Investigation into the Effects of Backrest Angle and Stick Location on Female Strength

Lo, Victor Ei-Wen; Chao, Shu-Min

doi:10.3390/ijerph19010007

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Studies have shown that leaning the seat backwards can slightly increase G-tolerance [31,49]. According to such research, seat backrest angles ranging from 13 deg to 30 deg are implemented in fighter aircraft such as the F-35 and F-16 [50]. Seat position within the HCS gondola can typically be adjusted to situate the end-effector at the desired position relative to the gondola CoR.…”

Section: Hcs Configurational Designmentioning

confidence: 99%

Performance Improvement of Human Centrifuge Systems through Multi-Objective Configurational Design Optimisation

Winter,

Mohajer,

Nahavandi

et al. 2023

Aerospace

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Human Centrifuge Systems (HCSs) are an effective training tool to improve the G-acceleration and Spatial Disorientation (SD) tolerance of aircrew. Though highly capable HCSs are available, their structure and performance are yet to be fully optimised to efficiently recreate the G-vectors produced using Aircraft Combat Manoeuvres (ACMs). To achieve this improvement, the relationship between configurational design and HCS performance should be profoundly investigated. This work proposes a framework for identifying the optimal configurational design of an active four Degree-of-Freedom (DoF) HCS. The relationship between configurational design parameters and objective criteria is established using inverse kinematics and dynamics. Then, a multi-objective evolutionary optimiser is used to identify the optimum arm length and seat position, minimising the Coriolis effect, relative acceleration ratio, and cost. The results of the work show that the applied optimisation step can significantly contribute to (1) efficiently replicating the aircraft motion, (2) minimising the detrimental effects generated during HCS motion, and (3) reducing the overall cost of the system. The applied methodology can be adapted to HCSs with different structures and DoFs.

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Section: Hcs Configurational Designmentioning

confidence: 99%

Performance Improvement of Human Centrifuge Systems through Multi-Objective Configurational Design Optimisation

Winter,

Mohajer,

Nahavandi

et al. 2023

Aerospace

View full text Add to dashboard Cite

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Smart Attendance Automation System

Shamila,

Prakash,

Ahmed

et al. 2023

E3S Web Conf.

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The smart attendance marking system, which is based on face recognition technology, is a modern solution that may aid in the automation of the attendance management process in a variety of organizations, including schools, colleges, and corporations. Using facial recognition technology, the research team hopes to create an efficient system for tracking and recording attendance. The system will be developed by gathering and analysing data in order to construct a database of faces that will be used to train deep learning algorithms to recognise faces effectively. Compared to traditional attendance tracking systems, the smart attendance marking system based on face recognition technology has various advantages. For starters, it saves time and lowers manual labour because attendance is automatically tracked. Second, it enhances precision, lowering the possibility of errors. Third, it delivers real-time attendance updates, making it simple for instructors or administration to monitor attendance and take appropriate action. The aim of the work is expected to improve attendance management in various organisations by optimising attendance monitoring methods while decreasing manual effort and errors. Because the system may be connected with existing attendance management systems or utilised as a standalone solution, it is a versatile and adaptable option for a variety of organisations. The paper’s effort is to design, develop, and implement an efficient system that can track and record attendance accurately using facial recognition technology, giving benefits such as time savings, accuracy, real-time updates, and better security.

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Predicting Smartphone Vision Syndrome: A Feasible Approach using Machine Learning Algorithms

Annapurna,

Rajeswari,

Likitha

et al. 2023

E3S Web Conf.

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Smartphone Vision Syndrome (SVS) is an evitable problem for people who spend a great deal of time watching digital screens. It is a major concern for rapid growth in technology where the burden is significantly greater due to factors such as limited access to and use of personal protective equipment, as well as lesser break time. The objective of the model is to achieve a feasible and higher level of eye health for people who are working long hours with digital screens. The dataset is obtained through an online survey form containing metrics that contribute to the occurrence of SVS. After applying Machine Learning algorithms, namely Logistic Regression, Random Forest Classifier, Naïve Bayes and Support Vector Machine (SVM), the model’s overall performance is assessed using the test sample. Accuracies obtained by Random Forest, Support Vector Machine, Logistic Regression, Naïve Bayes, and Gaussian Naïve Bayes are 98.75%, 97.5%, 77.5%, 95% and 96.25%.

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Investigation into the Effects of Backrest Angle and Stick Location on Female Strength

Cited by 3 publications

References 36 publications

Performance Improvement of Human Centrifuge Systems through Multi-Objective Configurational Design Optimisation

Performance Improvement of Human Centrifuge Systems through Multi-Objective Configurational Design Optimisation

Smart Attendance Automation System

Predicting Smartphone Vision Syndrome: A Feasible Approach using Machine Learning Algorithms

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