect detection, fewer late rework loops, thinner right tails of global duration, and an approximately threefold reduction in the expected number of rework cycles when AI is enabled. The framework yields interpretable, scenario-ready metrics for tuning quality-gate policies and automation levels in Agile/DevOps settings. Full article