Continuous Emotion Estimation for Human Machine Interaction

Lee, Hyun-Soon; Xu, Mingwei; Karuppusamy, Naveen Senniappan; Kang, Bo‐Yeong

doi:10.1166/asl.2015.5773

Cited by 2 publications

(1 citation statement)

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“…The Single point cutting tool along with flank wear is shown in Figure 4. Principal Component Analysis (PCA) is a wellestablished tool for dimensionality reduction [17]. It is a 50 35 process commonly used in face recognition [18][19][20][21][22][23].…”

Section: A Proposed Methodsmentioning

confidence: 99%

Tool wear and tool life estimation based on linear regression learning

Karuppusamy

Lee

et al. 2015

2015 IEEE International Conference on Mechatronics and Automation (ICMA)

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Tools have remained an integral part of the society without which stimulation of certain aspects of human evolution would not have been possible. In recent times the modern tools are used in the manufacturing of high precision components. We know that the accuracy and surface finish of these components can be achieved only by the usage of accurate tools. Sharp edged tools may loosen their sharpness due to repeated usage and machining parameters. Hence to address this issue we propose a system to monitor tool wear by using the captured image of cutting tool tip. We used vision system since it is the primitive method of predicting tool wear and two main machining parameters feed rate and depth of cut. The image of flank wear cutting edge at tool tip is captured by examining under profile projector. The system uses linear regression model to calculate tool wear which is mapped onto continuous 2-D coordinates with feed rate and depth of cut as axis from a captured digital image. Thus the proposed intelligent system uses profile projector and digital image processing methods to estimate tool wear continuously and predictively like humans rather than using strict rules. By estimating tool wear continuously the machine can better perform and machine components accurately by using the resultant values of feed rate and depth of cut as a threshold which are arrived as a result.

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Section: A Proposed Methodsmentioning

confidence: 99%

Tool wear and tool life estimation based on linear regression learning

Karuppusamy

Lee

et al. 2015

2015 IEEE International Conference on Mechatronics and Automation (ICMA)

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Human machine interactions: from past to future- a systematic literature review

Jain,

Gupta,

Temperini

et al. 2024

JMH

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Purpose This paper aims to provide insight into the evolving relationship between humans and machines, understanding its multifaceted impact on our lifestyle and landscape in the past as well as in the present, with implications for the near future. It uses bibliometric analysis combined with a systematic literature review to identify themes, trace historical developments and offer a direction for future human–machine interactions (HMIs). Design/methodology/approach To provide thorough coverage of publications from the previous four decades, the first section presents a text-based cluster bibliometric analysis based on 305 articles from 2,293 initial papers in the Scopus and Web of Science databases produced between 1984 and 2022. The authors used VOS viewer software to identify the most prominent themes through cluster identification. This paper presents a systematic literature review of 63 qualified papers using the PRISMA framework. Findings Next, the systematic literature review and bibliometric analysis revealed four major historical themes and future directions. The results highlight four major research themes for the future: from Taylorism to advanced technologies; machine learning and innovation; Industry 4.0, Society 5.0 and cyber–physical system; and psychology and emotions. Research limitations/implications There is growing anxiety among humankind that in the future, machines will overtake humans to replace them in various roles. The current study investigates the evolution of HMIs from their historical roots to Society 5.0, which is understood to be a human-centred society. It balances economic advancement with the resolution of social problems through a system that radically integrates cyberspace and physical space. This paper contributes to research and current limited knowledge by identifying relevant themes and offering scope for future research directions. A close look at the analysis posits that humans and machines complement each other in various roles. Machines reduce the mechanical work of human beings, bringing the elements of humanism and compassion to mechanical tasks. However, in the future, smart innovations may yield machines with unmatched dexterity and capability unthinkable today. Originality/value This paper attempts to explore the ambiguous and dynamic relationships between humans and machines. The present study combines systematic review and bibliometric analysis to identify prominent trends and themes. This provides a more robust and systematic encapsulation of this evolution and interaction, from Taylorism to Society 5.0. The principles of Taylorism are extended and redefined in the context of HMIs, especially advanced technologies.

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Continuous Emotion Estimation for Human Machine Interaction

Cited by 2 publications

References 0 publications

Tool wear and tool life estimation based on linear regression learning

Tool wear and tool life estimation based on linear regression learning

Human machine interactions: from past to future- a systematic literature review

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