• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.25-33
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• 2020

In most plant layout optimization researches, MILP(Mixed Integer Linear Programming) problems, in which the objective function includes the costs of pipelines connecting process equipment and cost associated with safety issues, have been employed. Based on these MILP problems, various optimization solvers have been applied to investigate the optimal solutions. To consider safety issues on the objective function of MILP problems together, the accurate information about the impact and the frequency of potential accidents in a plant should be required to evaluate the safety issues. However, it is really impossible to obtain accurate information about potential accidents and this limitation may reduce the reliability of a plant layout problem. Moreover, in real industries such as plant engineering companies, the plant layout is previously fixed and the considerations of various safety instruments and systems have been performed to guarantee the plant safety. To reflect these situations, the two step optimization problems have been designed in this study. The first MILP model aims to minimize the costs of pipelines and the land size as complying sufficient spaces for the maintenance and safety. After the plant layout is determined by the first MILP model, the optimal locations of blast walls have been investigated to maximize the mitigation impacts of blast walls. The particle swarm optimization technique, which is one of the representative sampling approaches, is employed throughout the consideration of the characteristics of MILP models in this study. The ethylene oxide plant is tested to verify the efficacy of the proposed model.

• Journal of the Korean Institute of Gas
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• v.22 no.4
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• pp.13-26
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• 2018

Currently, local regulations, such as KS Code, do not clearly specify how to calculate the range of hazardous area, so the dispersion modeling program should be used to select dispersion. The purpose of this study is to present a methodology of determining the range of hazardous area which is simpler and more reasonable than modelling by using representative materials and process conditions. Based on domestic and overseas regulations that are currently in effect, variables affecting distance to LFL(Lower Flammable Limit) were selected. A total of 16 flammable substances were modelled for substance variables, process conditions variables, and weather conditions variables, and the statistical analysis selected the variables that affect them. Using the selected variables, a three-step classification method was prepared to select the range of locations subject to explosion hazard.

• Bulletin of the Korean Chemical Society
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• v.35 no.12
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• pp.3637-3641
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• 2014

In this study, we analyzed the toxicity of mixtures of dimethylformamide (DMF) and methyl ethyl ketone (MEK) or DMF and toluene (TOL) and predicted their toxicity using quantitative structure-activity relationships (QSAR). A QSAR model for single substances and mixtures was analyzed using multiple linear regression (MLR) by taking into account the statistical parameters between the observed and predicted $EC_{50}$. After preprocessing, the best subsets of descriptors in the learning methods were determined using a 5-fold cross-validation method. Significant differences in physico-chemical properties such as boiling point (BP), specific gravity (SG), Reid vapor pressure (rVP), flash point (FP), low explosion limit (LEL), and octanol/water partition coefficient (Pow) were observed between the single substances and the mixtures. The $EC_{50}$ of the mixture of DMF and TOL was significantly lower than that of DMF. The mixture toxicity was directly related to the mixing ratio of TOL and MEK (MLR $EC_{50}$ equation = $1.76997-1.12249{\times}TOL+1.21045{\times}MEK$), as well as to SG, VP, and LEL (MLR equation $EC_{50}=15.44388-19.84549{\times}SG+0.05091{\times}VP+1.85846{\times}LEL$). These results show that QSAR-based models can be used to quantitatively predict the toxicity of mixtures used in manufacturing industries.

• 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 Korea Society of Environmental Toocicology Conference
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• 2003.10a
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• pp.91-93
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• 2003

It has been estimated that the equivalent of approximately $US 50 billion has been spent on research on the behavior and fate of pesticides in the environment since Rachel Carson published “Silent Spring” in 1962. Much of the resulting knowledge has been summarized explicitly in computer algorithms in a variety of empirical, deterministic, and probabilistic simulation models. These models describe and predict the transport, degradation and resultant concentrations of pesticides in various compartments of the environment during and after application. In many cases the known errors of model predictions are large. For this reason they are typically designed to be “conservative”, i.e., err on the side of over-prediction of concentrations in order to err on the side of safety. These predictions are then compared with toxicity data, from tests of the pesticide on a series of standard representative biota, including terrestrial and aquatic indicator species and higher animals (e.g., wildlife and humans). The models' predictions are good enough in some cases to provide screening of those compounds which are very unlikely to do harm, and to indicate those compounds which must be investigated further. If further investigation is indicated a more detailed (and therefore more complicated) model may be employed to give a better estimate, or field experiments may be required. A model may be used to explore “what if” questions leading to possible alternative pesticide usage patterns which give lower potential environmental concentrations and allowable exposures. We are currently at a maturing stage in this research where the knowledge base of pesticide behavior in the environmental is growing more slowly than in the past. However, innovative uses are being made of the explosion in available computer technology to use models to take ever more advantage of the knowledge we have. In this presentation, current developments in the state of the art as practiced in North America and Europe will be presented. Specifically, we will look at the efforts of the ‘Focus’ consortium in the European Union, and the ‘EMWG’ consortium in North America. These groups have been innovative in developing a process and mechanisms for discussion amongst academic, agriculture, industry and regulatory scientists, for consensus adoption of research advances into risk management methodology.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.21-29
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• 2020

The Topic Modeling research, the methodology for deduction keyword within literature, has become active with the explosion of data from digital society transition. The research objective is to investigate research trends in D.N.A.(Data, Network, Artificial Intelligence) field using DTM(Dynamic Topic Model). DTM model was applied to the 1,519 of research projects with SW·A.I technology classifications among ICT(Information and Communication Technology) field projects between 6 years(2015~2020). As a result, technology keyword for D.N.A. field; Big data, Cloud, Artificial Intelligence, extended keyword; Unstructured, Edge Computing, Learning, Recognition was appeared every year, and accordingly that the above technology is being researched inclusively from other projects can be inferred. Finally, it is expected that the result from this paper become useful for future policy·R&D planning and corporation's technology·marketing strategy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.1
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• pp.89-102
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• 2008

Internet becomes more and more popular, and most companies and institutes use web services for e-business and many other purposes. With the explosion of Internet, the attack of internet worm has grown. Simulation is one of the most widely used method to study internet worms. But, it is quite challenging to simulate very large-scale worm attacks because of various reasons. By this reason, we often use the modeling network simulation technique. But, it also has problem that it difficult to apply each worm attacks to simulation. In this paper, we propose worm attack representation and mapping methods for apply worm attack to simulation. The proposed method assist to achieve the simulation efficiency. And we can express each worm attacks more detail. Consequently, the simulation of worm attacks has the time-efficiency and the minuteness.

• Journal of the Korean Electrochemical Society
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• v.22 no.4
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• pp.139-147
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• 2019

Lithium-ion battery (LiB) with high energy density and efficiency has been utilized for the electric vehicle (EV) and energy storage system (ESS) as well as portable devices. However, as explosion accidents have frequently happened till lately, all-solid-state lithium secondary battery (ALSB) began to get in a spotlight because it can secure a very high safety and energy density by substituting flammable organic liquid electrolyte to nonflammable inorganic solid electrolyte. In spite of ALSB's certain merits, it has shown much poorer performance of cells than one of LiB due to some challenges, which have been small or never dealt with in the LiB system. Hence, although plenty of studies made progress to solve them, an approach about design of all-solid-state electrode (ASSE) has been limited on account of difficulty of ALSB's experiments. That is why the virtual 3D structure of an all-solid-state electrode has to be built and used for the prediction of cell performance. In this study, we elucidate how to form the 3D ASSE structure and what to be needed for the simulation of characteristics on ALSB. Furthermore, the ultimate orientation of 3D modeling and simulation for the study of ALSB are briefly suggested.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.867-872
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• 2020

Oil storage tanks are a major structure in chemical industrial complexes. Damage to the structure due to natural disasters or poor management can cause additional damage, such as leakage of chemicals, fire, and explosion, so it is essential to understand the deformation. In this study, data on oil storage tanks were acquired using a 3D laser scanner, and various analyzes were performed for storage tank management by comparing them with design data. Modeling of the oil storage tank was performed using the data and design drawings acquired by a 3D laser scanner. An inspection of the oil storage tank was effectively performed by overlapping. In addition, cross-sectional and exploded views of the deformation were produced to generate visible data on the deformation of the facility, and it was suggested that the oil storage tank had a maximum deformation of -7.16mm through quantitative analysis. Data that can be used for additional work was obtained by producing drawings to be precisely inspected for areas with large deformation. In the future, an inspection of oil storage tanks using 3D laser scanners is quantitative and visible data on oil storage tank deformation. This will greatly improve the efficiency of facility management by rebuilding it.

• Composites Research
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• v.29 no.2
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• pp.53-59
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• 2016

Aircraft wing structure is used as a fuel tank containing the fluid. Fuel tank and joint parts are consists of composite structure. Hydrodynamic Ram(HRAM) effect occurs when the high speed object pass through the aircraft wing or explosion and the high pressure are generated in the fuel tank by HRAM effect. High pressure can cause failure of the fuel tank and the joint parts as well as the aircraft wing structure. To ensure the aircraft survivability design, we shall examine the behavior of the joint parts in HRAM effect. In this study, static tensile tests were conducted on four kind of the composite T-Joints. The failure behavior of the composite T-joint was examined by strain gauges and high speed camera. We examine the validity of the Finite Element Modeling by comparing the results of FEA and static tensile tests. The failure stresses and failure pressure of the composite T-Joint were calculated by FEA.