Forthcoming and Online First Articles

International Journal of Human Factors Modelling and Simulation

International Journal of Human Factors Modelling and Simulation (IJHFMS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Human Factors Modelling and Simulation (2 papers in press)

Regular Issues

  • AI Model Design and the Challenging of CPU Acceleration by ChatGPT   Order a copy of this article
    by Zhenling Su, Qi Li, Song Wang, Lin Meng 
    Abstract: ChatGPT is a powerful AI language model that demonstrates an understanding of human-like text and generates responses based on an extensive corpus of training data. Deep learning has recently witnessed widespread adoption in applications such as object classification, object detection, appearance inspection, and anomaly detection, achieving remarkable accuracy. However, it is essential to acknowledge that all deep learning techniques rely on the underlying computer architecture and necessitate hardware utilisation to execute applications. Consequently, resource-scarce environments, such as edge CPU, can significantly impact the performance of these applications. This paper addresses the challenges of designing AI models using ChatGPT and explores methods for accelerating these models on CPUs. Specifically, we endeavor to engage ChatGPT in discussions to assist in creating an accurate and personalized AI model. Subsequently, we aim to collaborate with ChatGPT to generate assembly code tailored for MIPS CPUs.
    Keywords: ChatGPT; Deep Learning; AI Model Design; CPU Acceleration; MIPS CPUs.
    DOI: 10.1504/IJHFMS.2024.10063156
     
  • Modelling Human Variability in DES Using a Modified Multivariate Accommodation Technique   Order a copy of this article
    by Randall Hodkin, Michael Miller, Thomas Ford 
    Abstract: Current Discrete-Event Simulations (DES) models of human performance often assume human tasks are independent and identically distributed. However, the literature suggests that individual task times between tasks requiring similar human resources are correlated. Thus, this incorrect assumption may produce models that under predict the variability in human and total system performance. This research discusses proposed modifications of the Boundary Zone Method (BZM), a multivariate accommodation technique developed for anthropometrics, that can be combined with multi-stage simulation techniques to overcome this limitation of DES Human Performance Modelling (HPM). A modified BZM algorithm tailored for HPM is applied to multiple datasets and shown to characterise the variability of the performance envelope for individuals with differing performance. The modified Human Performance BZM (HPBZM) algorithm was effective in analysing data from three large human performance data sets. In a representative dataset, the algorithm was able to generate 23 representative boundary cases that characterise 86.5% of a highly correlated dataset containing data from 901 individuals while retaining information critical to modelling performance variability in DES.
    Keywords: Modelling; Human Variability; Discrete Event Simulation; DES; Multivariate Accommodation; Human Performance Modelling; HPM; Individual Differences; Correlation; Performance Envelope.
    DOI: 10.1504/IJHFMS.2024.10063900
     

 

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