Mental workload has become an important factor affecting the human error of ship operators. Controlling the mental workload of operators within a reasonable range can reduce human errors. The purpose of this study is to develop an evaluation method to evaluate the ship operator’s mental workload. First, the evaluation indices system was constructed according to three types of mental workload measurements. Second, the criteria importance though intercrieria correlation (CRITIC) method and analytic hierarchy process (AHP) method were used to determine the relative weight of each index. Finally, the fuzzy theory was used to calculate ship operator’s mental workload. The experiment results indicated that subjective workload assessment technique, physiological measurement (eye response), and error rate can be integrated into the comprehensive evaluation method to assess the mental workload of ship operators. Thus, this method can be used to comprehensively evaluate the mental workload level and improve the reliability of the assessment results.
In this article, we will first try to create a general development platform for embedded systems. The goal of this step is to establish an experimental platform that can support various peripheral modules and can be reused. The connection between the modules can be reconfigured to meet the different needs of embedded research and learning. On this basis, the system uses the audio frequency spectrum program as an input function to represent the dispersion pattern of signal energy, which can be tested with characteristic nodes simulated by convolutional neural network software. In addition, the auxiliary neural network software simulation core can continuously learn the detailed characteristics of the audio frequency spectrum, making it easy to recognize environmental sounds. In addition, the sound signal and the neural cycle are related in time. The neural network can study the relationship between different frames in the time domain to compensate for the defects caused by the complex neural network in modeling time series. Finally, this article focuses on the process of building an English translation platform based on the mobile cloud data model. The client is targeted at the Android platform, while the server is based on the laaS system. According to the model of mobile cloud data processing, the calculation of mobile phones in computer-intensive programs is studied. Through the hardware design and distribution of the system, we are able to use mobile cloud technology as a desktop-intensive program to solve the problem of effectiveness in the solution. The article promotes the development of an English translation platform by applying the research results of environmental sound recognition based on embedded system software simulation to the design of the English translation platform.
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