On the public citation graph

Limiting conditions prediction using machine learning for loss of condenser vacuum event

https://doi.org/10.1016/j.net.2023.08.038
0 signed · 24 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-04898-2_132openalex-snapshot
Bartlett’s Test
inferredcites →https://doi.org/10.1007/s10509-019-3602-4openalex-snapshot
A hybrid ensemble method for pulsar candidate classification
inferredcites →https://doi.org/10.1007/s11721-007-0002-0openalex-snapshot
Particle swarm optimization
inferredcites →https://doi.org/10.1016/j.anucene.2020.107395openalex-snapshot
Application of machine learning methods to predict a thermal conductivity model for compacted bentonite
inferredcites →https://doi.org/10.1016/j.anucene.2020.107871openalex-snapshot
An assembly-level neutronic calculation method based on LightGBM algorithm
inferredcites →https://doi.org/10.1016/j.net.2021.02.015openalex-snapshot
A study of predicting irradiation-induced transition temperature shift for RPV steels with XGBoost modeling
inferredcites →https://doi.org/10.1016/j.net.2021.11.006openalex-snapshot
Application of a combined safety approach for the evaluation of safety margin during a Loss of Condenser Vacuum event
inferredcites →https://doi.org/10.1080/03610919408813222openalex-snapshot
One-sided nonparametric tolerance limits
inferredcites →https://doi.org/10.1088/issn.1742-6596openalex-snapshot
Journal of Physics: Conference Series
inferredcites →https://doi.org/10.1109/icmla51294.2020openalex-snapshot
2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)
inferredcites →https://doi.org/10.1109/iske41214.2017openalex-snapshot
2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)
inferredcites →https://doi.org/10.1109/tcyb.2019.2950779openalex-snapshot
A Survey of Optimization Methods From a Machine Learning Perspective
inferredcites →https://doi.org/10.1126/science.220.4598.671openalex-snapshot
Optimization by Simulated Annealing
inferredcites →https://doi.org/10.1145/2939672openalex-snapshot
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
inferredcites →https://doi.org/10.11909/j.issn.1671-5411.2019.08.002openalex-snapshot
Current applications of big data and machine learning in cardiology.
inferredcites →https://doi.org/10.18637/jss.v036.i05openalex-snapshot
tolerance: AnRPackage for Estimating Tolerance Intervals
inferredcites →https://doi.org/10.3390/info9070149openalex-snapshot
Effective Intrusion Detection System Using XGBoost
inferredcites →https://doi.org/10.48550/arxiv.1603.02754openalex-snapshot
XGBoost: A Scalable Tree Boosting System
inferredcites →https://doi.org/10.5555/944790.944815openalex-snapshot
Classes of kernels for machine learning: a statistics perspective

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4658207openalex-extracted
Implementation of Probabilistic Machine Learning Algorithms to Predict and Evaluate Nuclear Accidents from the Perspective of the Vver-1200 Reactor
inferred← citeshttps://doi.org/10.1016/j.anucene.2026.112516openalex-snapshot
Thermal-hydraulic analysis of condenser transients in a large-scale LWR: impact of storm in the Persian Gulf, sea-side pump failures, and condensate pump trips of the BNPP
inferred← citeshttps://doi.org/10.1016/j.net.2025.103661openalex-snapshot
Hybrid machine learning model with optimization algorithm for predicting the incubation dose of void swelling in irradiated metals
inferred← citeshttps://doi.org/10.1016/j.pnucene.2024.105285openalex-snapshot
Application of artificial neural network for assembly homogenized equivalence parameter generation
inferred← citeshttps://doi.org/10.1016/j.pnucene.2024.105390openalex-snapshot
A hybrid model based on complementary ensemble empirical mode decomposition, sample entropy and long short-term memory neural network for the prediction of time series signals in NPPs
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.