RESEARCH OF STRUCTURAL AND MECHANICAL PROPERTIES OF MEAT AS AN OBJECT OF PROCESSING IN MEAT COMMINUTOR

Oleksandr Yaremchenko, P. Pukach
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Abstract

This paper proposes the use of micro mimics, small facial muscle movements that are difficult to detect with the naked eye, for the assessment of psychological states using artificial intelligence. The aim of the research is to develop and improve methods for the analysis of micro mimics to accurately recognize emotions and psychological states of individuals. In this study, we conducted an experimental investigation of the proposed method using video recordings of individuals in various emotional states. Additionally, our approach has several advantages over previous methods in micro mimics analysis. For instance, our method does not rely on manual annotation, which is time-consuming and prone to human error. Instead, our approach uses an automated process that is more efficient and consistent. In comparison with the previous studies, the proposed method has several advantages, including real-time analysis of micro mimics, the capability to handle large-scale datasets, and the ability to analyze complex facial expressions. Moreover, the proposed method overcomes the limitations of traditional machine learning algorithms, which are less effective in capturing the temporal dependencies in video data. Overall, the proposed method has demonstrated promising results in micro mimics analysis and provides a solid foundation for future research in the field of emotional AI. In conclusion, the results of our experiment and their comparison with the results of previous research demonstrate the effectiveness and applicability of our proposed method in analyzing micro mimics for assessing the psychological state using deep learning algorithms. This study contributes to the field of emotional AI and opens up new opportunities for the assessment of psychological states using micro mimics. The results of this study could be useful in a variety of applications, including mental health, human-computer interaction, and social robotics.
肉粉机加工对象肉的结构与力学性能研究
本文建议使用微模拟,即肉眼难以察觉的面部小肌肉运动,来使用人工智能来评估心理状态。本研究的目的是发展和改进微模仿的分析方法,以准确识别个体的情绪和心理状态。在这项研究中,我们利用不同情绪状态的个体录像对所提出的方法进行了实验调查。此外,我们的方法与以前的微模拟分析方法相比有几个优点。例如,我们的方法不依赖于手工注释,因为手工注释既耗时又容易出现人为错误。相反,我们的方法使用更有效和一致的自动化过程。与以往的研究相比,该方法具有实时分析微模拟、处理大规模数据集的能力以及分析复杂面部表情的能力等优点。此外,该方法克服了传统机器学习算法在捕获视频数据中的时间依赖性方面效率较低的局限性。总体而言,该方法在微模拟分析中显示出良好的结果,为未来情感人工智能领域的研究提供了坚实的基础。总之,我们的实验结果及其与先前研究结果的比较表明,我们提出的方法在利用深度学习算法分析微模拟以评估心理状态方面的有效性和适用性。这项研究为情感人工智能领域做出了贡献,并为使用微模拟来评估心理状态开辟了新的机会。这项研究的结果可用于各种应用,包括心理健康、人机交互和社交机器人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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