• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1031-1040
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• 2023

Propagation characteristics in line-of-sight(LOS) paths in 3, 6, 10, and 17 GHz frequency bands were measured and analyzed in two different indoor corridors: second floors of Buildings D2 and E2. The measurement was designed to measure when the receiving antenna moved at 0.5 m intervals from 3 m to 30 m, while the transmission antenna was fixed. The analysis of the two indoor corridors was compared by applying basic transmission loss, root mean square (RMS) delay spread, and K-factor. For basic transmission loss, the loss coefficient of the floating intercept path loss model was higher in the indoor corridor of Building E2 than in that of Building D2. Similarly, the RMS delay spread in the time domain was greater in the indoor corridor of Building E2. However, the indoor corridor of Building D2 exhibited higher K-factor in the 3, 6, and 17 GHz bands with lower wave propagation in the 10 GHz band. Despite the 2 indoor corridors being identical, the propagation characteristics varied due to different internal structures and materials. The results provide measurement data for ITU-R Recommendations regarding various indoor environments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.489-498
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• 2023

Advancements in hardware performance and computing technology have facilitated the progress of climate prediction models to address climate change. The Korea Meteorological Administration employs the GloSea6 model with supercomputer technology for operational use. Various universities and research institutions utilize the Low-GloSea6 model, a low-resolution coupled model, on small to medium-scale servers for weather research. This paper presents an analysis using Intel VTune Profiler on Low-GloSea6 to facilitate smooth weather research on small to medium-scale servers. The tri_sor_dp_dp function of the atmospheric model, taking 1125.987 seconds of CPU time, is identified as a hotspot. Nonlinear regression models, a machine learning technique, are applied and compared to existing functions conducting numerical operations. The K-Nearest Neighbors regression model exhibits superior performance with MAE of 1.3637e-08 and SMAPE of 123.2707%. Additionally, the Light Gradient Boosting Machine regression model demonstrates the best performance with an RMSE of 2.8453e-08. Therefore, it is confirmed that applying a nonlinear regression model to the tri_sor_dp_dp function during the execution of Low-GloSea6 could be a viable alternative.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.21-34
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• 2023

The purpose of this study is to examine the extent of Sustainable Development Goals (SDGs)-related research in the field of entrepreneurship globally since the adoption of the SDGs at the UN General Assembly, and to compare international and domestic research trends in order to determine the direction of SDGs-related research in entrepreneurship in Korea. Utilizing three databases-Web of Science (WoS), KCI, and DBpia- SDGs-related studies in entrepreneurship were extracted by employing specific search terms. After data purification, a total of 356 studies abroad and 4 studies in Korea were used for analysis. After data purification, a total of 356 international studies and 4 Korean studies were analyzed. Due to the limited number of domestic studies, the research trends were examined by conducting frequency analysis and keyword network analysis on international studies alone. Frequency analysis revealed that SDGs research in entrepreneurship primarily focused on sustainability-related terms and was conducted in conjunction with business models, innovation, entrepreneurship education, and strategies. Furthermore, yearly frequency analysis demonstrated an expansion of topics to encompass research on entrepreneurship and SDGs policies, the roles and capabilities of female entrepreneurs in SDGs implementation, energy start-ups and SDGs, directions for implementing SDGs in business schools and SDGs education, indicators for SDGs implementation and evaluation, and technologies for sustainability. The keyword network analysis identified central topics such as business, sustainability, SDGs, innovation, entrepreneurship, business models, and education, with research areas extending to entrepreneurship ecosystems, change and strategy, ethics, and climate. This study holds significance in establishing a foundation for SDGs research in entrepreneurship, which is currently an underexplored area in Korea, by presenting emerging research trends related to SDGs in entrepreneurship.

• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.108-121
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• 2024

In this study, we developed a sustainability education program employing a project-based learning strategy for prospective teachers and investigated its effectiveness. A total of 23 senior students from a university of education participated in the study. The investigation involved a pretest on their pro-environmental behavior and attitudes, followed by a five-week implementation of the program, during which students individually engaged in energy-saving projects. Following the program, a post-test, which used the same questionnaire as the pretest, was administered. In addition, we conducted individual interviews with nine students who actively engaged in the projects. We analyzed the interview contents, portfolios, and reports; identified sub-concepts related to the program's effectiveness and its causes; and then organized them into subcategories. Then, we extracted recurring relationships among the subcategories to formulate a tentative explanatory model. The results indicate that the program positively impacted students' pro-environmental behavior and values/attitudes. Notably, the students' "sense of achievement gained through success" emerged as a significant factor influencing their pro-environmental behavior. Furthermore, some causes were found to indirectly affect pro-environmental behavior through pro-environmental values and attitudes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.82-96
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• 2024

While analyzing traffic flow, speed, traffic volume, and density are important macroscopic indicators, and acceleration and spacing are the important microscopic indicators. The speed and traffic volume can be collected with the currently installed traffic information collection devices. However, acceleration and spacing data are necessary for safety and autonomous driving but cannot be collected using the current traffic information collection devices. 'You Look Only Once'(YOLO), an object recognition technique, has excellent accuracy and real-time performance and is used in various fields, including the transportation field. In this study, to measure acceleration and spacing using YOLO, we developed a model that measures acceleration and spacing through changes in vehicle speed at each interval and the differences in the travel time between vehicles by setting the measurement intervals closely. It was confirmed that the range of acceleration and spacing is different depending on the traffic characteristics of each point, and a comparative analysis was performed according to the reference distance and screen angle to secure the measurement rate. The measurement interval was 20m, and the closer the angle was to a right angle, the higher the measurement rate. These results will contribute to the analysis of safety by intersection and the domestic vehicle behavior model.

• Korean Association of Arts Management
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• no.50
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• pp.147-175
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• 2019

Gentrification is the process by which a working class or other disadvantaged area of a city changes into a middle class residential or commercial district. Gentrification, which has received much attention in arts management in recent years as part of a concern with urban regeneration, carries a generally negative connotation. In this paper, we interrogate this negative view of gentrification to explore ways arts entrepreneurship can convert the perceived threat of gentrification into opportunity. To this end, we examine the Mullae Cultural Salon in the gentrifying district of the Mullae Creative Village. Through a literature review of gentrification and arts entrepreneurship, we propose seven elements of art entrepreneurs responding to gentrification as an analytic framework for research. Our findings indicate that arts entrepreneurs were able to extend the maturity phase of gentrification and thus enhance the cultural and artistic value of the region for other artists and arts entrepreneurs.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.1-9
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• 2024

Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.

• Journal of Korean Society of Forest Science
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• v.113 no.2
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• pp.143-152
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• 2024

This study was conducted to evaluate the growth and nutrient dynamics in response to fertilization of four tree species (LT: Liriodendron tulipifera L.; PY: Prunus yedoensis Matsumura; QA: Quercus acutissima Ca rruth; a nd PT: Pinus thunbergii Parl.) planted in a fire-disturbed urban forest in Bongdaesan (Mt.), Ulsan Metropolitan Area, South Korea. The trees were planted in 2009, and compound fertilizers (N₆P₄K₁) were applied in April 2013 and March 2014. Tree growth, soil, and foliage nutrients were examined from March 2013 to October 2016. The regression coefficients for the increment of the diameter at breast height (DBH) significantly differed between the fertilized and unfertilized plots, suggesting the significant effects of fertilization. By contrast, fertilization did not affect the coefficients for height increments. Regarding soil nutrient contents, organic carbon and total nitrogen concentrations were lower in the fertilized plots than in the unfertilized plots, whereas available phosphorus, exchangeable calcium, and magnesium concentrations were higher in the fertilized plots than in the unfertilized plots. In foliage, nitrogen and phosphorus concentrations were higher in the fertilized plots than in the unfertilized plots, whereas potassium, calcium, and magnesium concentrations were not affected by fertilization. Nutrient concentration of foliage among the tree species were higher in LT and PY than in QA and PT. These results suggest that fertilizers may be used to enhance soil fertility and the growth and nutrient status of tree species planted in a fire-disturbed urban forest.

• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.25-39
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• 2006

Most student misconceptions about evolution are similar to misconceptions and disputes which early scientists had in science history. The aim of this study was to analyze student evolution conceptions based on science history, there by revealing for effectively teaching strategies on evolution. A test was developed according to Lee's three dimensional framework (2004) on evolution concept changes. Lee's framework had been constructed according to 4 stages of evolution concept changes in history in three-dimensional aspects such as mechanism, time, and subject: before Lamarck (stage 1), Lamarck (stage 2), Darwin (stage 3), and after Darwin (stage 4). Major results were as follows. First, the evolution conceptions of students appeared fixed to stage 2 regardless of grade. Moreover, students usually possessed Lamarckian thought and did not show consistency in evolution concepts among the three dimensional aspects of mechanism, time, and subject. Therefore, students were found to apply different conceptions of evolution to each different situation.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.