The paper deals with the problem of operator’s state estimating. For this purpose various approaches based on using deep convolutional neural networks are proposed. The approach using automatic emotion recognition methods is considered in the most detail. During the experiment video records of the operator’s face registered during operator performing the flight task on the flight simulator were processed. To determine the type of operator’s activity the studies based on using the emotional background of the face are also carried out. The experimental results of this approach confirmed the efficiency of the selected methods, especially for monitoring the operator’s state when falling asleep.
Current fluid film seal/bearing pressure numerical solutions taking into account both circumferential and axial lubricant flows are not in wide spread use. The most common method is to solve a two dimensional finite element method of Reynolds equation. However, this type of solution often leads to a long computer solution times when employed in an advanced seal/bearing code. A new approximate solution of Reynolds equation for oil seal or bearing flows is proposed in this paper which includes the axial flow modeling. The objective is achieved by means of an axial approximation that can be used to develop a one dimensional centerline circumferential pressure finite element solution to Reynolds equation. Optimization of the parameters associated with the approximate solution parameters is shown. Example seal/bearing pressure and load capacity calculations are presented and the solution verified by comparison with a full finite element 2-D solution. Also, the method of calculating the axial and circumferential lubricant flows as well as axial and circumferential power losses are presented and validated.
We analysed two approaches to estimating the state of a human operator according to video imaging of the face. These approaches, both using deep convolutional neural networks, are as follows: 1) automated emotion recognition; 2) analysis of blinking characteristics. The study involved assessing changes in the functional state of a human operator performing a manual landing in a flight simulator. During this process, flight parameters were recorded, and the operator’s face was filmed. Then we used our custom software to perform automated recognition of emotions (blinking), synchronising the emotions (blinking) recognised to the flight parameters recorded. As a result, we detected persistent patterns linking the operator fatigue level to the number of emotions recognised by the neural network. The type of emotion depends on unique psychological characteristics of the operator. Our experiments allow for easily tracing these links when analysing the emotions of "Sadness", "Fear" and "Anger". The study revealed a correlation between blinking properties and piloting accuracy. A higher piloting accuracy meant more blinks recorded, which may be explained by a stable psycho-physiological state leading to confident piloting
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