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The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale cooccurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%. [ABSTRACT FROM AUTHOR]

How to learn and identify new categories from a small number of training samples is a challenging problem for deep neural networks. For how to solve the problem of few-shot learning, this paper comprehensively summarized the existing few-shot learning methods based on deep neural networks, which covered various aspects such as models used in the methods, datasets and evaluation results. Specifically, for the few-shot learning method based on deep neural network, this paper divided it into four categories, named data enhancement method, migration learning method, metric learning method and meta-learning method. For each category, this paper further divided it into sub-categories and conducted a series of comparisons between each category and method to show the pros and cons of the various methods and their respective characteristics. Finally, the paper highlighted the limitations of existing methods and pointed to future research directions in the field of few-shot learning. [ABSTRACT FROM AUTHOR]

Traditional detection methods are not sensitive to small-sized tomato organs (flowers and fruits), because the immature green tomatoes are highly similar to the background color. The overlap among fruits and the occlusion of stems and leaves on tomato organs can lead to false and missing detection, which decreases the accuracy and generalization ability of the model. Therefore, a tomato organ recognition method based on improved Feature Pyramid Network was proposed in this paper. To begin with, multi-scale feature fusion was used to fuse the detailed bottom features and high-level semantic features to detect small-sized tomato organs to improve recognition rate. And then repulsion loss was used to take place of the original smooth L1 loss function. Besides, Soft-NMS (Soft non-maximum suppression) was adopted to replace non-maximum suppression to screen the bounding boxes of tomato organs to construct a recognition model of tomato key organ. Finally, the network was trained and verified on the collected image data set. The results showed that compared with the traditional Faster R-CNN model, the performance was greatly improved (mean average precision was improved from 90.7 to 99.5%). Subsequently, the training model can be compressed so that it can be embedded into the microcontroller to develop further precise pesticide targeting application system of tomato organs and the automatic picking device. [ABSTRACT FROM AUTHOR]

• A hybrid SIFC method is proposed to determine the unknown state or new load. • The isolated forest method is introduced to improve the judgment of unknown loads. • A shared parameter migration method is proposed to realize model self-updating. With the development of smart grid technology, optimization of energy structure, improvement of power efficiency and reduction of power consumption have become the major development trends. As a key process of power structure analysis, load identification is gaining increasing attention in smart grids. Although lots of load identification methods have been proposed, the introduction of unknown states or new load types is still a challenge for precise identification. In this paper, a similarity calculation method of the space convex hull overlap rate is proposed to deal with the identification of unknown loads based on the low-dimensional feature space model of Siamese neural networks. Moreover, transfer learning is introduced to realize the category-added learning of load pre-identification model for unknown load. The ultimate aim was to establish the identification and rapid modeling of unknown loads. The public datasets Plaid is used to verify the performance of the proposed method. As a result, the high accuracy of load identification for unknown load is proved. [ABSTRACT FROM AUTHOR]

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomatoes and the plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rates and poor generalizations of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network (CNN) has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and a K-means clustering method was used to adjust more appropriate anchor sizes than manual setting, to improve detection accuracy. The test results showed that the mean average precision (mAP) was significantly improved compared with the traditional Faster R-CNN model. The training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of a precise targeting pesticide application system and an automatic picking device. [ABSTRACT FROM AUTHOR]