Abstract.With the rapid development of information technology, the application of artificial intelligence in various industries is becoming increasingly extensive, and the tourism industry is also facing the opportunities and challenges of digital transformation. Against this background, this study aims to deeply analyze the mechanism of the role of artificial intelligence in tourism management. Through methods such as literature research, case analysis and empirical research, a systematic study is carried out on the application of artificial intelligence in various links such as tourism resource … Read more →

Abstract.Cooperative encirclement of moving targets using unmanned surface vehicle (USV) swarms plays a vital role in maritime patrol, active interception, and autonomous cluster confrontation missions. Conventional finite-horizon model predictive control strategies often suffer from short-sighted control decisions and gradual performance degradation in long-duration cooperative tasks, which may cause unstable formation evolution and potential constraint violations. To address these limitations, this paper proposes an extended horizon model predictive control (EH-MPC) method for USV swarm cooperative … Read more →

Abstract.Transformer oil leakage is a critical defect in power equipment inspection, yet its automatic detection remains challenging because real leakage samples are scarce, highly irregular in shape, variable in color, and easily confused with water stains, rust, shadows, and complex structural backgrounds. To alleviate the data scarcity problem and improve downstream segmentation performance, this paper proposes a multimodal semantic-heuristic diffusion framework, termed MSH-Diff, for automatic transformer oil-leakage image synthesis. The proposed framework exploits the visual reasoning capability … Read more →

Abstract.This study develops a fan vote share estimation model for competition settings in which weekly audience support is not directly observable. The model uses known judge scores, elimination outcomes, and rule mechanisms to infer contestant-level fan vote shares through constrained nonlinear optimization. The framework defines active contestants by elimination week, aggregates multi-judge scores, standardizes judge-score shares, and combines judge scores with estimated fan vote shares under percentage-based scoring rules. To address the non-uniqueness of inverse estimation, the model introduces … Read more →

Abstract.Addressing the course-keeping control problem of underactuated surface vessels subject to strong unknown environmental disturbances and asymmetric actuator saturation, this paper proposes an event-triggered (ETC) adaptive control scheme. A Radial Basis Function (RBF) neural network is constructed to approximate external disturbances and model uncertainties. To handle the impact of non-smooth asymmetric saturation nonlinearities, a continuously differentiable model based on the Gaussian error function is employed. An ETC is introduced to significantly reduce the frequency of controller updates, … Read more →

Abstract.Wireless sensor networks (WSNs) rely on battery-powered sensor nodes, making load balancing essential for prolonging network lifetime and ensuring reliable data delivery. Multi-sink WSNs can mitigate the hot-spot problem of single-sink architectures by distributing data collection points, but they also introduce complex imbalance among nodes, paths, cluster heads, and sink service regions. This paper reviews load balancing in multi-sink WSNs by first defining major load types and balancing objects, including communication, energy, path, cluster-head, and sink-region loads. Then, this paper … Read more →