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Conference
DSGN - Apparel Design

“A visible functional grasp to measure the complete hand”

Glove and tools are designed to protect our hands, but manufacturers
are limited by the available anthropometric hand data, which fails to reflect
functional measurement changes of the hand while performing tasks.
Advancements in 3D scanning technology have improved the ability to capture
data, but minimal research has focused on capturing functional hand dimensions.
The purpose of this study was to develop a comprehensive protocol to
capture the dorsal and palmar side of the hand in functional positions across a
large population using the Artec Leo. The development of this protocol considered
the following elements; scanning technology, hand positions, hand
support apparatuses, scanning platforms, and standardization across a population.
Two functional hand positions, splayed and grasp, were selected based on
clear visibility of the palmar side. The protocol and final scans contributed to a
robust anthropometric database to improve the design, fit, and function of
products for hands.
Details

589-596 pages 2020 Springer, Cham Colleen Pokorny Bethany Juhnke Linsey Griffin

Academic Journal
Business Analytics

“Additive Dynamic Models for Correcting Numerical Model Outputs”


Numerical air quality models are pivotal for the prediction and assessment of air pollution, but numerical model outputs may be systematically biased. An additive dynamic model is proposed to correct large-scale raw model outputs using data from other sources, including readings collected at ground monitoring networks and weather outputs from other numerical models. An additive partially linear model specification is employed for the nonlinear relationships between air pollutants and covariates. In addition, a multi-resolution basis function approximate is proposed to capture the different small-scale variations of biases, and a discretized stochastic
integro-differential equation is constructed to characterize the dynamic evolution of the random coefficients at each spatial resolution. An expectation-maximization algorithm is developed for parameter estimation and a multi-resolution ensemble-based scheme is embedded to accelerate the computation. For statistical inference, a conditional simulation technique is applied to quantify the uncertainty of parameter estimates and bias correction results. The proposed approach is used to correct the biased raw outputs of PM2.5 from the Community Multiscale Air
Quality (CMAQ) system for China’s Beijing-Tianjin-Hebei region. Our method improves the root mean squared error and continuous rank probability score by 43.70% and 34.76%, respectively. Compared to other statistical methods under different metrics, our model has advantages in both correction accuracy and computational efficiency.
Details

107799 pages 2023 Xiaohui Chang Yewen Chen Hui Huang Fangzhi Luo View on: Google Scholar