Facial expressions are indispensable in human cognitive behaviors since it can instantly reveal human emotions. Therefore, in this study, Multiple Convolutional Neural Networks using Improved Fuzzy Integral (MCNNs-IFI) were proposed for recognizing facial emotions. Since effective facial expression features are difficult to design; deep learning CNN is used in the study. Each CNN has its own advantages and disadvantages, thus combining multiple CNNs can yield superior results. Moreover, multiple CNNs combined with improved fuzzy integral, in which its fuzzy density value is optimized through particle swarm optimization (PSO), overcomes the majority decision drawback in the traditional voting method. Two Multi-PIE and CK+ databases and three main CNN structures, namely AlexNet, GoogLeNet, and LeNet, were used in the experiments. To verify the results, a cross-validation method was used, and experimental results indicated that the proposed MCNNs-IFI exhibited 12.84% higher accuracy than that of the three CNNs.

Cancer is the leading cause of death worldwide. Lung cancer, especially, caused the most death in 2018 according to the World Health Organization. Early diagnosis and treatment can considerably reduce mortality. To provide an efficient diagnosis, deep learning is overtaking conventional machine learning techniques and is increasingly being used in computer-aided design systems. However, a sparse medical data set and network parameter tuning process cause network training difficulty and cost longer experimental time. In the present study, the generative adversarial network was proposed to generate computed tomography images of lung tumors for alleviating the problem of sparse data. Furthermore, a parameter optimization method was proposed not only to improve the accuracy of lung tumor classification, but also reduce the experimental time. The experimental results revealed that the average accuracy can reach 99.86% after image augmentation and parameter optimization.

Various optimization methods and network architectures are used by convolutional neural networks (CNNs). Each optimization method and network architecture style have their own advantages and representation abilities. To make the most of these advantages, evolutionary-fuzzy-integral-based convolutional neural networks (EFI-CNNs) are proposed in this paper. The proposed EFI-CNNs were verified by way of face classification of age and gender. The trained CNNs’ outputs were set as inputs of a fuzzy integral. The classification results were operated using either Sugeno or Choquet output rules. The conventional fuzzy density values of the fuzzy integral were decided by heuristic experiments. In this paper, particle swarm optimization (PSO) was used to adaptively find optimal fuzzy density values. To combine the advantages of each CNN type, the evaluation of each CNN type in EFI-CNNs is necessary. Three CNN structures, AlexNet, very deep convolutional neural network (VGG16), and GoogLeNet, and three databases, computational intelligence application laboratory (CIA), Morph, and cross-age celebrity dataset (CACD2000), were used in experiments to classify age and gender. The experimental results show that the proposed method achieved 5.95% and 3.1% higher accuracy, respectively, in classifying age and gender.

In industrial processing, workpiece quality and processing time have recently become important issues. To improve the machining accuracy and reduce the cutting time, the cutting feed rate will have a significant impact. Therefore, how to plan a dynamic cutting feed rate is very important. In this study, a fuzzy control system for feed rate scheduling based on the curvature and curvature variation is proposed. The proposed system is implemented in actual cutting, and to verify the data an optical three-dimensional scanner is used to measure the cutting trajectory of the workpiece. Experimental results prove that the proposed fuzzy control system for dynamic cutting feed rate scheduling increases the cutting accuracy by 41.8% under the same cutting time; moreover, it decreases the cutting time by 50.8% under approximately the same cutting accuracy.

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.