| 1. |
- Saleh, Roxan
(författare)
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Towards Smart Maintenance : Machine-Learning Based Prediction of Retroreflectivity and Color of Road Traffic Signs
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Proper maintenance of road traffic signs is vital for safety, as their low visibility can cause accidents and fatalities. Many countries, including Sweden, lack a systematic approach for replacing signs due to the risky, costly, and complex methods needed to measure their color and retroreflectivity.This thesis introduces a predictive maintenance method for road traffic signs to ensure their visibility day and night. The proposed data-driven models predict sign degradation, helping maintain optimal visibility, decreasing accidents, and enhancing safety, and environmental sustainability by reducing material consumption and waste reduction.This thesis suggests using machine learning methods to predict the values of retroreflectivity (coefficient of retroreflection) and color (daylight chromaticity), and to estimate the status (rejected/accepted) and longevity according to color and retroreflectivity. Datasets collected in Sweden, Denmark, and Croatia were used in this research.Regression and classification models, employing Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN) utilized to predict the degradation of road traffic signs. ANN showed the highest performance, 94% R2 for retroreflectivity predictions and up to 94% accuracy for color and retroreflectivity status. SVM and RF also achieved acceptable accuracies.Statistical methods, including linear and logarithmic regression, were also applied to examine the impact of age on the retroreflectivity values and status, chromaticity, and color status of road traffic signs. Findings revealed age as a significant factor, with a generally linear relationship between chromaticity values and age, except for yellow signs which displayed non-linear patterns between 8 and 22 years. Logarithmic regression models achieved R2 values of 50% and 95%, which are more accurate than those from previous studies. These models reveal an annual decrease in retroreflectivity of 4-5% and a negative correlation with the sign's direction, indicating that signs facing south and west degrade faster due to more solar exposure.Logistic regression and Kaplan-Meier survival analyses were used to assess road traffic signs' longevity. The longevity based on retroreflectivity and color durability varies depending on color, retroreflective sheeting classes, direction, and location.In Sweden, the median lifespan of road traffic signs estimated based on retroreflectivity lasts up to 25 years for red, 20 for yellow, 20 for white, and 35 for blue sheeting. In Croatia, the lifespan is shorter, 12 years for red, 16 for yellow, and 17 for white, 20 for blue.Considering color degradation, the median lifespan of yellow road traffic signs is 45 years, 35 years for white, and blue signs, while red signs have a shorter lifespan. However, the red signs deteriorate in color before retroreflectivity with a median lifespan of 16 years, whereas other signs maintain their color longer. This emphasizes the effect of factors like pigment choice and environmental conditions on the durability of road traffic signs.
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| 2. |
- Saleh, Roxan, et al.
(författare)
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Assessing the color status and daylight chromaticity of road signs through machine learning approaches
- 2023
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Ingår i: IATSS Research. - 0386-1112. ; 47:3, s. 305-317
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Tidskriftsartikel (refereegranskat)abstract
- The color of road signs is a critical aspect of road safety, as it helps drivers quickly and accurately identify and respond to these signs. Properly colored road signs improve visibility during the day and make it easier for drivers to make informed decisions while driving. In order to ensure the safety and efficiency of road traffic, it is essential to maintain the appropriate color level of road signs. The objective of this study was to analyze the color status and daylight chromaticity of in-use road signs using supervised machine learning models, and to explore the correlation between road sign's age and daylight chromaticity. Three algorithms were employed: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The data used in this study was collected from road signs that were in-use on roads in Sweden. The study employed classification models to assess the color status (accepted or rejected) of the road signs based on minimum acceptable color levels according to standards, and regression models to predict the daylight chromaticity values. The correlation between road sign's age and daylight chromaticity was explored through regression analysis. Daylight chromaticity describes the color quality of road signs in daylight, that is expressed in terms of X and Y chromaticity coordinates. The study revealed a linear relationship between the road sign's age and daylight chromaticity for blue, green, red, and white sheeting, but not for yellow. The lifespan of red signs was estimated to be around 12 years, much shorter than the estimated lifespans of yellow, green, blue, and white sheeting, which are 35, 42, 45, and 75 years, respectively. The supervised machine learning models successfully assessed the color status of the road signs and predicted the daylight chromaticity values using the three algorithms. The results of this study showed that the ANN classification and ANN regression models achieved high accuracy of 81% and R2 of 97%, respectively. The RF and SVM models also performed well, with accuracy values of 74% and 79% and R2 ranging from 59% to 92%. The findings demonstrate the potential of machine learning to effectively predict the status and daylight chromaticity of road signs and their impact on road safety in the Swedish context. © 2023 International Association of Traffic and Safety Sciences
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| 3. |
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| 4. |
- Adami, C., et al.
(författare)
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Evolution and stability of altruist strategies in microbial games
- 2012
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Ingår i: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics. - 1539-3755 .- 1550-2376. ; 85:1
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Tidskriftsartikel (refereegranskat)abstract
- When microbes compete for limited resources, they often engage in chemical warfare using bacterial toxins. This competition can be understood in terms of evolutionary game theory (EGT). We study the predictions of EGT for the bacterial "suicide bomber" game in terms of the phase portraits of population dynamics, for parameter combinations that cover all interesting games for two-players, and seven of the 38 possible phase portraits of the three-player game. We compare these predictions to simulations of these competitions in finite well-mixed populations, but also allowing for probabilistic rather than pure strategies, as well as Darwinian adaptation over tens of thousands of generations. We find that Darwinian evolution of probabilistic strategies stabilizes games of the rock-paper-scissors type that emerge for parameters describing realistic bacterial populations, and point to ways in which the population fixed point can be selected by changing those parameters. © 2012 American Physical Society.
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| 5. |
- Adami, C., et al.
(författare)
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Evolutionary game theory using agent-based methods
- 2016
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Ingår i: Physics of Life Reviews. - : Elsevier B.V.. - 1571-0645 .- 1873-1457. ; 19, s. 1-26
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Tidskriftsartikel (refereegranskat)abstract
- Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection–strong mutation limit), but that mathematics is crucial to validate the computational simulations. © 2016 Elsevier B.V.
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| 6. |
- Adami, C., et al.
(författare)
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Evolutionary instability of zero-determinant strategies demonstrates that winning is not everything
- 2013
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Ingår i: Nature Communications. - : Nature Publishing Group. - 2041-1723. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Zero-determinant strategies are a new class of probabilistic and conditional strategies that are able to unilaterally set the expected payoff of an opponent in iterated plays of the Prisoner's Dilemma irrespective of the opponent's strategy (coercive strategies), or else to set the ratio between the player's and their opponent's expected payoff (extortionate strategies). Here we show that zero-determinant strategies are at most weakly dominant, are not evolutionarily stable, and will instead evolve into less coercive strategies. We show that zero-determinant strategies with an informational advantage over other players that allows them to recognize each other can be evolutionarily stable (and able to exploit other players). However, such an advantage is bound to be short-lived as opposing strategies evolve to counteract the recognition. © 2013 Macmillan Publishers Limited. All rights reserved.
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| 7. |
- Adami, C., et al.
(författare)
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Information content of colored motifs in complex networks
- 2011
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Ingår i: Artificial Life. - : MIT Press - Journals. - 1064-5462 .- 1530-9185. ; 17:4, s. 375-390
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Tidskriftsartikel (refereegranskat)abstract
- We study complex networks in which the nodes are tagged with different colors depending on their function (colored graphs), using information theory applied to the distribution of motifs in such networks. We find that colored motifs can be viewed as the building blocks of the networks (much more than the uncolored structural motifs can be) and that the relative frequency with which these motifs appear in the network can be used to define its information content. This information is defined in such a way that a network with random coloration (but keeping the relative number of nodes with different colors the same) has zero color information content. Thus, colored motif information captures the exceptionality of coloring in the motifs that is maintained via selection. We study the motif information content of the C. elegans brain as well as the evolution of colored motif information in networks that reflect the interaction between instructions in genomes of digital life organisms. While we find that colored motif information appears to capture essential functionality in the C. elegans brain (where the color assignment of nodes is straightforward), it is not obvious whether the colored motif information content always increases during evolution, as would be expected from a measure that captures network complexity. For a single choice of color assignment of instructions in the digital life form Avida, we find rather that colored motif information content increases or decreases during evolution, depending on how the genomes are organized, and therefore could be an interesting tool to dissect genomic rearrangements. © 2011 Massachusetts Institute of Technology.
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| 8. |
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| 9. |
- Adami, C., et al.
(författare)
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Thermodynamics of evolutionary games
- 2018
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Ingår i: Physical review. E. - : American Physical Society. - 2470-0045 .- 2470-0053. ; 97:6
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Tidskriftsartikel (refereegranskat)abstract
- How cooperation can evolve between players is an unsolved problem of biology. Here we use Hamiltonian dynamics of models of the Ising type to describe populations of cooperating and defecting players to show that the equilibrium fraction of cooperators is given by the expectation value of a thermal observable akin to a magnetization. We apply the formalism to the public goods game with three players and show that a phase transition between cooperation and defection occurs that is equivalent to a transition in one-dimensional Ising crystals with long-range interactions. We then investigate the effect of punishment on cooperation and find that punishment plays the role of a magnetic field that leads to an "alignment" between players, thus encouraging cooperation. We suggest that a thermal Hamiltonian picture of the evolution of cooperation can generate other insights about the dynamics of evolving groups by mining the rich literature of critical dynamics in low-dimensional spin systems. © 2018 American Physical Society.
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| 10. |
- Albantakis, L., et al.
(författare)
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Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity
- 2014
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Ingår i: PloS Computational Biology. - : Public Library of Science. - 1553-734X .- 1553-7358. ; 10:12
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Tidskriftsartikel (refereegranskat)abstract
- Natural selection favors the evolution of brains that can capture fitness-relevant features of the environment's causal structure. We investigated the evolution of small, adaptive logic-gate networks (“animats”) in task environments where falling blocks of different sizes have to be caught or avoided in a ‘Tetris-like’ game. Solving these tasks requires the integration of sensor inputs and memory. Evolved networks were evaluated using measures of information integration, including the number of evolved concepts and the total amount of integrated conceptual information. The results show that, over the course of the animats' adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks (“brains”) with many concepts, leading to an increase in their internal complexity. © 2014 Albantakis et al.
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