• 한국연소학회:학술대회논문집
• /
• 2002.06a
• /
• pp.85-94
• /
• 2002

Computational flow dynamics(CFD) has been frequently applied to the waste incinerators to understand the flow performance for various design and operating parameters. Though it needs many simplifications and complicated flow models, the reasonability of its results is not fully evaluated. For example, the inlet condition is calculated from an arbitrarily assumed properties of combustion gas release from the waste bed, since the combustion in the bed is difficult to be predicted. In this study, the computational modeling and calculation procedures of CFD for the grate type waste incinerator were evaluated using comparative simulations. Though the assumption method on the generation of the combustion gas directly affected the temperature and gas species concentrations, the overall flow pattern was dominated by the secondary air jets. The gaseous reaction could be included by assuming the release of the products of incomplete combusion from the bed. However, the reaction effficiency cannot not be directly evaluated from the species concentration, since it is not possible to simulate the actual co-existence of fuel rich or oxygen rich puffs over the bed. In predicting the turbulence, the higher order model, such as Reynolds stress model, gave difference shape of local recirculation zones, but similar results was acquired from the standard $k-{\varepsilon}$ model. Introducing radiation model was required for accurate temperature prediction, but it also caused heat imbalance due to the fixed temperature of the inlet, i.e. the waste bed. Thus, the computational modeling procedures on incinerators and the analysis of the predicted results should be progressed carefully. Though not validated experimentally, current simulation method is capable of comparative evaluation on the flow-related parameters such as the furnace shape and secondary air injection using identical inlet conditions. Quantitative analysis using measures of the residence time and mixing is essential to compare the flow performance efficiently.

• Journal of Agricultural Extension & Community Development
• /
• v.21 no.3
• /
• pp.181-213
• /
• 2014

This study analyzes the approach method for the excavation of the outstanding projects in order to improve the ODA quality in the agricultural sector in a situation of the recent quantitative expansion of the Korea's ODA. This analysis is based on the ODA development paradigm of the international society, Korea's ODA development trends, the problems of system, procedure and contents. The analysis drew five approaches, such as an approach based on the strategies, an approach based on the comparative advantage, an approach based on the partnership, an approach based on the networking, an approach of modeling. If each approach method is applied to the excavation of project closely linked to other approach methods, it would be easier to excavate outstanding project. And it could lead to enhancement of project effectiveness. This study introduced the KAPEX program which excavate the customized ODA projects through the application of these approach methods.

• Bulletin of the Korean Chemical Society
• /
• v.29 no.3
• /
• pp.647-655
• /
• 2008

Microsomal prostaglandin E2 synthase (mPGES-1) is a potent target for pain and inflammation. Various QSAR (quantitative structure activity relationship) analyses used to understand the factors affecting inhibitory potency for a series of MK886 analogues. We derived four QSAR models utilizing various quantum mechanical (QM) descriptors. These QM models indicate that steric, electrostatic and hydrophobic interaction can be important factors. Common pharmacophore hypotheses (CPHs) also have studied. The QSAR model derived by best-fitted CPHs considering hydrophobic, negative group and ring effect gave a reasonable result (q2 = 0.77, r2 = 0.97 and Rtestset = 0.90). The pharmacophore-derived molecular alignment subsequently used for 3D-QSAR. The CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) techniques employed on same series of mPGES-1 inhibitors which gives a statistically reasonable result (CoMFA; q2 = 0.90, r2 = 0.99. CoMSIA; q2 = 0.93, r2 = 1.00). All modeling results (QM-based QSAR, pharmacophore modeling and 3D-QSAR) imply steric, electrostatic and hydrophobic contribution to the inhibitory activity. CoMFA and CoMSIA models suggest the introduction of bulky group around ring B may enhance the inhibitory activity.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.23 no.4
• /
• pp.421-431
• /
• 2017

A process-centric simulation modeling method has been proposed to define a shipbuilding simulation model. Existing modeling methods have limitations for expressing various production information for a shipyard. The advanced process-centric modeling method proposed in this paper offers an improvement, effectively representing production information and constraints for a shipyard. To achieve this, a method and diagram components to define a process-centric simulation model in detail have been suggested. The modeling method can assign priorities when multiple products and facilities are assigned to the process. And layer concept was applied to express simulation model with hierarchical structure. To verify the effectiveness of the modeling method, comparative analysis has been performed and the actual shipbuilding process has been modeled using the proposed method. When a single facility was used for various purposes, we found that the proposed method was more advantageous than existing methods. As a result, it was possible to express constraints and flows that were difficult to identify with existing process-centric simulation modeling methods, and the methods were improved for use in shipyard production planning verification simulations.

• Journal of Information Technology Services
• /
• v.15 no.2
• /
• pp.169-184
• /
• 2016

Smartphone use is rapidly spreading due to the advantage of being able to connect to the Internet anytime, anywhere--and mobile app development is developing accordingly. The characteristic of the mobile app market is the ability to launch one's app into foreign markets with ease as long as the platform is the same. However, a large amount of prior research asserts that consumers behave differently depending on their culture and, from this perspective, various studies comparing the differences between consumer behaviors in different countries exist. Accordingly, this research, which uses online product reviews (OPRs) in order to analyze the cultural differences in consumer behavior comparatively by nationality, proposes to compare the U.S. and South Korea by selecting ten apps which were released in both countries in order to perform a sentimental analysis on the basis of star ratings and, based on those ratings, to interpret the sentiments in reviews. This research was carried out to determine whether, on the basis of ratings analysis, analysis of review contents for sentiment differences, analysis of LDA topic modeling, and co-occurrence analysis, actual differences in online reviews in South Korea and the U.S. exist due to cultural differences. The results confirm that the sentiments of reviews for both countries appear to be more negative than those of star ratings. Furthermore, while no great differences in high-raking review topics between the U.S. and South Korea were revealed through topic modeling and co-occurrence analyses, numerous differences in sentiment appeared-confirming that Koreans evaluated the mobile apps' specialized functions, while Americans evaluated the mobile apps in their entirety. This research reveals that differences in sentiments regarding mobile app reviews due to cultural differences between Koreans and Americans can be seen through sentiment analysis and topic modeling, and, through co-occurrence analysis, that they were able to examine trends in review-writing for each country.

• Knowledge Management Research
• /
• v.18 no.3
• /
• pp.101-124
• /
• 2017

Recently, 'happiness' has become a major issue of national level, exceeding the matter of personal issue. Especially, Korea has actually increased its GDP by focusing on the economic growth for decades, and now it has achieved the economic/technical development as an IT power. However, Korean people's satisfaction with life called 'happiness index' is moving back every year. Even though there have been continuous efforts to enhance the national happiness by mentioning it as an essential issue in the national level, there are not many researches related to it. This study drew measures to enhance happiness by extracting happiness factors and unhappiness factors of Korea through social network service. Especially, it aims to analyze, compare, and apply happiness factors and unhappiness factors of three countries such as the US, UK, and Brazil with higher happiness indexes than Korea. For this, through the topic modeling of text mining technique, postings including keywords about happiness and unhappiness were collected/analyzed from Twitter of Korea, the US, UK, and Brazil. The significance of this study is to discuss measures to increase happiness and to decrease unhappiness by mining/analyzing the actual public opinions about happiness and unhappiness in four countries like Korea, the US, UK, and Brazil by using the topic modeling. Through this, the quality of life of Korean people could be improved by suggesting measures to enhance happiness and to decrease unhappiness in the level of individual, family, society, and government.

• Geophysics and Geophysical Exploration
• /
• v.10 no.2
• /
• pp.154-160
• /
• 2007

This paper compares the characteristics of three different algorithms for three-dimensional (3D) magnetotelluric (MT) modeling. These methods are developed by Mackie et al. (1994), Sasaki (1999) and Nam et al. (2007). The first and second methods are based on the finite difference method (FDM), while the last one the finite-element method (FEM). MT responses, apparent resistivities and phases, for a COMMEMI 3D-2 model show a good agreement with integral equation solutions and only minor discrepancies are found over the anomalous bodies in the 3D model. The computation time of the two methods based on FDM is short and the static divergence correction contributes to speed up. The FEM modeling scheme is accurate but slow.

• The Journal of Society for e-Business Studies
• /
• v.24 no.2
• /
• pp.113-124
• /
• 2019

Today many companies are offering IoT-enabled products and place emphasis on security from the planning stage to protect their products and user information from external threats. The present security levels, however, remain low because the time and resources invested in developing security requirements for each device are far from enough to meet the needs of a wide range of IoT products. Nevertheless, vulnerabilities of IoT devices have been reported continuously, which calls for more detailed security requirements for home IoT devices. In this context, this research identified threats of home IoT systems by using Microsoft Threat Modeling Tool. It then suggested measures to enhance the security of home IoT devices by developing security assessment items through comparative analysis of the identified threats, domestic and global vulnerability assessment standards and related research. It also verified the effectiveness of the developed security requirements by testing them against the existing ones, and the results revealed the security requirements developed in this research proved to be more effective in identifying vulnerabilities.

• Nuclear Engineering and Technology
• /
• v.55 no.12
• /
• pp.4367-4381
• /
• 2023

Nuclear power plants are increasingly being equipped with digital I&C systems. Although some probabilistic safety assessment (PSA) models for the digital I&C of nuclear power plants have been constructed, there is currently no specific internationally agreed guidance for their modeling. This paper presents an initiative by the OECD Nuclear Energy Agency called "Digital I&C PSA - Comparative application of DIGital I&C Modelling Approaches for PSA (DIGMAP)", which aimed to advance the field towards practical and defendable modeling principles. The task, carried out in 2017-2021, used a simplified description of a plant focusing on the digital I&C systems important to safety, for which the participating organizations independently developed their own PSA models. Through comparison of the PSA models, sensitivity analyses as well as observations throughout the whole activity, both qualitative and quantitative lessons were learned. These include insights on failure behavior of digital I&C systems, experience from models with different levels of abstraction, benefits from benchmarking as well as major contributors to the core damage frequency and those with minor effect. The study also highlighted the challenges with modeling of large common cause component groups and the difficulties associated with estimation of key software and common cause failure parameters.

• Journal of the Korean Society of Radiology
• /
• v.15 no.4
• /
• pp.547-554
• /
• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.