Pine wilt disease is a highly contagious forest quarantine ailment that spreads rapidly. In this study, we designed a new Pine-YOLO model for pine wilt disease detection by incorporating Dynamic Snake Convolution (DSConv), the Multidimensional Collaborative Attention Mechanism (MCA), and Wise-IoU v3 (WIoUv3) into a YOLOv8 network. Firstly, we collected UAV images from Beihai Forest and Linhai Park in Weihai City to construct a dataset via a sliding window method. Then, we used this dataset to train and test Pine-YOLO. We found that DSConv adaptively focuses on fragile and curved local features and then enhances the perception of delicate tubular structures in discolored pine branches. MCA strengthens the attention to the specific features of pine trees, helps to enhance the representational capability, and improves the generalization to diseased pine tree recognition in variable natural environments. The bounding box loss function has been optimized to WIoUv3, thereby improving the overall recognition accuracy and robustness of the model. The experimental results reveal that our Pine-YOLO model achieved the following values across various evaluation metrics: MAP@0.5 at 90.69%, mAP@0.5:0.95 at 49.72%, precision at 91.31%, recall at 85.72%, and F1-score at 88.43%. These outcomes underscore the high effectiveness of our model. Therefore, our newly designed Pine-YOLO perfectly addresses the disadvantages of the original YOLO network, which helps to maintain the health and stability of the ecological environment.

Zhurong rover successfully landed on the southern of Utopia Planet of Mars on 15 May 2021. One laser-induced breakdown spectroscopy (LIBS) system, the main payload of the Mars Surface Composition Detector (MarSCoDe), was installed on the Zhurong rover aimed to measure the elements and their abundance in Martian regolith. Now, there are three sets of LIBS system (ChemCam, SuperCam and MarSCoDe) working on Mars at difference landing sites with diverse geologic features. For Mars exploration, cross-validation is necessary to expand the model compatibility, test data validity, and get more available data of the same type payloads. Spectral transformation approach is the first step and crucial for cross-validation of LIBS analysis model. Herein, a new 4-step spectral transformation approach was proposed to transform the LIBS spectra between three different LIBS systems (i.e., ChemCam, MarSCoDe, SDU-LIBS (recorded by self-built LIBS system)), whose data were partly different in spectral characteristics. Based on this approach, SDU-LIBS and MarSCoDe spectra data were transformed into ChemCam uniform and then the three kinds of LIBS data can have more similar spectral features and share one PLS (partial least squares) model for quantitative analysis. Our approach enables to make up the signal differences between different LIBS systems and gets acceptable quantitative analysis results of SDU-LIBS and MarSCoDe spectra using quantitative PLS model built by ChemCam calibration sample set. This work verified feasibility and availability of our approach for cross validation of different LIBS systems. Based on this method, MarSCoDe data were analyzed and got the preliminary satisfying results although no analysis model of laboratory replica payload was available under the existing conditions.

PID controller is used in most of the current-speed closed-loop control of permanent magnet synchronous motors (PMSM) servo system. However,Kp,Ki, andKdof PID are difficult to tune due to the multiple objectives. In order to obtain the optimal PID parameters, we adopt a NSGA-II to optimize the PID parameters in this paper. According to the practical requirement, several objective functions are defined. NSGA-II can search the optimal parameters according to the objective functions with better robustness. This approach provides a more theoretical basis for the optimization of PID parameters than the aggregation function method. The simulation results indicate that the system is valid, and the NSGA-II can obtain the Pareto front of PID parameters.