• Korean Journal of Heritage: History & Science
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• v.56 no.4
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• pp.216-228
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• 2023

The purpose of this study is to apply the handheld Raman spectrometer to identify the coloring materials used in a large Buddhist painting (of Maitreya Buddha) at Janggoksa Temple through cross-validation with HH-XRF. An in situ investigation was performed together with use of a digital microscope and HH-XRF analysis to verify the properties of pigments used in the gwaebul ("large Buddhist painting") via a non-destructive method. However, the identification of coloring materials composed of light elements and mixed or overlaid pigments is difficult using only non-destructive analysis data. Unlike in situ investigation, laboratory analysis often required samples yet the sampling is restricted to a small quantity due to the cultural heritage characteristic. Thus, it is necessary to develop a non-destructive in situ method to supplement the HH-XRF data. The large Buddhist painting at Janggoksa Temple was painted mainly using white, red, yellow, green, and blue colors. The Raman spectroscopy provides molecular information, while XRF spectroscopy provides information about elemental composition of the pigments. Analysis results identified various coloring materials: inorganic pigment, such as lead white, minium, cinnabar, and orpiment, as well as organic pigment such as gamboge and indigo. Therefore, it is possible to obtain more information for the identification of pigments; organic pigment and mixed or overlaid pigments, while at the same time minimizing the collection sample and simplifying the analysis procedure compared to previously used methods. The results of this study will be used as basic data for the analysis of painting cultural heritage through a non-destructive in situ method in the future.

• Journal of Venture Innovation
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• v.6 no.3
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• pp.1-20
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• 2023

This study conducted an empirical analysis of whether the accelerator incubation program (mentoring, seed investment) and entrepreneur self-efficacy affect competitive advantage performance for start-up representatives who have experienced the accelerator incubation program. At this time, the mediating effect of innovation capability was also examined. As for sample collection, 334 samples collected from representatives of start-ups nurtured by national accelerators and regional centers for creative economy and innovation in Korea were tested using the Smart PLS 4.0 program, and the analysis results are as follows. First, mentoring has no significant effect on competitive advantage performance, whereas seed investment and entrepreneur self-efficacy have a positive (+) effect on competitive advantage performance. Second, mentoring, seed investment, and entrepreneurial self-efficacy have a positive (+) effect on innovation capability. Third, innovation capability has a positive (+) effect on competitive advantage performance. And fourth, it was confirmed that innovation competency has a positive (+) effect between mentoring, seed investment, entrepreneurship self-efficacy and competitive advantage, and has a mediating effect. As a result, while mentoring did not have a direct significant effect on competitive advantage performance, it was confirmed that there was a significant effect mediated by innovation capability. Seed investment, entrepreneur self-efficacy, and innovation capability directly helped competitive advantage performance, and it was confirmed that innovation capability has an effect on the competitive advantage performance as a mediating effect. It is expected that this study will be able to suggest a direction that can help start-ups improve their competitive advantage performance.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.743-754
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• 2023

The global occurrence of myriad natural disasters and incidents, catalyzed by climate change and extreme meteorological conditions, has engendered substantial human and material losses. International organizations such as the International Charter have established an enduring collaborative framework for real-time coordination to provide high-resolution satellite imagery and geospatial information. These resources are instrumental in the management of large-scale disaster scenarios and the expeditious execution of recovery operations. At the national level, the operational deployment of advanced National Earth Observation Satellites, controlled by National Geographic Information Institute, has not only catalyzed the advancement of geospatial data but has also contributed to the provisioning of damage analysis data for significant domestic and international disaster events. This special edition of the National Disaster Management Research Institute delineates the contemporary landscape of major disaster incidents in the year 2023 and elucidates the strategic blueprint of the government's national disaster safety system reform. Additionally, it encapsulates the most recent research accomplishments in the domains of artificial satellite systems, information and communication technology, and spatial information utilization, which are paramount in the institution's disaster situation management and analysis efforts. Furthermore, the publication encompasses the most recent research findings relevant to data collection, processing, and analysis pertaining to disaster cause and damage extent. These findings are especially pertinent to the institute's on-site investigation initiatives and are informed by cutting-edge technologies, including drone-based mapping and LiDAR observation, as evidenced by a case study involving the 2023 landslide damage resulting from concentrated heavy rainfall.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.627-636
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• 2023

This study was conducted to lay the basis of the need of the self-directed learning ability, critical thinking, communicative ability, problem solving skills for nursing students by confirming the effect of problem based learning classes of nursing students. The data collection period was from March 1 to June 7, 2022. It was provided problem based learning classes to 165 nursing students located at B city. Problem based learning classes were conducted at total of 14 times, and 100 minutes/time. The collected data were analyzed using the frequency and percentage, Cronbach's α, mean and standard deviation with the SPSS Win 21.0 program, and the effectiveness verification of problem based learning classes was analyzed with a paired t-test. As a result of the effectiveness of the problem based learning class, the self-directed learning ability(t=-2.08, p=.039), critical thinking(t=-2.49, p=.014), communicative ability(t=-4.90, p<.001), problem solving skills(t=-4.84, p<.001) of nursing students who took 14 weeks of problem based learning was enhanced. Based on the results of this study, by applying it in various ways to first-year nursing students, it will be possible to use them to improve their competence, major satisfaction, and adapt to college life.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.935-940
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• 2023

Digital-twin technology is emerging as an innovative solution for all industries, including manufacturing and production lines. Therefore, this paper optimizes all the energy used in a biomass plant based on unused resources. We will then implement a digital-twin prototype for biomass plants and evaluate its performance in order to improve the efficiency of plant operations. The proposed digital-twin prototype applies a standard communication platform between the framework and the gateway and is implemented to enable real-time collaboration. and, define the message sequence between the client server and the gateway. Therefore, an interface is implemented to enable communication with the host server. In order to verify the performance of the proposed prototype, we set up a virtual environment to collect data from the server and perform a data collection evaluation. As a result, it was confirmed that the proposed framework can contribute to energy optimization and improvement of operational efficiency when applied to biomass plants.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.1-16
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• 2023

In 2005, the selection of 10 innovation cities was completed, and since 2013, public institutions began relocating to innovation cities. As a policy aimed at promoting balanced regional development, there were significant expectations from the regions. However, although the population moving to innovation cities has increased, it remains to be seen how much inflow is from the capital region and what spatial characteristics exist nationwide. Therefore, this study aims to analyze whether the innovation cities are fulfilling their roles by examining the patterns of inflow from the capital region and the spatial characteristics, and to reassess the policy direction for future innovation cities. We utilized the Microdata Integrated Service (MDIS) provided by Statistics Korea from 2013 to 2021. For the data collection reasons, we focused on analyzing the three cities. The results showed that in the initial stages of innovation city development, there was a significant influx of population from the capital region, leading to some effects on population dispersion and balanced regional development. However, over time, a phenomenon emerged where more people started to move back to the capital region, indicating a problematic trend. Furthermore, the Gyeongbuk Innovation City and Gwangju-Jeonnam Innovation City showed similarities in terms of reasons for migration, age of householder, and number of household members. However, the Gyeongnam Innovation City exhibited distinct characteristics compared to the other two cities. While the reasons for this phenomenon may be diverse, the current situation suggests that the goal of achieving "balanced national development" has reached its limits. Therefore, urgent measures need to be taken for improvement that take regional characteristics into account. Furthermore, in designing the second phase of the public institution relocation plan is required to avoid repeating the same issues and ensure a more thoughtful approach.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.119-131
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• 2024

Understanding accurate traffic performance is crucial for ensuring efficient highway operation and providing a sustainable mobility environment. On the other hand, an immediate and precise estimation of highway traffic performance faces challenges because of infrastructure and technological constraints, data processing complexities, and limitations in using integrated big data. This paper introduces a framework for estimating traffic performance by analyzing real-time data sourced from toll collection systems and dedicated short-range communications used on highways. In particular, this study addresses the data errors arising from segmented information in data, influencing the individual travel trajectories of vehicles and establishing a more reliable Origin-Destination (OD) framework. The study revealed the necessity of trip linkage for accurate estimations when consecutive segments of individual vehicle travel within the OD occur within a 20-minute window. By linking these trip ODs, the daily average highway traffic performance for South Korea was estimated to be248,624 thousand vehicle kilometers per day. This value shows an increase of approximately 458 thousand vehicle kilometers per day compared to the 248,166 thousand vehicle kilometers per day reported in the highway operations manual. This outcome highlights the potential for supplementing previously omitted traffic performance data through the methodology proposed in this study.

• Journal of Veterinary Clinics
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• v.41 no.2
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• pp.71-78
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• 2024

Changes in elimination behavior, including urination and defecation, are common clinical signs of numerous disorders in cats. Therefore, this study attempted to automatically measure the elimination behavior of cats using the litter box and develop an early warning system for the guardian in case of abnormalities. To construct an early warning system for abnormal changes through cat elimination behavior, it consisted of a litter box, an automatic sensor for data collection and data wifi transmission, a server for data analysis, and a mobile phone app for result transmission and early warning. To establish the reference interval (RI), the elimination behavior was monitored for more than 2 weeks using a motion sensor within a litter box in 37 healthy cats and 19 diseased cats. The data were expressed as daily total visits, daily total stay duration, average stay duration per elimination, weekly total visits, and weekly total stay duration. Healthy cats showed median daily total visits of 3 times/day (RI 1.0-7.0) and daily total stay duration of 192 s/day (RI 8.0-452.0). For weekly data, the median total visits were 20 times/week (RI 3.0-35.25) and the median total stay duration was 1,147 s/week (RI 80.0-2,249.5). The average stay duration per elimination was 59 s/elimination (RI 4.67-132.0). Diseased cats showed more frequent elimination behavior than healthy cats (p < 0.001). Otherwise, for each elimination, diseased cats had shorter stay durations than healthy cats (p < 0.001). This study established the RIs of elimination behavior parameters (frequency and duration) in healthy cats. The present study might help guardians and veterinarians detect changes in elimination behaviors in diseased cats at an early stage.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.85-93
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• 2024

The monitoring of environmental information (e.g. noise, dust, vibration, temperature, humidity) is crucial to the safe and sustainable operation of a construction site. However, commercial sensors exhibit certain drawbacks when applied on-site. First, the installation cost is prohibitively high. Second, these sensors have been engineered without considering the rugged and harsh conditions of a construction site, resulting in error-prone sensing. Third, construction sites are compelled to allocate additional resources in terms of manpower, expenses, and physical spaces to accommodate individual sensors. This research developed an integrated sensing module to measure the environmental information in construction site. The sensing module slashes the installation cost to 3.3%, is robust enough to harsh and outdoor sites, and consolidates multiple sensors into a single unit. The sensing module also supports GPS, LTE, and real-time sensing. The evaluation showed remarkable results including 97.5% accuracy and 99.9% precision in noise measurement, an 89.7% accuracy in dust measurement, and a 93.5% reliability in data transmission. This research empowers the collection of substantial volumes and high-quality environmental data from construction sites, providing invaluable support to decision-making process. These encompass objective regulatory compliance checking, simulations of environmental data dispersion, and the development of environmental mitigation strategies.

• Smart Media Journal
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• v.13 no.2
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• pp.52-61
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• 2024

In the shipyard, aerial images are acquired at regular intervals using Unmanned Aerial Vehicles (UAVs) for the management of external storage yards. These images are then investigated by humans to manage the status of the storage yards. This method requires a significant amount of time and manpower especially for large areas. In this paper, we propose an automated management technology based on a semantic segmentation foundation model to address these challenges and accurately assess the status of external storage yards. In addition, as there is insufficient publicly available dataset for external storage yards, we collected a small-scale dataset for external storage yards objects and equipment. Using this dataset, we fine-tune an object detector and extract initial object candidates. They are utilized as prompts for the Segment Anything Model(SAM) to obtain precise semantic segmentation results. Furthermore, to facilitate continuous storage yards dataset collection, we propose a training data generation pipeline using SAM. Our proposed method has achieved 4.00%p higher performance compared to those of previous semantic segmentation methods on average. Specifically, our method has achieved 5.08% higher performance than that of SegFormer.