Joon-Young Kim’s scientific contributions

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Publications (8)


Development of a Korean Offshore Wind Power HSE Risk Assessment Module based on Systems Engineering Approach
  • Article

August 2024

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5 Reads

International Journal of Naval Architecture and Ocean Engineering

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Keonwoo Nam

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[...]

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Joon-Young Kim

Fig. 2. Modeling scope As shown in Figure 2, in the view of CM equilibrium model, the modeling scope of AFWS is from requirements to facility configuration of AFWS, which covers the scope of Design Bases Document(DBD) of AFWS.
Fig. 14. Diagram showing relationships among DR, DBS, SSCS, DP, SOI/SDI and equipment properties
A Development Case of SysML Based Nuclear Power Plant Design Bases Model
  • Conference Paper
  • Full-text available

May 2018

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509 Reads

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Sensor Drift Detection in SNG Plant using Auto-Associative Kernel Regression

October 2017

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137 Reads

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2 Citations

With the rapid development of ICTs, condition monitoring has been used as a key technology in the plant industries. For reliable condition monitoring, sensors should output same values under same conditions regardless of time, but the sensitivity of sensors is gradually changed due to several factors such as temperature, humidity, contamination, aging, and etc. This type of situation is called as sensor drift problem. To solve this, several methods such as autoassociative neural network, auto-associative support vector regression, and etc. have been developed to detect sensor drifts earlier by estimating new input based on historical data. This study applied the auto-associative kernel regression model into a synthetic natural gas plant which produces synthetic natural gas from coals to detect sensor drifts during operation phase. To validate the auto-associative kernel regression model in the synthetic natural gas plant, a real data collected from an experimental operation are used. Based on the experimental results, the auto-associative kernel regression model can rapidly detect the sensor drift in the synthetic natural gas plant.


Plant Modeling Based on SysML Domain Specific Language

October 2017

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1,047 Reads

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3 Citations

Successful implementation of Model-based Systems Engineering(MBSE) obviously needs a model supporting efficient communication among engineers of various domains. The system modeling language standard, SysML is designed to create MBSE supporting models. However, SysML itself is not practical enough to be used for real-world engineering projects. As SysML is designed for generic systems and requires specialized knowledge, a model written in SysML has a limited capability to support communication between a systems engineer and a subsystem engineer. Our research’s main goal is to develop a SysML based plant model integrating most outputs from plant design phases. As mentioned, a standard SysML based plant model is not specific enough to be understood by plant engineers. To make the SysML model more practical, a customized SysML for the plant engineering domain is required. Unfortunately, current researches on SysML Domain Specific Language(DSL) for the plant engineering industry are still on the early stage. So, as a pilot, we have developed our own SysML-based Piping & Instrumentation Diagram (P&ID) creation environment and P&ID itself for a specific plant system, via widely known SysML modeling tool called MagicDraw. P&ID is one of the most important output during the plant design phase, which contains all information for the plant construction phase. So a SysML based P&ID has a great potential to bridge gaps between plant engineers.




A Study on Early Faults Detection of Pressurizer Pressure Control System using MTS

December 2016

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17 Reads

Pressurizer is one of the major equipment in nuclear power plant (NPP), which controls the reactor cooling system pressure within the allowable range. That is, faults of the pressurizer can be hugely affected to the NPP; thus early faults detection of the pressurizer is very significant to the NPP safety. For this, this study applies Mahalanobis Taguchi System (MTS) which is one of the promising pattern classification methods based on mahalanobis distance concept and Taguchi quality engineering theory to the early faults detection problem of pressurizer pressure control system. To validate the faults detection performance of the MTS, we conducted experiments using data from full scope NPP simulator based on a pressurizer pressure transmitter faults scenario. As a results, MTS can rapidly detect the faults compared to conventional faults detection based on single sensor monitoring.


Table 1 . Dataset description
Table 2 . Variable description
Table 3 . Normalized data of non-occupied(normal) in training dataset
Table 6 . Confusion matrix of test1&2 dataset
Table 9 . Confusion matrix of test1&2 dataset using useful variables
A Study on Occupancy Detection Method using MTS for Efficient Building Energy Savings

December 2016

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241 Reads

To deal with greenhouse gas reduction and limited energy resources, the necessity for efficient building energy management has recently been focused. For the efficient building energy management, accurate occupancy detection is required and also is well-know problem. This study proposed an accurate occupancy detection method based on Mahalanobis-Taguchi System (MTS) which is widely used pattern classification method in other faults diagnosis problems. To validate the MTS based occupancy detection method, we conducted the experiments using two test dataset. The experiential results show accuracies of 97.75%, 86.67% with all variables. However, after useful variable optimization, results shows accuracies of 97.64% and 96.28%.

Citations (2)


... The involvement of data science algorithms in decision making makes the chemical process industry "smart"; and a smart chemical plant is characterized by being faster, more flexible and more efficient to produce highquality services at low cost (Lin et al., 2017). For example, Kim (2017) reported that a smart chemical plant improved 0.5 to 2 times compared to the existing plant. However, data science tools must be considered within the design plan of new smart chemical processes (L. ...

Reference:

Developing a data science platform for pipeline applications Tennessee Eastman
Smart chemical plant architecture development based on a systems engineering
  • Citing Conference Paper
  • October 2017

... Similar researchers explored a methodological approach of MBSE and used UML, SysML, and MARTE modeling tools to solve complex systems (Rashid et al., 2015). By following this approach, MBSE can quickly identify the structural and behavioral aspects of the modeled system (Lee et al., 2017). Figure 2 shows the MBSE methodology framework and how it carries out the verified activity to accomplish and assess the correctness of the model/system. ...

Plant Modeling Based on SysML Domain Specific Language