Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions

M Aliramezani, CR Koch, M Shahbakhti - Progress in Energy and …, 2022 - Elsevier
A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization,
diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) …

Time-series forecasting with deep learning: a survey

B Lim, S Zohren - … Transactions of the Royal Society A, 2021 - royalsocietypublishing.org
Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

Strongsort: Make deepsort great again

Y Du, Z Zhao, Y Song, Y Zhao, F Su… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly,
remarkable progresses have been achieved. However, the existing methods tend to use …

Observation-centric sort: Rethinking sort for robust multi-object tracking

J Cao, J Pang, X Weng… - Proceedings of the …, 2023 - openaccess.thecvf.com
Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that
objects move linearly. While this assumption is acceptable for very short periods of …

Colloquium: Machine learning in nuclear physics

A Boehnlein, M Diefenthaler, N Sato, M Schram… - Reviews of modern …, 2022 - APS
Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Flow network based generative models for non-iterative diverse candidate generation

E Bengio, M Jain, M Korablyov… - Advances in Neural …, 2021 - proceedings.neurips.cc
This paper is about the problem of learning a stochastic policy for generating an object (like
a molecular graph) from a sequence of actions, such that the probability of generating an …

Advanced Bayesian multilevel modeling with the R package brms

PC Bürkner - arXiv preprint arXiv:1705.11123, 2017 - arxiv.org
The brms package allows R users to easily specify a wide range of Bayesian single-level
and multilevel models, which are fitted with the probabilistic programming language Stan …

A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve

D Yang, X Zhang, R Pan, Y Wang, Z Chen - Journal of Power Sources, 2018 - Elsevier
The state-of-health (SOH) estimation is always a crucial issue for lithium-ion batteries. In
order to provide an accurate and reliable SOH estimation, a novel Gaussian process …

[PDF][PDF] Deep learning

I Goodfellow - 2016 - synapse.koreamed.org
An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

[HTML][HTML] Forecasting carbon emissions due to electricity power generation in Bahrain

MR Qader, S Khan, M Kamal, M Usman… - … Science and Pollution …, 2021 - Springer
Global warming and climate change have become one of the most embarrassing and
explosive problems/challenges all over the world, especially in third-world countries. It is …