• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.215-223
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• 2012

Response surface methodology(RSM) is a very useful statistical techniques for improving and optimizing the product process. By this reason, RSM has been utilized extensively in the industrial world, particularly in the circumstances where several product variables potentially influence some quality characteristic of the product. In order to estimate the optimal condition of product variables, an experiment is being conducted defining appropriate experimental region. However, this experimental region can vary with the experimental circumstances and choice of a researcher. Response surface designs can be classified, according to the shape of the experimental region, into spherical and cuboidal. In the spherical case, the design is either rotatable or very near-rotatable. The central composite design(CCD)s widely used in RSM is an example of 5-level and spherical design. The cuboidal CCDs(CCDs with ${\alpha}=1$) is appropriate when an experimental region is cuboidal but this design dose not satisfy the rotatability as it is not spherical. Practically, a 3-level spherical design is often required in the industrial world where various level of experiments are not available. Box-Behnken design(BBD)s are a most popular 3-level spherical designs for fitting second-order response surfaces and satisfy the rotatability but the experimental region does not vary with the number of variables. The new experimental design with expanded experimental region can be considered if the predicting response at the extremes are interested. This paper proposes a new 3-level spherical RSM which are constructed to expand the experimental region together with number of product variables.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.256-261
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• 2008

The curtain-wall work has been more frequently applied in the construction industry since demand of high-rise buildings has been increased. The curtain-wall work is usually performed with the frame work simultaneously for reducing construction period, but it might be delayed because of several problems caused by interference of process. However, there is not an appropriate tool which can be used by a work manager for adjusting quantity of the construction equipments or the workers when the curtain-wall work was delayed. To resolve this problem a construction simulation anticipating and analyzing potential problems before starting the work can be applied in the curtain wall work. This research suggests a general model for the curtain-wall work by using construction simulation and produces a combination of construction equipments and workers which can estimate optimum work productivity.

• Membrane Journal
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• v.8 no.4
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• pp.235-242
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• 1998

Pervaporation separation of methyl tert-butyl ether (MTBE) and methanol (MeOH) mixture, of which the former compound is well known as the octane booster was carried out. Poly(vinyl alcohol) (PVA) membranes crosslinked with poly(acrylic acid) which have been successfully applied on the water-alcohol mixtures were used in this study. The PVA/PAA ratio in the crosslinked membranes was 95/5, 90/10, 85/15, 80/20, and 75/25 by weight. The operating temperatures were 30, 40, and 50$\circ$C, and the compositions of MTBE and MeOH to be separated were 95/5, 90/10, and 80/20 (MTBE/MeOH) solutions. PVA/PAA=85/15 membrane showed the separation factor $\alpha_{MeOH/MTBE}$=4000 and the permeation rate of 10.1 g/m$^2$hr for MTBE/MeOH=80/20 solution at 50$\circ$. When the same membrane was used, the separation factor and permeation rate for MTBE/MeOH=90/10 solution at 40$\circ$C were $\alpha_{MeOH/MTBE}$=6000 and 8.5 g/m$^2$hr, respectively. Also, the hydrophilic/hydrophobic balance of the membranes would take an important role in the relationships between the membranes and separation performances in terms of the flux and the separation factor.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.3
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• pp.191-197
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• 2019

As the shipbuilding and offshore plant industries grow larger and more complex, their maintenance and inspection systems become more important. Recently, maintenance and inspection systems based on augmented reality have been attracting much attention for improving worker's understanding of work and efficiency, but it is often difficult to work with because accurate matching between the augmented model and reality information is not. To solve this problem, marker based AR technology is used to attach a specific image to the model. However, the markers get damaged due to the characteristic of the shipbuilding and offshore plant industry, and the camera needs to be able to detect the entire marker clearly, and thus requires sufficient space to exist between the operator. In order to overcome the limitations of the existing AR system, in this study, a markerless AR was adopted to accurately recognize the actual model of the pipe system that occupies the most processes in the shipbuilding and offshore plant industries. The matching methodology. Through this system, it is expected that the twist phenomenon of the augmented model according to the attitude of the real worker and the limited environment can be improved.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.711-721
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• 2022

Due to a long-term pandemic of COVID-19 since 2019, our society finally goes into 'un-tact (non-contact) era' in earnest by increasing an importance of non-contacted activities which are able to live without contacts among people. Un-tact era has influenced to generate a huge structural change with digitalization in our daily life as well as industrial society based on digital twin as an essential technology. As a technology representing real world in virtual digital world by integrating various technologies applied to 4^th industrial revolution, digital twin leads an innovation in industrial society with diverse industrial processes. But this technique isn't actively used across all industries due to structural limitations and environmental restrictions of digital twin. Especially, the field of construction and facility is necessary to use digital twin because it requires periodic managements of buildings in daily life and is directly connected with casualties in cases of disaster. But issues faced in reality are acting as an obstacle for applying digital twin. Therefore, this study suggests the necessity applying digital twin in facility safety response based on these issues and emphasizes active applications of digital twin by describing the expected effects being created. Furthermore, it connects to create demands of digital twin by overcoming current issues and suggesting high sustainable development.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.62-67
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• 2023

Due to the prolonged impact of COVID-19, the demand for Information Technology (IT) products is increasing, and their production facilities are expanded. Consequently, the use of harmful and dangerous chemicals are increased, the risk of fire(s) and explosion(s) is also elevated. In order to mitigate these risks, the government sets standards, such as KS C IEC 60079-10-1, and manages explosion-prone hazardous facilities where flammable substances are manufactured, used, and handled. However, using the standards of KS, it is difficult to predict the actual possibility of an explosion in a facility, because ventilation (an important factor) is not considered when setting up a hazardous work environment. In this study, the SEMI S6, Tracer Gas Test was applied to the chemical vapor deposition (CVD) facility, a major part of the display industry, to evaluate ventilation performance and to confirm the possibility of creating a less explosive environment. Based on the results, it was confirmed that the ventilation performance in the assumed scenarios met the standards stipulated in SEMI S6, along with supporting the possibility of creating a less explosive working condition. Therefore, it is recommended to use the prediction tool using engineering techniques, as well as KS standards, in such hazardous environments to prevent accidents and/or reduce economic burden following accidents.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.388-393
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• 2023

The analysis of engineering data using neural network based on supervised learning has been utilized in various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport phenomena system. The supervised learning requires training data, and the performance of the supervised learning is affected by the composition and the configurations of the given training data. Among the frequently observed engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the log-scaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction performance. The optimal loss functions and method for how to compose training data studied in this work would be applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of colloidal suspension.

• Journal of the Korea Society for Simulation
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• v.33 no.2
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• pp.1-11
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• 2024

A cyber-physical system is a technology that connects the physical systems of a manufacturing environment with a cyber space to enable simulation. One of the major challenges in this technology is the seamless communication between these two environments. In complex manufacturing processes, it is crucial to adapt to various protocols of manufacturing equipment and ensure the transmission and reception of a large volume of data without delays or errors. In this study, we propose a method for constructing agent models for real-time simulation-capable cyberphysical systems. To achieve this, we design data collection units as independent agent models and effectively integrate them with existing simulation tools to develop the overall system architecture. To validate the proposed structure and ensure reliability, we conducted empirical testing by integrating various equipment from a real-world smart microfactory system to assess the data collection capabilities. The experiments involved testing data delay and data gaps related to data collection cycles. As a result, the proposed approach demonstrates flexibility by enabling the application of various internal data collection methods and accommodating different data formats and communication protocols for various equipment with relatively low communication delays. Consequently, it is expected that this approach will promote innovation in the manufacturing industry, enhance production line efficiency, and contribute to cost savings in maintenance.

• Information Systems Review
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• v.26 no.1
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• pp.151-172
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• 2024

Metaverse, which supports social and economic activities in the virtual world, is being cited as the core of future Web 3.0 businesses. However, most of the major metaverse platforms currently adhere to the Web 2.0 system and are operated in a centralized manner. Accordingly, this study investigated the form of metaverse operation by examining the case of Decentraland, a metaverse platform operated as DAO, a decentralized autonomous organization in the form of Web 3.0. From the case analysis, we found that Decentraland had positive characteristics such as a horizontal operating structure, fair profit distribution, and transparency, but there were challenges including the possibility of returning to centralization in the operating process, the possibility of abuse of an autonomous system, and inefficiency in decision-making. Therefore, in this study, the timing of transition to DAO governance was discussed, and DAO's efforts for business continuity and the need for adjustment for autonomous operation were suggested as implications. The implications presented in this study are expected to contribute to materializing not only practical but also theoretical aspects of platform operation aimed at web 3.0 as well as the metaverse.