Achieving high-performance numerical weather prediction (NWP) is important for people’s livelihoods and for socioeconomic development. However, NWP is obtained by solving differential equations with globally observed data without capturing enough local and spatial information at the observed station. To improve the forecasting performance, we propose a novel spatial lightGBM (Light Gradient Boosting Machine) model to correct the numerical forecast results at each observation station. By capturing the local spatial information of stations and using a single-station single-time strategy, the proposed method can incorporate the observed data and model data to achieve high-performance correction of medium-range predictions. Experimental results for temperature and wind prediction in Hainan Province show that the proposed correction method performs well compared with the ECWMF model and outperforms other competing methods.

Near-infrared (NIR) spectroscopy can improve the efficiency of soil property prediction, such as that of soil total nitrogen (TN) content. However, soil spectra are very sensitive to soil moisture content, which is a crucial factor affecting the accuracy of soil nutrient composition prediction. In response to this issue, the goal of this study is to identify the best model to correct the effect of soil moisture for predicting soil total nitrogen by near-infrared spectroscopy. The 107 collected soil samples were divided into six different water content (0%, 5%, 10%, 15%, 20%, and 25%) sample groups. Then, five correction methods, including direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), spectral space transformation (SST), and slope/bias (S/B), were executed. Finally, partial least squares regression (PLSR) models were established to forecast TN content. The results showed that SST could minimize the influence of moisture. Furthermore, SST–PLSR had the best TN content prediction accuracy: $R_p^2$ (the coefficient of determination of the prediction set) in the range of 0.81–0.82, RMSEP (the root mean square error of the prediction set) in the range of 0.09–0.10 g/kg, and RPD (ratio of performance to deviation) in the range of 2.32–2.40. Therefore, the dry soil prediction model is competent for wet soil samples and could achieve preciseness in TN content prediction. The use of SST can effectively eliminate the influence of moisture and achieve high-precision TN prediction in wet soil samples. Additionally, the introduction of SST expands the application scope of soil nutrient prediction models and increases model robustness.

In motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance the classification performance of motor imagery BCIs. However, the problem of subject-specific bandpass frequency selection frequently arises in Riemannian manifold-based methods. In this study, we propose a multiple Riemannian graph fusion (MRGF) model to optimize the subject-specific frequency band for a Riemannian manifold. After constructing multiple Riemannian graphs corresponding to multiple bandpass frequency bands, graph embedding based on bilinear mapping and graph fusion based on mutual information were applied to simultaneously extract the spatial and spectral features of the EEG signals from Riemannian graphs. Furthermore, with a support vector machine (SVM) classifier performed on learned features, we obtained an efficient algorithm, which achieves higher classification performance on various datasets, such as BCI competition IIa and in-house BCI datasets. The proposed methods can also be used in other classification problems with sample data in the form of covariance matrices.

The nutritional status of rubber trees (Hevea brasiliensis) is inseparable from the production of natural rubber. Nitrogen (N) and potassium (K) levels in rubber leaves are 2 crucial criteria that ...

Soil total nitrogen (TN) is a vital nutrient element that affects the growth and rubber production of rubber trees. Especially in the coastal environment, soil nutrients will show significant differences. Using hyperspectral technology to detect soil nitrogen ion content in the offshore environment can provide technical support for nutrient management. Preprocessing hyperspectral data is a crucial step in accurate spectral model estimation. At the same time, it is considered that the traditional first-order and second-order derivatives are easily unbalanced between the signal-to-noise ratio, resulting in the loss of adequate information. Therefore, this work focuses on the feasibility of fractional order derivative (FOD) combined with partial least squares regression (PLSR) to estimate its TN content. By collecting soil samples from rubber plantations, the TN content of the soil samples was determined, and the spectral reflectance was measured. The FOD of the original spectrum was preprocessed with an interval of 0.2, and 11 spectral curves were obtained. Then, successive projections algorithm (SPA) was used to extract spectral features, and partial least squares regression (PLSR) models of soil TN content were established. The research results show that compared with the traditional integer derivative, FOD has a tremendous advantage in balancing spectral information and noise and can provide more abundant characteristic variables, which helps establish a more robust estimation model. In the range of orders 0–2, the model established by the 1.8-order is the best. Under that circumstance, the determination coefficients of validation (R²v) is 0.649, and the ratio of the performance to deviation (RPD) is 1.72. Combined with FOD, it is feasible and practical to establish an accurate and rapid estimation model of soil TN content, which can provide an important reference for large-scale detection of soil TN content in rubber plantations.

One of the basic requirements of an automated parking system is to quickly and accurately detect parking spaces. With the development of deep convolutional neural networks, parking space detection systems are becoming increasingly mature. However, the existing detection systems are often too complicated, resulting in a long detection time. In order to solve this problem, this paper proposes a two-stage parking space detection system, which aims to combine the advantages of deep learning and traditional computer vision and treat parking space detection as a two-stage process. First, we take the vehicle’s circumnavigation image as input and extract the target box of the parking space key points from the global image through the YoloV11 network. Then, using the special convolution kernel proposed in this paper, the local image is convolved to obtain the direction information of the parking space. Finally, the correct parking space is deduced based on its characteristic parameters. The method in this paper achieved a detection accuracy of 98.24% on the public data set ps2.0 (Parking-slot 2.0), and the parking space inference time on a single image was only 12.3 ms. Compared with the existing methods, the two-stage parking space detection system proposed in this paper not only demonstrates higher robustness, but also significantly reduces the time required for parking space inference.