Search Results (2)

Tangerine peel, rich in moisture (75–90%) and medicinal value, requires drying to prevent spoilage and extend shelf life. Traditional heat pump drying often causes uneven airflow, leading to inconsistent drying and nutrient loss, compromising product quality and storage stability. In this study, a prediction model of drying moisture ratio of tangerine peel based on Kolmogorov–Arnold network bidirectional long short-term memory (KAN-BiLSTM) and multimodal feature fusion is proposed. A pre-trained visual geometry group U-shaped network (VGG-UNet) is employed to segment tangerine peel images and extract color, contour, and texture features, while airflow distribution is simulated using finite element analysis (FEA) to obtain spatial location information. These multimodal features are fused and input into a KAN-BiLSTM model, where the KAN layer enhances nonlinear feature representation and a multi-head attention (MHA) mechanism highlights critical temporal and spatial features to improve prediction accuracy. Experimental validation was conducted on a dataset comprising 432 tangerine peel samples collected across six drying batches over a 480 min period, with image acquisition and mass measurement performed every 20 min. The results showed that the pre-trained VGG-UNet achieved a mean intersection over union (MIoU) of 93.58%, outperforming the untrained model by 9.41%. Incorporating spatial features improved the coefficient of determination (R²) of the time series model by 0.08 ± 0.04. The proposed KAN-BiLSTM model achieved a mean absolute error (MAE) of 0.024 and R² of 0.9908, significantly surpassing baseline models such as BiLSTM (R² = 0.9049, MAE = 0.0476) and LSTM (R² = 0.8306, MAE = 0.0766), demonstrating superior performance in moisture ratio prediction. Full article

Research has found kernel anomaly regions in the power spectrum images of ionospheric electric fields in space, which are widely distributed. To effectively detect these kernel abnormal regions, this paper proposes a new kernel abnormal region detection method, KANs-Unet, based on KANs and U-net networks. The model embeds the KAN-Conv convolutional module based on KANs in the encoder section, introduces the feature pyramid attention module (FPA) at the junction of the encoder and decoder, and introduces the CBAM attention mechanism module in the decoder section. The experimental results show that the improved KANs-Unet model has a mIoU improvement of about 10% compared to the PSPNet algorithm and an improvement of about 7.8% compared to the PAN algorithm. It has better detection performance than the currently popular semantic segmentation algorithms. A higher evaluation index represents that the detected abnormal area is closer to the label value (i.e., the detected abnormal area is more complete), indicating better detection performance. To further investigate the characteristics of kernel anomaly areas and the differences in features during magnetic storms, the author studied the characteristics of kernel anomaly areas during two different intensities of magnetic storms: from November 2021 to October 2022 and from 1 May 2024 to 13 May 2024 (large magnetic storm), and from 11 October 2023 to 23 October 2023 (moderate magnetic storm). During a major geomagnetic storm, the overall distribution of kernel anomaly areas shows a parallel trend with a band-like distribution. The spatial distribution of magnetic latitudes is relatively scattered, especially in the southern hemisphere, where the magnetic latitudes are wider. Additionally, the number of orbits with kernel anomaly areas during ascending increases, especially during peak periods of major geomagnetic storms. The overall spatial distribution of moderate geomagnetic storms does not change significantly, but the global magnetic latitude distribution is relatively concentrated. Full article