T. Song

2015–2016

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Whom T. Song trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Chih-Jen Lin (in Evaluation & Benchmarks)
  2. Vladimir Naumovich Vapnik (in Statistical Learning Theory)

Papers from T. Song's perspective

Ordered by how strongly the researchers T. Song trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles (IEEE Transactions on Vehicular Technology)
  2. Methods for state-of-charge determination and their applications
  3. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking (IEEE Transactions on Signal Processing)
  4. The Nature of Statistical Learning
  5. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods
  6. Intelligent prognostics for battery health monitoring based on sample entropy (Expert systems with applications)
  7. LIBSVM: A library for support vector machines
  8. The WEKA data mining software: an update
  9. Comparison of prognostic algorithms for estimating remaining useful life of batteries
  10. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles

Their papers