Aiming at the problem that existing motion control strategies cannot guarantee high-precision control for independently driven caster-type omnidirectional mobile platform， a double closed-loop trajectory tracking control strategy was proposed by combining Model Predictive Control （MPC） and Proportion Integral Differential （PID） control. Firstly， kinematic geometric relationship was used to establish three-degree-of-freedom kinematic model of independently driven caster-type omnidirectional mobile platform in world coordinate system， and based on orthogonal decomposition method， inverse kinematic model of platform in robot coordinate system was established to reflect the relationship between center point speed of platform and rotation speed of each caster. Secondly， MPC was used to design position controller based on three-degree-of-freedom kinematic model， so that platform could track positions of desired trajectory， and the optimal control quantity was solved through the position controller while taking multi-objective constraints into account. Finally， PID was used to design speed controller to track desired speed output by position controller. Desired caster speed was calculated through the inverse kinematic model of platform， thereby driving platform to achieve omnidirectional motion. The effectiveness of the proposed control strategy was verified through simulation， and the platform could effectively track linear trajectories and circular trajectories. Simulation results show that compared with position single-loop trajectory tracking control strategy that decouples drive caster speed through angle inverse kinematic model of platform， the system overshoot is reduced by 97.23% and the response time is shortened by 36.84% after adding speed inner loop.

The existing image aesthetic quality evaluation methods widely use Convolution Neural Network （CNN） to extract image features. Limited by the local receptive field mechanism， it is difficult for CNN to extract global features from a given image， thereby resulting in the absence of aesthetic attributes like global composition relations， global color matching and so on. In order to solve this problem， an image aesthetic quality evaluation method based on SSViT （Self-Supervised Vision Transformer） model was proposed. Self-attention mechanism was utilized to establish long-distance dependencies among local patches of the image and to adaptively learn their correlations， and extracted the global features so as to characterize the aesthetic attributes. Meanwhile， three tasks of perceiving the aesthetic quality， namely classifying image degradation， ranking image aesthetic quality， and reconstructing image semantics， were designed to pre-train the vision Transformer in a self-supervised manner using unlabeled image data， so as to enhance the representation of global features. The experimental results on AVA （Aesthetic Visual Assessment） dataset show that the SSViT model achieves 83.28%， 0.763 4， 0.746 2 on the metrics including evaluation accuracy， Pearson Linear Correlation Coefficient （PLCC） and SRCC （Spearman Rank-order Correlation Coefficient）， respectively. These experimental results demonstrate that the SSViT model achieves higher accuracy in image aesthetic quality evaluation.

In the few-shot object detection task based on transfer learning， due to the lack of attention mechanism to focus on the object to be detected in the image， the ability of the existing models to suppress the surrounding background area of the object is not strong， and in the process of transfer learning， it is usually necessary to fine-tune the meta-features to achieve cross-domain sharing， which will cause meta-feature shift， and lead to the decline of the model’s ability to detect large-sample images. To solve the above problems， an improved meta-feature transfer model Up-YOLOv3 based on the attention mechanism and the meta-feature secondary reweighting mechanism was proposed. Firstly， the Convolution Block Attention Module （CBAM）-based attention mechanism was introduced in the original meta-feature transfer model Base-YOLOv2， so that the feature extraction network was able to focus on the object area in the image and pay attention to the detailed features of the image object class， thereby improving the model’s detection performance for few-shot image objects. Then， the Squeeze and Excitation-Secondary Meta-Feature Reweighting （SE-SMFR） module was introduced to reweight the meta-features of the large-sample image for the second time in order to obtain the secondary reweighted meta-features， so that the model was not only able to improve the performance of few-shot object detection， but also able to reduce the weight shift of the meta-feature information of the large-sample image. Experimental results on PASCAL VOC2007/2012 dataset show that， compared with Base-YOLOv2， Up-YOLOv3 has the detection mean Average Precision （mAP） for few-shot object images increased by 2.3 to 9.1 percentage points； compared with the original meta-feature transfer model based on YOLOv3 Base-YOLOv3， mAP for large-sample object images increased by 1.8 to 2.4 percentage points. It can be seen that the improved model has good generalization ability and robustness for both large-sample images and few-shot images of different classes.

In order to improve the efficiency of processing small files in Hadoop Distributed File System (HDFS), a new efficient approach named SmartFS was proposed. By analyzing the file accessing log to obtain the accessing behavior of users, SmartFS established a probability model of file associations. This model was the reference of merging algorithm to merge the relevant small files into large files which would be stored on HDFS. When a file was accessed, SmartFS prefetched the related files according to the prefetching algorithm to accelerate the access speed. To guarantee the enough cache space, a cache replacement algorithm was put forward. The experimental results show that SmartFS can save the metadata space of NameNode in HDFS, reduce the interaction between users and HDFS, and improve the storing and accessing efficiency of small files on HDFS.

Integrity is a critical parameter for Global Positioning System (GPS)/ Strapdown Inertial Navigation System (SINS) tight coupling system. In order to reduce satellites' failure detection time, an innovation extrapolation method based on the innovation test method was proposed. By disposing the innovation produced in the extrapolation process, the innovation extrapolation method's test statistics that has been used for failure detection was formed. Applying the proposed method in GPS/SINS tightly coupled system, the simulation results show that innovation extrapolation method can detect slowly growing failure faster than the innovation test method, and innovation extrapolation method can undermine the effect of outliers for failure detection.

To exploit the visual features of multi-modal industrial weld seam images and further improve the registration effect through modal translation， a modal translation-based network for pixel-level registration of multi-modal weld seam images was proposed. Firstly， a cross-modal translation module was designed to make the network have the capability to capture shared features of different modalities of industrial images. Then， the shared features were captured to perform multi-modal image registration. At the same time， adversarial loss and multi-level contrastive loss were used to improve the modal translation effect. Additionally， the cross-modal translation module was integrated with the unimodal image registration module， and reconstruction loss was employed to improve pixel-level registration performance. Finally， a multi-modal industrial weld seam image dataset was constructed， and experiments were conducted using this dataset for comparison. Experimental results demonstrate that the proposed network significantly outperforms the existing advanced multi-modal image registration models such as DFMIR （Discriminator-Free-Medical-Image-Registration） and IMSE （Indescribable Multi-modal Spatial Evaluator）， achieving 3.9 and 3.2 percentage point increases in mean Intersection over Union （mIoU） and 16- and 11-pixel registration accuracy improvements in average Euclidean distance （aEd）， thereby obtaining good results in pixel-level registration.