Similarity measures for identifying material parameters from hysteresis loops using inverse analysis

Jekel, Charles F.; Venter, Gerhard; Venter, Martin Philip; Stander, Nielen; Haftka, Raphael T.

doi:10.1007/s12289-018-1421-8

Cited by 93 publications

(43 citation statements)

References 36 publications

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“…The Frechet distance was then subtracted from 1 in order to produce a metric that increases as the shape of the curves becomes more similar. The discrete Frechet distance was computed using the ‘similarity measures’ Python package 51 .…”

Section: Methodsmentioning

confidence: 99%

Contextual dependencies expand the re-usability of genetic inverters

et al. 2021

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The implementation of Boolean logic circuits in cells have become a very active field within synthetic biology. Although these are mostly focussed on the genetic components alone, the context in which the circuit performs is crucial for its outcome. We characterise 20 genetic NOT logic gates in up to 7 bacterial-based contexts each, to generate 135 different functions. The contexts we focus on are combinations of four plasmid backbones and three hosts, two Escherichia coli and one Pseudomonas putida strains. Each gate shows seven different dynamic behaviours, depending on the context. That is, gates can be fine-tuned by changing only contextual parameters, thus improving the compatibility between gates. Finally, we analyse portability by measuring, scoring, and comparing gate performance across contexts. Rather than being a limitation, we argue that the effect of the genetic background on synthetic constructs expands functionality, and advocate for considering context as a fundamental design parameter.

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Section: Methodsmentioning

confidence: 99%

Contextual dependencies expand the re-usability of genetic inverters

et al. 2021

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“…To look at eye-hand following behavior, we extracted finger and eye movement paths in one-finger typing where the eyes stay in the keyboard area. We examined the dissimilarity between the finger movement and eye movement path by means of the Partial Curve Mapping (PCM) method, which uses a combination of arc length and area to determine the similarity between curves [63,27]. We found a positive correlation between WPM and dissimilarity, β = 0.16, p < .001.…”

Section: Eye-hand Followingmentioning

confidence: 97%

How We Type: Eye and Finger Movement Strategies in Mobile Typing

Jiang

Jokinen

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Relatively little is known about eye and finger movement in typing with mobile devices. Most prior studies of mobile typing rely on log data, while data on finger and eye movements in typing come from studies with physical keyboards. This paper presents new findings from a transcription task with mobile touchscreen devices. Movement strategies were found to emerge in response to sharing of visual attention: attention is needed for guiding finger movements and detecting typing errors. In contrast to typing on physical keyboards, visual attention is kept mostly on the virtual keyboard, and glances at the text display are associated with performance. When typing with two fingers, although users make more errors, they manage to detect and correct them more quickly. This explains part of the known superiority of two-thumb typing over one-finger typing. We release the extensive dataset on everyday typing on smartphones.

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“…There are also some other specifically designed measurement methods like cosine distance and Hausdorff distance, which focus on geometric features, or Hamming distance, Jaccard distance, and correlation distance, which focus on statistical properties. The area between a pair of trajectories can also be used as a distance measure [32]. However, most of these methods ignore the temporal property of trajectories.…”

Section: Geometric Distance Metricmentioning

confidence: 99%

“…1. Distance-based measure: Hausdorff distance, Frechet distance, DTW [33], LCSS [34], ERP [35], EDR [36], and area-based distance [32]. 2.…”

Section: Experiments Setupmentioning

confidence: 99%

UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

Guo

Shekhar

Xiong

et al. 2019

IJGI

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Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model which takes both interpolation error and positioning error into account by making use of motion features of trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called UTSM based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.

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Similarity measures for identifying material parameters from hysteresis loops using inverse analysis

Cited by 93 publications

References 36 publications

Contextual dependencies expand the re-usability of genetic inverters

Contextual dependencies expand the re-usability of genetic inverters

How We Type: Eye and Finger Movement Strategies in Mobile Typing

UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

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