ABSTRACT To address the challenges of slow positioning speed and inaccurate localisation of underground pipeline corridors in complex environments using ultra‐wideband (UWB) absolute positioning, this paper proposes a Hybrid UWB‐IMU Adaptive Localisation Algorithm (HUIALA) for precise underground pipeline corridor positioning. The positioning method uses UWB as absolute positioning, IMU and odometer trajectory calculation as relative positioning (predictive positioning), and updates the observation noise by calculating the fuzzy distance to the triangle centroid to adaptively allocate weights. At the same time, dynamically adjust the intervention and exit of predicted positioning based on system operation, and filter out interference such as UWB positioning drift and absolute positioning failure. The proposed method is based on the simulation and experiment of a wheeled inspection vehicle system using UWB and inertial navigation. The experimental result shows that the proposed method maintains better response speed and high positioning accuracy during dynamic testing in simulated interference environments. The positioning speed is improved by 98.9% compared to single UWB positioning, and the positioning accuracy is improved by about 45.84% and 27.96% compared to single UWB positioning and KF fusion positioning, respectively.

With the incredible growth of the scale and complexity of datasets, creating proper visualizations for users becomes more and more challenging in large datasets. Though several visualization recommendation systems have been proposed, so far, the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the industry. In this paper, we proposed AVA, an open-sourced web-based framework for Automated Visual Analytics. AVA contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights respectively. The code is available at https://github.com/antvis/AVA.

Authoring graph visualization poses great challenges to developers due to its high requirements on both domain knowledge and development skills. Although existing libraries and tools reduce the difficulty of generating graph visualization, there are still many challenges. We work closely with developers and formulate several design goals, then design and implement G6, a web-based library for graph visualization. It combines template-based configuration for high usability and flexible customization for high expressiveness. To enhance development efficiency, G6 proposes a range of optimizations, including state management and interaction modes. We demonstrate its capabilities through an extensive gallery, a quantitative performance evaluation, and an expert interview. G6 was first released in 2017 and has been iterated for 317 versions. It has served as a web-based library for thousands of applications and received 8312 stars on GitHub.

In recent years, green product issues have received increasing attention. Both government regulations and consumer behaviors have a strong influence on the product green degree decisions of manufacturers’ products. In order to find how government regulations and individual’s green product purchase behavior affect manufacturers’ green degree decisions and the market evolution characteristics, this paper proposes a multiagent model that considers the interaction among government, consumers, and manufacturers. The simulation results show that, firstly, the product green degree decision‐making of manufacturers needs the guidance and regulation of the government. Secondly, product price subsidies are the most effective way to affect the manufacturers’ product green degree decisions. In contrast to giving green cost subsidies to manufacturers, the government employs various publicity means to improve the environmental awareness of consumers is also an effective way to enhance the green degree of manufacturers’ products. Thirdly, there is a “Crowding Out Effect” on the other qualities of manufacturers’ products when manufacturers focus on the green degree of their products.