Cheol Young Park

Predictive situation awareness model for smart manufacturing

Smart manufacturing relies on a combination of different sources providing key information to support diverse activities throughout the manufacturing process. Most smart manufacturing systems focus on activities directly related to the management of robots, conveyor belts, maintenance logs, and others that ensure the process runs smoothly. An initial step to support such smart manufacturing systems is an awareness process for estimating current situations and predicting future situations in manufacturing, called Predictive Manufacturing Situation Awareness (MSAW). Our research addresses developing an MSAW system with the goal of enhancing industrial competitiveness (e.g., lower cost in shorter time with higher quality) for the manufacturing industry. This requires constant monitoring of market conditions, prices of manufacturing assets, and other inputs that would help to define how the production line behaves. This input is highly stochastic, which makes fusing the data from the diverse sources a challenge. In such situations, the MSAW system requires efficient knowledge representation for various situations and expeditious reasoning methods for estimating current situations as well as predicting future situations. In this paper, we provide an overview of the data fusion process supporting MSAW, including the representation of situations with associated uncertainty, and reasoning methods to support improved manufacturing processes.

A new research tool for hybrid Bayesian networks using script language

While continuous variables become more and more inevitable in Bayesian networks for modeling real-life applications in complex systems, there are not much software tools to support it. Popular commercial Bayesian network tools such as Hugin, and Netica etc., are either expensive or have to discretize continuous variables. In addition, some free programs existing in the literature, commonly known as BNT, GeNie/SMILE, etc, have their own advantages and disadvantages respectively. In this paper, we introduce a newly developed Java tool for model construction and inference for hybrid Bayesian networks. Via the representation power of the script language, this tool can build the hybrid model automatically based on a well defined string that follows the specific grammars. Furthermore, it implements several inference algorithms capable to accommodate hybrid Bayesian networks, including Junction Tree algorithm (JT) for conditional linear Gaussian model (CLG), and Direct Message Passing (DMP) for general hybrid Bayesian networks with CLG structure. We believe this tool will be useful for researchers in the field.

Message passing for Hybrid Bayesian Networks using Gaussian mixture reduction

Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., artificial intelligence, data fusion, medical diagnosis, fraud detection, etc). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks. Inference in CG networks can be NP-hard even for special-case structures, such as poly-trees, where inference in discrete Bayesian networks can be performed in polynomial time. This paper presents an extension to the Hybrid Message Passing inference algorithm for general CG networks (i.e., networks with loops and many discrete parents). The extended algorithm uses Gaussian mixture reduction to prevent an exponential increase in the number of Gaussian mixture components. Experimental results compare performance of the new algorithm with existing algorithms.

A reference model for model-based design of critical infrastructure protection systems

Today’s war field environment is getting versatile as the activities of unconventional wars such as terrorist attacks and cyber-attacks have noticeably increased lately. The damage caused by such unconventional wars has also turned out to be serious particularly if targets are critical infrastructures that are constructed in support of banking and finance, transportation, power, information and communication, government, and so on. The critical infrastructures are usually interconnected to each other and thus are very vulnerable to attack. As such, to ensure the security of critical infrastructures is very important and thus the concept of critical infrastructure protection (CIP) has come. The program to realize the CIP at national level becomes the form of statute in each country. On the other hand, it is also needed to protect each individual critical infrastructure. The objective of this paper is to study on an effort to do so, which can be called the CIP system (CIPS). There could be a variety of ways to design CIPS’s. Instead of considering the design of each individual CIPS, a reference model-based approach is taken in this paper. The reference model represents the design of all the CIPS’s that have many design elements in common. In addition, the development of the reference model is also carried out using a variety of model diagrams. The modeling language used therein is the systems modeling language (SysML), which was developed and is managed by Object Management Group (OMG) and a de facto standard. Using SysML, the structure and operational concept of the reference model are designed to fulfil the goal of CIPS’s, resulting in the block definition and activity diagrams. As a case study, the operational scenario of the nuclear power plant while being attacked by terrorists is studied using the reference model. The effectiveness of the results is also analyzed using multiple analysis models. It is thus expected that the approach taken here has some merits over the traditional design methodology of repeating requirements analysis and system design.