• Journal of the Korean Society for Precision Engineering
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• v.23 no.12 s.189
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• pp.88-94
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• 2006

In this study, the dynamic impact analysis for the passenger air-bag(PAB) module has been carried out by using FEM to predict the dynamic characteristics of vehicle ride safety against head impact. The impact performance of vehicle air-bag is directly related to the design parameters of passenger air-bag module assembly, such as the tie bar bracket's width and thickness, respectively, However, the product's design of PAB module parameters are estimated through experimental trial and error according to the designer's experience, generally. Therefore, the dynamic analysis of head impact test of the passenger air-bag module assembly of automobile is needed to construct the analytical methodology At first, the FE models, which are consist of instrument panel, PAB Module, and head part, are combined to the whole module system. Then, impact analysis is carried out by the explicit solution procedure with assembled FE model. And the dynamic characteristics of the head impact are observed to prove the effectiveness of the proposed method by comparing with the experimental results. The better optimized impact performance characteristics is proposed by changing the tie bracket's width md thickness of module. The proposed approach of impact analysis will provides an efficient vehicle to improve the design quality and reduce the design period and cost. The results reported herein will provide a better understanding of the vehicle dynamic characteristics against head impact.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.141-150
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• 2018

Crop type classification is essential for supporting agricultural decisions and resource monitoring. Remote sensing techniques, especially using hyperspectral imagery, have been effective in agricultural applications. Hyperspectral imagery acquires contiguous and narrow spectral bands in a wide range. However, large dimensionality results in unreliable estimates of classifiers and high computational burdens. Therefore, reducing the dimensionality of hyperspectral imagery is necessary. In this study, the Random Forest (RF) classifier was utilized for dimensionality reduction as well as classification purpose. RF is an ensemble-learning algorithm created based on the Classification and Regression Tree (CART), which has gained attention due to its high classification accuracy and fast processing speed. The RF performance for crop classification with airborne hyperspectral imagery was assessed. The study area was the cultivated area in Chogye-myeon, Habcheon-gun, Gyeongsangnam-do, South Korea, where the main crops are garlic, onion, and wheat. Parameter optimization was conducted to maximize the classification accuracy. Then, the dimensionality reduction was conducted based on RF variable importance. The result shows that using the selected bands presents an excellent classification accuracy without using whole datasets. Moreover, a majority of selected bands are concentrated on visible (VIS) region, especially region related to chlorophyll content. Therefore, it can be inferred that the phenological status after the mature stage influences red-edge spectral reflectance.