Search Results (92)

Injurious birds pose a significant threat to food production and the agricultural economy. To address the challenges posed by their small size, irregular shape, and frequent occlusion in complex farmland environments, this paper proposes YOLO-AWK, an improved bird detection model based on YOLOv11n. Firstly, to improve the ability of the enhanced model to recognize bird targets in complex backgrounds, we introduce the in-scale feature interaction (AIFI) module to replace the original SPPF module. Secondly, to more accurately localize and identify bird targets of different shapes and sizes, we use WIoUv3 as a new loss function. Thirdly, to remove the noise interference and improve the extraction of bird residual features, we introduce the Kolmogorov–Arnold network (KAN) module. Finally, to improve the model’s detection accuracy for small bird targets, we add a small target detection head. The experimental results show that the detection performance of YOLO-AWK on the farmland bird dataset is significantly improved, and the final precision, recall, mAP@0.5, and mAP@0.5:0.95 reach 93.9%, 91.2%, 95.8%, and 75.3%, respectively, which outperforms the original model by 2.7, 2.3, 1.6, and 3.0 percentage points, respectively. These results demonstrate that the proposed method offers a reliable and efficient technical solution for farmland injurious bird monitoring. Full article

System identification—originating in the 1950s from statistical theory—has since developed a wealth of algorithms, insights, and practical expertise. We introduce Kolmogorov–Arnold neural networks (KANs) as an interpretable alternative for model discovery. Leveraging KANs’ inherent property to approximate data and interpret it by employing learnable activation functions and decomposition of multivariate mappings into univariate transforms, we test its ability to recover the step responses of first- and second-order systems both numerically and symbolically. We employ synthetic datasets, both noise-free and with Gaussian noise, and find that KANs can achieve very low RMSE and parameter error with simple architectures. Our results demonstrate that KANs combine ease of implementation with symbolic transparency, positioning them as a compelling bridge between classical identification and modern machine learning. Full article

Wi-Fi sensing leverages variations in CSI (channel state information) to infer human activities in a contactless and low-cost manner, with growing applications in smart homes, healthcare, and security. While deep learning has advanced macro-motion sensing tasks, micro-motion sensing such as keypad stroke recognition remains underexplored due to subtle inter-class CSI variations and significant intra-class variance. These challenges make it difficult for existing deep learning models typically relying on fully connected MLPs to accurately recognize keypad inputs. To address the issue, we propose a novel approach that combines a discriminative feature extractor with a Kolmogorov–Arnold Network (KAN)-based classifier. The combined model is trained to reduce intra-class variability by clustering features around class-specific centers. The KAN classifier learns nonlinear spline functions to efficiently delineate the complex decision boundaries between different keypad inputs with fewer parameters. To validate our method, we collect a CSI dataset with low-cost Wi-Fi devices (ESP8266 and Raspberry Pi 4) in a real-world keypad sensing environment. Experimental results verify the effectiveness and practicality of our method for keypad input sensing applications in that it outperforms existing approaches in sensing accuracy while requiring fewer parameters. Full article

Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov–Arnold Networks (KANs) to address the trade-off between accuracy and interpretability in lathe tool wear modeling. Three KAN variants (KAN-A, KAN-B, and KAN-C) with varying complexities are proposed, using feed rate, depth of cut, and cutting speed as input variables to model flank wear. The proposed KAN-based framework generates interpretable mathematical expressions for tool wear, enabling transparent decision-making. To evaluate the performance of KANs, this research systematically compares prediction errors, topological evolutions, and mathematical interpretations of derived symbolic formulas. For benchmarking purposes, MLP-A, MLP-B, and MLP-C models are developed based on the architectures of their KAN counterparts. A comparative analysis between KAN and MLP frameworks is conducted to assess differences in modeling performance, with particular focus on the impact of network depth, width, and parameter configurations. Theoretical analyses, grounded in the Kolmogorov–Arnold representation theorem and Cybenko’s theorem, explain KANs’ ability to approximate complex functions with fewer nodes. The experimental results demonstrate that KANs exhibit two key advantages: (1) superior accuracy with fewer parameters compared to traditional MLPs, and (2) the ability to generate white-box mathematical expressions. Thus, this work bridges the gap between empirical models and black-box machine learning in manufacturing applications. KANs uniquely combine the adaptability of data-driven methods with the interpretability of physics-based models, offering actionable insights for researchers and practitioners. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article

Motor imagery (MI) EEG decoding is a key application in brain–computer interface (BCI) research. In cross-session scenarios, the generalization and robustness of decoding models are particularly challenging due to the complex nonlinear dynamics of MI-EEG signals in both temporal and frequency domains, as well as distributional shifts across different recording sessions. While multi-scale feature extraction is a promising approach for generalized and robust MI decoding, conventional classifiers (e.g., multilayer perceptrons) struggle to perform accurate classification when confronted with high-order, nonstationary feature distributions, which have become a major bottleneck for improving decoding performance. To address this issue, we propose an end-to-end decoding framework, MCTGNet, whose core idea is to formulate the classification process as a high-order function approximation task that jointly models both task labels and feature structures. By introducing a group rational Kolmogorov–Arnold Network (GR-KAN), the system enhances generalization and robustness under cross-session conditions. Experiments on the BCI Competition IV 2a and 2b datasets demonstrate that MCTGNet achieves average classification accuracies of 88.93% and 91.42%, respectively, outperforming state-of-the-art methods by 3.32% and 1.83%. Full article

Centralized and distributed optimization methods used by traditional virtual power plants (VPPs) in power system dispatching face issues such as high computational complexity, difficulties in privacy protection, and slow iterative convergence. There is an urgent need to propose an accurate and efficient acceleration method for generating VPP operational dispatching strategies. This paper proposes a deep learning-based acceleration method for generating VPP operational dispatching strategies. By using the equivalent projection method to solve the operation feasible region of the VPP, the objective function and constraints of the VPP are transformed into constraints of coordination variables and submitted to the system dispatching center for optimization, thereby avoiding the slow convergence problem of iterative computation methods. The Kolmogorov–Arnold Network (KAN) is employed to predict the batch operation feasible regions of the VPP, addressing the inefficiency of individually calculating feasible regions. Tests on a 13,659-node system show that the proposed method reduces solution time by 64.40% while increasing the objective function value by only 4.74%, verifying its accuracy and speed. Full article

This study proposes a deep learning-based, non-contact method for detecting elevated intraocular pressure (IOP) by integrating Scheimpflug images with corneal biomechanical features. Glaucoma, the leading cause of irreversible blindness worldwide, requires accurate IOP monitoring for early diagnosis and effective treatment. Traditional IOP measurements are often influenced by corneal biomechanical variability, leading to inaccurate readings. To address these limitations, we present a multi-modal framework incorporating CycleGAN for data augmentation, Swin Transformer for visual feature extraction, and the Kolmogorov–Arnold Network (KAN) for efficient fusion of heterogeneous data. KAN approximates complex nonlinear relationships with fewer parameters, making it effective in small-sample scenarios with intricate variable dependencies. A diverse dataset was constructed and augmented to alleviate data scarcity and class imbalance. By combining Scheimpflug imaging with clinical parameters, the model effectively integrates multi-source information to improve high IOP prediction accuracy. Experiments on a real-world private hospital dataset show that the model achieves a diagnostic accuracy of 0.91, outperforming traditional approaches. Grad-CAM visualizations identify critical anatomical regions, such as corneal thickness and anterior chamber depth, that correlate with IOP changes. These findings underscore the role of corneal structure in IOP regulation and suggest new directions for non-invasive, biomechanics-informed IOP screening. Full article

Accurate oil production forecasting is essential for optimizing field development and supporting efficient decision-making. However, traditional methods often struggle to capture the complex dynamics of reservoirs, and existing machine learning models rely on large parameter sets, resulting in high computational costs and limited scalability. To address these limitations, we propose the Kolmogorov–Arnold Network (KAN) for oil production forecasting, which replaces traditional weights with spline-based learnable activation functions to enhance nonlinear modeling capabilities without large-scale parameter expansion. This design reduces training costs and enables adaptive scaling. The KAN model was applied to forecast oil production from wells 15/9-F-11 and 15/9-F-14 in the Volve field, Norway. The experimental results demonstrate that, compared to the best-performing baseline model, the KAN reduces the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) by 78.5% and 89.5% for well 15/9-F-11 and by 80.1% and 91.8% for well 15/9-F-14, respectively. These findings suggest that the KAN is a robust and efficient multivariate forecasting method capable of capturing complex dependencies in oil production data, with strong potential for practical applications in reservoir management and production optimization. Full article

Kolmogorov–Arnold networks (KANs) represent a promising modeling framework for applications requiring interpretability. In this study, we investigate the use of KANs to analyze time series of water quality parameters obtained from a publicly available dataset related to an aquaponic environment. Two water quality indices (WQIs) were computed—a linear case based on the weighted average WQI, and a non-linear case using the weighted quadratic mean (WQM) WQI, both derived from three water parameters: pH, total dissolved solids (TDS), and temperature. For each case, KAN models were trained to predict the respective WQI, yielding explicit algebraic expressions with low prediction errors and clear input–output mathematical relationships. Model performance was evaluated using standard regression metrics, with $R^2$ values exceeding 0.96 on the hold-out test set across all cases. Specifically for the non-linear WQM case, we trained 15 classical regressors using the LazyPredict Python library. The top three models were selected based on validation performance. They were then compared against the KAN model and its symbolic expressions using a 5-fold cross-validation protocol on a temporally shifted test set (approximately one month after the training period), without retraining. Results show that KAN slightly outperforms the best tested baseline regressor (multilayer perceptron, MLP), with average $R^2$ scores of $0.998\pm 0.001$ and $0.996\pm 0.001$, respectively. These findings highlight the potential of KAN in terms of predictive performance, comparable to well-established algorithms. Moreover, the ability of KAN to extract data-driven, interpretable, and lightweight symbolic models makes it a valuable tool for applications where accuracy, transparency, and model simplification are critical. Full article

Time series forecasting serves a critical function in domains such as energy, meteorology, and power systems by leveraging historical data to predict future trends. However, existing methods often prioritize long-term dependencies while neglecting the integration of local features and global patterns, resulting in limited accuracy for short-term predictions of non-stationary multivariate sequences. To address these challenges, this paper proposes a time series forecasting model named VBTCKN based on variational mode decomposition and a dual-channel cross-attention network. First, the model employs variational mode decomposition (VMD) to decompose the time series into multiple frequency-complementary modal components, thereby reducing sequence volatility. Subsequently, the BiLSTM channel extracts temporal dependencies between sequences, while the transformer channel captures dynamic correlations between local features and global patterns. The cross-attention mechanism dynamically fuses features from both channels, enhancing complementary information integration. Finally, prediction results are generated through Kolmogorov–Arnold networks (KAN). Experiments conducted on four public datasets demonstrated that VBTCKN outperformed other state-of-the-art methods in both accuracy and robustness. Compared with BiLSTM, VBTCKN reduced RMSE by 63.32%, 68.31%, 57.98%, and 90.76%, respectively. Full article

Microscopic cell classification is a fundamental challenge in both clinical diagnosis and biological research. However, existing methods still struggle with the complexity and morphological diversity of cellular images, leading to limited accuracy or high computational costs. To overcome these constraints, we propose an efficient classification method that balances strong feature representation with a lightweight design. Specifically, an Inception-Linear Attention-based Lightweight Vision Transformer (ILViT) model is developed for microscopic cell classification. The ILViT integrates two innovative modules: Dynamic Inception Convolution (DIC) and Contrastive Omni-Kolmogorov Attention (COKA). DIC combines dynamic and Inception-style convolutions to replace large kernels with fewer parameters. COKA integrates Omni-Dimensional Dynamic Convolution (ODC), linear attention, and a Kolmogorov-Arnold Network(KAN) structure to enhance feature learning and model interpretability. With only 1.91 GFLOPs and 8.98 million parameters, ILViT achieves high efficiency. Extensive experiments on four public datasets are conducted to validate the effectiveness of the proposed method. It achieves an accuracy of 97.185% on BioMediTech dataset for classifying retinal pigment epithelial cells, 97.436% on ICPR-HEp-2 dataset for diagnosing autoimmune disorders via HEp-2 cell classification, 90.528% on Hematological Malignancy Bone Marrow Cytology Expert Annotation dataset for categorizing bone marrow cells, and 99.758% on a white blood cell dataset for distinguishing leukocyte subtypes. These results show that ILViT outperforms the state-of-the-art models in both accuracy and efficiency, demonstrating strong generalizability and practical potential for cell image classification. Full article

Due to the scarcity of labeled samples, the practical engineering application of deep learning-based hydraulic pump fault diagnosis methods is extremely challenging. This study proposes a semi-supervised learning method based on data augmented consistency regularization (DACR) to address the issue of lack of labeled data in diagnostic models. It utilizes augmented data obtained from the improved symplectic geometry modal decomposition method as additional perturbations, expanding the feature space of limited labeled samples under different operating conditions of the pump. A high-confidence label prediction process is formulated through a threshold determination strategy to estimate the potential label distribution of unlabeled samples. Consistent regularization loss is introduced in labeled and unlabeled data, respectively, to regularize model training, reducing the sensitivity of the classifier to additional perturbations. The supervised loss term ensures that the predictions of the augmented labeled samples are consistent with the true labels. Meanwhile, the unsupervised loss term can be used to minimize the difference between the distributions of unlabeled samples for different augmented versions. Finally, the proposed method is combined with Kolmogorov–Arnold Network (KAN). Comparative experiments based on data from two models of hydraulic pumps verify the superior recognition performance of this method under low label rate. Full article

Accurate classification of electrocardiogram (ECG) signals is vital for reliable arrhythmia diagnosis and informed clinical decision-making, yet real-world datasets often suffer severe class imbalance that degrades recall and F1-score. To address these limitations, we introduce MAK-Net, a hybrid deep learning framework that combines: (1) a four-branch multiscale convolutional module for comprehensive feature extraction across diverse waveform morphologies; (2) an efficient channel attention mechanism for adaptive weighting of clinically salient segments; (3) bidirectional gated recurrent units (BiGRU) to capture long-range temporal dependencies; and (4) Kolmogorov–Arnold Network (KAN) layers with learnable spline activations for enhanced nonlinear representation and interpretability. We further mitigate imbalance by synergistically applying focal loss and the Synthetic Minority Oversampling Technique (SMOTE). On the MIT-BIH arrhythmia database, MAK-Net attains state-of-the-art performance—0.9980 accuracy, 0.9888 F1-score, 0.9871 recall, 0.9905 precision, and 0.9991 specificity—demonstrating superior robustness to imbalanced classes compared with existing methods. These findings validate the efficacy of multiscale feature fusion, attention-guided learning, and KAN-based nonlinear mapping for automated, clinically reliable arrhythmia detection. Full article

Semantic segmentation of remote sensing images is a key technology for land cover interpretation and target identification. Although convolutional neural networks (CNNs) have achieved remarkable success in this field, their inherent limitation of local receptive fields restricts their ability to model long-range dependencies and global contextual information. As a result, CNN-based methods often struggle to capture the comprehensive spatial context necessary for accurate segmentation in complex remote sensing scenes, leading to issues such as the misclassification of small objects and blurred or imprecise object boundaries. To address these problems, this paper proposes a new hybrid architecture called KPV-UNet, which integrates the Kolmogorov–Arnold Network (KAN) and the Pyramid Pooling Visual State Space Attention (PP-VSSA) block. KPV-UNet introduces a deep feature refinement module based on KAN and incorporates PP-VSSA to enable scalable long-range modeling. This design effectively captures global dependencies and abundant localized semantic content extracted from complex feature spaces, overcoming CNNs’ limitations in modeling long-range dependencies and inter-national context in large-scale complex scenes. In addition, we designed an Auxiliary Local Monitoring (ALM) block that significantly enhances KPV-UNet’s perception of local content. Experimental results demonstrate that KPV-UNet outperforms state-of-the-art methods on the Vaihingen, LoveDA Urban, and WHDLD datasets, achieving mIoU scores of 84.03%, 51.27%, and 62.87%, respectively. The proposed method not only improves segmentation accuracy but also produces clearer and more connected object boundaries in visual results. Full article