Feature selection, as one of the essential dimensionality reduction techniques, hasbecome one popular yet challenging area in the field, such as data mining andmachine learning. Unlike feature extraction (such as principle component analysis andnon-negative matrix factorization), preserving the entire information but losing thefeature relevance. Data processing in feature selection will lose information, leading tothe need to develop lightweight, efficient, and practical methods that preserve the datainformation as much as possible while performing dimensional reduction. In this paper,we propose a rankability-based feature selection method. The rankability concept wasproposed in 2019, similar to the entropy concept, and has not been studied widely yet.The proposed method is lightweight in terms of complexity, which requires no iterativeoptimization or auxiliary estimator tools.We experimented with sixteen datasets and compared our results with four otheralgorithms. The results show that our rankability-based feature selection methodoutperforms the fuzzy entropy-based method on five datasets in eight, and the averageaccuracy increased by 0.1482, 0.1078, and 0.1157, respectively. Then, in the varieddimension-reducing experiments, the proposed method shows superiority on fourdatasets out of eight and is competitive with others on two datasets out of eight.

Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly balances the abilities of exploration and exploitation. It utilizes the grey wolf optimizer (GWO) to further search the optimal solutions acquired by equilibrium optimizer (EO) and does not add extra evaluation of objective function. The short-term prediction of traffic flow has the characteristics of high non-linearity and uncertainty and has a strong correlation with time. This paper adopts the structure of LSTM and EO-GWO to implement the prediction, and the hyper parameters of the LSTM are optimized by EO-GWO to transcend the problems of backpropagation. Experiments show that the algorithm has achieved wonderful results in the accuracy and computation time of the three prediction models in the highway intersection.

While it is risky considering spacecraft constraints and unknown environment on asteroid, surface sampling is an important technique for asteroid exploration. One of the sample return missions is to seek an optimal landing site, which may be in hazardous terrain. Since autonomous landing is particularly challenging, it is necessary to simulate the effectiveness of this process and prove the onboard optical hazard avoidance is robust to various uncertainties. This paper aims to generate realistic surface images of asteroids for simulations of asteroid exploration. A SinGAN-based method is proposed, which only needs a single input image for training a pyramid of multi-scale patch generators. Various images with high fidelity can be generated, and manipulations such as shape variation, illumination direction variation, super resolution generation are well achieved. The method's applicability is validated by extensive experimental results and evaluations. At last, the proposed method has been used to help set up a test environment for landing site selection simulation.