• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.269-277
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• 2024

With the advancement of modern IT technologies, the operation of autonomous vehicles is becoming a reality, and route planning is essential for this. Generally, route planning involves proposing the shortest path to minimize travel distance and the quickest path to minimize travel time. However, the quality of these routes depends on the topological characteristics of the road network graph. If the connectivity structure of the road network is not rational, there are limits to the performance improvement that routing algorithms can achieve. Real drivers consider psychological factors such as the number of turns, surrounding environment, traffic congestion, and road quality when choosing routes, and they particularly prefer routes with fewer turns. This paper introduces a simple path algorithm that seeks routes with the fewest turns, in addition to the traditional shortest distance and quickest time routes, to evaluate the characteristics of road networks. Using this simple path algorithm, we compare and evaluate the connectivity characteristics of road networks in 20 major cities worldwide. By analyzing these road network characteristics, we can identify the strengths and weaknesses of urban road networks and develop more efficient and safer route planning algorithms. This paper comprehensively examines the quality of road networks and the efficiency of route planning by analyzing and comparing the road network characteristics of each city using the proposed simple path algorithm.

• Korean Journal of Environment and Ecology
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• v.38 no.2
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• pp.127-142
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• 2024

This study aimed to understand the interrelationships between tree species in plant communities through Plant Social Network (PSN) analysis using a large amount of vegetation data surveyed in an island area belonging to a warm-temperate boreal forest. The Machilus thunbergii, Castanopsis sieboldii, and Ligustrum japonicum, which belong to the canopy layer, Pittosporum tobira and Ardisia japonica, which belong to the shrub layer and Trachelospermum asiaticum and Stauntonia hexaphylla, which belong to the vines, appearing in evergreen broad-leaved climax forest community, showed strong positive association(+) with each other. These tree species had a negative association or no friendly relationship with deciduous broad-leaved species due to the large difference in location environments. Divided into 4 group modularizations in the PSN sociogram, evergreen broad-leaved tree species in Group I and deciduous broad-leaved tree species in Group II showed high centrality and connectivity. It was analyzed that the arrangement of tree species (nodes) and the degree of connection (grouping) of the sociogram can indirectly estimate environmental factors and characteristics of plant communities like DCA. Tree species with high centrality and influence in the PSN included T. asiaticum, Eurya japonica, Lindera obtusiloba, and Styrax japonicus. These tree species are common with a wide range of ecological niches and appear to have the characteristics and survival strategies of opportunistic species that commonly appear in forest gaps and damaged areas. They will play a major role in inter-species interactions and structural and functional changes in plant communities. In the future, long-term research and in-depth discussions are needed to determine how these species actually influence plant community changes through interactions

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.33-40
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• 2024

The live weight of livestock is important information for managing their health and housing conditions, and it can be used to determine the optimal amount of feed and the timing of shipment. In general, it takes a lot of human resources and time to weigh livestock using a scale, and it is not easy to measure each stage of growth, which prevents effective breeding methods such as feeding amount control from being applied. In this paper, we aims to improve the accuracy of weight measurement of piglets, weaned pigs, nursery pigs, and fattening pigs by collecting, analyzing, learning, and predicting video and image data in animal husbandry and pig farming. For this purpose, we trained using Pytorch, YOLO(you only look once) 5 model, and Scikit Learn library and found that the actual and prediction graphs showed a similar flow with a of RMSE(root mean square error) 0.4%. and MAPE(mean absolute percentage error) 0.2%. It can be utilized in the mammalian pig, weaning pig, nursery pig, and fattening pig sections. The accuracy is expected to be continuously improved based on variously trained image and video data and actual measured weight data. It is expected that efficient breeding management will be possible by predicting the production of pigs by part through video reading in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.3
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• pp.1-16
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• 2024

In the present highway design guidelines suggest that the capacity of one-lane hi-pass lanes is 2,000 veh/h for mainline toll plaza and 1,700 veh/h for interchange toll plaza. However, in a study conducted in early 2010, capacity of the mainline toll plaza was presented with 1,476 veh/h/ln to 1,665 veh/h/ln, and capacity of the interchange toll plaza was presented as 1,443 veh/h/ln. Accordingly, this study examined the feasibility of the capacity of the currently proposed highway one-lane hi-pass lane. Based on the 2021 individual vehicle passing data collected from the one-lane hi-pass gantry, the speed-traffic volume relationship graph and headway were used to calculate and compare capacity. In addition, the bootstrapping technique was introduced to utilize the headway and new processing methods for collected data were reviewed. As a result of the analysis, the one-lane hi-pass capacity could be estimated at 1,700 veh/h/ln for the interchange toll plaza, and at least 1,700 veh/h/ln for the mainline toll plaza. In addition, by using the bootstrap technique when using headway data, it was possible to present an estimated capacity similar to the observed capacity.

• Education of Primary School Mathematics
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• v.27 no.3
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• pp.227-246
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• 2024

The purposes of this study were to analyze the levels of cognitive demand and questioning types in tasks of 'Data and Chance' presented in elementary mathematics textbooks in Korea, the United States, and Australia. The levels of cognitive demand of textbook tasks were analyzed according to the knowledge and process and thinking types required in the tasks. The tasks were also analyzed for questioning types, answer types, and response types. As a result, in terms of knowledge and process and thinking types in tasks, all three countries had something in common: the percentage of tasks requiring 'representation' and process was the highest, and the percentage of tasks requiring 'basic application of skill/concept' was also the highest. From a thinking types perspective, differences were found between textbook tasks in the three countries in graph and chance learning. The results of analyzing questioning types showed that in all three textbooks, the percentage of observational reasoning questions was highest, followed by the percentage of factual questions. The proportions and characteristics of the constructing questions included in the U.S. and Australian textbooks differed from those in the Korean textbooks. Based on these results, this study presents implications for constructing elementary mathematics textbook tasks in 'Data and Chance.'

• Journal of the Health Care and Life Science
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• v.10 no.1
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• pp.1-14
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• 2022

This study is a study on the possibility of using big data-related education programs in health and medical administration-related departments using health and medical big data. This paper intends to examine the health and medical big data from five perspectives. 1st, in addition to the aforementioned 'Health and Medical Big Data Open System', I would like to examine the characteristics and application technologies of public big data disclosed by 'Korea Welfare Panel', 'Public Big Data', 'Seoul City Big Data', 'Statistical Office Big Data', etc. 2nd, it is intended to examine the appropriateness of whether the applicable health and medical big data can be used as living data in regular subjects of health and medical administration and health information related departments of junior colleges. 3rd, we want to select the most appropriate tool for classroom lectures using existing statistical processing packages and programming languages. Fourth, finally, by using verified health and medical big data and appropriate tools, we want to test the possibility of expressing graphs, etc. in class and the steps from writing a report. 4th, I would like to describe the relative advantages of R language that can satisfy portability, installability, cost effectiveness, compatibility, and big data processing potential.

• The Mathematical Education
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• v.63 no.3
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• pp.549-571
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• 2024

As digital·AI-based teaching and learning is emphasized, discussions on the educational use of generative AI are becoming more active. This study analyzed the mathematical performance of ChatGPT 4, Claude 3 Opus, and Gemini Advanced on solving examples and problems from five first-year high school math textbooks. As a result of examining the overall correct answer rate and characteristics of each skill for a total of 1,317 questions, ChatGPT 4 had the highest overall correct answer rate of 0.85, followed by Claude 3 Opus at 0.67, and Gemini Advanced at 0.42. By skills, all three models showed high correct answer rates in 'Find functions' and 'Prove', while relatively low correct answer rates in 'Explain' and 'Draw graphs'. In particular, in 'Count', ChatGPT 4 and Claude 3 Opus had a correct answer rate of 1.00, while Gemini Advanced was low at 0.56. Additionally, all models had difficulty in explaining using Venn diagrams and creating images. Based on the research results, teachers should identify the strengths and limitations of each AI model and use them appropriately in class. This study is significant in that it suggested the possibility of use in actual classes by analyzing the mathematical performance of generative AI. It also provided important implications for redefining the role of teachers in mathematics education in the era of artificial intelligence. Further research is needed to develop a cooperative educational model between generative AI and teachers and to study individualized learning plans using AI.

• Journal of Christian Education in Korea
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• v.76
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• pp.193-212
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• 2023

Purpose of Study: This study is a qualitative study to understand the essential experiences of missionary children related to national identity. Research Contents and Methods: Ten children of missionaries who were re-entered to their home countries to receive university education were selected as participants for the study. Two preliminary surveys (2016, 2019) were conducted to determine the direction and subject of the study. Two in-depth interviews and one non-face-to-face survey were conducted with the study participants. Based on preliminary research and prior research, the questionnaire explored identity experiences by discovering four areas: language, culture, group, and place. In addition, rich research results were derived with schematic interview data, surveys using Phinney's 1992 national identity test tool, non-face-to-face surveys with parents of study participants, and self-report identity graphs. Conclusions and suggestions: As a result of the study, missionary kids as TCKs were able to know their names in identity confusion by sequentially experiencing international mobility, separation, and discrepancy in four areas. After all, TCK seems to suffer from identity difficulties because it remains primarily 'minority' in relation to the four domains. This study is meaningful in that it specifically revealed the support needed for TCK missionary children with multicultural background by revealing the importance of providing visiting experience in Korea and schoo(herd)l experience before entering Korean universities to re-adaptate TCK.

• Applied Chemistry for Engineering
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• v.35 no.3
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• pp.222-229
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• 2024

The use of low-toxic, hypoallergenic, and environmentally friendly natural pigments has increased. With growing interest in health, research on natural extracts containing beneficial substances for the human body is actively underway. In this study, natural pigments were extracted from sprout barley using a solvent extraction method and CCD-RSM was used to optimize the extraction process. The experiment's independent variables included extraction temperature, alcohol/ultra-pure volume ratio, and extraction time. The response variables were set to achieve a target chromaticity (L = 45, a = -35, b = 45), and to maximize DPPH radical scavenging activity evaluating the antioxidant capacity. The statistical significance of the main effect, interaction effect, and effect on the response value was evaluated and analyzed through the F and P values for the regression equation variables calculated using RSM optimization. Additionally, the reliability of the experiment was also confirmed through the P values of the probability plot graph. The extraction conditions for optimizing the four reaction values are 76.1 vol.% alcohol/ultra pure water volume ratio, an extraction temperature of 52.9 ℃ , and an extraction time of 49.6 min. Under these conditions, the theoretical values of the reaction values are L = 45.4, a = -36.8, and b = 45.0 DPPH radical scavenging activity = 30.9%. When the actual experiment was conducted under these optimal extraction conditions and analyzed, the measured values were L = 46.2, a = -36.1, and b = 48.2, and antioxidant capacity = 31.1% with an average error rate of 2.9%.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.30 no.2
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• pp.101-106
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• 2024

Current cold chain logistics relying on organic or eutectic materials within the 2~8℃ range as secondary fluids often face limitations in heat storage capacity, necessitating high energy consumption and large volume capacity. An effective approach to address this challenge is by incorporating polymers to enhance the heat storage capacity of eutectic materials. In this study, we investigated the impact of polyethylene glycols (PEGs) on phase change materials using Fourier transform infrared spectroscopy (FT-IR), differential scanning calorimeter (DSC), analyses of endothermic and exothermic phase change processes, and an accelerated thermal cycling test. Our findings indicate that the introduction of PEGs into the phase change materials can lead to improvements in latent heat, thermal conductivity, and 2~8℃ retention time. This enhancement is attributed to the high latent heat and thermal conductivity of the polymer, along with its ability to inhibit crystal formation in the eutectic mixture.