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Sökning: WFRF:(Ghosh Soumitra)

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1.
  • Banerjee, Sourasekhar, et al. (författare)
  • Application of deep learning for energy management in smart grid
  • 2022
  • Ingår i: Deep learning in data analytics. - Cham : Springer. - 9783030758547 - 9783030758554 ; , s. 221-239
  • Bokkapitel (refereegranskat)abstract
    • In the modern electronic power system, energy management and load forecasting are important tasks. Energy management systems are designed concerning monitoring and optimizing the energy requirement in smart systems. This research work is divided into two parts. The first part will contain load forecasting and energy management in a smart grid. Load forecasting in the smart grid can be divided into three parts long-term, mid-term, and short-term load forecasting. The second part will describe energy usage optimization for the electric vehicle. Here we will show grids to vehicle energy demand management and optimization. This chapter will first introduce different deep learning techniques and then discuss their applications related to smart-grid and smart vehicle.
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2.
  • Sakornsakolpat, Phuwanat, et al. (författare)
  • Genetic landscape of chronic obstructive pulmonary disease identifies heterogeneous cell-type and phenotype associations
  • 2019
  • Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 51:3, s. 494-505
  • Tidskriftsartikel (refereegranskat)abstract
    • Chronic obstructive pulmonary disease (COPD) is the leading cause of respiratory mortality worldwide. Genetic risk loci provide new insights into disease pathogenesis. We performed a genome-wide association study in 35,735 cases and 222,076 controls from the UK Biobank and additional studies from the International COPD Genetics Consortium. We identified 82 loci associated with P < 5 x 10-8; 47 of these were previously described in association with either COPD or population-based measures of lung function. Of the remaining 35 new loci, 13 were associated with lung function in 79,055 individuals from the SpiroMeta consortium. Using gene expression and regulation data, we identified functional enrichment of COPD risk loci in lung tissue, smooth muscle, and several lung cell types. We found 14 COPD loci shared with either asthma or pulmonary fibrosis. COPD genetic risk loci clustered into groups based on associations with quantitative imaging features and comorbidities. Our analyses provide further support for the genetic susceptibility and heterogeneity of COPD.
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