Deep learning achieves unprecedented success involves many fields, whereas the high requirement of memory and time efficiency tolerance have been the intractable challenges for a long time. On the other hand, quantum computing shows its superiorities in some computation problems owing to its intrinsic properties of superposition and entanglement, which may provide a new path to settle these issues. In this paper, a quantum deep convolutional neural network (QDCNN) model based on the quantum parameterized circuit for image recognition is investigated. In analogy to the classical deep convolutional neural network (DCNN), the architecture that a sequence of quantum convolutional layers followed by a quantum classified layer is illustrated. Inspired by the variational quantum algorithms, a quantum-classical hybrid training scheme is demonstrated for the parameter updating in the QDCNN. The network complexity analysis indicates the proposed model provides the exponential acceleration comparing with the classical counterpart. Furthermore, the MNIST and GTSRB datasets are employed to numerical simulation and the quantitative experimental results verify the feasibility and validity.
Polystyrene electrospun optical fibrous membrane (EOF) was fabricated using a one-step electrospinning technique, functionalized with glucose oxidases (GOD/EOF), and used as a quick and highly sensitive optical biosensor. Because of the doped iridium complex, the fibrous membrane emitted yellow luminescence (562 nm) when excited at 405 nm. Its luminescence was significantly enhanced with the presence of extremely low concentration glucose. The detection limit was of 1.0 × 10(-10) M (S/N = 3), superior to that of reported glucose biosensor with 1.2 × 10(-10) M. A linear range between the relative intensity increase and the logarithm of glucose concentration was exhibited from 3.0 × 10(-10) M to 1.3 × 10(-4) M, which was much wider than reported results. Notably, the response time was less than 1 s. These high sensitivity and fast response were attributed to the high surface-area-to-volume of the porous fibrous membrane, the efficient GOD biocatalyst reaction on the fibers surface, as well as the fast electron or energy transfer between dissolved oxygen and the optical fibrous membrane.
Results showed that ESEHT possesses better effects on migration, proliferation, and osteogenic differentiation of mesenchymal stem cells in vitro but similar promotion effect on periodontal regeneration in vivo compared with PAB, suggesting that ESEHT may be one of the most effective graft materials for periodontal regeneration.
In view of the limitations of existing rotating machine fault diagnosis methods in single-scale signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) and multi-channel fusion convolutional neural networks (MCFCNN) is proposed. First, MPE quantitatively analyzes the vibration signals of rotating machine at different scales, and obtains permutation entropy (PE) to construct feature vector sets. Then, considering the structure and spatial information between different sensor measurement points, MCFCNN constructs multiple channels in the input layer according to the number of sensors, and each channel corresponds to the MPE feature sets of different monitored points. MCFCNN uses convolutional kernels to learn the features of each channel in an unsupervised way, and fuses the features of each channel into a new feature map. At last, multi-layer perceptron is applied to fuse multi-channel features and identify faults. Through the health monitoring experiment of planetary gearbox and rolling bearing, and compared with single channel convolutional neural networks (CNN) and existing CNN based fusion methods, the proposed method based on MPE and MCFCNN model can diagnose faults with high accuracy, stability, and speed.
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