• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.143-150
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• 2014

Cloud computing is fast becoming the wave of the future for both home and business computing. Because of this growing acceptance, we can expect an explosion of diverse cloud service providers in the coming years. However, the cloud is not a single entity, rather it is a set of many disconnected islands of application (SaaS), infrastructure (IaaS), and different platform (PaaS) services. Cloud brokering mechanisms are essential to transform the heterogeneous cloud market into a commodity-like service. Cloud service brokers (CSBs) are the new form of business entities to help to aggregate the scattered set of cloud services and make conveniently available to their diverse users. CSBs can reserve a certain percentage of their clients' (users') demand and satisfy the remaining portion in an on-demand basis. In doing so, they need to minimize cost of both reserved and on-demand instances as well as the distance of a link between the cloud service provider (CSP) and the user. The study proposes a reservation approach with a mixed integer model to optimizes the cloud service cost and quality.

• Journal of Energy Engineering
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• v.25 no.4
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• pp.85-92
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• 2016

Since the power density of the VHTR(Very High Temperature Reactor) is lower, there is less possibility of core melt. VHTR has no risk of explosion caused by hydrogen generation when the loss of coolant accident occurs, which is another advantage. Along with safety benefit, it can be used as a process heat supplier near demand facilities because coolant temperature is very high enough to be used for industrial purpose. In this paper, we designed the primary system using VHTR and the secondary system providing electricity and process heat. Based on that 350 MW thermal reactor proposed by NGNP(Next Generation Nuclear Part), we developed conceptual model that the IHX(Intermediate Heat Exchanger) loop transports 300 MW thermal energy to the secondary system. In addition, we analyzed thermodynamic behavior and performed the efficiency analysis and optimization study depending on major parameters.

• Journal of the Korean Institute of Gas
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• v.13 no.3
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• pp.49-53
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• 2009

Relief systems can improve the process safety because it has the function for the prevention of overpressure. Flare stacks is necessary to avoid explosion, radiation, or toxicity by waste-gas emitted from relief system. Safe combustion is one of the important factors to improve safety and the quantity and velocity emitted is ruled in the API code 521. Due to the pressure of released gas and mass flow, a flame from flare stack is similar to jet fire. In this study, we have investigated the effect of flame form on complete combustion and heat emission. API code was similar to jet fire model in flame length, the flame had an effect on the ground.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.248-256
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• 2020

In this study, predictive models based on machine learning for evaluating the extent of hazardous zone of explosive gases are developed. They are able to provide important guidelines for installing the explosion proof apparatus. 1,200 research data sets including 12 combustible gases and their extents of hazardous zone are generated to train predictive models. The extent of hazardous zone is set to an output variable and 12 variables affecting an output are set as input variables. Multiple linear regression, principal component regression, and artificial neural network are employed to train predictive models. Mean absolute percentage errors of multiple linear regression, principal component regression, and artificial neural network are 44.2%, 49.3%, and 5.7% and root mean square errors are 1.389m, 1.602m, and 0.203 m respectively. Therefore, it can be concluded that the artificial neural network shows the best performance. This model can be easily used to evaluate the extent of hazardous zone for explosive gases.

• Journal of KIISE
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• v.44 no.11
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• pp.1178-1193
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• 2017

It is very important to understand system behaviors in collective pattern for each knowledge domain. However, there are structural limitations to represent collective behaviors because of the size of system components and the complexity of their interactions, causing the state explosion problem. Further composition with other systems is mostly impractical because of exponential growth of their size and complexity. This paper presents a practical method to model the collective behaviors, based on a new concept of domain engineering: behavior ontology. Firstly, the ontology defines each collective behavior of a system from active ontology. Secondly, the behaviors are formed in a quantifiably abstract lattice, called common regular expression. Thirdly, a lattice can be composed with other lattices based on quantifiably common elements. The method can be one of the most innovative approaches in representing system behaviors in collective pattern, as well as in minimization of system states to reduce system complexity. For implementation, a prototype tool, called PRISM, has been developed on ADOxx Meta-Modelling Platform.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.933-937
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• 2002

The fiber reinforced composite material is widely used in the multi-industrial field where the weight reduction of the infrastructure is demanded because of their high specific modulus and specific strength. Pressure vessels using this composite material in comparison with conventional metal vessels can be applied in the field where lightweight and the high pressure are demanded from the defense and aerospace industry to rocket motor case due to the merits which are energy cutdown the weight reduction and decrease of explosive damage preceding to the sudden explosion which is generated by the pressure leakage condition). In this paper, for nonlinear finite element analysis of E-glass/epoxy filament winding composite pressure vessel receiving an internal pressure, the standard interpretation model is developed by using the ANSYS, general commercial software, which is verified as the accuracy and useful characteristic of the solution based on Auto LISP and ANSYS APDL. Both the preprocessor for doing exclusive analysis of filament winding composite pressure vessel and postprocessor that simplifies result of analysis have been developed to help the design engineers.

• Journal of The Korean Astronomical Society
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• v.34 no.4
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• pp.309-311
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• 2001

Strong thermal X-ray emission, called Galactic Ridge X-ray Emission, is observed along the Galactic plane (Koyama et al. 1986). The origin of hot ($\~$7 keV) component of GRXE is not known, while cool ($\~$0.8 keV) one is associated with supernovae (Kaneda et al. 1997, Sugizaki et al. 2001). We propose a possible mechanism to explain the origin; locally strong magnetic fields of $B_{local}\;\~30{\mu}G$ heat interstellar gas to $\~$7 keV via magnetic reconnection (Tanuma et al. 1999). There will be the small-scale (< 10 pc) strong magnetic fields, which can be observed as $(B)_{obs} \;\~3{\mu}G$ by integration of Faraday Rotation Measure, if it is localized by a volume filling factor of f $\~$ 0.1. In order to examine this model, we solved three-dimensional (3D) resistive magnetohydrodynamic (MHD) equations numerically to examine the magnetic reconnect ion triggered by a supernova shock (fig.l). We assume that the magnetic field is Bx = 30tanh(y/20pc) $\mu$G, By = Bz = 0, and the temperature is uniform, at the initial condition. We put a supernova explosion outside the current sheet. The supernova-shock, as a result, triggers the magnetic reconnect ion, and the gas is heatd to > 7 keV. The magnetic reconnect ion heats the interstellar gas to $\~$7 keV in the Galactic plane, if it occurs in the locally strong magnetic fields of $B_{local}\;\~30{\mu}G$. The heated plasma is confined by the magnetic field for $\~10^{5.5} yr$. The required interval of the magnetic reconnect ions (triggered by anything) is $\~$1 - 10 yr. The magnetic reconnect ion will explain the origin of X-rays from the Galactic ridge, furthermore the Galactic halo, and clusters of galaxies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.939-949
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• 2017

Prognostics and health management(PHM) is actively utilized by industry as an essential technology focusing on accurately monitoring the health state of a system and predicting the remaining useful life(RUL). An effective PHM is expected to reduce maintenance costs as well as improve safety of system by preventing failure in advance. With these advantages, PHM can be applied to the battery system which is a core element to provide electricity for devices with mobility, since battery faults could lead to operational downtime, performance degradation, and even catastrophic loss of human life by unexpected explosion due to non-linear characteristics of battery. In this paper we mainly review a recent progress on various models for predicting RUL of battery with high accuracy satisfying the given confidence interval level. Moreover, performance evaluation metrics for battery prognostics are presented in detail to show the strength of these metrics compared to the traditional ones used in the existing forecasting applications.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.103-116
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• 2003

States explosion due to composition of spaces of data, temporal, and locational values is one of the well-known critical problems which cause difficulty in understanding and analysing real-time systems specified with state-based formal methods. In order to overcome this problem, this paper presents an abstraction method for state minimization based on an abstraction in system specification and an abstraction in system execution. The first is named the syntactic in system specification and an abstraction in system execution. The first is named the syntactic abstraction, through which the patterns of the unconditionally internalized computation and the repetition and selection structures are abstracted. The latter is named the semantic abstraction, through which the patterns of the execution space represented with data. Through the abstractions, the components of a system in specification and execution model is hierarchically organized. The system can be analyzed briefly in the upper level in an skeleton manner with low complexity. The system, however, can be abstraction method for the state minimization and the decrease in analysis complexity through the abstraction with examples.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.5
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• pp.376-384
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• 2020

An energy storage system is composed of lithium-ion batteries in modern applications. Batteries are regarded as storage devices for renewable and residual energy. The failure of batteries can cause the performance reduction and explosion of battery systems. High maintenance cost is essential when dealing with the problem of battery safety. Therefore an accurate health diagnosis is required to ensure the high reliability of battery systems. A battery pack is a combination of single cells in series and parallel connections. A battery pack has to consider various factors to assess battery health. Battery health involves conventional factors and additional factors, such as cell-to-cell imbalance. For large applications, state-of-health (SOH) can be inaccurate because of the lack of factors that indicate the state of the battery pack. In this study, six characterization factors are proposed for improving the SOH estimation of battery packs. The six proposed characterization factors can be regarded as health indicators (HIs). The six HIs are applied to the principal component analysis (PCA) algorithm. To reflect information regarding capacity, voltage, and temperature, the PCA algorithm extracts new degradation factors by using the six HIs. The new degradation factors are applied to a multiple regression model. Results show the advancement and improvement of SOH estimation.