Human Factors Principles Underlying Glyph Design: A Review of the Literature and an Agenda for Future Research

Siva, Navaneethan; Chaparro, Alex; Palmer, Evan M.

doi:10.1037/e572172013-345

Cited by 2 publications

(2 citation statements)

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“…Siva, Palmer, & Chaparro (2012) reviewed the available literature on glyph perception and defined an agenda for further investigation to optimize their design. That work identified four categories of glyphbased tasks that pertain to interfaces: (i) searching for a particular glyph or glyph feature, (ii) comparing glyphs, (iii) holistically perceiving glyph displays to ascertain global patterns, and (iv) tracking dynamically changing glyph and glyph features over time.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of MILSTD 2525 Glyph Features in a Visual Search Paradigm

Siva

Chaparro

Palmer

2014

Proceedings of the Human Factors and Ergonomics Society Annual

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MILSTD 2525 is a document that outlines the composition and use of a set of standardized symbology by the US Department of Defense to represent vehicles, equipment and personnel on tactical interfaces. These symbols are primarily multivariate glyphs that visualize the status of military units. This study selected a subset of commonly used glyph features in order to investigate their relative efficiency in a search paradigm. Performance across the different features as well as within levels of each feature was examined. Stimuli were tested using an oddball search paradigm with set sizes of 6, 12 and 18. The dependent variables of interest were search efficiency, RT, and accuracy. Results show that search asymmetries occur with MILSTD 2525 glyph features and that these features differ in search efficiency from one another. The authors discuss the relative search performance of these features and implications for glyph design.

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Section: Introductionmentioning

confidence: 99%

Evaluation of MILSTD 2525 Glyph Features in a Visual Search Paradigm

Siva

Chaparro

Palmer

2014

Proceedings of the Human Factors and Ergonomics Society Annual

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“…The authors selected the features for the training phase based on several methods, such as autocorrelation; however, as methods such as autocorrelation are linear, the role of the features in the construction of a nonlinear model remains questionable. According to a review published in [7], the major factors that affect energy consumption are climate, building system, occupants, and socioeconomic characteristics. Although the studies identified a set of five main categories and other sub-factors affecting building energy consumption, the subfactors can be a major factor, depending on the case study.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Forecasting in a University Office by Artificial Intelligence Techniques: An Analysis of the Exogenous Data Effect on the Modeling

Sadeghian Broujeny,

Ben Ayed,

Matalah

2023

Energies

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The forecasting of building energy consumption remains a challenging task because of the intricate management of the relevant parameters that can influence the performance of models. Due to the powerful capability of artificial intelligence (AI) in forecasting problems, it is deemed to be highly effective in this domain. However, achieving accurate predictions requires the extraction of meaningful historical knowledge from various features. Given that the exogenous data may affect the energy consumption forecasting model’s accuracy, we propose an approach to study the importance of data and selecting optimum time lags to obtain a high-performance machine learning-based model, while reducing its complexity. Regarding energy consumption forecasting, multilayer perceptron-based nonlinear autoregressive with exogenous inputs (NARX), long short-term memory (LSTM), gated recurrent unit (GRU), decision tree, and XGboost models are utilized. The best model performance is achieved by LSTM and GRU with a root mean square error of 0.23. An analysis by the Diebold–Mariano method is also presented, to compare the prediction accuracy of the models. In order to measure the association of feature data on modeling, the “model reliance” method is implemented. The proposed approach shows promising results to obtain a well-performing model. The obtained results are qualitatively reported and discussed.

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Human Factors Principles Underlying Glyph Design: A Review of the Literature and an Agenda for Future Research

Cited by 2 publications

References 0 publications

Evaluation of MILSTD 2525 Glyph Features in a Visual Search Paradigm

Evaluation of MILSTD 2525 Glyph Features in a Visual Search Paradigm

Energy Consumption Forecasting in a University Office by Artificial Intelligence Techniques: An Analysis of the Exogenous Data Effect on the Modeling

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