Zobrazeno 1 - 10
of 55
pro vyhledávání: '"V. A. Samaranayake"'
A Multi-Step Approach to Modeling the 24-hour Daily Profiles of Electricity Load using Daily Splines
Autor:
Abdelmonaem Jornaz, V. A. Samaranayake
Publikováno v:
Energies, Vol 12, Iss 21, p 4169 (2019)
Forecasting of real-time electricity load has been an important research topic over many years. Electricity load is driven by many factors, including economic conditions and weather. Furthermore, the demand for electricity varies with time, with diff
Externí odkaz:
https://doaj.org/article/c6a88abcc62b422a9673e9c6b80d9887
Publikováno v:
PLoS ONE, Vol 18, Iss 5, p e0285769 (2023)
A serially dependent Poisson process with time-varying zero-inflation is proposed. Such formulations have the potential to model count data time series arising from phenomena such as infectious diseases that ebb and flow over time. The model assumes
Externí odkaz:
https://doaj.org/article/4fba91a665e14b64878989adc312da02
Publikováno v:
PLoS ONE, Vol 13, Iss 2, p e0193247 (2018)
Human exposure to volatile organic compounds (VOCs) via vapor intrusion (VI) is an emerging public health concern with notable detrimental impacts on public health. Phytoforensics, plant sampling to semi-quantitatively delineate subsurface contaminat
Externí odkaz:
https://doaj.org/article/b3f9e6fe7b80446fb0a316f5ccb13287
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 32:4323-4333
In this article, a novel learning methodology is introduced for the problem of classification in the context of high-dimensional data. In particular, the challenges introduced by high-dimensional data sets are addressed by formulating a $L_{1}$ regul
A serially dependent Poisson process with time-varying zero-inflation is proposed. Such formulations have the potential to model count data time series arising from phenomena such as infectious diseases that ebb and flow over time. The model assumes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e84f18584f114ee02b27319db558a3a
http://arxiv.org/abs/2207.10114
http://arxiv.org/abs/2207.10114
Publikováno v:
INNS Conference on Big Data
In this brief, heterogeneity and noise in big data are shown to increase the generalization error for a traditional learning regime utilized for deep neural networks (deep NNs). To reduce this error, while overcoming the issue of vanishing gradients,
Publikováno v:
INNS Conference on Big Data
In this paper, a novel dimension-reduction approach is presented to overcome challenges such as nonlinear relationships, heterogeneity, and noisy dimensions. Initially, the $p$ p attributes in the data are first organized into random groups. Next, to
Publikováno v:
Stat. 10
Publikováno v:
Journal of Non-Crystalline Solids. 495:107-116
Mathematical models for the chemical durability–composition relation for 5-component iron phosphate glasses, containing a nuclear waste similar to that of the high sulfate (~17 wt%), high soda (~80 wt%) Hanford AZ 102 LAW, have been developed using
Autor:
Liang Wang, V. A. Samaranayake, Nathan W. Weidner, Sisi Que, Kwame Awuah-Offei, Shaochun Yuan
Publikováno v:
Journal of Cleaner Production. 189:30-39
The literature on mining community preferences for mineral development, which is the basis for engaging local communities, mainly focuses on rural communities, and may not provide enough insight into an urban community's needs, concerns, and preferen