• Journal of Conservation Science
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• v.35 no.3
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• pp.217-225
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• 2019

The chimi(ridge-end tile) of Wangheungsa temple is the oldest in our country. The upper part of the chimi was excavated from the southern side of Wangheungsa temple and the lower part from the northern side. These parts are considered to be portions of the same chimi, because they are similar in shape and are excavated from two sides of the same temple structure. However, the original shape of the chimi cannot be determined owing to substantial deterioration. Hence, in this study, replicas of the deteriorated chimi portions of Wangheungsa temple were fabricated by employing 3D scanning technology and the computer numerical control machining method. While observing the bending phenomenon of the chimi, the proposed model was warped realistically on the basis of the bending direction of the actual chimi. Consequently, the restoration process was modified several times. The results indicated that no gaps can be found between the upper and lower parts, and the corresponding patterns connect naturally. Furthermore, the proposed method is contactless, safe, operable, reproducible, and appropriate for restoration of artifacts. Additionally, the modeling data is semi-permanent. Hence, if modelling data is appropriately applied as per the characteristics of artifacts, it can be utilized in various fields such as virtual exhibitions, hands-on exhibitions, cultural heritage restoration, and production of teaching aids and souvenirs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2169-2190
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• 2022

The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.

• The Korean Journal of Franchise Management
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• v.13 no.2
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• pp.53-66
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• 2022

Purpose: How to build the attitude on brand is very important, because it affects the positive word of mouth and revisit intention. Brand attachment, brand name, and image congruence play important role on consumer behavior in terms of reinforcing consumers' perception of food service companies and differentiating them from competing brands. Following the planned behavior theory, this paper examines the effect of linking brand attitude to word-of-mouth and revisit intentions in the restaurant sector. Research design, data, and methodology: This paper examines the structural relationship among brand attachment, brand name, image congruence, brand attitude, WOM, and revisit intention. In order to test the purposes of this study, research model and hypotheses were developed. The questionnaire items were modified and used according to the content of this study based on previous studies. All constructs were measured by multiple items tested and developed in the previous research. The study is based on the quantitative method and considered 519 questionnaires fulfilled by customers of restaurants. The data were explored employing the partial least square-structural equation modelling (PLS-SEM). Frequency analysis was conducted to identify the general characteristics of the survey subjects. To measure the reliability and validity of the measurement tools, confirmatory factor analysis was conducted. Structural model analysis was conducted to verify the research model. Result: The findings demonstrate that brand attachment and brand name had positive effects on attitude while image congruence did not have. Also, attitude had positive effect on WOM and revisit intention. Conclusions: This study expands the literature about WOM and revisit intentions. This study expands prior research in a similar field to which the theory of planned behavior (TPB) is applied, and reveals that brand attachment, brand name, and brand image congruence play an important role in developing brand attitude that affect revisit intention and WOM. And provide guidelines on how to enhance competitiveness in the restaurant sector based on understanding of linking brand attitude to customer loyalty and repeat business. By putting into practice these suggestions in the restaurant industry, brands can easily build up their attitude and boost a positive WOM and the intention to revisit.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.609-625
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• 2021

It has now been more than a year since the start of the COVID-19 pandemic in Korea, which has claimed thousands of lives and changed every aspect of life. The corona pandemic not only caused physical damages but also psychological one which is a collective social stress phenomenon often termed as 'corona blue'. The purpose of this study is to examine how empathy affects anxiety and depression through risk perception and indirect trauma, which are psychological variables related to the corona pandemic as a disaster. The survey data from 214 people were analyzed with a structural equation modelling. The results shows that 53.3 % of the participants experienced anxiety and 35.7% suffered from depression, which were about 6 times higher than ones from the 2019 government data. Affective empathy had a significant effect on risk perception, and cognitive empathy had a significant effect on indirect trauma. Risk perception and indirect trauma both had a significant effect on anxiety, and anxiety had a significant impact on depression. Only cognitive empathy had a significant indirect effect on anxiety and depression. This study provides an important insight into understanding a social phenomenon of 'corona blue' from a empathic perspective.

• Nuclear Engineering and Technology
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• v.26 no.3
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• pp.401-410
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• 1994

Validation study of two radionuclide migration models for single fracture developed in geologic medium the hydrodynamic dispersion diffusion model(HDDM) and the channeling dispersion diffusion model(CDDM), was studied by migration experiment of tracers through an artificial granite fracture on the labolatory scale. The tracers used were Uranine and Sodium lignosulfonate know as nonsorbing material. The flow rate ranged 0.4 to 1.5 cc/min. Related parameters for the models were estimated by optimization technique. Theoretical breakthrough curves with experimental data were compared. In the experiment, it was deduced that the surface sorption for both tracers did not play an important role while the diffusion of Uranine into the rock matrix turned out to be an important mass transfer mechanism. The parameter characterizing the rock matrix diffusion of each model agreed well The simulated result showed that the amount of flow rate could not tell the CDDM from the HDDM quantitatively. On the other hand, the variation of fracture length gave influence on the two models in a different degree. The dispersivity of breakthrough curve of the CDDM was more amplified than that of the CDDM when the fracture length was increased. A good agreement between the models and experimental data gave a confirmation that both models were very useful in predicting the migration system through a single fracture.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.760-769
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• 2023

A numerical simulations were performed to investigate the storm surge during the passage of Typhoon Maemi on the coast of Busan. The typhoon landed on the southern coasts of Korean Peninsula at 21:00, September 12, 2003 with a central pressure of 950 hPa, and the typhoon resulted on the worst coastal disaster on the coast of Busan in the last decades. Observed storm surges at Busan, Yeosu, Tongyoung, Masan, Jeju and Seogwipo harbors during the passage of the typhoon were compared with the computed data. The simulated storm surge time series were in good agreement with the observations. The simulated peak storm surges were estimated to be 230 cm at Masan harbor, 200 cm at Yeosu harbor and Tongyoung harbor, and 75 cm at Busan harbor. The computed storm surges along the east coast of Busan measure 52 to 55 cm, exhibiting a gradual reduction in surge height as one moves further from the coast of Busan. Therefore, coastal inundation due to the storm surge in the semi-enclosed bay can induce great disasters, and the simulated results can be used as the important data to reduce the impact of a typhoon-induced coastal disaster in the future.

• Korean Journal of Ecology and Environment
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• v.56 no.3
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• pp.268-279
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• 2023

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.359-365
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• 2013

Estimate breeding value can be used as single trait animal model was developed directly using the Fortran language program. The program is based on data computed by using the indirect method repeatedly. The program develops a common algorithm and imprves efficiency. Algorithm efficiency was compared between the two programs. Estimated using the solution is easy to farm and brand the service, pedigree data base was associated with the development of an improved system. The existing program that uses the single trait animal model and the comparative analysis of efficiency is weak because the estimation of the solution and the conventional algorithm programmed through regular formulation involve many repetition; therefore, the newly developed algorithm was conducted to improve speed by reducing the repetition. Single trait animal model was used to analyze Gauss-Seidel iteration method, and the aforesaid two algorithms were compared thorough the mixed model equation which is used the most commonly in estimating the current breeding value by applying the procedures such as the preparation of information necessary for modelling, removal of duplicative data, verifying the parent information of based population in the pedigree data, and assigning sequential numbers, etc. The existing conventional algorithm is the method for reading and recording the data by utilizing the successive repetitive sentences, while new algorithm is the method for directly generating the left hand side for estimation based on effect. Two programs were developed to ensure the accurate evaluation. BLUPF90 and MTDFREML were compared using the estimated solution. In relation to the pearson and spearman correlation, the estimated breeding value correlation coefficients were highest among all traits over 99.5%. Depending on the breeding value of the high correlation in Model I and Model II, accurate evaluation can be found. The number of iteration to convergence was 2,568 in Model I and 1,038 in Model II. The speed of solving was 256.008 seconds in Model I and 235.729 seconds in Model II. Model II had a speed of approximately 10% more than Model I. Therefore, it is considered to be much more effective to analyze large data through the improved algorithm than the existing method. If the corresponding program is systemized and utilized for the consulting of farm and industrial services, it would make contribution to the early selection of individual, shorten the generation, and cultivation of superior groups, and help develop the Hanwoo industry further through the improvement of breeding value based enhancement, ultimately paving the way for the country to evolve into an advanced livestock country.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.241-263
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• 2010

KoFlux is a Korean network of micrometeorological tower sites that use eddy covariance methods to monitor the cycles of energy, water, and carbon dioxide between the atmosphere and the key terrestrial ecosystems in Korea. KoFlux embraces the mission of AsiaFlux, i.e. to bring Asia's key ecosystems under observation to ensure quality and sustainability of life on earth. The main purposes of KoFlux are to provide (1) an infrastructure to monitor, compile, archive and distribute data for the science community and (2) a forum and short courses for the application and distribution of knowledge and data between scientists including practitioners. The KoFlux community pursues the vision of AsiaFlux, i.e., "thinking community, learning frontiers" by creating information and knowledge of ecosystem science on carbon, water and energy exchanges in key terrestrial ecosystems in Asia, by promoting multidisciplinary cooperations and integration of scientific researches and practices, and by providing the local communities with sustainable ecosystem services. Currently, KoFlux has seven sites in key terrestrial ecosystems (i.e., five sites in Korea and two sites in the Arctic and Antarctic). KoFlux has systemized a standardized data processing based on scrutiny of the data observed from these ecosystems and synthesized the processed data for constructing database for further uses with open access. Through publications, workshops, and training courses on a regular basis, KoFlux has provided an agora for building networks, exchanging information among flux measurement and modelling experts, and educating scientists in flux measurement and data analysis. Despite such persistent initiatives, the collaborative networking is still limited within the KoFlux community. In order to break the walls between different disciplines and boost up partnership and ownership of the network, KoFlux will be housed in the National Center for Agro-Meteorology (NCAM) at Seoul National University in 2011 and provide several core services of NCAM. Such concerted efforts will facilitate the augmentation of the current monitoring network, the education of the next-generation scientists, and the provision of sustainable ecosystem services to our society.

• Geophysics and Geophysical Exploration
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• v.14 no.1
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• pp.98-104
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• 2011

Numerical simulation in exploration geophysics provides important insights into subsurface wave propagation phenomena. Although elastic wave simulations take longer to compute than acoustic simulations, an elastic simulator can construct more realistic wavefields including shear components. Therefore, it is suitable for exploration of the responses of elastic bodies. To overcome the long duration of the calculations, we use a Graphic Processing Unit (GPU) to accelerate the elastic wave simulation. Because a GPU has many processors and a wide memory bandwidth, we can use it in a parallelised computing architecture. The GPU board used in this study is an NVIDIA Tesla C1060, which has 240 processors and a 102 GB/s memory bandwidth. Despite the availability of a parallel computing architecture (CUDA), developed by NVIDIA, we must optimise the usage of the different types of memory on the GPU device, and the sequence of calculations, to obtain a significant speedup of the computation. In this study, we simulate two- (2D) and threedimensional (3D) elastic wave propagation using the Finite-Difference Time-Domain (FDTD) method on GPUs. In the wave propagation simulation, we adopt the staggered-grid method, which is one of the conventional FD schemes, since this method can achieve sufficient accuracy for use in numerical modelling in geophysics. Our simulator optimises the usage of memory on the GPU device to reduce data access times, and uses faster memory as much as possible. This is a key factor in GPU computing. By using one GPU device and optimising its memory usage, we improved the computation time by more than 14 times in the 2D simulation, and over six times in the 3D simulation, compared with one CPU. Furthermore, by using three GPUs, we succeeded in accelerating the 3D simulation 10 times.