Information retrieval-based question answering (IRQA) and knowledge-based question answering (KBQA) are the main forms of question answering (QA) systems. The answer generated by the IRQA system is extracted from the relevant text but has a certain degree of randomness, while the KBQA system retrieves the answer from structured data, and its accuracy is relatively high. In the field of policy and regulations such as household registration, the QA system requires precise and rigorous answers. Therefore, we design a QA system based on the household registration knowledge graph, aiming to provide rigorous and accurate answers for relevant household registration inquiries. The QA system uses a semantic analysis-based approach to simplify one question into a simple problem consisting of a single event entity and a single intention relationship, and quickly generates accurate answers by searching in the household registration knowledge graph. Due to the scarcity and imbalance of QA corpus data in the field of household registration, we use GPT3.5 to augment the collected questions dataset and explore the impact of data augmentation on the QA system. The experiment results show that the accuracy rate of the QA system using the augmented dataset reaches 93%, which is 6% higher than before.

Path planning plays a crucial role in determining the shortest and safest route for autonomous mobile robots, and metaheuristic optimization algorithms have demonstrated their efficacy in solving complex problems involving numerous and variably shaped obstacles. The dung beetle optimizer (DBO) emerges as a novel heuristic optimization algorithm, drawing inspiration from the behavior patterns exhibited by dung beetles. This paper presents an improved algorithm called the enhanced DBO (EDBO) designed to address the mobile robot path planning problem. First, a node selection strategy based on the search radius is proposed to reduce the probability of path reciprocation while concurrently enhancing the initial population quality. Second, the conventional ball-rolling strategy employed in DBO is replaced with a dynamic step size search strategy. This strategy dynamically adjusts the step size based on the relative distance between the current position and the worst position of each iteration, enabling the population to update its position adaptively in response to environmental changes. Additionally, the global optimal position is incorporated to guide the search process, thereby improving the global search performance of DBO. Finally, a path fine-tuning strategy is implemented to refine the new generated individuals, with geometric principles of triangles being introduced to locally adjust the optimized path and guide the population out of local optima. Experiments are conducted to validate the effectiveness of the three improvement strategies, comparing the path optimization performance of EDBO with five other algorithms across five different complexity environments. Additionally, two statistical testing methods are employed to evaluate the experimental results. The experimental results indicate that, in all five environments, EDBO outperforms the other five algorithms in terms of path length and achieves superior path smoothness in 80% of the cases. Moreover, EDBO consistently demonstrates better overall performance than DBO, excelling in both convergence accuracy and speed.

The effectiveness of a cruise ship’s emergency evacuation is greatly influenced by the way people interact; this paper uses the social force model to simulate two different evacuation scenarios considering the impact of small groups. It uses an agent to simulate the behavior of a single occupant, and leverages the social force model to quantify the effect of group behavior on the group members. According to the influence of the group on the members, this paper corrects the expected speed of the members to determine the speed of crowd evacuation. It uses the SAFEGUARD cruise ship as the evacuation platform to simulate the process of evacuating the passengers to the boarding station and assembly station, respectively, and calculates the evacuation time, congestion area, and congestion duration of passengers under the action of groups. The simulation results of the two scenarios show that the group effect increases the average evacuation time by 15.29% and 21.79%, and increases the average detour distance by 24.54% and 17.89%, respectively.

The paper presents the problem of multi-target tracking with heavy-tailed process and measurement noises. Such heavy-tailed noises can reflect unmodeled anomalies, sudden disturbance, or temporary sensor failures. A robust Students t-based labeled multi-Bernoulli (RSTLMB) filter is designed for such systems, where the state predicted probability density and measurement likelihood function of individual targets are modeled as Students t distributions. A closed form recursion of the RSTLMB filter to jointly estimate the target state and the parameters of the Students t distribution is derived in the variational Bayesian framework. Simulations on multi-target tracking with heavy tailed process and measurement noises demonstrate the effectiveness and superiority of the proposed RSTLMB filter.