In addition to being sensitive to humidity, humidity sensors with moisture sensitive elements are also sensitive to ambient temperature. The fusion of temperature and humidity data is an effective way to improve the accuracy of humidity sensors. In view of the problem of insufficient adaptive ability and poor universality in the current compensation algorithm, a piecewise processing of measured error at different temperatures by using multiple linear regression is proposed in this paper. The least squares method and back propagation (BP) neural network improved by a genetic simulated annealing algorithm (GSA-BP) were used to compensate the measured humidity data of different temperature ranges. The efficiency of the GSA-BP algorithm was tested, and the compensation function model was established. The compensation accuracy was also compared with the accuracies obtained by other methods. The experimental results show that the adaptive segmentation compensation method can significantly improve the measured error of the humidity sensor over a wide temperature range.

The measurement of the atmospheric electric field is of great significance for the study of thunderstorm cloud charge models. Traditional electric field meters can only measure the vertical component of the atmospheric electric field, and thus it is difficult to invert the structure of the thunderstorm cloud. A three-dimensional atmospheric electric field meter was developed to simultaneously measure the horizontal and vertical components of the atmospheric electric field in this paper. The effective measurement linearly relates the measured electric field to the induced voltage, and the nonlinear equations of the three-dimensional atmospheric electric field and the thunderstorm cloud-charging model parameters were derived. The particle swarm optimization algorithm (PSO) and the three-dimensional atmospheric electric field were used to invert the thunderstorm clouds. Experimental observations of the three-dimensional electric field in a cloud during a thunderstorm were analyzed. Combined with the typical charged structure model, parameters such as the charge and relative distance of the thunderstorm cloud were determined. The results showed that the value of the inversion fitness function reached 0.7288, and the charge structure was even. The measurement of the three-dimensional atmospheric electric field provides a new means of observation for the study of atmospheric electricity.

In this paper, we consider a particular class of optimal switching problem for the linear-quadratic switched system in discrete time, where an optimal switching sequence is designed to minimize the quadratic performance index of the system with a switching cost. This is a challenging issue and studied only by few papers. First, we introduce a total variation function with respect to the switching sequence to measure the volatile switching action. In order to restrain the switching magnitude, it is added to the cost functional as a penalty. Then, the particular optimal switching problem is formulated. With the positive semi-definiteness of matrices, we construct a series of exact lower bounds of the cost functional at each time and the branch and bound method is applied to search all global optimal solutions. For the comparison between different global optimization methods, some numerical examples are given to show the efficiency of our proposed method.

Ground-based remote sensing cloud image data can be used to analyze regional cloud type variation trends, thereby predicting future water resource supply capacity. However, existing cloud classification methods based on ground-based remote sensing imagery often suffer from limited recognition accuracy due to insufficient fine-grained feature extraction, and their large model parameter counts hinder deployment on embedded terminals. To address these issues, this study proposes CloudMViT, a lightweight hybrid network architecture fusing a dual-pooling channel attention module and cross-scale self-attention, which enhances both local and global feature representation of cloud images while optimizing computational efficiency. Specifically, the model suppresses sky background interference and strengthens cloud edge features via the dual-pooling channel attention module that combines global average pooling (GAP) and global max pooling (GMP); captures cross-channel detailed features (e.g., cirrus fibril structures and stratocumulus shadows) using depthwise separable convolution and a decoupling mechanism; and further reduces model parameters by introducing CloudGhost cascade compression technology through linear feature redundancy elimination.Experiments on the World Meteorological Organization (WMO)-compliant HBMCD (10 standard cloud genera) and GCD (7 sky conditions) datasets demonstrate that CloudMViT achieves classification accuracies of 98.81 % and 95.13 %, respectively, significantly outperforming lightweight models such as MobileViT and EfficientNet. Ablation experiments validate the effectiveness of the dual-pooling channel attention module (improving accuracy by 5.31 %) and the CloudGhost module (increasing inference speed by 50 %). When deployed on the RK3588 embedded platform, the INT8-quantized CloudMViT enables real-time inference, maintaining an accuracy of 94.79 % with only 0.47 MB of memory occupation. The proposed cloud classification method and hardware acceleration strategy provide a feasible solution for the development of portable ground-based cloud observation and classification devices.

Ground-based remote sensing cloud image data can be used to analyze regional cloud type variation trends, thereby predicting future water resource supply capacity. However, existing cloud classification methods based on ground-based remote sensing imagery often suffer from limited recognition accuracy due to insufficient fine-grained feature extraction, and their large model parameter counts hinder deployment on embedded terminals. To address these issues, this study proposes CloudMViT, a lightweight hybrid network architecture fusing a dual-pooling channel attention module and cross-scale self-attention, which enhances both local and global feature representation of cloud images while optimizing computational efficiency. Specifically, the model suppresses sky background interference and strengthens cloud edge features via the dual-pooling channel attention module that combines global average pooling (GAP) and global max pooling (GMP); captures cross-channel detailed features (e.g., cirrus fibril structures and stratocumulus shadows) using depthwise separable convolution and a decoupling mechanism; and further reduces model parameters by introducing CloudGhost cascade compression technology through linear feature redundancy elimination.Experiments on the World Meteorological Organization (WMO)-compliant HBMCD (10 standard cloud genera) and GCD (7 sky conditions) datasets demonstrate that CloudMViT achieves classification accuracies of 98.81 % and 95.13 %, respectively, significantly outperforming lightweight models such as MobileViT and EfficientNet. Ablation experiments validate the effectiveness of the dual-pooling channel attention module (improving accuracy by 5.31 %) and the CloudGhost module (increasing inference speed by 50 %). When deployed on the RK3588 embedded platform, the INT8-quantized CloudMViT enables real-time inference, maintaining an accuracy of 94.79 % with only 0.47 MB of memory occupation. The proposed cloud classification method and hardware acceleration strategy provide a feasible solution for the development of portable ground-based cloud observation and classification devices.

Optimal switching is a special class of optimal control problems for hybrid dynamic systems. In this paper, we consider the optimal switching problem of linear-quadratic switched systems. The aim is to design a suitable switching strategy with the constraint on the number of switchings so that the quadratical performance achieves the minimum value. This problem is difficult to be solved because of the tight coupling between the continuous switching time and the discrete switching sequence. In our method, we first divide this hybrid optimization problem into two subproblems. In each of them, only one type of variable is considered. Then, we develop a gradient-based method with the time-scaling transformation to process the optimal switching time problem and a branch and bound method based on a series of exact lower bounds to handle the optimal switching sequence problem, respectively. By solving these two subproblems alternatively, the optimal switching strategy satisfying the constraint on the number of switchings can be obtained. Numerical examples are given to demonstrate the efficiency of the proposed method.