Session-Based Recommendation (SBR), aiming at predicting user next action in view of anonymous behavior sequences, is a critical task for online services (e.g. e-commerce, streaming media). Recently, Graph Neural Networks (GNN) have achieved significant success in session-based recommendation modeling tasks. However, the graph structure data cannot adequately reflect the sequential information in original sessions, and it is also hard for GNN to capture high-order item relationships. Focusing on the challenges mentioned, we proposed a method called Sequence-enhanced Graph neural networks for Session-Based Recommendation (SG-SBR) to provide an effective solution. First, we introduced positional embedding mechanism to merge sequential information and session representations generated by GNN. Second, we proposed self-attention network to capture both the sequential information and high-order item relationships, which improves the ability to capture sequential information for GNN. The above method not only improves the performance of recommendation, but also can be effortlessly integrated into other session-based recommendation combined with GNN. We conducted extensive experiments on two real datasets, and the results show that SG-SBR outperforms baseline methods consistently.

This study introduces a novel climbing strategy, reconfigurable parallel-type cable-driven climbing designed for long-span, large-scale bridge stay cable robotic applications, which has the potential to revolutionize the stay cable inspection and maintenance practice. The proposed methodology features the development of a Collaborative Climbing Robot Squad (CCRobot-S), which builds upon the design principles of the previous CCRobot series. In this study, CCRobot-S implements a parallel-type cable-driven manipulation design, allowing for reconfigurable kinematic morphology by its movable anchor bases and realizing the capacity of crossing over the stay cables for its flying platform. The collaborative robot squad design liberates the dimensions and scales of the robot's reachable workspace and moves the part of the robotic system that indeed needs to be moved, enhancing the working efficiency and climbing agility. This strategy also utilizes controllable adhesion instead of friction to interact with the bridge cable surface for the flying platform, realizing force multiplication for forceful manipulation. Toward bringing high efficiency and heavy-duty capacity, we propose the applicable climbing frameworks (zero-downtime climbing gait for cable inspection and spider-like climbing gait for cable maintenance) and the optimization frameworks (optimal anchor configuration for the movable anchor bases and optimal grasp arrangement for the flying gripper). This article includes the exploration of the design and climbing gaits of CCRobotS, the formulation of the CCRobot-S model, a comprehensive analysis of its workspace, and its climbing strategy and optimization. Extensive experiments have assessed the proposed climbing strategy's effectiveness and showcased CCRobot-S' capabilities.

This paper presents CCRobot-V, the fifth version of CCRobot, a cooperative serial multi-robot system for bridge cable inspection and maintenance that uses silkworm-like locomotion to climb the entire length of super-long stay cable at high speeds while carrying heavy inspection/maintenance equipment. CCRobot-V consists of one climbing precursor robot, one inspection/maintenance robot, several cable-carrying robots, and a power-tethered cable guiding system. The pre-cursor robot is the “head,” which leads the affiliated sub-robots along the bridge cable. Every sub-robot possesses a pair of self-locking palms. When a sub-robot grips on the bridge cable with its palms, it becomes a fixed anchor point that allows the adjacent sub-robots in front and back to use winches and steel wires to pull themselves upward. With this cooperative multi-robot system, cable inspection/maintenance tasks can be divided into several functional units, with each inspection/maintenance equipment installed separately on a customized sub-robot. Thus, CCRobot-V provides a complete mobile inspection/maintenance work line for a bridge cable. The experimental and field tests demonstrate CCRobot-V's high climbing speed, high payload capacity, and full-length cable moving capability. It has the potential application value for the actual bridge cable inspection/maintenance.