Search results for: Eun-Ji Lee

Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

Abstract:

In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

Procedia PDF Downloads 226

Authors: Ki-Yong Oh, Eunji Kwak, Due Su Son, Siheon Jung

Abstract:

A pouch type of 10 Ah LiFePO₄ battery cell is characterized with two mechanical responses: swelling and bulk force. Both responses vary upon the state of charge significantly, whereas voltage shows flat responses, suggesting that mechanical responses can become a sensitive gauge to characterize microstructure transformation of a battery cell. The derivative of swelling s with respect to capacity Q, (ds/dQ) and the derivative of force F with respect to capacity Q, (dF/dQ) more clearly identify phase transitions of cathode and anode electrodes in the overall charge process than the derivative of voltage V with respect to capacity Q, (dV/dQ). Especially, the force versus swelling curves over the state of charge clearly elucidates three different stiffness over the state of charge oriented from phase transitions: the α-phase, the β-phase, and the metastable solid-solution phase. The observation from mechanical responses suggests that macro-scale mechanical responses of a battery cell are directly correlated to microscopic transformation of a battery cell.

Keywords: force response, LiFePO₄ battery, strain response, stress response, swelling response

Procedia PDF Downloads 139

Authors: Jae-Hyung Jin, Kyung-Tae Lee, Yee-Seul So, Eunji Jeong, Yeonseon Lee, Dongpil Lee, Dong-Gi Lee, Yong-Sun Bahn

Abstract:

Cryptococcus neoformans causes cryptococcal meningoencephalitis mainly in immunocompromised patients as well as immunocompetent people. But therapeutic options are limited to treat cryptococcosis. Some signaling pathways including cyclic AMP pathway, MAPK pathway, and calcineurin pathway play a central role in the regulation of the growth, differentiation, and virulence of C. neoformans. To understand signaling networks regulating the virulence of C. neoformans, we selected the 114 putative phosphatase genes, one of the major components of signaling networks, in the genome of C. neoformans. We identified putative phosphatases based on annotation in C. neoformans var. grubii genome database provided by the Broad Institute and National Center for Biotechnology Information (NCBI) and performed a BLAST search of phosphatases of Saccharomyces cerevisiae, Aspergillus nidulans, Candida albicans and Fusarium graminearum to Cryptococcus neoformans. We classified putative phosphatases into 14 groups based on InterPro phosphatase domain annotation. Here, we constructed 170 signature-tagged gene-deletion strains through homologous recombination methods for 91 putative phosphatases. We examined their phenotypic traits under 30 different in vitro conditions, including growth, differentiation, stress response, antifungal resistance and virulence-factor production.

Keywords: human fungal pathogen, phosphatase, deletion library, functional genomics

Procedia PDF Downloads 329