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This article is concerned with the trajectory optimization problem of a drilling process, which is of significance to drilling efficiency and safety. Due to the difference between an actual trajectory and a planned trajectory, this problem is a multiobjective optimization problem (MOP) with parameter uncertainties. To solve the problem, this study devises a new approach named nondominated sorting genetic Algorithm II using outlier removal (OR-NSGA-II). First, the optimization problem with parameter uncertainties is formulated, including two objective functions: 1) a trajectory length and 2) an expected value of drill-string torque. Then, an outlier-removal mechanism is devised in the sorting process of NSGA-II to reduce the negative effects of parameter uncertainties. Next, the crowding distance calculation in NSGA-II is improved to ensure population diversity. Comparison of simulation results show that our method is effective on the MOP with parameter uncertainties. [ABSTRACT FROM AUTHOR]

The weight-adapted convolution neural network (WACNN) is proposed to extract discriminative expression representations for recognizing facial expression. It aims to make good use of the convolution neural network’s (CNN’s) potential performance in avoiding local optima and speeding up convergence by the hybrid genetic algorithm (HGA) with optimal initial population, in such a way that it realizes deep and global emotion understanding in human–robot interaction. Moreover, the idea of novelty search is introduced to solve the deception problem in the HGA, which can expend the search space to help genetic algorithm jump out of local optimum and optimize large-scale parameters. In the proposal, the facial expression image preprocessing is conducted first, then the low-level expression features are extracted by using a principal component analysis. Finally, the high-level expression semantic features are extracted and recognized by WACNN which is optimized by HGA. In order to evaluate the effectiveness of WACNN, experiments on JAFFE, CK+, and static facial expressions in the wild 2.0 databases are carried out by using ${k}$ -fold cross validation, and experimental results show the recognition accuracies of the proposal are superior to that of the state-of-the-art, such as local directional ternary pattern and weighted mixture deep neural network (DNN), which aim to extract discriminative and are the DNN-based methods. Moreover, recognition accuracies of the proposal are also higher than the deep CNN without HGA, which indicates that the proposal has better global optimization ability. Meanwhile, preliminary application experiments are also carried out by using the proposed algorithm on the emotional social robot system, where nine volunteers and two-wheeled robots experience the scenario of emotion understanding. Application results indicate that the wheeled robots can recognize basic expressions, such as happy, surprise, and so on. [ABSTRACT FROM AUTHOR]

Accurate prediction of rate of penetration is a prerequisite for optimization of drilling parameters. However, characteristics such as multiple drilling conditions, inconsistency in data length and various drilling variables can lead to inaccurate prediction of rate of penetration in actual drilling process. Therefore, it is important to use appropriate methods to predict rate of penetration for the above characteristics. This paper proposes an online prediction method of rate of penetration based on drilling conditions identification. First, data preprocessing and correlation analysis are employed to handle various drilling variables to obtain optimal model inputs. Then, dynamic time warping method is used to solve inconsistency in data length. Further, for multiple drilling conditions, the fuzzy c-means and dynamic time warping are combined to identify drilling conditions. Next, different extreme learning machine models optimized by genetic algorithm are built to predict rate of penetration for different drilling conditions. Finally, the results involving actual data illustrate that the prediction error of the proposed hybrid method of fuzzy c-means, dynamic time warping, extreme learning machine and genetic algorithm meets the requirements. The proposed hybrid method outperforms the comparative methods in four performance metrics, which verify the rationality and necessity of considering drilling conditions when predicting rate of penetration. [ABSTRACT FROM AUTHOR]