Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.

Detection of pulmonary nodules is an important aspect of an automatic detection system. Incomputer-aided diagnosis (CAD) systems, the ability to detect pulmonary nodules is highly important, which plays an important role in the diagnosis and early treatment of lung cancer. Currently, the detection of pulmonary nodules depends mainly on doctor experience, which varies. This paper aims to address the challenge of pulmonary nodule detection more effectively. A method for detecting pulmonary nodules based on an improved neural network is presented in this paper. Nodules are clusters of tissue with a diameter of 3 mm to 30 mm in the pulmonary parenchyma. Because pulmonary nodules are similar to other lung structures and have a low density, false positive nodules often occur. Thus, our team proposed an improved convolutional neural network (CNN) framework to detect nodules. First, a nonsharpening mask is used to enhance the nodules in computed tomography (CT) images; then, CT images of 512×512 pixels are segmented into smaller images of 96×96 pixels. Second, in the 96×96 pixel images which contain or exclude pulmonary nodules, the plaques corresponding to positive and negative samples are segmented. Third, CT images segmented into 96×96 pixels are down-sampled to 64×64 and 32×32 size respectively. Fourth, an improved fusion neural network structure is constructed that consists of three three-dimensional convolutional neural networks, designated as CNN-1, CNN-2, and CNN-3, to detect false positive pulmonary nodules. The networks’ input sizes are 32×32×32, 64×64×64, and 96×96×96 and include 5, 7, and 9 layers, respectively. Finally, we use the AdaBoost classifier to fuse the results of CNN-1, CNN-2, and CNN-3. We call this new neural network framework the Amalgamated-Convolutional Neural Network (A-CNN) and use it to detect pulmonary nodules. Our team trained A-CNN using the LUNA16 and Ali Tianchi datasets and evaluated its performance using the LUNA16 dataset. We discarded nodules less than 5mm in diameter. When the average number of false positives per scan was 0.125 and 0.25, the sensitivity of A-CNN reached as high as 81.7% and 85.1%, respectively.

In the field of music generation, generating structured music is a highly challenging research topic. Music generation methods are currently learned in Euclidean space and usually modeled as a time series without structural properties, but due to the limitations of the time series representation in Euclidean space, the hierarchical structure of music is difficult to learn, and the generated music is poorly structured. Therefore, based on hyperbolic theory, this paper proposes a Hyperbolic Music Transformer model, which considers the hierarchy in music and models the structured components of music in hyperbolic space. Meanwhile, in order for the network to have sufficient capacity to learn music data with hierarchical and power regular structure, a hyperbolic attention mechanism is proposed, which is an extension of the attention mechanism in hyperbolic space based on the definition of hyperboloid and Klein model. Subjective and objective experiments show that the model proposed in this paper is able to generate high-quality music with structure.

As an important part of the quadruped robot, the leg determines its performance. Flexible legs or flexible joints aid in the buffering and adaptability of robots. At present, most flexible quadruped robots only have two-dimensional flexibility or use complex parallel structures to achieve three-dimensional flexibility. This research will propose a new type of three-dimensional flexible structure. This passive compliant three-dimensional flexibility reduces the weight and complex structure of the robot. The anti-impact performance of the robot is verified by a side impact experiment. The simulation and experiments show that the robot still has good stability even under a simple algorithm and that the flexible leg can reduce the impact on the quadruped robot and improve the environmental adaptability of the robot.

Convolutional neural networks have achieved great successes in many visual tasks, as well as a good performance in various applications. However, research has yet to solve the practical problem of how to improve the recognition rate by increasing the depth of the network. The method proposed in this paper is based on the idea of a residual network, which shows that if a fast connection is added between network layers, the network will be deeper, the accuracy will be higher, and the training will be more efficient. The proposed Densely Squeeze-and-Excitation Network (DSENet) combines densely connected networks with the structure of a Squeeze-and-Excitation Network (SENet). The proposed structure is based on layers comprising blocks of compressed networks, and DSENet enables each of these layers to connect directly to all previous layers with adaptive weights. DSENet has many advantages, such as a high utilization rate of parameters and the ability to alleviate the disappearance of a gradient. The performance of the proposed network is evaluated on the CIFAR-10 and CIFAR-100 datasets, and experiments show that DSENet achieves better results on those datasets when compared with the most popular convolutional networks in current use.

The field-of-view (FOV) of compound eyes is an important index for performance evaluation. Most artificial compound eyes are optical, fabricated by imitating insect compound eyes with a fixed FOV that is difficult to adjust over a wide range. The compound eye is of great significance in the field of tracking high-speed moving objects. However, the tracking ability of a compound eye is often limited by its own FOV size and the reaction speed of the rudder unit matched with the compound eye, so that the compound eye cannot better adapt to tracking high-speed moving objects. Inspired by the eyes of many organisms, we propose a soft-array, surface-changing compound eye (SASCE). Taking soft aerodynamic models (SAM) as the carrier and an infrared sensor as the load, the basic model of the variable structure infrared compound eye (VSICE) is established using an array of infrared sensors on the carrier. The VSICE model is driven by air pressure to change the array surface of the infrared sensor. Then, the spatial position of each sensor and its viewing area are changed and, finally, the FOV of the compound eye is changed. Simultaneously, to validate the theory, we measured the air pressure, spatial sensor position, and the FOV of the compound eye. When compared with the current compound eye, the proposed one has a wider adjustable FOV.