Search Results (5,014)

In response to the growing demand for efficient and eco-friendly golf carts, this paper presents an optimized design of a permanent magnet synchronous machine (PMSM) for multiple operating conditions. The application scenarios of the golf cart were first analyzed, identifying the power requirements under three driving conditions such as unloaded on flat roads, fully loaded on flat roads, and fully loaded on slopes. Then, a 36-slot 8-pole interior PMSM is developed, and a systematic two-stage optimization strategy using a Multi-Objective Genetic Algorithm (MOGA) is applied to enhance both no-load and rated-load performance. By adjusting key rotor parameters to balance competing objectives, the optimized machine demonstrates notable improvements in cogging torque reduction, output torque, torque ripple minimization, and operational efficiency. Specifically, the results show that the optimized machine achieves a cogging torque reduction of over 60%, an increase in maximum output torque by 7.3%, and a peak efficiency improvement of 1.2 percentage points under high-load conditions. Experimental results validate the effectiveness of the design and confirm its suitability for the complex operating conditions of golf carts. Full article

Carbon fiber reinforced silicon carbide (C/SiC) composite material exhibits exceptional properties, including high strength, high stiffness, low density, outstanding high-temperature performance, and corrosion resistance. Consequently, they are widely used in aerospace, defense, and automotive engineering. However, their anisotropic, high hardness, and brittle characteristics make them a typical difficult-to-machine material. This paper focuses on achieving high-quality micro hole machining of C/SiC composite material via electrical discharge machining. It systematically investigates electrical discharge machining characteristics and innovatively develops a hollow internal flow helical electrode reaming process. Experimental results reveal four typical chip morphologies: spherical, columnar, blocky, and molten. The study uncovers a multi-mechanism cutting process: the EDM ablation of the composite involves material melting and explosive vaporization, the intact extraction and fracture of carbon fibers, and the brittle fracture and spalling of the SiC matrix. Discharge energy correlates closely with surface roughness: higher energy removes more SiC, resulting in greater roughness, while lower energy concentrates on m fibers, yielding higher vaporization rates. C fiber orientation significantly impacts removal rates: processing time is shortest at θ = 90°, longest at θ = 0°, and increases as θ decreases. Typical defects such as delamination were observed between alternating 0° and 90° fiber bundles or at hole entrances. Cracks were also detected at the SiC matrix–C fiber interface. The proposed hole-enlargement process enhances chip removal efficiency through its helical structure and internal flushing, reduces abnormal discharges, mitigates micro hole taper, and thereby improves forming quality. This study provides practical references for the EDM of C/SiC composite material. Full article

Background: Despite advances in myoelectric control of hand prostheses, their dropout rate remains high. Methods: We analyzed 37 features extracted from surface electromyography (sEMG) recordings from 15 participants, distributed into three groups: non-impaired individuals, impaired individuals with limb loss due to trauma, and impaired individuals with limb loss due to electrical burn. Feature relationships were examined with correlation heatmaps and two feature-selection methods (ReliefF and Minimal Redundancy Maximum Relevance), and classification performance was evaluated using machine-learning models to characterize sEMG behavior across groups. Results: Individuals with electrical-burn injury exhibited increased forearm co-contraction on the affected side across normalized isometric contractions, indicating altered motor coordination and likely higher energetic cost for prosthetic control. Feature selection and model results revealed etiology-dependent differences in the most informative sEMG features, underscoring the need for personalized, etiology-aware myoelectric control strategies. Conclusions: These findings inform the design of adaptive prosthetic controllers and targeted rehabilitation protocols that account for injury-specific motor control adaptations. Full article

Soil salinization is an important negative factor that reduces the fertility of irrigated arable land. The fields in southern Kazakhstan are at high risk of salinization due to the dry arid climate. In some cases, even the top layer of soil has a significant degree of salinization. The use of a UAV equipped with a multispectral camera can help in the rapid and highly detailed mapping of salinity in cultivated arable land. This article describes the process of preparing the labeled data for assessing the salinity of the top layer of soil and the comparative results achieved due to using machine learning methods in two different districts. During an expedition to the fields of the Turkestan region of Kazakhstan, fields were surveyed using a multispectral camera mounted on a UAV; simultaneously, the soil samples were collected. The electrical conductivity of the soil samples was then measured in laboratory conditions, and a set of programs was developed to configure machine learning models and to map the obtained results subsequently. A comparative analysis of the results shows that local conditions have a significant impact on the quality of the models in different areas of the region, resulting in differences in the composition and significance of the model input parameters. For the fields of the Zhetisay district, the best result was achieved using the extreme gradient boosting regressor model (linear correlation coefficient Rp = 0.86, coefficient of determination R² = 0.42, mean absolute error MAE = 0.49, mean square error MSE = 0.63). For the fields in the Shardara district, the best results were achieved using the support vector machines model (Rp = 0.82, R² = 0.22, MAE = 0.41, MSE = 0.46). This article presents the results, discusses the limitations of the developed technology for operational salinity mapping, and outlines the tasks for future research. Full article

Network microstructure titanium matrix composites (NMTMCs) possess excellent performance and are promising for aerospace applications, yet their microstructural heterogeneity poses substantial challenges to achieving high-quality micro-machined surfaces. The aim of this study is to evaluate electrochemical machining (ECM) as a post-processing method for improving the surface quality of NMTMCs after electrical discharge machining (EDM). This study systematically examines the effects of electrolyte concentration, machining voltage, and pulse frequency on surface roughness. Electrochemical measurements in NaCl and NaNO₃ revealed that standalone electrochemical machining causes severe selective corrosion due to the large dissolution rate mismatch between TiBw reinforcements and the Ti-6Al-4V matrix, making it unsuitable for direct finishing. Accordingly, ECM was applied to EDM-prepared surfaces, and under optimized conditions (10 wt.% NaCl, 4.5 V, 200 kHz), ECM effectively mitigates the protrusions at the edges of discharge pits caused by the EDM process. Surface roughness (Sa) is significantly reduced from 0.90 μm to 0.45 μm, and the surface morphology becomes more uniform. These results demonstrate that ECM is a viable post-EDM finishing strategy for achieving high-quality micro-machining of NMTMCs. Full article

With the rapid development of precision seeding technology, which plays a vital role in promoting large-scale cultivation, reducing seed loss, increasing crop yield, and improving land use efficiency, a maize precision seed metering control system based on KF-RTS fusion speed measurement has been developed to address the issues of ground wheel slippage and chain bounce in Chinese precision planters during high-speed operation, as well as the problems of speed measurement delay, motor control lag, and susceptibility to interference in existing electric drive seeders. The system comprises an STM32 master controller, a speed acquisition unit, a seed metering drive unit, and a human–machine interaction interface. By employing a multi-rate KF-RTS (Kalman Filter-Rauch-Tung-Striebel Smoother) fusion algorithm that integrates RTK-GNSS and accelerometer data, it significantly enhances the accuracy and real-time performance of forward speed measurement. A control strategy combining Kalman filtering with a fuzzy PID controller, optimized by a particle swarm algorithm, enables the control system to converge rapidly within 0.10 s with a steady-state error below 0.55%, achieving precise and stable regulation of the seed metering shaft speed. Field test results demonstrate that the qualified index of seed spacing reaches no less than 94.11% under the fusion speed measurement method. Compared to the RTK-GNSS speed measurement alone, the coefficient of variation in seed spacing is reduced by 3.85% to 6.93%, effectively resolving seed spacing deviations caused by speed measurement delays and improving seeding uniformity. Full article

To ensure reliable, efficient and sustainable operation of modern power networks, accurate load forecasting is an important task in system planning and control. It is also a crucial task for the efficient operation of smart grids to maintain a balance between load shifting, load management and power dispatch. In this regard, this research study aims to investigate the efficiency of various machine learning models for whole-house energy consumption prediction and appliance-level load disaggregation using Non-Intrusive Load Monitoring (NILM). The primary objective is to determine which model offers the most accurate forecasts for both individual appliance consumption patterns and the total amount of energy used by the household. The empirical study presents comparative performance analysis of machine learning models, i.e., Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Gradient Boosting and Support Vector Regressor (SVR) for load forecasting and load disaggregation. This research is conducted on PRECON: Pakistan Residential Electricity Dataset consisting of 42 Pakistani households. The dataset was recorded originally as one minute per sample, but the proposed study aggregated it to hourly samples to evaluate models’ alignment with the typical sampling rate of smart meters in Pakistan. It enables the models to more accurately depict implementation scenarios in real-world settings. The statistical measures MAE, MSE, RMSE and R² have been employed for performance evaluation. The proposed Random Forest algorithm out-performs all other employed models, with the lowest error values (MAE: 0.1316, MSE: 0.0367, RMSE: 0.1916) and the highest R² score of 0.9865. Furthermore, for detecting appliance events from aggregate power data, ensemble models such as Random Forest performed better than other models for ON/OFF prediction. To evaluate the suitability of machine learning models for real-time, appliance-level energy forecasting using Non-Intrusive Load Monitoring (NILM), this study presents a novel evaluation framework that combines learning speed and edge adaptability with conventional performance metrics (e.g., R², MAE). This paper introduces a NILM-based approach for load forecasting and appliance-level ON/OFF prediction, representing its capacity to improve residential energy efficiency and encourage sustainable energy consumption, while emphasizing operational metrics for implementation in embedded smart grid systems—an area mainly neglected in prior NILM-based research articles. The results provide useful information for improving demand-side energy management, facilitating more effective load disaggregation, and maximizing the energy efficiency and responsiveness of smart grids. Full article

Electricity is fundamental to the functioning of modern economies, yet its price volatility presents significant challenges for both long-term investment planning and short-term operational decision-making. In this study we examine electricity price dynamics across seven diverse European bidding zones, selected through principal component analysis to reflect a broad spectrum of energy mix characteristics. The analysis explores the relationship between the structure of national energy mixes—classified according to the controllability of generation sources—and the volatility and predictability of electricity prices during 2023–2024. Using ENTSO-E data, adaptive machine learning models were developed to forecast day-ahead electricity prices, with the Random Forest algorithm consistently achieving the highest predictive accuracy. The results indicate that bidding zones dominated by low-controllability renewable generation exhibit greater price volatility and reduced forecast accuracy, whereas zones with a higher share of controllable sources, such as natural gas, demonstrate more stable prices and improved model performance. These findings underscore the crucial role of the energy mix composition in shaping market dynamics and highlight the necessity of adopting adaptive, mix-sensitive forecasting methodologies in increasingly diversified electricity systems. Full article

The Northeast region of Thailand covers approximately 16.89 million hectares, with about 6.17 million hectares of seasonal rice cultivation and 2.85 million hectares affected by soil salinity—a major constraint to agricultural productivity in this region. This study develops an integrated data fusion framework combining multi-temporal Landsat-8 and Sentinel-2 imagery to train machine learning (ML) models for the prediction of rice yield and soil salinity, allowing for an analysis of their relationship. The field data comprised 380 rice yield and 625 soil electrical conductivity (EC) samples collected in 2023. Three ML models—Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Regression (SVR)—were applied for variable reduction and optimal predictor selection. RF achieved the highest accuracy for yield prediction (R² = 0.86, RMSE = 0.19 t ha⁻¹) and salinity estimation (R² = 0.93, RMSE = 0.87 dS/m) when using fused Landsat–Sentinel data. Spatial analysis of 5000 matched points showed a strong negative relationship between seedling stage EC and yield (R² = 0.71), with yields declining sharply above 5 dS/m and remaining below 1.5 t ha⁻¹ beyond 15 dS/m. These results demonstrate the potential of multi-sensor fusion and ensemble ML approaches for precise soil salinity monitoring and sustainable rice production. Full article

Access to Information and Communication Technologies (ICTs) is a critical component for social inclusion and population development. This study aimed to analyze ICT access in Argentine households, considering its distribution according to deprivation conditions and area of residence (urban–rural) at the regional level, and incorporating a spatial association perspective at the departmental level. The percentage of households with Internet access, computers (or tablets), and cell phones with connectivity was examined at the regional level, according to household deprivation type and area of residence. At the departmental level, the analysis was conducted through thematic maps and the estimation of spatial autocorrelation patterns (global and local Moran’s Index). Indicators were constructed using data from the 2022 Population, Household, and Housing Census. Results revealed significant disparities in ICT access, attributable to deprivation conditions and the geographic distribution of households. Spatial autocorrelation patterns with low ICT access were mainly identified in the Northwest (NOA) and Northeast (NEA) regions, while the highest coverage levels were concentrated in the Buenos Aires Metropolitan Area (AMBA), the Pampeana, and Patagonia regions. The evidence highlights the need to design public policies aimed at reducing digital divides. Full article

Sensors in plant and crop monitoring play a key role in improving agricultural efficiency by enabling the collection of data on environmental conditions, soil moisture, temperature, sunlight, and nutrient levels. Traditionally, wide-scale wireless sensor networks (WSNs) gather this information in real-time, supporting the optimization of cultivation processes and plant management. Our paper proposes a novel “plant-to-machine” interface, which uses a plant-based biosensor as a primary data source. This model allows for direct monitoring of the plant’s physiological parameters and environmental interactions via Electrical Impedance Spectroscopy (EIS), aiming to reduce the reliance on extensive sensor networks. We present simple data-gathering hardware, a non-invasive single-wire connection, and a machine learning-based framework that supports the automatic analysis and interpretation of collected data. This approach seeks to simplify monitoring infrastructure and decrease the cost of digitizing crop monitoring. Preliminary results demonstrate the feasibility of the proposed model in monitoring plant responses to sunlight exposure. Full article

The processing performance of deep narrow grooves by electrical discharge machining (EDM) needs to be further improved, mainly reflected in the serious electrode wear and low processing efficiency. This study firstly conducted a single-factor experiment on electrical parameters to analyze the influence of electrical parameters on electrode length wear and electrode sharp corner wear, respectively. It was found that the increase in pulse width and duty cycle could reduce electrode length wear, but at the same time led to an increase in electrode sharp corner wear. The reason is that bubbles and debris tend to accumulate at the sharp corner of the electrode. It causes short circuits and arcing phenomena, intensifying the sharp corner wear of the electrode. To address this issue, it is proposed to use a rounded corner electrode to facilitate the exclusion of bubbles and debris from the machining gap, reduce the occurrence of short circuits and arcing phenomena, thereby lowering the electrode length and sharp corner wear, and enhancing processing efficiency. Through the simulation of the flow field in the machining gap, it is theoretically proven that the rounded corner electrode can promote the movement of bubbles and debris towards the outlet of the machining gap and slow down the accumulation of bubbles and debris. Through the EDM of deep narrow groove, it is proven that the electrode wear and processing efficiency of the rounded corner electrode are both superior to those of the sharp corner electrode, and the electrode wear and processing efficiency increase with the increase in the rounded corner radius of the electrode. The research results have contributed to improving the performance of deep narrow grooves by EDM. Full article

Industrial electric motors powered by variable frequency drives (VFDs) offer better controllability as compared to the conventional sinusoid-fed motors. However, the switching transients of VFDs induce shaft voltage in electric motors, which can lead to bearing failure. This may cause the machine to shut down and pose a serious threat to the system’s reliability. Several shaft voltage mitigation strategies are suggested in the literature, including insulated bearings, grounding brushes, copper shields, and filters. Although mitigation strategies have been extensively studied, shaft voltage signal processing remains relatively underexplored. This review introduces diffusion models (DMs), a new generative learning technique, as an effective solution for processing shaft voltage signals. These models are good at reducing noise, handling uncertainty, and capturing complex patterns over time. DMs offer robust performance under dynamic conditions as compared to traditional machine learning (ML) and deep learning (DL) techniques. In summary, the review outlines the sources and causes of shaft voltage, its existing mitigation strategies, and the theory behind DMs for shaft voltage analysis. Thus, by combining insights from electrical engineering and artificial intelligence (AI), this work addresses an important gap in the existing literature and provides a strong path forward for improving the reliability of industrial motor systems. Full article

This study presents the development process of a digital twin for a complex dynamic object using Artificial Neural Networks. A separately excited DC motor is considered as an example, which, despite its well-known electromechanical properties, remains a non-trivial object for neural network modeling. It is shown that describing the motor using a generalized neural network with various configurations does not yield satisfactory results. The optimal solution was based on a separation into two distinct nonlinear autoregressive with exogenous inputs (NARX) artificial neural networks with cross-connections for the two main machine variables: one for modeling the armature current with exogenous inputs of voltage and armature speed, and another for modeling the angular speed with inputs of voltage and armature current. Both neural networks are characterized by a relatively small number of neurons in the hidden layer and a time delay of no more than 3 time steps. This solution, consistent with the physical understanding of the motor as an object where electromagnetic energy is converted into thermal and mechanical energy (and vice versa), allows the model to be calibrated for the ideal no-load mode and subsequently account for the influence of torque loads of various natures and changes in the control object parameters over a wide range. The study demonstrates that even for modeling an object such as a DC electric drive with cascaded control, reducing errors at the boundaries of the known operating range requires generating test signals covering approximately 120% of the nominal speed range and 250–400% of the nominal current. Analysis of various test signals revealed that training with a sequence of step changes and linear variations across the entire operating range of armature current and speed provides higher accuracy compared to training with random or uniform signals. Furthermore, to ensure the neural network model’s functionality under varying load torque, a mechanical load observer was developed, and a model architecture incorporating an additional input for disturbance was proposed. The SEDCM_NARX_LOAD neural network model demonstrates a theoretically justified response to load application, although dynamic and static errors arise. In the experiment, the current error was 7.4%, and the speed error was 0.5%. The practical significance of the research lies in the potential use of the proposed model for simulating dynamic and static operational modes of electromechanical systems, tuning controllers, and testing control strategies without employing a physical motor. Full article

Magnetic field measurements are essential for space science missions but are often contaminated by transient stray fields from spacecraft subsystems such as electrical and control units. Traditional mitigation approaches—including strict magnetic cleanliness programs, deployable long booms, and dual-sensor gradient systems—suffer from inherent limitations, as cleanliness programs and long booms impose high cost and system complexity. To overcome these challenges, we propose Multi-Source Adaptive Gradiometry (MSAG), an enhanced gradiometry technique that integrates a neural network-based interference classification framework. The trained network identifies interference types and applies adaptive correction coefficients, enabling accurate multi-source disturbance correction without requiring manual segmentation of long-duration data. We validate MSAG using realistic synthetic data, generated by superimposing key transient interference types—modeled from SMILE ground tests—onto actual THEMIS satellite measurements, and test it through 220 Monte Carlo simulations. MSAG reduces the median RMSE from 0.457 nT to 0.014 nT and achieves a median correlation coefficient of 0.999994 with the ground truth. This improved accuracy could alleviate constraints on magnetic cleanliness and boom length in future missions, highlighting the advantage of MSAG over conventional methods and underscoring the potential of combining machine learning with gradiometry for high-fidelity magnetic field recovery. Full article