• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.123-130
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• 2018

With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.233-242
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• 2014

A magnetically levitated vehicle (Maglev) system is under commercialization as a new transportation system in Korea. The Maglev is operated by an unmanned automatic control system. Therefore, the plan of train operation should be carefully established and validated in advance. In general, when making a train operation plan, statistically predicted traffic data is used. However, a traffic wave often occurs in real train service, and demand-driven simulation technology is required to review a train operation plan and service quality considering traffic waves. We propose a method and model to simulate Maglev operation considering continuous demand changes. For this purpose, we employed a discrete event model that is suitable for modeling the behavior of railway passenger transportation. We modeled the system hierarchically using discrete event system specification (DEVS) formalism. In addition, through implementation and an experiment using the DEVSim++ simulation environment, we tested the feasibility of the proposed model. Our experimental results also verified that our demand-driven simulation technology can be used for a priori review of train operation plans and strategies.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2292-2313
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• 2023

With improving computing performance, various digital platforms are being developed to enable easily utilization of high-performance computing environments. EDISON 1.0 is an online simulation platform widely used in computational science and engineering education. As the research paradigm changes, the demand for developing the EDISON 1.0 platform centered on simulation into the EDISON 2.0 platform centered on data and artificial intelligence is growing. Herein, a data framework, a core module for data-centric research on EDISON 2.0 digital platform, is proposed. The proposed data framework provides the following three functions. First, it provides a data repository suitable for the data lifecycle to increase research reproducibility. Second, it provides a new data model that can integrate, manage, search, and utilize heterogeneous data to support a data-driven interdisciplinary convergence research environment. Finally, it provides an exploratory data analysis (EDA) service and data enrichment using an AI model, both developed to strengthen data reliability and maximize the efficiency and effectiveness of research endeavors. Using the EDISON 2.0 data framework, researchers can conduct interdisciplinary convergence research using heterogeneous data and easily perform data pre-processing through the web-based UI. Further, it presents the opportunity to leverage the derived data obtained through AI technology to gain insights and create new research topics.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.3
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• pp.156-163
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• 2009

In this study, comparative analysis on the back-layer phenomena in the tunnel-fire driven flow is performed using numerical simulation with LES and RANS. FDS(Fire Dynamics Simulator) code is employed to calculate the fire-driven turbulent flow for LES and Smartfire code is used for RANS. Hwang and Wargo's data of scaling tunnel fire experiment are employed to compare with the present numerical simulation. The modeled tunnel is 5.4m(L) ${\times}$ 0.4m(W) ${\times}$ 0.3m(H). Heat Release Rate (HRR) of fire is 3.3kW and ventilation-velocity is 0.33m/s in the main stream. The various grid-distributions are systematically tested with FDS code to analyze the effects of grid size. The LES method with FDS provides an improved back-layer flow behavior in comparison with the RANS (${\kappa}-{\epsilon}$) method by Smartfire. The FDS solvers, however, overpredict the velocity in the center region of flow which is caused by the defects in the tunnel-entrance turbulence strength and in the near-wall turbulent flow in FDS code.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.475-480
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• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

• Journal of the Korea Society for Simulation
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• v.4 no.2
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• pp.61-68
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• 1995

The performance of event-driven logic simulation frequently used for VLSI design verification depends on the data structures for event queues. This paper improves the existing Timing Wheel as a data structure for an event queue. In case of the use of B+ tree, an efficient node degree is also presented based on the experiment results. A new Timing Wheel index structure, which eliminates the insertion and deletion overhead of B+ tree, is proposed and analyzed.

• Journal of the Korean Society for Railway
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• v.10 no.2 s.39
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• pp.224-230
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• 2007

The performance and applicability of FDS code is analyzed for flow simulation in railway tunnel. FDS has been built in NIST(USA) for simulation of fire-driven flow. RANS and DNS's results are compared with FDS's. AJL non-linear ${\kappa}-{\epsilon}$[7,8] model is employed to calculate the turbulent flow for RANS. DNS data by Moser et al.[9] are used to prove the FDS's applicability in the near wall region. Parallel plate is used for simplified model of railway tunnel. Geometrical variables are non-dimensionalized by the height (H) of parallel plate. The length of streamwise direction is 50H and the length of spanwise direction is 5H. Selected Re numbers are 10,667 for turbulent flow and 133 for laminar low. The characteristics of turbulent boundary layer are introduced. AJL model's predictions of turbulent boundary layer are well agreed with DNS data. However, the near wall turbulent boundary layer is not well resolved by FDS code. Slip conditions are imposed on the wall but wall functions based on log-law are not employed by FDS. The heavily dense grid distribution in the near wall region is necessary to get correct flow behavior in this region for FDS.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.50-55
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• 2008

The comparative analysis of fire-driven flow simulation for Dae-Gu subway station was performed using FDS and Fluent. The boundary condition was obtained from analyzed data for Dae-Gu subway fire accident which had been outbreaked in 2003 year. The smoke flow in the second and third basement has been analyzed. The CO and temperature distribution in the train units and station platform have been obtained with FDS and FLUENT and compared with each other. Total simulation time is 600s and the results are compared of each 10sec The analyzed data will be applied to the passenger evacuation simulation for Dae-Gu subway station and used to optimal design method.

• Journal of ILASS-Korea
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• v.19 no.2
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• pp.64-68
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• 2014

A driving vehicle performance which is driven by FTP-75 mode was simulated by computer. Throttle valve position, engine speed, air mass flow rate, fuel consumption et al. were computer simulated. A set of engine part load performance data, automatic transmission shift map and vehicle specifications were used for the computer simulation. Throttle valve position, engine speed, air mass flow rate et al. measured for evaluating the computer simulation results by driving the vehicle with FTP-75 mode on a chassis dynamometer. GT-Power$^{(R)}$ software was used for the computer simulation of the driving vehicle performance. Experimental fuel consumption rate was measured by using an ECU HILS fuel injection system. The experimental data and simulation results were compared. The computer simulation of the driving vehicle performance predicts the measured data well comparatively.