Miltos Alamaniotis

/ / Published in
Assistant Professor
AET 2.346
210-458-6237
miltos.alamaniotis@utsa.edu
https://ceid.utsa.edu/malamaniotis/
 

Ai LAB Website: https://sites.google.com/view/aai-lab/home

Areas of Teaching Interest

Applied Artificial Intelligence

Areas of Research Interest

  • Applied Artificial Intelligence
  • Smart Electric Power Systems and Smart Grids
  • Machine Learning in National Security Applications
  • Intelligent Control Methods in Power Systems
  • Intelligent Systems for Signal Processing, and Detection Algorithms
  • Machine Learning Applications:
  • Nuclear Security
  • Smart Cities

Educational Background

Ph.D., Applied Intelligent Systems
School of Nuclear Engineering, Purdue University West Lafayette, IN, USA

Selected Publications

  • Alamaniotis, M., “Fuzzy Leaky Bucket System for Intelligent Management of Consumer Electricity Elastic Load in Smart Grids,” Frontiers in Artificial Intelligence – Fuzzy Systems, January 2020, pp. 16.
  • Alamaniotis, M., & Karagiannis, G., “Application of Fuzzy Multiplexing of Learning Gaussian Processes for the Interval Forecasting of Wind Speed,” IET Renewable Power Generation – Special Issue from Medpower 2018, January 2020, vol. 14 (1), pp. 100-109.
  • Alamaniotis, M., & Gatsis, N., “Evolutionary Multiobjective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition,” Energies – Special Issue Selected Papers from Medpower 2018, MDPI, June 2019, vol. 12, pp. 2470(1-18).
  • Alamaniotis, M., Bourbakis, N., & Tsoukalas, L.H., “Enhancing Privacy in Smart Cities through Morphing of Anticipated Demand Utilizing Self-Elasticity and Genetic Algorithms,” Sustainable Cities and Society, Elsevier, April 2019, vol. 46, pp. (101426)1-12.
  • Mathew, J., Griffin, J., Alamaniotis, M., Kanarachos, S., & Fitzpatrick, M., “Prediction of welding residual stresses using machine learning: Comparison between neural networks and neuro-fuzzy systems,” Applied Soft Computing Journal, Elsevier, vol. 70, September 2018, pp. 131-146.
  • Alamaniotis, M., Mathew, J., Chroneos, A., Fitzpatrick, M., & Tsoukalas, L.H., “Probabilistic Kernel Machines for Predictive Monitoring of Weld Residual Stress in Energy Systems,” Engineering Applications of Artificial Intelligence, Elsevier, vol. 71, May 2018, pp. 138-154.
  • Mathew, J., Parfitt, D., Wilford, K., Riddle, N., Alamaniotis, M., Chroneos, A., Fitzpatrick, M., “Reactor Pressure Vessel Embrittlement: Insights from Neural Network Modelling,” Journal of Nuclear Materials, Elsevier, vol. 502, April 2018, pp. 311-322.
  • Alamaniotis, M., Gatsis, N., & Tsoukalas, L.H., “Virtual Budget: Integration of Electricity Load and Price Anticipation for Load Morphing in Price-Directed Energy Utilization,” Electric Power Systems Research, Elsevier, vol. 158, May 2018, pp. 284-296.
  • Alamaniotis, M., & Cappelli, M., “Intelligent Identification of Boiling Water Reactor State Utilizing Relevance Vector Machines,” ASME Journal of Nuclear Engineering and Radiation Science, American Society of Mechanical Engineers, vol. 4, April 2018, pp. (020904)1-9.
  • Alamaniotis, M., & Karagiannis, G., “Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short-Term Wind Speed Forecasting in Smart Power,” International Journal of Monitoring and Surveillance Technologies Research, IGI-Global, vol. 5(3), 2017, pp. 1-14.
  • Nasiakou, A., Alamaniotis, M., & Tsoukalas, L.H., “Extending the K-means Clustering Algorithm to improve the Compactness of the Clusters,” Journal of Pattern Recognition Research, vol. 11(1), pp. 61-73, 2016.
  • Fainti, R., Nasiakou, A., Alamaniotis, M., & Tsoukalas, L.H., “Hierarchical Method based on Artificial Neural Networks for Power Output Prediction of a Combined Cycle Power Plant,” International Journal of Monitoring and Surveillance Technologies Research, IGI-Global, vol. 4(4), October 2016, pp. 20-32.
  • Lagari, P.L., Sobes, V., Alamaniotis, M., & Tsoukalas, L.H., “Application of Artificial Neural Networks for Reliable Nuclear Data for Nonproliferation Modeling and Simulation,” International Journal of Monitoring and Surveillance Technologies Research, IGI-Global, vol. 4(4), October 2016, pp. 54-64.
  • Alamaniotis, M., & Tsoukalas, L.H., “Fusion of Gaussian Process Kernel Regressors for Fault Prediction in Intelligent Energy Systems,” International Journal on Artificial Intelligence Tools, World Scientific Publishing Company, vol. 25 (4), August 2016, pp. (#1650023)1-17.
  • Fainti, R., Alamaniotis, M., & Tsoukalas, L.H., “Backpropagation Neural Network for Interval Prediction of Three-Phase Ampacity Level in Power Systems,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global, vol. 4(3), July 2016, pp. 1-20.
  • Lagari, L., Nasiakou, A., & Alamaniotis, M., “Evaluation of Human Machine Interface (HMI) on a Digital and Analog Control Room in Nuclear Power Plants Using a Fuzzy Logic Approach,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global, 2016, vol. 4(2), April 2016, pp. 50-68.
  • Alamaniotis, M., Bargiotas, D., & Tsoukalas, L.H., “Towards Smart Energy Systems: Application of Kernel Machine Regression for Medium Term Electricity Load Forecasting,” SpringerPlus – Engineering, Springer, vol. 5 (1), January 2016, pp. 1-15.
  • Eklund, M., Alamaniotis, M., Hernandez, H., & Jevremovic, T., “Method of Characteristics – A Review with Application to Science and Nuclear Engineering Computation,” Progress in Nuclear Energy, Elsevier, vol. 85, November 2015, pp. 548-567.
  • Chrysikou, V., Alamaniotis, M., & Tsoukalas, L.H., “A Review of Incentive based Demand Response Methods in Smart Electricity Grids,” International Journal of Monitoring and Surveillance Technologies Research, IGI Global Publications, October 2015, pp. 62-73.
  • Alamaniotis, M., Bargiotas, D., Bourbakis, N., & Tsoukalas, L.H., “Genetic Optimal Regression of Relevance Vector Machines for Electricity Price Forecasting in Smart Grids,” IEEE Transactions on Smart Grid, Institute of Electrical and Electronic Engineers, vol. 6(6), November 2015, pp. 2997-3005.