• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.337-345
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• 2022

For data such as rainfall and soil moisture, it is important to obtain the values of all points required as geostatistical data. Spatial interpolation is generally performed in this process, and commercial software such as ArcGIS is often used. However, commercial software has fatal drawbacks due to its high expertise and cost. In this study, R, an open source-based environment with ArcGIS, a commercial software, was used to compare the differences according to the processing environment when performing spatial interpolation. The data for spatial interpolation was weather forecast data calculated through Land-Atmosphere Modeling Package (LAMP)-WRF model, and soil moisture data calculated for each cumulative rainfall scenario. There was no difference in the output value in the two environments, but there was a difference in user interface and calculation time. The results of spatial interpolation work in the test bed showed that the average time required for R was 5 hours and 1 minute, and for ArcGIS, the average time required was 4 hours and 40 minutes, respectively, showing a difference of 7.5%. The results of this study are meaningful in that researchers can derive the same results in a commercial software environment and an open source-based environment, and can choose according to the researcher's environment and level.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.163-180
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• 2022

Real estate data can be big data. Because the amount of real estate data is growing rapidly and real estate data interacts with various fields such as the economy, law, and crowd psychology, yet is structured with complex data layers. The existing Relational Database tends to show difficulty in handling various relationships for managing real estate big data, because it has a fixed schema and is only vertically extendable. In order to improve such limitations, this study constructs the real estate data in a Graph Database and verifies its usefulness. For the research method, we modeled various real estate data on MySQL, one of the most widely used Relational Databases, and Neo4j, one of the most widely used Graph Databases. Then, we collected real estate questions used in real life and selected 9 different questions to compare the query times on each Database. As a result, Neo4j showed constant performance even in queries with multiple JOIN statements with inferences to various relationships, whereas MySQL showed a rapid increase in its performance. According to this result, we have found out that a Graph Database such as Neo4j is more efficient for real estate big data with various relationships. We expect to use the real estate Graph Database in predicting real estate price factors and inquiring AI speakers for real estate.

• Journal of the Korean GEO-environmental Society
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• v.23 no.12
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• pp.5-15
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• 2022

Conventionally, because evaluation methods of the bearing capacity for double steel pipe-concrete composite pile design have not been established, the conventional vertical bearing capacity equations for steel hollow pile are used. However, there are severe differences between the predictions from these equations, and the most conservative one among vertical bearing capacity predictions are conventionally adopted as a design value. Consequently, the current prediction method for vertical bearing capacity of composite pile prediction composite pile causes design reliability and economical feasibility to be low. This paper investigated mechanical behaviors of a new composite pile, with a cross-section composed of double steel pipes filled with concrete (DSCT), vertical bearing capacities were analyzed for several DSCT pile conditions. Axisymmetric finite element models for DSCT pile and surrounding ground were created and they were used to analyze effects on behaviors of DSCT pile pile by embedding depth, stiffness of plugging material at pile tip, height of plugging material at pile tip, and rockbed material. Additionally, results from conventional design prediction equations for vertical bearing capacity at steel hollow pile tip were compared with that from numerical results, and the use of the conventional equations for steel hollow pile was examined to apply to that for DSCT pile.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1137-1147
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• 2022

River and groundwater stages are the main elements in the hydrologic cycle. They are spatially correlated and can be used to evaluate hydrological and agricultural drought. Stochastic simulation is often performed independently on hydrological variables that are spatiotemporally correlated. In this setting, interdependency across mutual variables may not be maintained. This study proposes the Bayesian vector autoregression model (VAR) to capture the interdependency between multiple variables over time. VAR models systematically consider the lagged stages of each variable and the lagged values of the other variables. Further, an autoregressive model (AR) was built and compared with the VAR model. It was confirmed that the VAR model was more effective in reproducing observed interdependency (or cross-correlation) between river and ground stages, while the AR generally underestimated that of the observed.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.148-163
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• 2021

The diversification of the New Deal urban regeneration projects, that started in 2017 in accordance with the "Special Act on Urban Regeneration Activation and Support", generated the increased demand for the accuracy of data-driven diagnosis and project type forecast. Thus, this research was conducted to develop an application model able to identify the most appropriate New Deal project type for "eup", "myeon" and "dong" across the country. Data for application model development were collected through Statistical geographic information service(SGIS) and the 'Urban Regeneration Comprehensive Information Open System' of the Urban Regeneration Information System, and data for the analysis model was constructed through data pre-processing. Four models were derived and simulations were performed through polynomial regression analysis and multinomial logistic regression analysis for the application of the appropriate New Deal project type. I verified the applicability and validity of the four models by the comparative analysis of spatial distribution of the previously selected New Deal projects by targeting the sites located in Seoul by each model and the result showed that the DI-54 model had the highest concordance rate.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.453-462
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• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.159-166
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• 2022

Background: A quantitative structure-activity relationship (QSAR) model was adopted in the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH, EU) regulations as well as the Act on Registration, Evaluation, etc. of Chemicals (AREC, Republic of Korea). It has been previously used in the registration of chemicals. Objectives: In this study, we investigated the correlation between the predicted data provided by three prediction programs using a QSAR model and actual experimental results (acute fish, daphnia magna toxicity). Through this approach, we aimed to effectively conjecture on the performance and determine the most applicable programs when designating toxic substances through the AREC. Methods: Chemicals that had been registered and evaluated in the Toxic Chemicals Control Act (TCCA, Republic of Korea) were selected for this study. Two prediction programs developed and operated by the U.S. EPA - the Ecological Structure-Activity Relationship (ECOSAR) and Toxicity Estimation Software Tool (T.E.S.T.) models - were utilized along with the TOPKAT (Toxicity Prediction by Komputer Assisted Technology) commercial program. The applicability of these three programs was evaluated according to three parameters: accuracy, sensitivity, and specificity. Results: The prediction analysis on fish and daphnia magna in the three programs showed that the TOPKAT program had better sensitivity than the others. Conclusions: Although the predictive performance of the TOPKAT program when using a single predictive program was found to perform well in toxic substance designation, using a single program involves many restrictions. It is necessary to validate the reliability of predictions by utilizing multiple methods when applying the prediction program to the regulation of chemicals.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.34 no.3
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• pp.82-92
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• 2022

In recent years, coastal and port structures have attempted to prevent wave-overtopping or provide waterfront areas by installing superstructures on the structural crowns. In general, in the design stage, the Goda formula acting on the front the structure is applied to calculate the wave pressure acting on the superstructure in consideration of the wave-runup of the design wave. However, the wave pressure exceeding the Goda wave pressure could generate depending on the installation location of the superstructure where the wave-overtopping occurs. This study analyzed the applicability of the Goda formula to the wave pressure calculation for the superstructure of the vertical structures through hydraulic model experiments and numerical simulations. Furthermore, this study investigated the magnitude of the wave pressure acting on the superstructure based on detailed numerical results. As a result, the wave pressure acting on the superstructure was up to 120% higher than the maximum wave pressure on the still water surface. In addition, the wave pressure increases exponentially with the Froude number computed by the overtopping water depth at the crown of the structure, and we proposed an empirical formula for predicting the wave pressure based on the Froude number.

• Korean Journal of Cognitive Science
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• v.33 no.4
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• pp.169-203
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• 2022

As artificial intelligence (AI) technology advances, the number of cases in which AI becomes an object or subject of moral judgment is increasing, and this trend is expected to accelerate. Although the area of AI in human society expands, relatively few studies have been conducted on how people perceive and respond to AI. Three studies examined the effect of the anthropomorphism of AI on its responsibility. We predicted that anthropomorphism would increase the responsibility perception, and perceived agency and perceived patiency for AI would mediate this effect. Although the manipulation was not effective, multiple analyses confirmed the indirect effect of perceived patiency. In contrast, the effect of perceived agency of AI was somewhat mixed, which makes the hypothesis partially supported by the overall result. This result shows that for the moral status of artificial agents, perceived patiency is relatively more critical than perceived agency. These results support the organic perspective on the moral status that argues the importance of patiency, and show that patiency is more important than agency in the anthropomorphism related study of AI and robots.

• Journal of Life Science
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• v.33 no.2
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• pp.199-207
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• 2023

To prepare for the threat of a future epidemic in the post-COVID-19 era, research based on the one-health concept (i.e., the health of humans, animals, and the environment as "one") is essential. Cross-species infections are being identified as a result of the high infection rate and viral load of SARS-CoV-2 in humans. The possibility of transmission of SARS-CoV-2 from humans to mink has been determined. In addition, the transmission of SARS-CoV-2 from humans to cats through contact has been considered possible. The data so far show that livestock and poultry are less likely to be infected with SARS-CoV-2. However, if infections are established through a new mutation, the resulting diseases are expected to have enormous ripple effects on various fields, such as human food security, the economy, and trade. In addition, there are concerns about the endemic prospect of SARS-CoV-2 and the high accessibility of companion animals. This is because the evolution of the virus likely occurs in animal hosts. Once SARS-CoV-2 is established in other species, they might serve as intermediate hosts for the re-emergence of the virus in the human population. Thus, it is necessary to ensure a rapid response to future outbreaks by accumulating research data on the animal infection of SARS-CoV-2. These data can have implications for the development of animal models for vaccines and therapeutics against SARS-CoV-2. Therefore, in this study, epidemiological reviews were analyzed, and response strategies against SARS-CoV-2 infection in animals were presented using the One-health approach.