In some specific conditions, UAVs are required to obtain comprehensive information of an area or to operate in the area in an all-round way. In this case, the coverage path planning (CPP) is required. This paper proposes a solution to solve the problem of short endurance time in the coverage path planning (CPP) problem of multi-solar unmanned aerial vehicles (UAVs). Firstly, the energy flow efficiency based on the energy model is proposed to evaluate the energy utilization efficiency during the operation. Moreover, for the areas with and without obstacles, the coverage path optimization model is proposed based on the undirected graph search method. The constraint equation is defined to restrict the UAV from accessing the undirected graph according to certain rules. A mixed integer linear programming (MILP) model is proposed to determine the flight path of each UAV with the objective of minimizing operation time. Through the simulation experiment, compared with the Boustrophedon Cellular Decomposition method for coverage path planning, it is seen that the completion time is greatly improved. In addition, considering the impact of the attitude angle of the solar powered UAV when turning, the operation time and the total energy flow efficiency are defined as the optimization objective. The bi-objective model equation is established to solve the problem of the CPP. A large number of simulation experiments show that the optimization model in this paper selects different optimization objectives and applies to different shapes of areas to be covered, which has wide applicability and strong feasibility.

To study the dynamic changes of nutrient consumption and aflatoxin B₁ (AFB₁) accumulation in peanut kernels with fungal colonization, macro hyperspectral imaging technology combined with microscopic imaging was investigated. First, regression models to predict AFB₁ contents from hyperspectral data ranging from 1000 to 2500 nm were developed and the results were compared before and after data normalization with Box-Cox transformation. The results indicated that the second-order derivative with a support vector regression (SVR) model using competitive adaptive reweighted sampling (CARS) achieved the best performance, with R_C² = 0.95 and R_V² = 0.93. Second, time-lapse microscopic images and spectroscopic data were captured and analyzed with scanning electron microscopy (SEM), transmission electron microscopy (TEM), and synchrotron radiation-Fourier transform infrared (SR-FTIR) microspectroscopy. The time-lapse data revealed the temporal patterns of nutrient loss and aflatoxin accumulation in peanut kernels. The combination of macro and micro imaging technologies proved to be an effective way to detect the interaction mechanism of toxigenic fungus infecting peanuts and to predict the accumulation of AFB₁ quantitatively.

Aiming at solving the control problem caused by the large-scale change of the Hypersonic Glide Vehicle (HGV) parameters, this paper proposes a design method of backstepping variable structure attitude controller based on Nonlinear Extended State Observer (NESO), with the characteristics of HGV model and the idea of uncertainty estimation and compensation associated. Firstly, the design of the second-order NESO is studied. Due to the large number of NESO parameters, a systematic method for determining the second-order NESO parameters is given in this paper, and the stability of the observer is proved completely using the piecewise Lyapunov analysis. Then, the NESO-assisted backstepping variable structure attitude controller employs the reduced-order modeling idea to decompose the whole system design problem into two first-order subsystem design problem, and classifies the nonlinear dynamic changes caused by the large-scale changes of the aircraft parameters into the aggregated uncertain terms of the two subsystems. The simulation results show that the backstepping attitude controller based on NESO can realize the stable and accurate tracking of the flight attitude when the aircraft parameters change in a large range.