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A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators' system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.

A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be achieved when a branch is pruned. Our experiments show that the proposed method can significantly reduce ensemble size and improve ensemble accuracy, no matter whether ensembles are constructed by a certain algorithm such as bagging or obtained by an ensemble selection algorithm, no matter whether each decision tree is pruned or unpruned.

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Smoking in public places not only brings about some safety hazards, but also does harm to people’s lives, property and living environment. A smoking behavior detection model based on deep learning is trained for the concern of environment and safety. First, a vertical rotation data enhancement method is adopted in the preprocessing stage to extend the dataset and increase the objects of detection. Then, the channel attention module is introduced in backbone network to calibrate the feature response. Finally, added a small target detection layer to the YOLOv5 algorithm. This paper analyzes the network structure of the YOLOv5s, and the model is trained and tested by utilizing the YOLOv5s network. Experimental results show that the mAP value of the algorithm is improved by 5.3% over the original algorithm.