Teaching

COM529 – Research Studies and Project Management

COM566 – Natural Language Processing

Studies Adviser for first and second year students

Supervision of final year Degree, MSc and PhD students

Research Areas

• Intelligent health monitoring and fault diagnosis

• Pattern recognition, neural networks, signal processing

• Semantic similarity analysis, conversational agents

Research Projects

• Selection of most Relevant Signal Components for Fault Classification (2006-2008), supported by The Nuffield Foundation.

• Identifying Key Variables for Effective Monitoring and Process Control (2007-2010), supported by Seagate Technology (Ireland) and DEL.

• PhD projects

Selected Publications

• Yuhua Li, Maguire L. (2011)
  "Selecting critical patterns based on local geometrical and statistical information,"
  IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1189-1201.

• Yuhua Li, (2011)
  “Selecting training points for one-class support vector machines,”
  Pattern Recognition Letters, vol. 32, no.11, pp. 1517-1522.

• Kui Zhang, Yuhua Li, Philip Scarf, Andrew Ball (2011)
  "Feature selection for high-dimensional machinery fault diagnosis data using multiple models and radial basis function networks,"
  Neurocomputing, vol. 74, no. 17, pp. 2941-2952.

• Yuhua Li, McLean D, Bandar Z, O'Shea J, Crokett K (2006)
  “Sentence similarity using semantic nets and corpus statistics,”
  IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 8, pp. 1138-1150.

• Yuhua Li, Gu F, Harris G, Ball AD, Bennett N (2005)
  “The measurement of instantaneous angular speed,”
  Mechanical Systems and Signal Processing, vol. 19, no. 4, pp. 786-805.

• Yuhua Li, Bandar Z, McLean D (2003)
  “An approach for measuring semantic similarity using multiple information sources,”
  IEEE Transactions on Knowledge and Data Engineering, vol. 15, no.4, pp. 871-882.
   
  (Cited by over 500 times)

• Yuhua Li, Pont MJ, Jones NB (2002)
  “Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where ‘unknown’ faults may occur,”
  Pattern Recognition Letters, vol.23, no.5, pp. 569-577.