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Park H, Seo J, Kim K, Kim T. Predicting the Wear Amount of Tire Tread Using 1D-CNN. SENSORS (BASEL, SWITZERLAND) 2024; 24:6901. [PMID: 39517798 PMCID: PMC11548380 DOI: 10.3390/s24216901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2024] [Revised: 10/18/2024] [Accepted: 10/24/2024] [Indexed: 11/16/2024]
Abstract
Since excessively worn tires pose a significant risk to vehicle safety, it is crucial to monitor tire wear regularly. This study aimed to verify the efficient tire wear prediction algorithm proposed in a previous modeling study, which minimizes the required input data, and use driving test data to validate the method. First, driving tests were conducted with tires at various wear levels to measure internal accelerations. The acceleration signals were then screened using empirical functions to exclude atypical data before proceeding with the machine learning process. Finally, a tire wear prediction algorithm based on a 1D-CNN with bottleneck features was developed and evaluated. The developed algorithm showed an RMSE of 5.2% (or 0.42 mm) using only the acceleration signals. When tire pressure and vertical load were included, the prediction error was reduced by 11.5%, resulting in an RMSE of 4.6%. These findings suggest that the 1D-CNN approach is an efficient method for predicting tire wear states, requiring minimal input data. Additionally, it supports the potential usefulness of the intelligent tire technology framework proposed in the modeling study.
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Affiliation(s)
| | | | | | - Taewung Kim
- Department of Mechanical Design Engineering, Tech University of Korea, Siheung-si 15073, Gyeonggi-do, Republic of Korea
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Rafiei F, Shekhar M, Rahnev D. The neural network RTNet exhibits the signatures of human perceptual decision-making. Nat Hum Behav 2024; 8:1752-1770. [PMID: 38997452 DOI: 10.1038/s41562-024-01914-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 05/13/2024] [Indexed: 07/14/2024]
Abstract
Convolutional neural networks show promise as models of biological vision. However, their decision behaviour, including the facts that they are deterministic and use equal numbers of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behaviour. Here we develop a new neural network, RTNet, that generates stochastic decisions and human-like response time (RT) distributions. We further performed comprehensive tests that showed RTNet reproduces all foundational features of human accuracy, RT and confidence and does so better than all current alternatives. To test RTNet's ability to predict human behaviour on novel images, we collected accuracy, RT and confidence data from 60 human participants performing a digit discrimination task. We found that the accuracy, RT and confidence produced by RTNet for individual novel images correlated with the same quantities produced by human participants. Critically, human participants who were more similar to the average human performance were also found to be closer to RTNet's predictions, suggesting that RTNet successfully captured average human behaviour. Overall, RTNet is a promising model of human RTs that exhibits the critical signatures of perceptual decision-making.
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Affiliation(s)
- Farshad Rafiei
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Medha Shekhar
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Dobromir Rahnev
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
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Bowers JS, Malhotra G, Dujmović M, Montero ML, Tsvetkov C, Biscione V, Puebla G, Adolfi F, Hummel JE, Heaton RF, Evans BD, Mitchell J, Blything R. Clarifying status of DNNs as models of human vision. Behav Brain Sci 2023; 46:e415. [PMID: 38054298 DOI: 10.1017/s0140525x23002777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature. In our view, these latter issues are contributing to many unjustified claims regarding DNN-human correspondences in vision and other domains of cognition. We explore all these issues in this response.
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Affiliation(s)
- Jeffrey S Bowers
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Gaurav Malhotra
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Marin Dujmović
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Milton L Montero
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Christian Tsvetkov
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Valerio Biscione
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | | | - Federico Adolfi
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
| | - John E Hummel
- Psychology Department, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Rachel F Heaton
- Psychology Department, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Benjamin D Evans
- Department of Informatics, School of Engineering and Informatics, University of Sussex, Brighton, UK
| | - Jeffrey Mitchell
- Department of Informatics, School of Engineering and Informatics, University of Sussex, Brighton, UK
| | - Ryan Blything
- School of Psychology, Aston University, Birmingham, UK
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Bowers JS, Malhotra G, Dujmović M, Llera Montero M, Tsvetkov C, Biscione V, Puebla G, Adolfi F, Hummel JE, Heaton RF, Evans BD, Mitchell J, Blything R. Deep problems with neural network models of human vision. Behav Brain Sci 2022; 46:e385. [PMID: 36453586 DOI: 10.1017/s0140525x22002813] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data.
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Affiliation(s)
- Jeffrey S Bowers
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Gaurav Malhotra
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Marin Dujmović
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Milton Llera Montero
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Christian Tsvetkov
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Valerio Biscione
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Guillermo Puebla
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
| | - Federico Adolfi
- School of Psychological Science, University of Bristol, Bristol, UK ; https://jeffbowers.blogs.bristol.ac.uk/
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
| | - John E Hummel
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Rachel F Heaton
- Department of Psychology, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Benjamin D Evans
- Department of Informatics, School of Engineering and Informatics, University of Sussex, Brighton, UK
| | - Jeffrey Mitchell
- Department of Informatics, School of Engineering and Informatics, University of Sussex, Brighton, UK
| | - Ryan Blything
- School of Psychology, Aston University, Birmingham, UK
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