That individuals focus on emotionally negative events more than to neutral or positive events, which is designated as emotional negativity bias, has been demonstrated by many behavioral studies. For example, snakes and spiders were detected more rapidly than flowers and mushrooms in a visual search task, suggesting that threat-relevant stimuli in the environment are preferentially perceived [1]. Researchers assumed this privileged access to negative stimulation may be driven by the pre-attentive analysis of stimuli as being threat-related (e.g., snakes, spiders), and that the underlying mechanism may consequently lead to an automatic shift of attention resources to the location of the threatening object [2,3]. Similarly, the attention bias towards negative stimuli is reflected by impaired reaction time (RT) in an emotional Stroop task which requires participants to name the color ink of words varying in emotional value (e.g., neutral versus threat-related words), wherein the RTs between word types are compared [4]. Typically, the longer the RTs, the more psychological resources are put into the processing of negative information. Because participants are presumably distracted by the nature of the words, the RTs of the color-naming task are correspondingly prolonged.RT studies establish the presence of an attention bias. ERPs provided a better examination than RTs of the time course to information processing during sensory and cognitive stages [5]. Negativity bias in earlier ERP components has also been documented by some literature. One of ERP's components, the P2 which appeared approximately at 200 -250 ms poststimulus onset, would be a good index to mirror attention bias. Thomas et al. [6] reported that the amplitude of P2 was specific to stimulus valence and larger P2 amplitudes were required for threat words than for neutral words. Likewise, Huang and Luo [7] found that enlarged P2 amplitudes are solely observed for negative rather than for positive and neutral stimuli. Carretie et al.[8] explored the ERPs for positive, negative and neutral pictorial stimuli in normal participants. They found that the P2 post-target component had the highest amplitudes for negative stimuli. It remains uncertain whether or not enhanced P2 verifies negative stimuli receive more attention than positive stimuli. Chen et al. [9] reported participants who experienced either positive or negative moods elicited smaller P2 amplitudes from negative pictures compared to positive pictures. A smaller P2 elicited by negative but not neutral stimuli was also observed in anxious and female participants [10,11]. Another possible candidate for this effect is the late positive
In order to ensure the safety of the underwater unmanned vehicle (UUV) and reduce the risk of damage or loss during operation, the safety system will be equipped on the UUV. It can accurately locate the fault when the submersible encounters dangerous situations, such as equipment failure and cabin flooding, assess the dangerous situation and make emergency measures, so as to help the underwater unmanned submersible realize self-rescue. Fault diagnosis expert system is a knowledge-based fault diagnosis method, which is suitable for complex systems with incomplete knowledge. However, because the working environment of the underwater unmanned underwater vehicle is very complex and the fault types are diverse and change in real time, the traditional fault diagnosis expert system may not meet the response time requirements of the safety system due to the long response time required. Therefore, the finite state machine technology is applied to the diagnostic expert system to improve the response speed of the diagnostic expert system, and at the same time make the system more flexible and convenient to expand its functions. In addition, the model-based design method was applied to establish the model of the security system in stateflow, and the model verification was carried out. With the help of PLC Coder tool, the code of the target controller was generated and the algorithm was deployed.
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