Search Results (548)

The predictive accuracy of the Bekker–Wong model for wheel traction is highly dependent on the precision of the wheel–soil contact angle parameters. These parameters are typically identified through extensive and costly single wheel–soil tests, which are limited by poor experimental repeatability and site-specific constraints. This study proposes a method for obtaining contact angle parameters through numerical simulation. Firstly, a finite element model of an off-road tire is established. The Drucker–Prager (D-P) constitutive model parameters of clay under different moisture were calibrated by soil mechanical tests. And then the moist clay was modeled through the SPH algorithm. An FEM–SPH interaction model was developed to define the tire–moist clay interaction. Meanwhile, the tire–moist clay interaction model was verified by a single wheel–soil test device. To identify the empirical parameters of tire–soil interaction, numerical simulations were conducted for multiple operating conditions involving different slip ratios, soil moisture contents, and vertical loads. By processing the simulated wheel–soil contact characteristic images, the contact angles for each condition were extracted. Finally, the contact angle parameters in the Bekker–Wong model were identified. The empirical parameters were integrated into the Bekker–Wong model to predict traction. The results indicate that the maximum relative error of traction force between the prediction and experiment did not exceed 13.6%, which validated the reliability of the proposed method. Full article

Projects integrating Science, Technology, Engineering, and Mathematics (STEM) are essential to interdisciplinary research. This study presents a STEM (Science, Technology, Engineering, and Mathematics) methodology with the primary objective of designing, constructing, and validating a functional cannabinoid extraction device. To inform the device’s drying parameters, the dehydration kinetics of female hemp buds or flowering buds (FHB) were first analyzed using infrared drying at 100 °C for different durations. The plants were cultivated and harvested in accordance with good agricultural practices using Dinamed CBD Autoflowering seeds. The FHB were harvested and prepared by manually separating them from the stems and leaves. Six 5 g samples were prepared, each with a slab geometry of varying surface area and thickness. Two of these samples were ground: one into a fine powder and the other into a coarse powder. Mathematical fits were obtained for each resulting curve using either an exponential decay model or the logarithmic equation $y\left(t\right)=A\left(e^{-kt}\right)+y_0$ calculate the equilibrium moisture (m_E). The Moisture Rate (MR) was calculated, and by modelling with the logarithmic equation, the constant k and the effective diffusivity (Deff) were determined with the analytical solution of Fick’s second law. The Deff values (ranging from 10⁻⁷ to 10⁻⁵) were higher than previously reported. The coarsely ground powder sample yielded the highest k and Deff values and was selected for oil extraction. The device was then designed using Quality Function Deployment (QFD), specifically the House of Quality (HoQ) matrix, to systematically translate user requirements into technical specifications. A 200 g sample of coarsely ground, dehydrated FHB was prepared for ethanol extraction. Chemical results obtained by Liquid Chromatography coupled with Photodiode Array Detection (LC-PDA) revealed the presence of THC, CBN, CBC, and CBG. The extraction device design was validated using previous results showing the presence of CBD and CBDA. The constructed device successfully extracted cannabinoids, including Δ9-THC, CBG, CBC, and CBN, from coarsely ground FHB, validating the integrated STEM approach. This work demonstrates a practical framework for developing accessible agro-technical devices through interdisciplinary collaboration. Full article

This paper presents the design and optimization, based on electromagnetic simulation, of a fifth-order bandpass filter centered at 30 GHz, implemented using Gap Waveguide (GWG) technology and pole-type cylindrical resonators, intended for applications in 5G communication systems and high-frequency satellite links. Unlike conventional Chebyshev designs reported in the literature, this study proposes an integrated methodology that combines theoretical synthesis, full-wave electromagnetic modeling, and multivariable optimization, while accounting for manufacturing constraints. The developed method encompasses the electromagnetic characterization of individual resonators, the extraction of cavity–cavity coupling parameters, and the complete implementation of the filter using full-wave electromagnetic simulations. A distinctive aspect of the proposed approach is the explicit incorporation of practical manufacturing effects, such as rounded corners induced by machining processes, alongside analytical and numerical geometric compensation procedures that preserve the device’s electrical response. Furthermore, a combination of gradient-based optimization algorithms and evolutionary strategies is employed to align the electromagnetic response with the target theoretical behavior. The results obtained through electromagnetic simulation indicate that the designed filter achieves return losses exceeding 20 dB and a fractional bandwidth of 4.95%, consistent with the reference Chebyshev response. Finally, the potential extension of the proposed approach to higher frequency bands is discussed conceptually, laying the groundwork for future work that includes experimental validation. Full article

This article explores the high-frequency characteristics of gallium nitride (GaN) power-switching devices and evaluates their application performance using a double-pulse test (DPT) circuit model. With the increasing adoption of GaN power-switching devices in high-performance and miniaturized electronic products, their low junction capacitance makes them highly suitable for high-frequency applications. However, parasitic inductance in the power loop can introduce resonance phenomena, impacting system stability and switching performance. To address this, this study integrates the parasitic parameters of printed circuit boards (PCBs) with the nonlinear junction capacitance characteristics of GaN devices. Finite element analysis (FEA) is employed to extract PCB parasitic inductance values and analyze their effects on GaN power-switching behavior. The findings indicate that precise extraction and analysis of parasitic inductance are critical for optimizing the performance of GaN switching devices. Additionally, this study investigates mitigation strategies to minimize parasitic inductance, ultimately enhancing GaN device design and reliability. The insights from this research provide valuable guidance for the development of GaN power devices in high-frequency applications. Full article

Accurate and non-contact identification of individual sheep is important for intelligent livestock management, but remains challenging due to subtle inter-individual differences, breed-dependent facial morphology, and complex farm environments. This study proposes a lightweight sheep face detection and keypoint recognition framework based on an improved YOLO11 architecture, termed SAMS-KLA-YOLO11. The model incorporates a Sheep Adaptive Multi-Scale Convolution (SAMSConv) module to enhance feature extraction across breed-dependent facial scales, a Keypoint-Aware Lightweight Attention (KLAttention) mechanism to emphasize biologically discriminative facial landmarks, and the Efficient IoU (EIoU) loss to stabilize bounding box regression. A dataset of 3860 images from 68 individuals belonging to three breeds (Hu, Dorper, and Dorper × Hu crossbreeds) was collected under unconstrained farm conditions and annotated with five facial keypoints. On this dataset, the proposed model achieves higher precision, recall, and mAP than several mainstream YOLO-based baselines, while reducing FLOPs and parameter count compared with the original YOLO11. Additional ablation experiments confirm that each proposed module provides complementary benefits, and OKS-based evaluation shows accurate facial keypoint localization. All results are obtained on a single, site-specific dataset without external validation or on-device deployment benchmarks, so the findings should be viewed as an initial step toward practical sheep face recognition rather than definitive evidence of large-scale deployment readiness. Full article

Conventional RC snubber design relies on oscillation frequency-based estimation, which is often influenced by uncontrolled parasitic elements and can therefore limit the accuracy of surge voltage prediction in GaN HEMT flyback converters. To overcome this limitation, a drain-voltage assessment-based design approach is introduced, in which the snubber parameters are extracted directly from the measured voltage characteristics during the turn off transition. This method allows the surge voltage to be modeled more precisely and enables the snubber capacitance to be selected without unnecessary oversizing. Simulation results using the GaN Systems GS66516T device show that the proposed approach reduces the total power loss by 27.67% and 21.84% relative to two empirical design methods and achieves up to 53.64% lower loss compared with other RC combinations in the explored design space. The method suppresses the surge voltage from 877 V to 556 V, which closely aligns with the design target of 550 V, whereas the empirical methods result in maximum voltages of 637 V and 603 V. Finally, the thermal feasibility of the snubber resistor is analytically assessed, indicating that the estimated temperature rise remains within the safe operating range of commercial components. Full article

High-resolution range profile (HRRP) sequence recognition in radar automatic target recognition faces several practical challenges, including severe category imbalance, degradation of robustness under complex and variable operating conditions, and strict requirements for lightweight models suitable for real-time deployment on resource-limited platforms. To address these problems, this paper proposes a lightweight spatiotemporal fusion-based (LSTF) HRRP sequence target recognition method. First, a lightweight Transformer encoder based on group linear transformations (TGLT) is designed to effectively model temporal dynamics while significantly reducing parameter size and computation, making it suitable for edge-device applications. Second, a transform-domain spatial feature extraction network is introduced, combining the fractional Fourier transform with an enhanced squeeze-and-excitation fully convolutional network (FSCN). This design fully exploits multi-domain spatial information and enhances class separability by leveraging discriminative scattering-energy distributions at specific fractional orders. Finally, an adaptive focal loss with label smoothing (AFL-LS) is constructed to dynamically adjust class weights for improved performance on long-tail classes, while label smoothing alleviates overfitting and enhances generalization. Experiments on the MSTAR and CVDomes datasets demonstrate that the proposed method consistently outperforms existing baseline approaches across three representative scenarios. Full article

To address issues such as slow response speed and low detection accuracy in fallen walnut picking robots in complex orchard environments, this paper proposes a detection and path planning method that integrates star-shaped lightweight convolution with NDT-RRT. The method includes the improved lightweight detection model YOLO-FW and an efficient path planning algorithm NDT-RRT. YOLO-FW enhances feature extraction by integrating star-shaped convolution (Star Blocks) and the C3K2 module in the backbone network, while the introduction of a multi-level scale pyramid structure (CA_HSFPN) in the neck network improves multi-scale feature fusion. Additionally, the loss function is replaced with the PIoU loss, which incorporates the concept of Inner-IoU, thus improving regression accuracy while maintaining the model’s lightweight nature. The NDT-RRT path planning algorithm builds upon the RRT algorithm by employing node rejection strategies, dynamic step-size adjustment, and target-bias sampling, which reduces planning time while maintaining path quality. Experiments show that, compared to the baseline model, the YOLO-FW model achieves precision, recall, and mAP@0.5 of 90.6%, 90.4%, and 95.7%, respectively, with a volume of only 3.62 MB and a 30.65% reduction in the number of parameters. The NDT-RRT algorithm reduces search time by 87.71% under conditions of relatively optimal paths. Furthermore, a detection and planning system was developed based on the PySide6 framework on an NVIDIA Jetson Xavier NX embedded device. On-site testing demonstrated that the system exhibits good robustness, high precision, and real-time performance in real orchard environments, providing an effective technological reference for the intelligent operation of fallen walnut picking robots. Full article

Frequent occurrences of pests and diseases in tomatoes severely restrict yield and quality improvements. Traditional detection methods are labor-intensive and prone to errors, while advancements in deep learning provide a promising solution for rapid and accurate identification. However, existing deep learning-based models often face high computational complexity and a large number of parameters, which hinder their deployment on resource-constrained edge devices. To overcome this limitation, we propose a novel lightweight detection model named YOLOv11n-KL based on the YOLOv11n framework. In this model, the feature extraction capability for small targets and the overall computational efficiency are enhanced through the integration of the Conv_KW and C3k2_KW modules, both of which incorporate the KernelWarehouse (KW) algorithm, and the Detect_LSCD detection head is employed to enable parameter sharing and adaptive multi-scale feature calibration. The results indicate that YOLOv11n-KL achieves superior performance in tomato pest and disease detection, balancing lightweight design and detection accuracy. The model achieves an mAP@0.5 of 92.5% with only 3.0 GFLOPs and 5.2 M parameters, reducing computational cost by 52.4% and improving mAP@0.5 by 0.9% over YOLOv11n. With its low complexity and high precision, YOLOv11n-KL is well-suited for resource-constrained applications. The proposed YOLOv11n-KL model offers an effective solution for detecting tomato pests and diseases, serving as a useful reference for agricultural automation. Full article

Cup rupture failures in strip steel welds can lead to strip breakage, resulting in unplanned downtime of high-speed continuous rolling mills and scrap steel losses. Manual visual inspection suffers from a high false positive rate and cannot meet the production cycle time requirements. This paper proposes a lightweight online cup rupture visual inspection method based on an improved YOLOv10 algorithm. The backbone feature extraction network is replaced with ShuffleNetV2 to reduce the model’s parameter count and computational complexity. An ECA attention mechanism is incorporated into the backbone network to enhance the model’s focus on cup rupture micro-cracks. A Slim-Neck design is adopted, utilizing a dual optimization with GSConv and VoV-GSCSP, significantly improving the balance between real-time performance and accuracy. Based on the results, the optimized model achieves a precision of 98.8% and a recall of 99.2%, with a mean average precision (mAP) of 99.5%—an improvement of 0.2 percentage points over the baseline. The model has a computational load of 4.4 GFLOPs and a compact size of only 3.24 MB, approximately half that of the original model. On embedded devices, it achieves a real-time inference speed of 122 FPS, which is about 2.5, 11, and 1.8 times faster than SSD, Faster R-CNN, and YOLOv10n, respectively. Therefore, the lightweight model based on the improved YOLOv10 not only enhances detection accuracy but also significantly reduces computational cost and model size, enabling efficient real-time cup rupture detection in industrial production environments on embedded platforms. Full article

To develop a high-performance detection system for automated harvesting on resource-limited edge devices, we introduce FSA-DETR-P, a lightweight detection framework that addresses challenges such as illumination inconsistency, occlusion, and scale variation in complex orchard environments. Unlike traditional computationally intensive architectures, this model optimizes real-time detection transformers by integrating an efficient backbone for fast feature extraction, a simplified aggregation structure to minimize complexity, and an adaptive mechanism for multi-scale feature fusion. The optimized backbone improves early-stage texture extraction while reducing computational demands. The streamlined aggregation design enhances multi-level interactions without losing spatial detail, and the adaptive fusion module strengthens the detection of small, partially occluded, or ambiguous fruits. We created a domain-specific pomegranate dataset, expanded to 13,840 images with a rigorous 8:1:1 split for training, validation, and testing. The results show that the pruned and optimized model achieves a Mean Average Precision (mAP50) of 0.928 and mAP50–95 of 0.632 with reduced parameters (13.73 M) and lower computational costs (34.6 GFLOPs). It operates at 24.6 FPS on an NVIDIA Jetson Orin Nano, indicating a strong balance between accuracy and deployability, making it well-suited for orchard monitoring and robotic harvesting in real-world applications. Full article

We propose a Hybrid Parallel Temporal–Spatial CNN-LSTM (HPTS-CL) architecture for optimized indoor environment modeling in sports halls, addressing the computational and scalability challenges of high-resolution spatiotemporal data processing. The sports hall is partitioned into distinct zones, each processed by dedicated CNN branches to extract localized spatial features, while hierarchical LSTMs capture both short-term zone-specific dynamics and long-term inter-zone dependencies. The system integrates model and data parallelism to distribute workloads across specialized hardware, dynamically balanced to minimize computational bottlenecks. A gated fusion mechanism combines spatial and temporal features adaptively, enabling robust predictions of environmental parameters such as temperature and humidity. The proposed method replaces monolithic CNN-LSTM pipelines with a distributed framework, significantly improving efficiency without sacrificing accuracy. Furthermore, the architecture interfaces seamlessly with existing sensor networks and control systems, prioritizing critical zones through a latency-aware scheduler. Implemented on NVIDIA Jetson AGX Orin edge devices and Google Cloud TPU v4 pods, HPTS-CL demonstrates superior performance in real-time scenarios, leveraging lightweight EfficientNetV2-S for CNNs and IndRNN cells for LSTMs to mitigate gradient vanishing. Experimental results validate the system’s ability to handle large-scale, high-frequency sensor data while maintaining low inference latency, making it a practical solution for intelligent indoor environment optimization. The novelty lies in the hybrid parallelism strategy and hierarchical temporal modeling, which collectively advance the state of the art in distributed spatiotemporal deep learning. Full article

Planetary gear is the mainstream deceleration transmission device, and its derivative form of high contact ratio internal gear adopts the structure of full internal meshing. While improving the compactness and efficiency of the transmission, it is necessary to focus on its lubrication characteristics and churning power consumption. In this paper, based on the actual meshing state of high contact ratio internal gear, combined with its geometric parameters, motion speed, and pressure bearing state, the Computational Fluid Dynamics (CFD) model is used to analyze the oil distribution during gear motion. According to the oil state, the oil pressure and viscous force on the gear surface are extracted, the churning loss of the gear is calculated, and the influence of different parameters on the churning loss is analyzed. Finally, based on the influence of the oil churning parameters on the lubrication performance, the representative oil churning parameters are selected for the test. The test results are consistent with the results obtained by the simulation analysis, which provides data support for the study of the lubrication of high contact ratio internal gears. Full article

Simpson’s seventh-order integration has been implemented in an automatically executable program to integrate the current minus the short-circuit current. Then, a regression of this integral to a second-degree polynomial in two variables, namely the voltage and the short-circuit current, is performed, obtaining six regression constants. The series ($R_s$) and shunt resistance ($R_{sh}$), the ideality factor (n), and the saturation (I_sat) and light current (I_lig) are extracted from these regression constants. The standard errors of these five photovoltaic device parameters are also calculated. $R_s$, $R_{sh}$, n, I_lig, and I_sat can be extracted with less than 1% error when the percentage noise is $p_n<$ 0.05%, with just $N\ge 26$ ($N\ge 101$ for I_sat), in contrast with a value of $N\ge 751$, in the case of the trapezoidal integration method being used. The program calculates the photovoltaic device parameters in less than a second for 1001 data points, four seconds for 10,001 data points, and nineteen seconds for 20,001 data points, which is in striking contrast with the tenths of minutes when using the trapezoidal integration provided by the software Origin, as it has to be performed manually. It is worth mentioning that for the case of $p_n\ge$ 0.1%, both trapezoidal and Simpson seventh-order integration practically yield the same accuracy; nevertheless, the program outstands the trapezoidal integration, as it achieves the extraction in nineteen seconds or less. The results reported in this article are valid for the one-diode solar cell model, and might not be valid for other models. Full article

In Search and Rescue (SAR) missions, UAVs must be capable of detecting small objects from complex and noise-prone maritime images. Existing small object detection methods typically rely on super-resolution techniques or complex structural designs, which often demand significant computational resources and fail to meet the real-time requirements for small mobile devices in SAR tasks. To address this challenge, we propose SAQ-YOLO, an efficient small object detection model based on the YOLO framework. We design a Small Object Auxiliary Query branch, which uses deep semantic information to guide the fusion of shallow features, thereby improving small object capture efficiency. Additionally, SAQ-YOLO incorporates a series of lightweight channel, spatial, and group (large kernel) gated attention mechanisms to suppress background clutter in complex maritime environments, enhancing feature extraction at a low computational cost. Experiments on the SeaDronesSee dataset demonstrate that, compared to YOLOv11s, SAQ-YOLO reduces the number of parameters by approximately 70% while increasing mAP@50 by 2.1 percentage points. Compared to YOLOv11n, SAQ-YOLO improves mAP@50 by 8.7 percentage points. When deployed on embedded platforms, SAQ-YOLO achieves an inference latency of only 35 milliseconds per frame, meeting the real-time requirements of maritime SAR applications. These results suggest that SAQ-YOLO provides an efficient and deployable solution for UAV SAR operations in vast and highly dynamic marine environments. Future work will focus on enhancing the robustness of the detection model. Full article