In this paper, we present an autonomous control system for Unmanned Aerial Vehicles (UAVs), specifically designed to inspect a detected suspicious vessel and capture information-rich images in a maritime environment. The maritime environment is ever-changing and uncertain, making it challenging to perform maritime monitoring tasks efficiently and reliably. The proposed UAV control system consists of multiple modules, including path planning, vessel searching, image processing, and image optimization. A novel image optimization approach utilizing deep reinforcement learning (DRL) is proposed to enhance the quality of the captured images by jointly controlling the movement of the UAV and camera orientation. The effectiveness and efficiency of the proposed system were validated and evaluated by searching the vessel and optimizing the captured images in the self-developed simulation environment in Gazebo.

VSLAM is one of the key technologies for indoor mobile robots, used to perceive the surrounding environment, achieve accurate positioning and mapping. However, traditional VSLAM algorithms based on the assumption of a static environment still face certain challenges. The movement, occlusion, and appearance changes of dynamic objects can lead to feature point-matching errors, making data association difficult and causing biases in motion estimation. In order to address this challenge, this paper proposes a dynamic feature point removal method and a closed-loop detection method for high dynamic scenes, aiming to effectively improve the robustness and positioning accuracy in dynamic environments. First, the YOLOv7-tiny object detection network and LK optical flow algorithm are combined to detect the dynamic area, and the adaptive threshold keyframe selection method is adopted to solve the problem of poor quality of keyframe caused by the existing heuristic threshold selection method. Then, this paper proposes a dynamic keyframe sequence creation method based on the angle difference between keyframes, which reduces the workload of loop back detection and accelerates the efficiency of loop back detection in the system. Next, the ParC_NetVLAD image matching algorithm is proposed. In this paper, ConvNeXt-Tiny network is used for feature extraction of images, and ParC-Net network and CBAM attention mechanism are added to the feature extraction network. Finally, NetVLAD is used to cluster the extracted local features to obtain global features that can represent images. Experiments are conducted on public TUM RGB-D datasets and in real-world situations. The proposed algorithm reduces the ATE (Absolute Trajectory Error) by 96.4% and the RPE (Relative Trajectory Error) by 82.8% on average in highly dynamic scenarios. In the Pittsburgh30k dataset, the average accuracy of loop closure detection has been improved by 2.6%.

An image enhancement method, which is solving the issue of detail loss caused by the inability of existing image enhancement methods to balance brightness and saturation in Wireless Capsule Endoscope (WCE) low-light environment, is proposed. Firstly, we design a multi-scale fast guided filter to estimate the illumination component and utilize the OTSU method to determine the function parameters based on the grayscale information of the illumination component. Secondly, we construct a brightness enhancement function based on the Weber-Fechner law to achieve brightness enhancement of the V component image. At the same time, we designed the brightness enhancement coefficient and combined with Haar wavelet to operate the S component image to balance the brightness and saturation of the WCE enhanced image. Finally, the image enhancement result is obtained by merging the channels and converting to the RGB color space. Comprehensive experimental results show that compared with existing methods, our proposed method improves the mean, standard deviation and information entropy evaluation criteria by 18.2, 5.81 and 0.26 respectively. Furthermore, the feature point detection and matching numbers of the enhanced images increased by an average of 59.3% and 32.9% respectively. Moreover, the effectiveness of this method is further verified through the improvement of experimental results of single-image depth estimation accuracy.

Due to the contradiction between limited bandwidth and huge transmission parameters, federated Learning (FL) has been an ongoing challenge to reduce the model parameters that need to be transmitted to server in clients for fast transmission. Existing works that attempt to reduce the amount of transmitted parameters have limitations: 1) the reduced number of parameters is not significant; 2) the performance of the global model is limited. In this paper, we propose a novel method called Fed-KGF that significantly reduces the amount of model parameters while improving the global model performance. Our goal is to reduce those transmitted parameters by reducing the number of convolution kernels. Specifically, we construct an incomplete model with a few representative convolution kernels, and propose Kernel Generation Function (KGF) to generate other convolution kernels to render the incomplete model to be a complete one. We discard those generated kernels after training local models, and solely transmit those representative kernels during training, thereby significantly reducing the transmitted parameters. Furthermore, there is a client-drift in the traditional FL because of the averaging method, which hurts the global model performance. We innovatively select one or few modules from all client models in a permutation way, and only aggregate the uploaded modules rather than averaging all modules to reduce client-drift, thus improving the global model performance and further reducing the transmitted parameters. Experimental results on both non-Independent and Identically Distributed (non-IID) and IID scenarios for image classification and object detection tasks demonstrate that our Fed-KGF outperforms SOTA FL models.

As an effective method to solve the cold start problem of recommender systems, cross-domain recommendation has received more and more attention and research. Currently, most cross-domain recommendation models rely on the unidirectional knowledge transfer between the same users in the source and target domains. The rich information in the source domain is transferred to the target domain with sparse information to achieve better recommendation effect. However, the performance of cross-domain recommendation heavily depends on the number of overlapping active users, and these models cannot fully utilize the useful knowledge behind active users in a single domain. This limitation makes it difficult for the model to achieve ideal results in real-world scenarios. To solve the above problems and optimize cross-domain recommendation, we propose a Two-way Cross-domain Recommendation with Central Social Influence(CST-CDR) and the concept of super-user. Through the idea of clustering, user circles are formed centering on active super-users, so the potential common preferences of single-field active users, dual-field active users, and super-users are fully explored, alleviating the dependence of the model on overlapping and active users. At the same time, the cross-domain recommendation is extended to realize two-way information migration, so that users who are active in only one domain can have a preliminary preference judgment. Finally, the effectiveness of the proposed method is demonstrated on two real datasets.

Session-based recommendation, which uses the interactive information of anonymous users in a period to predict user preferences, has attracted extensive attention. Existing methods mainly exploit the strengths of graph neural networks (GNNs) in capturing structured data features to model complex item transitions for recommendations. However, there still remain some challenges: 1) many methods neglect to exploit cross-session interactions and fail to integrate the latent intra- and inter-session information effectively. 2) some methods barely explore fine-grained contextual factors underlying in the sessions for session-based recommendation, thus failing to capture more comprehensive user preferences. To solve these issues, we propose a novel method called Global Context-Aware Graph Neural Networks (GCA-GNN) which captures the local-session and cross-session preferences respectively, and captures fine-grained global contextual factors to complement user preferences. Specifically, GCA-GNN models user preferences from two different views: the cross-session view and the local-session view. The former learns collaborative user preferences from a cross-session graph, while the latter is designed to learn users' personal preferences from local-session graphs. Furthermore, a context-aware Capsule Graph Neural Network is employed to extract fine-grained contextual factors, serving as complementary information. And we introduce an auxiliary self-supervised learning task to enhance user preferences. Experiments on benchmark datasets demonstrate the strength of our model over the state-of-the-art methods.

In-situ aeroengine maintenance works (e.g. inspection, repair) are highly beneficial as it can significantly reduce currently accepted maintenance cycle which is extensive and costly due to the need to remove engines from the wing of an aircraft. However, feeding in/out via inspection ports and performing a multi-axis movement of an end-effector in a very constrained environment such as aeroengine combustion chamber is a fairly challenging task. This paper presents the design and development of a highly slender (i.e., low diameter-to-length ratio) dual-structure continuum robot with 16 degrees of freedom (DoFs) to provide the feeding motion needed to navigate into confined environments and then perform a required configuration shape for further repair operation. This continuum robot is a compact system and presents a set of innovative mechatronic solutions such as: (i) two-stage tendon-driven structure with bevelled disk design to perform required configuration shape and to provide selective stiffness for the ability of taking high payloads; (ii) various compliant joints to enable different flexibility requirement in each stage; (iii) three commanding cables for each 2-DoF section to minimise the number of actuators with a precise actuation. To be able to achieve the desired configuration shape, a kinematic model has been established and the configuration-cable kinematics has been implemented. Finally, the continuum robot has been built and tested for performing the predefined configuration shape.