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Ensemble learning is to employ multiple individual classifiers and combine their predictions, which could achieve better performance than a single classifier. Considering that different base classifier gives different contribution to the final classification result, this paper assigns greater weights to the classifiers with better performance and proposes a weighted voting approach based on differential evolution. After optimizing the weights of the base classifiers by differential evolution, the proposed method combines the results of each classifier according to the weighted voting combination rule. Experimental results show that the proposed method not only improves the classification accuracy, but also has a strong generalization ability and universality.

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Automatic detection of steel surface defects is very important for product quality control in the steel industry. However, the traditional method cannot be well applied in the production line, because of its low accuracy and slow running speed. The current, popular algorithm (based on deep learning) also has the problem of low accuracy, and there is still a lot of room for improvement. This paper proposes a method combining improved ResNet50 and enhanced faster region convolutional neural networks (faster R-CNN) to reduce the average running time and improve the accuracy. Firstly, the image input into the improved ResNet50 model, which add the deformable revolution network (DCN) and improved cutout to classify the sample with defects and without defects. If the probability of having a defect is less than 0.3, the algorithm directly outputs the sample without defects. Otherwise, the samples are further input into the improved faster R-CNN, which adds spatial pyramid pooling (SPP), enhanced feature pyramid networks (FPN), and matrix NMS. The final output is the location and classification of the defect in the sample or without defect in the sample. By analyzing the data set obtained in the real factory environment, the accuracy of this method can reach 98.2%. At the same time, the average running time is faster than other models.

Nomenclature f i = lift of ith rotor, N g = acceleration of gravity, kg · m∕s 2 h = altitude of helicopter, m J x , J y , J z = moment of inertia around roll, pitch, and yaw axes of helicopter frame, kg · m 2 K i = maximum lift of ith rotor, N k μ = ratio between reactive torque and lift of rotors L, M, N = airframe roll, pitch, and yaw torque of helicopter, N · m m = number of rotors m a = mass of helicopter, kg p, q, r = roll, pitch, and yaw angular velocities of helicopter, rad∕s r i = distance from center of ith rotor to center of mass, m T = total thrust of helicopter, N v h = vertical velocity of helicopter, m∕s η i = efficiency parameter of ith rotor ϕ, θ, ψ = roll, pitch, and yaw angles of helicopter, rad