• Conservation Science in Museum
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• v.24
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• pp.55-74
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• 2020

The Sacred Bell of Great King Seongdeok is required digital precision recording of conservation conditions because of corrosion and partial abrasion of its patterns and inscriptions. Therefore, this study performed digital documentation of the bell using four types of scanning and unmanned aerial vehicle (UAV) photogrammetry technologies, and performed the various shape analyses through image processing. The modeling results of terrestrial laser scanning and UAV photogrammetry were merged and utilized as basic material for monitoring earthquake-induced structural deformation because these techniques can construct mutual spatial relationships between the bell and its tower. Additionally, precision scanning at a resolution four to nine times higher than that of the previous study provided highly valuable information, making it possible to visualize the patterns and inscriptions of the bell. Moreover, they are well-suited as basic data for identifying surface conservation conditions. To actively apply three-dimensional scanning results to the conservation of the original bell, the time and position of any changes in shape need to be established by further scans in the short-term. If no change in shape is detected by short-term monitoring, the monitoring should continue in medium- and long-term intervals.

• Journal of Korean Society of Forest Science
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• v.111 no.2
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• pp.302-310
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• 2022

Here we aimed to classify the major coniferous tree species (Pinus densiflora, Pinus koraiensis, and Larix kaempferi) by tree measurement information and machine learning algorithms to establish an efficient forest management plan. We used national forest monitoring information amassed over nine years for the measurement information of trees, and random forest (RF), XGBoost (XGB), and light GBM (LGBM) as machine learning algorithms. We compared and evaluated the accuracy of the algorithm through performance evaluation using the accuracy, precision, recall, and F1 score of the algorithm. The RF algorithm had the highest performance evaluation score for all tree species, and highest scores for Pinus densiflora, with an accuracy of about 65%, a precision of about 72%, a recall of about 60%, and an F1 score of about 66%. The classification accuracy for the dominant trees was higher than about 80% in the crown classes, but that of the co-dominant trees, the intermediate trees, and the overtopper trees was evaluated as low. We consider that the results of this study can be used as reference data for decision-making in the selection of thinning trees for forest management.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.39-49
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• 2022

Purpose: As part of these efforts, in-depth research is needed on efficient and practical utilization of underground spaces and underground shopping malls. In addition, efforts are being made to find effective alternatives to various problems currently occurring in underground shopping malls, but it is not easy. In addition, the development entity and the maintenance entity are different from each other, and the management is not unified, making it difficult to maintain underground shopping malls. From this point of view, it can be said that it is time to actively and specifically discuss ways to revitalize underground shopping malls. Data and methodology: In the domestic distribution environment, traditional markets and shops are stagnating due to rapid changes in consumption patterns, such as the spread of large companies with advanced distribution techniques such as hypermarkets, shopping malls, and SCM, the rapid increase in Internet and home shopping, and the importance of convenience for young consumers. In order to revitalize underground shopping malls, it is necessary to strengthen the organization and self-rescue efforts of merchants' associations, change consciousness through merchant education, change to specialized markets, find nuclear stores and representative restaurants, and support the hardware sector. Results: The connection of underground shopping malls in each region of the country, where commercial districts are separated from each other, will also play an important role in reviving the function of the city in the future. To do this, it is first necessary to connect underground shopping malls that have been cut off. In other words, connection between connectable underground shopping malls should be promoted. Of course, long-term projects should be promoted step by step, and many consultations should be made on how to connect with the ground for each local government. Conclusion: This is because in the future, the underground space cannot just be a walking place, but another space of the Korean Wave where you can experience satisfying the five senses. K-shopping Hallyu content can be created by creating a characteristic story for each underground shopping mall in the city, permanently this story-oriented event, and creating a safe and elegant environment. If there is a story, so-called "Senomi Shopping" will be possible. A new Korean Wave will be created that can satisfy "the fun of writing, the fun of seeing, and the fun of feeling" at the same time.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.378-390
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• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.146-153
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• 2022

Purpose: In the case of domestic port facilities, port structures that have been in use for a long time have many problems in terms of safety performance and functionality due to the enlargement of ships, increased frequency of use, and the effects of natural disasters due to climate change. A big data analysis method was studied to develop an approximate model that can predict the aging pattern of a port facility based on the maintenance history data of the port facility. Method: In this study, member-level maintenance history data for caisson-type quay walls were collected, defined as big data, and based on the data, a predictive approximation model was derived to estimate the aging pattern and deterioration of the facility at the project level. A state-based aging pattern prediction model generated through Gaussian process (GP) and linear interpolation (SLPT) techniques was proposed, and models suitable for big data utilization were compared and proposed through validation. Result: As a result of examining the suitability of the proposed method, the SLPT method has RMSE of 0.9215 and 0.0648, and the predictive model applied with the SLPT method is considered suitable. Conclusion: Through this study, it is expected that the study of predicting performance degradation of big data-based facilities will become an important system in decision-making regarding maintenance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.239-247
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• 2022

Cities are becoming more complex due to rapid industrialization and population growth in modern times. In particular, urban areas are rapidly changing due to housing site development, reconstruction, and demolition. Thus accurate road information is necessary for various purposes, such as High Definition Map for autonomous car driving. In the case of the Republic of Korea, accurate spatial information can be generated by making a map through the existing map production process. However, targeting a large area is limited due to time and money. Road, one of the map elements, is a hub and essential means of transportation that provides many different resources for human civilization. Therefore, it is essential to update road information accurately and quickly. This study uses Semantic Segmentation algorithms Such as LinkNet, D-LinkNet, and NL-LinkNet to extract roads from drone images and then apply hyperparameter optimization to models with the highest performance. As a result, the LinkNet model using pre-trained ResNet-34 as the encoder achieved 85.125 mIoU. Subsequent studies should focus on comparing the results of this study with those of studies using state-of-the-art object detection algorithms or semi-supervised learning-based Semantic Segmentation techniques. The results of this study can be applied to improve the speed of the existing map update process.

• Applied Chemistry for Engineering
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• v.33 no.6
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• pp.563-573
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• 2022

This work aimed to develop a new equation for turbidity (Turb) simulation and prediction using statistical methods based on principal component analysis (PCA) and multiple linear regression (MLR). For this purpose, water samples were collected monthly over a five year period from Cheurfa dam, an important reservoir in Northwestern Algeria, and analyzed for 12 parameters, including temperature (T°), pH, electrical conductivity (EC), turbidity (Turb), dissolved oxygen (DO), ammonium (NH₄⁺), nitrate (NO₃^-), nitrite (NO₂^-), phosphate (PO₄^3-), total suspended solids (TSS), biochemical oxygen demand (BOD₅) and chemical oxygen demand (COD). The results revealed a strong mineralization of the water and low dissolved oxygen (DO) content during the summer period. High levels of TSS and Turb were recorded during rainy periods. In addition, water was charged with phosphate (PO₄^3-) in the whole period of study. The PCA results revealed ten factors, three of which were significant (eigenvalues >1) and explained 75.5% of the total variance. The F1 and F2 factors explained 36.5% and 26.7% of the total variance, respectively and indicated anthropogenic pollution of domestic agricultural and industrial origin. The MLR turbidity simulation model exhibited a high coefficient of determination (R² = 92.20%), indicating that 92.20% of the data variability can be explained by the model. TSS, DO, EC, NO₃^-, NO₂^-, and COD were the most significant contributing parameters (p values << 0.05) in turbidity prediction. The present study can help with decision-making on the management and monitoring of the water quality of the dam, which is the primary source of drinking water in this region.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.161-180
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• 2021

The purpose of this study is to define Juan Mayorga's play Hamelin as a Post-Epic Theatre and to study the practical directing technique for Hamelin as a Post-Epic Theatre. Post-Epic Theatre, which appeared after the Post-drama, has the purpose of presenting social issues, communicating interactively between the actors and the audience, and making the audience think about the issues presented by the techniques of immersion and alienation. To this end, after examining the theoretical background of the Post-Epic Theatre, the characteristics of the Post-Epic Theatre of Hamelin were identified and based on these features, '1. Building a visual image based on a Cubistic multifocal concept' and '2. The concept of directing was derived from reinforcing Meta-drama through role-playing'. Next, the actual directing technique was discussed, focusing on the chain action of immersion and alienation that occurs in the form of communication between actors and audiences. '1. Presenting the characteristics of the work through Post-Epic Theatre scenography', '2. Co-existence of actors and characters', '3. Building and utilizing body-centered gestus' are them. As a result, demanding an active attitude from the audience, various experiences such as critical thinking of the audience, strengthening the characteristics of post-epic dramas, and active meaning creation were made possible.

• Journal of Korea Water Resources Association
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• v.55 no.2
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• pp.159-170
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• 2022

As the severity of water-related disasters increases in urban watersheds due to climate change, reducing flood damage in urban watersheds is one of the important issues. This study focuses on prioritizing the optimal site for permeable pavement to maximize the efficiency of reducing flood damage in urban watersheds in the future climate environment using multi-criteria decision making techniques. The Mokgamcheon watershed which is considerably urbanized than in the past was selected for the study area and its 27 sub-watersheds were considered as candidate sites. Six General Circulation Model (GCM) of Coupled Model Intercomparison Project 6(CMIP6) according to two Shared Socioeconomic Pathway (SSP) scenarios were used to estimate future monthly precipitation for the study area. The Driving force-Pressure-State-Impact-Response (DPSIR) framework was used to select the water quantity evaluation criteria for prioritizing permeable pavement, and the study area was modeled using ArcGIS and Storm Water Management Model (SWMM). For the values corresponding to the evaluation criteria based on the DPSIR framework, data from national statistics and long-term runoff simulation value of SWMM according to future monthly precipitation were used. Finally, the priority for permeable pavement was determined using the Fuzzy TOPSIS and Minimax regret method. The high priorities were concentrated in the downstream sub-watersheds where urbanization was more progressed and densely populated than the upstream watersheds.