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This study examines the course-keeping control of directionally unstable large oil tankers involving a pole in the right half plane. Treated as an unstable plant in control engineering, tankers are theoretically and experimentally investigated during the controller design process. First, the unstable plant is mirror-mapped to its corresponding stable minimum phase plant using the mirror-mapping technique, which enables an easy controller design. Then, a linear proportional-differential and a first-order filter controller is designed based on the closed-loop gain shaping algorithm, which requires only one controller parameter to be properly selected based on the system’s characteristics. Numerical simulation results confirmed that the designed controller can successfully stabilise an unstable plant subjected to external wind and wave disturbances. The controller designed with the proposed method is suitable for course-keeping control of directionally unstable large tankers. The controller design method is simple with an uncomplicated structure that can easily be implemented in engineering endeavours. Moreover, the rudder motion is small and soft.

Dedicated equipment, which is widely used in many different types of vehicles, is the core system that determines the combat capability of special vehicles. Therefore, assuring the normal operation of dedicated equipment is crucial. With the increase in battlefield complexity, the demand for equipment functions is increasing, and the complexity of dedicated equipment is also increasing. To solve the problem of fault diagnosis of dedicated equipment, a fault diagnosis algorithm based on CNN-LSTM was proposed in this paper. CNN and LSTM are used in the model adopted by the algorithm to extract spatial and temporal features from the data. CBAM is used to enhance the model's accuracy in identifying faults for dedicated equipment. Data on dedicated equipment faults were obtained from a hardware-in-loop simulation platform to verify the model. It is demonstrated that the proposed fault diagnosis algorithm has high recognition ability for dedicated equipment by comparing it to other neural network models. [ABSTRACT FROM AUTHOR]

It is controversial whether sarcasm processing should go through literal meaning processing. There is also a lack of eye movement evidence for Chinese sarcasm processing. In this study, we used eye movement experiments to explore the processing differences between sarcastic and literal meaning in Chinese text and whether this was regulated by sentence complexity. We manipulated the variables of complexity and literality. We recorded 33 participants' eye movements when they were reading Chinese text and the results were analyzed by a linear mixed model. We found that, in the early stage of processing, there was no difference between the processing time of the sarcastic meaning and the literal meaning of simple remarks, whereas for complex remarks, the time needed to process the sarcastic meaning was longer than that needed to process the literal meaning. In the later stage of processing, regardless of complexity, the processing time of the sarcastic meaning was longer than that of the literal meaning. These results suggest that sarcastic speech processing in Chinese is influenced by literal meaning, and the effect of literal meaning on sarcastic remarks is regulated by complexity. Sarcastic meaning was expressed differently in different stages of processing. These results support the hierarchical salience hypothesis of the serial modular model. [ABSTRACT FROM AUTHOR]

Objective: Irony as an indirect language with unpredictability consumes more cognitive resources, and is more difficult to understand than literal language. This study aims to explore the processing differences between irony and literal sentences using event-related potential (ERP) technology., Materials and Methods: Three types of sentences were involved: sentences with predictable literal meaning, sentences with unpredictable literal meaning, and sentences with ironic meaning. The neural responses of the subjects were recorded when they read sentences., Results: Compared to predictable literal meaning sentences, unpredictable literal meaning sentences and ironic meaning sentences elicited larger amplitude of N400 components. The difference was not significant between the latter two. In addition, there was no significant difference in P600 evoked by the three sentences., Conclusion: In the initial stage of irony processing, the low predictability may result in the difficulty in semantic comprehension, in which the processing patterns of unpredictable and ironic sentences are rather close. In the later stage of processing, ironic integration is not harder compared to literal sentence integration., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Shi and Li.)