In practical applications, tower cranes often operate in outdoor environments and are subject to a variety of disturbances. Additionally, the control performance is significantly affected by uncertain dynamics, such as frictional forces and payload mass. To address these challenges, this paper presents a novel adaptive fuzzy control mechanism for nonlinear 5-degree of freedom (5-DOF) tower cranes with variable cable length. The proposed fuzzy adaptive mechanism leverages fuzzy logic to compensate for dynamic uncertainties and external disturbances thereby enhancing the overall robustness of the controlled system. Moreover, to ensure safe operation and avoid collisions, auxiliary constraint terms are introduced to restrict the actuated state variables within predefined bounds throughout the transportation process. In particular, an adaptive law is developed to accurately estimate the payload mass， which is critical for precise cable positioning. To the best of our knowledge, this work proposes a novel adaptive fuzzy closed-loop control framework for 5-DOF tower crane systems with varying cable lengths. The framework systematically integrates online payload mass estimation, state variable constraints, and fuzzy adaptive compensation to improve system stability and robustness. Based on the Lyapunov technique and Barbalat’s lemma, the system stability is theoretically analyzed and proven. Finally, the effectiveness of the proposed controller is validated by hardware experiments.

This paper proposes a novel energy-efficient control method for vehicle suspensions that addresses key application-oriented issues, including inevitable disturbance, inherent nonlinearity, state-coupling effects, and energy consumption. Unlike conventional schemes, the proposed method explicitly quantifies the influence of disturbance and state-coupling by designing dedicated effect indicators and exploiting the beneficial aspects of nonlinear dynamics. Positive effects are harnessed, while negative effects are transformed into advantageous contributions, thereby delivering superior robustness, lower energy consumption, and improved response rapidity. Specifically, a fuzzy disturbance observer is developed to accurately estimate the disturbance factors, including both parametric/unmolded uncertainty and external disturbance. Effect indicators are introduced to characterize the positive and negative impacts of disturbance and coupling on the active suspension system, and these insights are incorporated into the design of a novel adaptive controller. In addition, biologically inspired nonlinear reference model is deliberately introduced to make use of favorable nonlinear stiffness and damping effects. Furthermore, Lyapunov’s theory is employed to ensure the asymptotic stability of the overall suspension system. Experimental validation demonstrated that the proposed approach achieves excellent transient performance and significant energy savings, with reductions of up to 80% or more.

Fake news detection has received increasing attention from researchers in recent years, especially in the area of multimodal fake news detection involving both text and images. However, many previous studies have simply fed the semantic features of both text and image modalities into a binary classifier after applying basic concatenation or attention mechanisms, where these features often contain a significant amount of inherent noise. This, in turn, leads to both intra- and inter-modal uncertainty. In addition, while methods based on simple concatenation of the two modalities have achieved notable results, they often ignore the drawback of applying fixed weights across modalities, which causes some high-impact features to be ignored. To address these issues, we propose a novel semantic-level m ultimodal d ynamic f usion framework for f ake n ews d etection ( MDF-FND ). To the best of our knowledge, this is the first attempt to develop a dynamic fusion framework for semantic-level multimodal fake news detection. Specifically, our model consists of two main components: (1) the U ncertainty E stimation M odule ( UEM ), which is an uncertainty modeling module that uses a multi-head attention mechanism to model intra-modal uncertainty, and (2) the D ynamic F usion N etwork, which is based on Dempster–Shafer evidence theory ( DFN ) and is designed to dynamically integrate the weights of both text and image modalities. To further enhance the dynamic fusion framework, a graph attention network is employed for inter-modal uncertainty modeling before DFN. Extensive experiments have demonstrated the effectiveness of our model across three datasets, with a performance improvement of up to 4% on the Twitter dataset, achieving state-of-the-art performance. We also conducted a systematic ablation study to gain insights into our motivation and architectural design. Our model is publicly available at https://github.com/CoisiniStar/MDF-FND .

In the study of dialogue system, personalized dialogue mainly focuses on the semantic matching degree between response and role. However, various factors, such as semantic style, dialogue noise, and sparse personality information, can affect the performance of the model. For this purpose, we construct a novel personalized dialogue model. Based on the personality description information and conversation history, it uses the information enhancement algorithm to cluster the personality description text into a number of fine sparse categories, and uses the feature classifier to precisely select the features highly relevant to the current dialogue situation according to the input query content. At the same time, the history information selector will fine-filter the conversation history, retaining the parts that are valuable for generating replies. We combine the processed personality description text with the filtered historical context information, and send it to the decoder through the feature cue learning strategy for deep processing to generate personalized responses. Our model integrates the proposed algorithms, classifiers, selectors and feature cue learning strategies to build a complete dialogue system. Experiments on two datasets show that our model is superior to other models in terms of consistency and coherence.