Modern Heuristics Research Group - MHRG

Modern Heuristics Research Group - MHRG Modern Heuristics Research Group (MHRG) is a research group that focuses on theoretical and applied

Our research group focuses on theoretical as well as applied research in Computational Intelligence algorithms, such as Artificial Neural Networks, Fuzzy Logic Systems and Unsupervised Learning techniques. We are applying these methods in areas of Energy, Cyber Security, Human-Machine Interfacing,, Intelligent Control Systems, Software Defined Networks, Robotics, Visualizations and others. Please check the list of publications of our recently published conference and journal papers.

The Virtual Reality lab of MHRG
09/12/2017

The Virtual Reality lab of MHRG

Check out the new Virtual Reality Lab in the VCU Engineering Computer Science Dept. on Friday:

MHRG's machine learning based process optimization for Biomass Process Demonstration Unit at Idaho National Laboratory
09/06/2017

MHRG's machine learning based process optimization for Biomass Process Demonstration Unit at Idaho National Laboratory

Graduate students Kasun Amarasinghe and Daniel Marino sit in a computer control room watching readouts from a grinder as it devours bale after bale of corn stover.

04/22/2015

RICHMOND, Virginia (April 21, 2015) – Since joining Virginia Commonwealth University School of Engineering in August 2014, Milos Manic, Ph.D., has secured

04/14/2015

Idaho National Laboratory Heavy Vehicle Simulator located at the Center for Advanced Energy Studies.

Prof. Milos Manic (MHRG Director)
01/30/2014

Prof. Milos Manic (MHRG Director)

Implementation of tools for increased state awareness of building managers. The tools enable easy to use state awareness...
01/30/2014

Implementation of tools for increased state awareness of building managers. The tools enable easy to use state awareness with linguistic anomaly detection as well as occupant comfort reporting with subjective and objective data.

References:
D. Wijayasekara, M. Manic, C. Rieger, "Fuzzy Linguistic Knowledge Based Behavior Extraction for Building Energy Management Systems," in Proc. IEEE Symposium on Resilience Control Systems, ISRCS 2013, San Francisco, California, Aug. 13-15, 2013.
http://mhrg.if.uidaho.edu/papers/2013/ISRCS13_WijaManicReiger_TEMSTBehaviorExtraction.pdf

O. Linda, D. Wijayasekara, M. Manic, C. Rieger, "Computational Intelligence based Anomaly Detection for Building Energy Management Systems," in Proc. IEEE Symposium on Resilience Control Systems, ISRCS 2012, Salt Lake City, Utah, Aug. 14-16, 2012.
http://mhrg.if.uidaho.edu/papers/2012/ISRCS2012_LindaWijaManic_CIBEMS_Anomaly.pdf

Read More: http://mhrg.if.uidaho.edu/RS_EnergyR_TEMST.html

01/20/2014
Using advanced Software Defined Networking (SDN) to achieve true application visualization for high quality, reliable an...
01/20/2014

Using advanced Software Defined Networking (SDN) to achieve true application visualization for high quality, reliable and controllable emergency communication systems

http://nsec.if.uidaho.edu/

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401 W Main Street
Richmond, VA
23284

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