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Tropical cyclone (TC) motion has an important impact on both human lives and infrastructure. Predicting TC intensity is crucial, especially within the 24 h warning time. TC intensity change prediction can be regarded as a problem of both regression and classification. Statistical forecasting methods based on empirical relationships and traditional numerical prediction methods based on dynamical equations still have difficulty in accurately predicting TC intensity. In this study, a prediction algorithm for TC intensity changes based on deep learning is proposed by exploring the joint spatial features of three-dimensional (3D) environmental conditions that contain the basic variables of the atmosphere and ocean. These features can also be interpreted as fused characteristics of the distributions and interactions of these 3D environmental variables. We adopt a 3D convolutional neural network (3D-CNN) for learning the implicit correlations between the spatial distribution features and TC intensity changes. Image processing technology is also used to enhance the data from a small number of TC samples to generate the training set. Considering the instantaneous 3D status of a TC, we extract deep hybrid features from TC image patterns to predict 24 h intensity changes. Compared to previous studies, the experimental results show that the mean absolute error (MAE) of TC intensity change predictions and the accuracy of the classification as either intensifying or weakening are both significantly improved. The results of combining features of high and low spatial layers confirm that considering the distributions and interactions of 3D environmental variables is conducive to predicting TC intensity changes, thus providing insight into the process of TC evolution.

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With the rapid development of on-orbit services and space situational awareness, there is an urgent demand for multisatellite flyby inspection (MSFI) that can obtain information about a large number of space targets with little fuel consumption in a short time. There are two kinds of constraints, namely inspection constraints (ICs) at each flyby point and transfer process constraints (TPCs) in the actual mission. Further considering the influence of discrete and continuous variables such as inspection sequence, time, and maneuver scheme, it is complex and difficult to solve MSFI. To optimize it efficiently, the task flow and the problem model are defined firstly. Then, the algorithm framework based on constraint repairing is given, which contains repair methods of the ICs and the TPCs. Finally, the proposed method is compared with the nonrepair optimization method in two numerical examples. The results indicate that when the constraints are hard to meet, it is better and more efficient than the nonrepair method.

The detection of wheel sinkage has great significance for rover mobility optimization control and prevention of wheel sinking. A new wheel sinkage detection method is proposed based on planetary rovers' wheel-soil boundary. The model of wheel sinkage calculation is built. The machine vision method is proposed to extract wheel-soil boundary. Wheel-soil interaction image is processed into binary image, and the wheel-soil boundary is extracted according to its morphological features. The wheel sinkage depth, entrance angle and departure angle are calculated through the model of wheel sinkage calculation. The method's applicability has been validated by experiments under various terrain conditions, which are flat terrain, raised terrain, hollow terrain, and rough terrain. Accuracy tests are done with the flat terrain, the results of the experiments indicate that the relative errors of the sinkage depth are around 10% and the relative errors of terrain interface angles are around 5%, when the actual sinkage depth is above 5mm.