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Skeleton-based hand gesture recognition is an active research topic in computer graphics and computer vision and has a wide range of applications in VR/AR and robotics. Although the spatial–temporal graph convolutional network has been successfully used in skeleton-based hand gesture recognition, these works often use a fixed spatial graph according to the hand skeleton tree or use a fixed graph on the temporal dimension, which may not be optimal for hand gesture recognition. In this paper, we propose a two-stream graph attention convolutional network with spatial–temporal attention for hand gesture recognition. We adopt pose stream and motion stream as the two input streams for our network. In pose stream, we use the joint in each frame as the input; In motion stream, we use the joint offsets between neighboring frames as the input. We propose a new temporal graph attention module to model the temporal dependency and also use a spatial graph attention module to construct dynamic skeleton graph. For each stream, we adopt graph convolutional network with spatial–temporal attention to extract the features. Then, we concatenate the feature of the pose stream and motion stream for gesture recognition. We achieve the competitive performance on the main hand gesture recognition benchmark datasets, which demonstrates the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Manufacturers of printed circuit boards (PCBs) typically use automated optical inspection (AOI) machines to test their PCBs. However, AOI machines employ conventional image-processing methods. If the integrated circuit (IC) components are not identical to the golden samples, then the AOI machine registers those IC components as flaws. Conventional image-processing methods cause misjudgments and increase the cost of manual reviews. Character-verification and image-classification systems are proposed in this paper for detecting misplaced, missing, and reversed-polarity parts. The regions of IC components can be identified on PCBs by using the contour border-detection method. Through the proposed convolutional neural network (CNN) structure and refinement mechanism, the characters can be successfully recognized. The image-classification system was applied only to images with blurry characters. Different CNN learning structures were used in both systems, and the refinement mechanism was used in both systems to improve the results. The proposed character-verification and image-classification methods achieved 98.84% and 99.48% passing rates, and the amount of required training time was less than that of other methods, demonstrating the proposed methods' greater effectiveness. [ABSTRACT FROM AUTHOR]

When photographs are being taken in an outdoor environment, the medium in air will cause light attenuation and further reduce image quality, and this impact is especially obvious in a hazy environment. Reduction of image quality results in the loss of information, which renders an image recognition system unable to identify objects in the image. In order to eliminate the hazy effect on images and improve the visual quality, this paper presents an efficient method combining the fuzzy inference system and the neural network filter to solve image dehazing. During dehazing, the fuzzy inference system is adopted to estimate the variations in light attenuation, and the erosion of morphological operation and the neural network filter are used to eliminate the halation and achieve optimization in transmission map refinement. Finally, the brightest 1% of the atmospheric light is utilized to calculate the color vector of atmospheric light to eliminate color cast. Experimental results indicate that the proposed method is superior to other dehazing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy cerebellar model articulation controller using a strategy-adaptation-based bacterial foraging optimization (SABFO) algorithm to solve classification problems. A strategic approach to the chemotaxis step in the SABFO algorithm was adopted: in this approach, each virtual bacterium swims on different run-lengths, and bacterial diversity is increased. The simulation results indicated that the performance of the proposed method was more favorable than that of other methods. [ABSTRACT FROM AUTHOR]

This study presents a new evolutionary learning algorithm to optimize the parameters of the neural fuzzy classifier (NFC). This new evolutionary learning algorithm is based on a hybrid of bacterial foraging optimization and particle swarm optimization. It is thus called bacterial foraging particle swarm optimization (BFPSO). The proposed BFPSO method performs local search through the chemotactic movement operation of bacterial foraging whereas the global search over the entire search space is accomplished by a particle swarm operator. The NFC model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. Finally, the proposed neural fuzzy classifier with bacterial foraging particle swarm optimization (NFC-BFPSO) is adopted in several classification applications. Experimental results have demonstrated that the proposed NFC-BFPSO method can outperform other methods. [ABSTRACT FROM AUTHOR]

By applying the recurrent functional neural fuzzy network (RFNFN) and a novel evolutionary learning algorithm this study presents an evolutionary neural fuzzy network (NFN). The proposed new evolutionary learning algorithm is based on an effective combination of the modified differential evolution (MDE) and cultural algorithm, which is called the cultural-based MDE (CMDE) method. After the four individuals are randomly chosen from the population for mutation, the proposed CMDE method can search the whole solution space more efficiently. In addition, during the evolutionary process of the cultural algorithm, the performance enhancement is highly affected by the belief space extraction and use of the corresponding information. The proposed RFNFN model uses functional-link neural networks (FLNN) as the consequent part of the fuzzy rules, while in this study the orthogonal polynomials and linearly independent functions are adapted as in a functional expansion of the FLNNs. In summary, the RFNFN model is not only able to generate the consequent part as a nonlinear combination of input variables, but also capable of tackling complicated temporal problem through the added feedback connections. Experimental results in prediction and control applications have shown that the proposed RFNFN-CMDE mechanism outperforms other compared approaches, in terms of convergent speed and root-mean-squared error. [ABSTRACT FROM AUTHOR]

This paper presents multiple functional neural fuzzy networks (FNFN) fusion using fuzzy integral (FI). Since the classifiers are able to complement each other, the fusion of multiple classifiers overcomes the limitations of applying a single classifier. In addition, the FI is a better decision-combination scheme than the majority voting method that uses the subjectively defined relevance of classifiers. A combination of multiple FNFN classifiers with FI is proposed to achieve data classification with higher accuracy than existing traditional methods. The advantage of the proposed method is that not only are the classification results combined but the relative importance of the different networks is also considered. Computer simulations for the Iris, Wisconsin breast cancer, and wine classifications show that the fusion of multiple FNFNs using FI can perform better than existing traditional methods. [ABSTRACT FROM AUTHOR]

This study presents an evolutionary neural fuzzy system (NFS) for nonlinear system control. The proposed NFS model uses functional link neural networks (FLNNs) as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. A learning algorithm, which consists of structure learning and parameter learning, is presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the particle swarm optimization (PSO) algorithm, can adjust the shape of the membership function and the corresponding weighting of the FLNN. The distance-based mutation operator, which strongly encourages a global search giving the particles more chance of converging to the global optimum, is introduced. The simulation results have shown the proposed method can improve the searching ability and is very suitable for the nonlinear system control applications. [ABSTRACT FROM AUTHOR]

In this paper, a recurrent functional neural fuzzy network (RFNFN) with symbiotic particle swarm optimization (SPSO) is proposed for solving identification and prediction problems. The proposed RFNFN model has feedback connections added in the membership function layer that can solve temporal problems. Moreover, an efficient learning algorithm, called symbiotic particle swarm optimization (SPSO), combined symbiotic evolution and modified particle swarm optimization for tuning parameters of the RFNFN. Simulation results show that the converging speed and root mean square error (RMS) of the proposed method has a better performance than those of other methods. [ABSTRACT FROM AUTHOR]