Selected Publications


Bae, J. W., Bae, S. W., Moon, I. C., & Kim, T. G. (2016). Efficient Flattening Algorithm for Hierarchical and Dynamic Structure Discrete Event Models. ACM Transactions on Modeling and Computer Simulation (TOMACS)26(4), 25.
 
Abstract:
Discrete event models are widely used to replicate, analyze, and understand complex systems. DEVS (Discrete Event System Specification) formalism enables hierarchical modeling, so it provides an efficiency in the model development of complex models. However, the hierarchical modeling incurs prolonged simulation executions due to indirect event exchanges through the model hierarchy. Although direct event paths are applied to mitigate this overhead, the situation becomes even worse when a model changes its structures during simulation execution, called a dynamic structure model. This article suggests Coupling Relation Graph (CRG) and Strongly Coupled Component (SCC) concepts to improve hierarchical and dynamic structure DEVS simulation execution. CRG is a directed graph representing DEVS model structure, and SCC is a group of connected components in a CRG. Using CRG and SCC, this article presents (1) how to develop CRG from a DEVS model and (2) how to construct and update direct event paths with respect to dynamic structural changes. In particular, compared to the previous works, the proposed method focuses on the reduction of the updating costs for the direct event paths. Through theoretical and empirical analyses, this article shows that the proposed method significantly reduces the simulation execution time, especially when a simulation model contains lots of components and changes its model structures frequently. We expect that the proposed method would support the faster simulation executions of complex hierarchical and dynamic structure models.
 
Source Webpage : 
 
Bibtex:
@article{Bae:2016:EFA:2892241.2875356,
 author = {Bae, Jang Won and Bae, Sang Won and Moon, Il-Chul and Kim, Tag Gon},
 title = {Efficient Flattening Algorithm for Hierarchical and Dynamic Structure Discrete Event Models},
 journal = {ACM Trans. Model. Comput. Simul.},
 issue_date = {March 2016},
 volume = {26},
 number = {4},
 month = feb,
 year = {2016},
 issn = {1049-3301},
 pages = {25:1--25:25},
 articleno = {25},
 numpages = {25},
 url = {http://doi.acm.org/10.1145/2875356},
 doi = {10.1145/2875356},
 acmid = {2875356},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {DEVS, Flattening algorithm, dynamic structure model, graph-based acceleration, hierarchical model},
} 
 
No. Subject
14 J. W. Bae et al., "Layered Behavior Modeling via Combining Descriptive and Prescriptive Approaches: A Case Study of Infantry Company Engagement," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 7, pp. 2551-2565, July 2020 file
13 Kyungwoo Song, Wonsung Lee, and Il-Chul Moon. Neural Ideal Point Estimation Network. AAAI Conference on Artificial Intelligence (AAAI 2018). New Orleans. Feb 2-7 file
12 Sungrae Park, Jun-Keon Park, Su-Jin Shin, and Il-Chul Moon. Adversarial Dropout for Supervised and Semi-Supervised Learning. AAAI Conference on Artificial Intelligence (AAAI 2018). New Orleans. Feb 2-7 file
11 Wonsung Lee, Kyungwoo Song, and Il-Chul Moon. Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information, ACM Conference on Information and Knowledge Management (CIKM 2017), Singapore, Nov 6-10 file
10 J. W. Bae et al., "Evaluation of Disaster Response System Using Agent-Based Model With Geospatial and Medical Details," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 9, pp. 1454-1469, Sept. 2018. file
9 Su-Jin Shin, and Il-Chul Moon, "Guided HTM: Hierarchical Topic Model with Dirichlet Forest Priors", IEEE Transactions on Knowledge & Data Engineering. vol. 29 no. 2, p. 330-343, Feb., 2017 file
8 Lee, W.S., Lee, Y.M., Kim, H.Y., and Moon, I.-C., 2016. Bayesian Nonparametric Collaborative Topic Poisson Factorization for Electronic Health Records-Based Phenotyping. IJCAI 2016. pp.2544-2552, New York, USA file
» Bae, J. W., Bae, S. W., Moon, I. C., & Kim, T. G. (2016). Efficient Flattening Algorithm for Hierarchical and Dynamic Structure Discrete Event Models. ACM Transactions on Modeling and Computer Simulation (TOMACS), 26(4), 25. file
6 Bae, J. W., & Moon, I. C. (2016). LDEF Formalism for Agent-Based Model Development. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(6), 793–808. file
5 Lee, W.S., Park, S.R. and Moon, I.-C., 2014. Modeling Multiple Fields of Collective Emotions with Brownian Agent-Based Model. In AAMAS. Paris, France. file
4 Shinjae Yoo, Yiming Yang, Frank Lin, and Il-Chul Moon, Mining Social Networks for Personalized Email Prioritization, ACM SIGKDD Conference, Paris, France, Jun, 28, 2009 file
3 Il-Chul Moon and Kathleen M. Carley, Self-Organizing Social and Spatial Networks under What-if Scenarios, Autonomous Agent and Multi-Agent Systems (AAMAS’07), Honolulu, Hawaii, May 14-18, 2007, pp 127-134 file
2 Yiming Yang, Shinjae Yoo, Frank Lin, and Il-Chul Moon, Personalized Email Prioritization Based on Content and Social Network Analysis, IEEE Intelligent Systems, Special Issue on Social Learning, Vol. 25, Issue 4, pp 12-18, Jul/Aug 2010 file
1 Il-Chul Moon and Kathleen M. Carley, Modeling and Simulation of Terrorist Networks in Social and Geospatial dimensions, IEEE Intelligent Systems, Special Issue on Social Computing, Vol. 22, pp 40-49, Sep/Oct. 2007 file