Three-dimensional (3D) ground-penetrating radar is an effective method for detecting internal crack damage in pavement structures. Inefficient manual interpretation of radar images and high personnel requirements have substantially restrained the generalization of 3D ground-penetrating radar. An improved Crack Unet model based on the Unet semantic segmentation model is proposed herein for 3D ground-penetrating radar crack image processing. The experiment showed that the MPA, MioU, and accuracy of the model were improved, and it displayed better capacity in the radar image crack segmentation task than current mainstream algorithms do, such as deepLabv3, PSPNet, and Unet. In the test dataset without cracks, Crack Unet is on the same level as deepLabv3 and PSPNet, which can meet engineering requirements and display a significant improvement compared with Unet. According to the ablation experiment, the MPA and MioU of Unet configured with PMDA, MC-FS, and RS modules were larger than those of Unet configured with one or two modules. The PMDA module adopted by the Crack Unet model showed a higher MPA and MioU than the SE module and the CBAM module did, respectively. The results show that the Crack Unet model has a better segmentation ability than the current mainstream algorithms do in the task of the crack segmentation of radar images, and the performance of crack segmentation is significantly improved compared with the Unet model. The Crack Unet model has excellent engineering application value in the task of the crack segmentation of radar images.

Because unmanned forklifts need to recognise and locate pallets in warehouses, a detection algorithm based on deep learning framework was proposed. First, the authors collected a large number of pictures including people and pallet in the real warehouse and marked the corresponding label to build a logistics warehouse pallet database. Second, the object detection algorithm based on a single shot multibox detector is improved and trained by the database. In the prediction phase, the network combines the multiscale feature maps with different resolution, which enhances the adaptability of the network to the detection task. Third, the algorithm is an end‐to‐end detection network, i.e. uses a single network for detection tasks, which can be easily combined with other systems which need detection tasks. The experimental results show that the accuracy of the improved pallet detection algorithm can reach 92.7% and the test rate is 42 frames per second, which can meet the requirements of the efficiency and accuracy of the pallet detection while using the TITAN X GPU.

Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model’s ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

We generalize Musiker–Schiffler–Williams’ expansion formula to quantum cluster algebras from unpunctured surfaces. In particular, we give a combinatorial proof of the positivity for such class of quantum cluster algebras.

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Sensor data pre‐processing is an essential phase of crowd sensing application. Existing studies do not effectively solve the problem, and there still exist various sensor data pre‐processing optimisation problems at the acquisition end in crowd‐sensing process. This study presents an improved sliding average method to achieve data compression and reduce the time complexity by using a dynamic window with improved processing time. Through adopting locally sorting and gradient change of the filter window, an improved extremum median filtering method is proposed to relieve the time‐consuming problem when denoising high pixel images. A transmission strategy for optimisation is also proposed, in which only the demarcation points of each group of data and the data points with large differences when comparing with the demarcation points are recorded. This strategy reduces the storage pressure and the amount of data transmission of mobile terminal and improves the efficiency of data transmission. The experimental results show that their methods have higher speed and lower cost, and thus they can run better in crowd‐sensing environment.