• Journal of The Korean Association For Science Education
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• v.39 no.1
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• pp.89-99
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• 2019

The purpose of this study is to investigate the effect of students' formation of evolutionary concept and learning on the development of digital teaching materials. The explanation of biological evolution, which explains the changes that living organisms undergo over a long period of time, can provide various contents for use in a book. The production and editing of images in digital textbooks would provide explanation of difficult concepts in a fun way. For this study, we designed instructional materials consisting of four class hours using iBooks Author, an electronic book authoring tool based on the 5E learning cycle model. In order to verify the effectiveness of the developed digital textbooks, we compared instructions by the general textbooks to those using digital textbooks. Both teaching through general textbook form and teaching using digital textbook materials had a significant effect on the formation of the concept of evolution, but interest in biological science and evolution increased significantly only in the group taught using digital textbooks. As a result of testing the instruction effect by the digital textbooks by classifying the students by type, the group that is familiar with smart devices was more active and interesting in class depending on digital literacy. The satisfaction of the developed digital textbooks also showed a positive score in the group with high digital literacy. The results of this study suggest that the development of digital textbooks in the unit of evolution can be an instructional material for easy and interesting approach to difficult concepts in the teaching of evolution.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.3117-3126
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• 2018

The operating system update is a basic step to maintain a safe internet use environment. This study analyzed whether the implementation of the operating system update was related to gender and age group during the violation accident prevention act in relation to information protection on the internet, and tried to verify the validity of these factors by analyzing the influence of gender and age group. In this study, logistic regression analysis was conducted based on the information security survey data surveyed by the Korea Internet & Security Agency in 2016. As a result, gender and age were surveyed as factors related to the implementation of operating system updates. As a result of analyzing the impact on the implementation of operating system updates by gender, it is estimated that the odds are 0.419 times higher for women than for men. According to the analysis of the operating system update by age group based on the 50s, which is a vulnerable group of information, the result is that the odds are 13.266 times higher in the 20s than the 50s.

• Database Research
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• v.34 no.3
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• pp.148-158
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• 2018

The optical character recognition (OCR) is a technique to extract and recognize texts from images. It is an important preprocessing step in data analysis since most actual text information is embedded in images. Many OCR engines have high recognition accuracy for images where texts are clearly separable from background, such as white background and black lettering. However, they have low recognition accuracy for images where texts are not easily separable from complex background. To improve this low accuracy problem with complex images, it is necessary to transform the input image to make texts more noticeable. In this paper, we propose a method to segment an input image into text lines to enable OCR engines to recognize each line more efficiently, and to determine the final output by comparing the recognition rates of CLAHE module and Two-step module which distinguish texts from background regions based on image processing techniques. Through thorough experiments comparing with well-known OCR engines, Tesseract and Abbyy, we show that our proposed method have the best recognition accuracy with complex background images.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.156-162
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• 2021

The number of smart farms has increased to save labor in agricultural production as the subsidy become available from central and local governments. The number of illegal greenhouses has also increased, which causes serious issues for the local governments. In the present study, we developed Mask-RCNN model to detect greenhouses based on satellite images. Greenhouses in the satellite images were labeled for training and validation of the model. The Mask-RC NN model had the average precision (AP) of 75.6%. The average precision values for 50% and 75% of overlapping area were 91.1% and 81.8%, respectively. This results indicated that the Mask-RC NN model would be useful to detect the greenhouses recently built without proper permission using a periodical screening procedure based on satellite images. Furthermore, the model can be connected with GIS to establish unified management system for greenhouses. It can also be applied to the statistical analysis of the number and total area of greenhouses.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.441-447
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• 2021

Construction sites are complex and dangerous because heavy equipment and workers perform various operations simultaneously within limited working areas. Solitary works of heavy equipment in complex job sites can cause fatal accidents, and thus they should interact with spotters and obtain information about surrounding environments during operations. Recently, many computer vision technologies have been developed to automatically monitor construction equipment and detect their interactions with other resources. However, previous methods did not take into account the interactions between equipment and spotters, which is crucial for identifying solitary works of heavy equipment. To address the drawback, this research develops a computer vision-based solitary work detection model that considers interactive operations between heavy equipment and spotters. To validate the proposed model, the research team performed experiments using image data collected from actual construction sites. The results showed that the model was able to detect workers and equipment with 83.4 % accuracy, classify workers and spotters with 84.2 % accuracy, and analyze the equipment-to-spotter interactions with 95.1 % accuracy. The findings of this study can be used to automate manual operation monitoring of heavy equipment and reduce the time and costs required for on-site safety management.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.361-370
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• 2020

Long-term observation of the life cycle of plants allows the identification of critical signals of the effects of climate change on plants. Indeed, plant phenology is the simplest approach to detect climate change. Observation of seasonal changes in plants using digital repeat imaging helps in overcoming the limitations of both traditional methods and satellite remote sensing. In this study, we demonstrate the utility of camera-based repeat digital imaging in this context. We observed the biological events of plants and quantified their phenophases in the northern temperate type deciduous broadleaf forest of Jeombong Mountain. This study aimed to identify trends in seasonal characteristics of Quercus mongolica (deciduous broadleaf forest) and Pinus densiflora (evergreen coniferous forest). The vegetation index, green chromatic coordinate (GCC), was calculated from the RGB channel image data. The magnitude of the GCC amplitude was smaller in the evergreen coniferous forest than in the deciduous forest. The slope of the GCC (increased in spring and decreased in autumn) was moderate in the evergreen coniferous forest compared with that in the deciduous forest. In the pine forest, the beginning of growth occurred earlier than that in the red oak forest, whereas the end of growth was later. Verification of the accuracy of the phenophases showed high accuracy with root-mean-square error (RMSE) values of 0.008 (region of interest [ROI]1) and 0.006 (ROI3). These results reflect the tendency of the GCC trajectory in a northern temperate type deciduous broadleaf forest. Based on the results, we propose that repeat imaging using digital cameras will be useful for the observation of phenophases.

• Journal of The Korean Association For Science Education
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• v.40 no.6
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• pp.611-620
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• 2020

The purpose of the study is to find creativity factors of students in the process of small group modeling and relate them to the types of interactions among students. In order to capture students' detailed interactions, this study was conducted as an 'essential case study' through qualitative analysis. We have developed the modules of nine lessons about terraforming, and they were used in an actual classroom. In order to understand the creativity of the students in the process of modeling, students' discourses and interview data were analyzed using 19 creative factors or abilities. The findings are as follows. Frequently found creativity factors are Elaboration, Evaluation, Visualization, Resist premature closer, Originality, Analysis and Concentration. And students' interactions that affect students' creativity in the process of modeling can be classified into four categories: Suggestion, Agreement, Questioning, Refutation, and Conversion. Through each interaction, students demonstrated the process of expressing and modifying their own thoughts and ideas in the modeling process. The findings of the study suggest that it is important to the teachers to understand types of interactions among students and the relationship between the types of interaction and creativity factors for students' creative modeling in modeling-based learning.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.27-32
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• 2020

Along with the aging of the population, the number of dementia patients is increasing, and the social and economic burden is also increasing. Currently, the effective way to manage dementia patients is to identify patients with dementia early. However, in rural and island areas where medical staff are scarce, there is a problem that it is difficult to visit a hospital and get an early examination. Therefore, we propose a remote early detection system for dementia to solve the problems. The remote dementia early diagnosis system is a system that allows a patient to receive examination and treatment from a remote dementia expert using remote medical technology based on real-time image communication. The remote early diagnosis system for dementia consists of a local client system used by medical staff at health centers in the island, an image server that transmits, stores and manages images, and an expert client used by remote dementia experts. The local client subsystem satisfies the current medical law's remote collaboration by allowing the patient to use it with the health center's medical staff. In addition, expert clients are used by dementia experts, and can store/manage patient information, analyze patient history information, and predict the degree of dementia progression in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.141-148
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• 2021

In some intersections or busy traffic roads, there are more pedestrians in a specific period of time, and there are many traffic accidents caused by road congestion. Especially at the intersection where there are schools nearby, it is particularly important to protect the traffic safety of students in busy hours. In the past, when designing traffic lights, the safety of pedestrians was seldom taken into account, and the identification of motor vehicles and traffic optimization were mostly studied. How to keep the road smooth as far as possible under the premise of ensuring the safety of pedestrians, especially students, will be the key research direction of this paper. This paper will focus on person, motorcycle, bicycle, car and bus recognition research. Through investigation and comparison, this paper proposes to use YOLO v4 network to identify the location and quantity of objects. YOLO v4 has the characteristics of strong ability of small target recognition, high precision and fast processing speed, and sets the data acquisition object to train and test the image set. Using the statistics of the accuracy rate, error rate and omission rate of the target in the video, the network trained in this paper can accurately and effectively identify persons, motorcycles, bicycles, cars and buses in the moving images.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.63-71
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• 2020

In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.