• Korean Journal of Soil Science and Fertilizer
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• v.44 no.6
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• pp.1035-1041
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• 2011

Salinity of soil under the plastic film houses in Korea is known as a significant factor to lower the crop production and to hamper the sustainable agricultural land management. In this study we propose a field monitoring technique to examine the methods applied to minimize the adverse effect of salts in soil based on the relationship between soil electrical characteristics and soil properties. Field experiments for 4 different treatments (water only, fertilizer only, DTPA only, and DTPA and fertilizer together) were conducted on soils at the plastic film house built for cultivating a cucumber plant located at Chunan-si, Chungchungnam-do in Korea. The electrical resistivity was measured by both a dipole-dipole and wenner multi-electrodes array method. After the electrical resistivity measurement we also measured the soil water content, temperature, and electrical conductivity on surface soil. The resulted image of the interpreted resistivity by the inversion technique presented a unique spatial distribution depending on the treatment, implying the effect of the different chemical components. It was also highly suspected that resistivity response changed with the nutrients level, suggesting that our proposed technique could be the effective tool for the monitoring soil water as well as nutrient during the cropping period. Especially, subsoils under DTPA treatment at 40 to 60 cm depth typically presented lower soil water accumulation comparing to subsoils under non-DTPA treatment. It is considered that DTPA resulted in increase of a root water uptake. However, our demonstrated results were mainly based on qualitative comparison. Further experiments need to be conducted to monitor temporal changes of electrical resistivity using time lapse analysis, providing that a plant root activity difference based on changes of soil water and nutrients level in time.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.491-498
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• 2021

In this study, the effective dose was measured using the PCXMC v2.0 program by examining the conditions used to set the diagnostic reference level for intraoral imaging recommended by the government, and the effect of the Al additive filter was confirmed. In oral imaging, the largest effective dose was calculated from the oral mucosa among 11 organs. The effect of the Al additive filter showed an excellent radiation reduction effect at 2mm rather than 1mm. In the case of children aged 5 years, the overall effective dose was calculated to be high in all 11 organs because they are more sensitive to radiation than adults. And as a result of evaluating the image quality according to the use of an additional filter during intraoral imaging, there was no significant difference in SNR and CNR changes compared to before the additional filter was used. Based on this study, it is thought that additional filter settings can be recommended for intraoral imaging.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.

• Journal of Korean Home Economics Education Association
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• v.34 no.1
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• pp.97-111
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• 2022

The purpose of this study is to systematically develop an impression management education program for career women by applying the Educational System Development Model [ESD] (Kwon, 1997) based on the research results of Hwang and Lee(2019) on social attractiveness. Since the previous impression management education programs were generally developed by content experts, few of them were developed systematically by considering the learner characteristics, clearly setting the educational goals, and structuring the contents accordingly. In this study, 'social attractiveness' defined by Hwang and Lee (2019) was considered as the educational goal of the impression management education program. In particular, this study focused on the design stage of the ESD model, set teaching goals based on the components of social attractiveness derived from previous studies, and conducted an analysis of teaching activities, the establishment of the educational goals at the specific function level, allocation of time, and establishment of an evaluation plan. The research process was intended to improve the learners' social image directly related to success through a systematic educational program designed to enhance the social attractiveness of working women with various hands-on activities and information. The impression management education program designed in this study has the educational advantage of a learner-centered education program configured to meet the needs and goals of career women. In addition, based on the ESD model, the sub-factors necessary for impression management were identified, and the curriculum was configured to reflect them through mutual communication between the instructor and learners. Therefore, this education program meets the basic requirements of impression management education necessary for career women and is expected to contribute to enhancing the social attractiveness of working women.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.175-187
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• 2020

The purpose of this study is to discuss the elements and meanings of the performative scenography which has been revealed through the new directorial interpretation and deconstruction process of the musical 『All Shook Up』. The performative scenography characteristics after postmodernism aim to create individual active perceptions and various social meanings through audience's voluntary and particular emergence. To this end, the theoretical foundation of scenography was examined by periods in advance. Based on this, I attempted to establish performative scenography for synthesized scenic and media design through the reconstruction process for the 『All Shook Up Travelers』. As a result, I set up visual narrative based world of 『All Shook Up Travelers』 which was produced by text-based intense images for a direct medium in order to expand actors' inner narrative and established unique performative scenography of its own: 1. enhancing the adapted one's narratives for the actors' and audience's co-existence and detachment, 2. delivering its own independent meanings which have double meanings, 3. encouraging audience's critical and active perception experiences through collage and montage of media.

• Journal of Platform Technology
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• v.11 no.2
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• pp.26-38
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• 2023

This paper is a study on a transport robot capable of autonomously driving to a destination using a QR code in an indoor environment. The transport robot was designed and manufactured by attaching a lidar sensor so that the robot can maintain a certain distance during movement by detecting the distance between the camera for recognizing the QR code and the left and right walls. For the location information of the delivery robot, the QR code image was enlarged with Lanczos resampling interpolation, then binarized with Otsu Algorithm, and detection and analysis were performed using the Zbar library. The QR code recognition experiment was performed while changing the size of the QR code and the traveling speed of the transport robot while the camera position of the transport robot and the height of the QR code were fixed at 192cm. When the QR code size was 9cm × 9cm The recognition rate was 99.7% and almost 100% when the traveling speed of the transport robot was less than about 0.5m/s. Based on the QR code recognition rate, an experiment was conducted on the case where the destination is only going straight and the destination is going straight and turning in the absence of obstacles for autonomous driving to the destination. When the destination was only going straight, it was possible to reach the destination quickly because there was little need for position correction. However, when the destination included a turn, the time to arrive at the destination was relatively delayed due to the need for position correction. As a result of the experiment, it was found that the delivery robot arrived at the destination relatively accurately, although a slight positional error occurred while driving, and the applicability of the QR code-based destination self-driving delivery robot was confirmed.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1111-1123
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• 2023

In recent times, semantic image segmentation methods using deep learning models have been widely used for monitoring changes in surface attributes using remote sensing imagery. To enhance the performance of various UNet-based deep learning models, including the prominent UNet model, it is imperative to have a sufficiently large training dataset. However, enlarging the training dataset not only escalates the hardware requirements for processing but also significantly increases the time required for training. To address these issues, transfer learning is used as an effective approach, enabling performance improvement of models even in the absence of massive training datasets. In this paper we present three transfer learning models, UNet-ResNet50, UNet-VGG19, and CBAM-DRUNet-VGG19, which are combined with the representative pretrained models of VGG19 model and ResNet50 model. We applied these models to building extraction tasks and analyzed the accuracy improvements resulting from the application of transfer learning. Considering the substantial impact of learning rate on the performance of deep learning models, we also analyzed performance variations of each model based on different learning rate settings. We employed three datasets, namely Kompsat-3A dataset, WHU dataset, and INRIA dataset for evaluating the performance of building extraction results. The average accuracy improvements for the three dataset types, in comparison to the UNet model, were 5.1% for the UNet-ResNet50 model, while both UNet-VGG19 and CBAM-DRUNet-VGG19 models achieved a 7.2% improvement.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.225-234
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• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1764-1776
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• 2021

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

• Education of Primary School Mathematics
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• v.27 no.3
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• pp.303-320
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• 2024

This study presents the development and performance evaluation of a custom GPT-based chatbot tailored to provide solutions following Polya's problem-solving stages. A beta version of the chatbot was initially deployed to assess its mathematical capabilities, followed by iterative error identification and correction, leading to the final version. The completed chatbot demonstrated an accuracy rate of approximately 89.0%, correctly solving an average of 57.8 out of 65 image-based problems from a 6th-grade elementary mathematics textbook, reflecting a 4 percentage point improvement over the beta version. For a subset of 50 problems, where images were not critical for problem resolution, the chatbot achieved an accuracy rate of approximately 91.0%, solving an average of 45.5 problems correctly. Predominant errors included problem recognition issues, particularly with complex or poorly recognizable images, along with concept confusion and comprehension errors. The custom chatbot exhibited superior mathematical performance compared to the general-purpose ChatGPT. Additionally, its solution process can be adapted to various grade levels, facilitating personalized student instruction. The ease of chatbot creation and customization underscores its potential for diverse applications in mathematics education, such as individualized teacher support and personalized student guidance.