Methodology for Identifying Post-Traumatic Stress Disorder Indicators in Text Data

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dc.contributor.authorMazurets, O.V.
dc.contributor.authorOvcharuk, O.M.
dc.contributor.authorМазурець, Олександр Вікторович
dc.date.accessioned2025-10-26T20:03:10Z
dc.date.available2025-10-26T20:03:10Z
dc.date.issued2025
dc.description.abstractThe article develops an engineering method for adjusting (or readjusting during operation) the controllers of electric drives of manipulator mobility units, which takes into account the presence of significant nonlinearities. This method prevents the occurrence of “primary self-oscillations” in the automatic control system of electric drives of manipulator mobility units, which stimulate the occurrence of resonant elastic vibrations and self-oscillations (self-oscillation effect). The proposed method allows not only to eliminate the cause of the autoelasticity effect, but also to do so at the engineering level of mastery of mathematical apparatus, computer mathematics systems, and programming skills. The manifestation of the autoelasticity effect is associated with the presence of factors such as: the dynamic properties of the drive of the mobility nodes; the elastic flexibility of manipulators; significant nonlinearities of a structural and technological nature or those that arise during operation in mechanical and electrical devices. The engineering simplicity and convenience of the method is expressed in the fact that the adjustment of the electric drive controllers of the mobility nodes during the manufacture of the manipulator or their readjustment during operation does not require specialized scientific research, but can be performed by a specialist with an engineering level of mathematical training in interactive mode in a short time.
dc.identifier.citationMazurets O.V., Ovcharuk O.M. Methodology for Identifying Post-Traumatic Stress Disorder Indicators in Text Data. Information Control Systems and Intelligent Technologies. Advances and Applications. Monograph. Liha-Pres. 2025. P.43-58. ISBN 978-966-397-538-2
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/19733
dc.language.isoen
dc.subjectPost-traumatic stress disorder
dc.subjectNLP
dc.subjectneural network
dc.subjecttext data
dc.titleMethodology for Identifying Post-Traumatic Stress Disorder Indicators in Text Data
dc.typeСтаття
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