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Park, Hoon;Suk, Chul-Gi;Kim, Seung-Kon
- Explosives and Blasting
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- v.26 no.1
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- pp.7-14
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- 2008
In order to complete successfully the demolition of a silo structure by means of felling method, structural properties and the geometric design of blast mouth have to be considered. In this study, a commercial software, 3-dimensional applied element analysis (3D AEM), was used to investigate the effect of the geometrical parameters of blast mouth on the collapse behavior of the silo structure.
PDF KSCI

Kibar, Hakan
- Structural Engineering and Mechanics
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- v.62 no.4
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- pp.487-496
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- 2017
The focus of this study were to investigate patterns between wall pressures and stresses with grain moisture of soybean and rice varieties widespread cultivated in Turkey in order to determine needed designing parameters for structure analysis in silos at filling and discharge. In this study, the wall pressures and stresses were evaluated as a function of moisture contents in the range of 8-14% and 10-14% d.b. The pressures and von Mises stresses affected as significant by the change of grain moisture content. The main cause of pressure and stress drops is changed in bulk density. Therefore is extremely important bulk density and moisture content of the product at the structural design of the silos. 4 mm wall thickness, were determined to be safe for von Mises stresses in both soybean and rice silos is smaller than 188000 kPa.
https://doi.org/10.12989/sem.2017.62.4.487 인용 KSCI

Firdaus, Muhammad;Latt, Cho Nwe Zin;Aguilar, Mariz;Rhee, Kyung-Hyune
- Proceedings of the Korea Information Processing Society Conference
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- 2022.05a
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- pp.264-267
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- 2022
Recently, federated learning (FL) has increased prominence as a viable approach for enhancing user privacy and data security by allowing collaborative multi-party model learning without exchanging sensitive data. Despite this, most present FL systems still depend on a centralized aggregator to generate a global model by gathering all submitted models from users, which could expose user privacy and the risk of various threats from malicious users. To solve these issues, we suggested a safe FL framework that employs differential privacy to counter membership inference attacks during the collaborative FL model training process and empowers blockchain to replace the centralized aggregator server.
https://doi.org/10.3745/PKIPS.y2022m05a.264 인용 PDF