• Journal of the Korean Chemical Society
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• v.60 no.3
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• pp.203-214
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• 2016

The purpose of this study was to investigate brain activation pattern and functional connectivity during convergence thinking based creative problem solving and chemistry problem solving to identify characteristic convergence thinking that is backbone of creative problem solving using functional magnetic resonance imaging(fMRI). A fMRI paradaigm inducing convergence thinking and chemistry problem solving was developed and adjusted on 17 highschool students, and brain activation image during task was analyzed. According to the results, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, medial frontal gyrus, cingulate gyrus, precuneus and caudate nucleus body in left hemisphere and cuneus and caudate nucleus body in right hemisphere were significantly activated during convergence thinking. The other hand, middle frontal gyrus, medial frontal gyrus and caudate nucleus in left hemisphere and middle frontal gyrus, lingual gyrus, caudate nucleus, thalamus and culmen of cerebellum in right hemisphere were significantly activated during chemistry problem solving. As results of analysis functional connectivity, all of areas activated during convergence thinking were functionaly connected, whereas scanty connectivity of chemistry problem solving between right middle frontal gyrus, bilateral nucleus caudate tail and culmen. The results show that logical thinking, working memory, planning, imaging, languge based thinking and learning motivation were induced during convergence thinking and these functions and regions were synchronized intimately. Whereas, logical thinking and inducing learning motivation functioning during chemistry problem solving were not synchronized. These results provide concrete information about convergence thinking.

• Journal of the Korean Dietetic Association
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• v.15 no.2
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• pp.97-112
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• 2009

The purpose of this study was to develop a nutrition education curriculum with teacher's guide which includes discretionary activities for obese children. A survey was carried out to investigate the recognition of body image and food behaviors according to the obesity index (mild, moderate, severe) in school children (4~6th grade, 158 boys and 60 girls) who were selected based on a physical examination in May, 2006 in the Gyeongnam province. Next, a nutrition education curriculum with teacher's guide was developed on the basis of the findings from the survey and from preceding researches. The results are summarized as follow. The results of this study showed the existence of some nutritional problems such as overeating, prejudice, skipping meals, snacking patterns, etc, which indicate the need for nutritional management for obese children. Most overweight children (80.3%) showed the most interest in the nutrition education program, particularly with regards to dieting for weight control (64.7%). The developed nutrition education curriculum consisted of 8 main subjects and 13 subtitles. The curriculum was prepared for 13 lessons and included songs and singing, making-up lyrics, games about nutrition, discussions of the experience of eating (satiety, thirst, hunger), debates on dietary habits, writing and others to promote the interest for learning. We aimed to develop this program in an attempt to improve the dietary habits of obese school children. This is very important because once a dietary habit is formed in adults, it is difficult to change and the best adjustable stage is during childhood. Therefore, early nutrition education during elementary school can change and build-up the awareness of health in young elementary school children.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.959-974
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• 2021

Forel-Ule Index (FUI) is an index which classifies the colors of inland and seawater exist in nature into 21 gradesranging from indigo blue to cola brown. FUI has been analyzed in connection with the eutrophication, water quality, and light characteristics of water systems in many studies, and the possibility as a new water quality index which simultaneously contains optical information of water quality parameters has been suggested. In thisstudy, Ocean Colour-Climate Change Initiative (OC-CCI) based 4 km FUI was spatially downscaled to the resolution of 500 m using the Geostationary Ocean Color Imager (GOCI) data and Random Forest (RF) machine learning. Then, the RF-derived FUI was examined in terms of its correlation with various water quality parameters measured in coastal areas and its spatial distribution and seasonal characteristics. The results showed that the RF-derived FUI resulted in higher accuracy (Coefficient of Determination (R²)=0.81, Root Mean Square Error (RMSE)=0.7784) than GOCI-derived FUI estimated by Pitarch's OC-CCI FUI algorithm (R²=0.72, RMSE=0.9708). RF-derived FUI showed a high correlation with five water quality parameters including Total Nitrogen, Total Phosphorus, Chlorophyll-a, Total Suspended Solids, Transparency with the correlation coefficients of 0.87, 0.88, 0.97, 0.65, and -0.98, respectively. The temporal pattern of the RF-derived FUI well reflected the physical relationship with various water quality parameters with a strong seasonality. The research findingssuggested the potential of the high resolution FUI in coastal water quality management in the Korean Peninsula.

• Journal of the Korean Society of Radiology
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• v.15 no.1
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• pp.45-54
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• 2021

This study was written to create educational content in non-destructive fields based on Mixed Reality. Currently, in the field of radiation, there is almost no content for educational Mixed Reality-based educational content. And in the field of non-destructive inspection, the working environment is poor, the number of employees is often 10 or less for each manufacturer, and the educational infrastructure is not built. There is no practical training, only practical training and safety education to convey information. To solve this, it was decided to develop non-destructive worker education content based on Mixed Reality. This content was developed based on Microsoft's HoloLens 2 HMD device. It is manufactured based on the resolution of 1280 ⁎ 720, and the resolution is different for each device, and the Side is created by aligning the Left, Right, Bottom, and TOP positions of Anchor, and the large image affects the size of Atlas. The large volume like the wallpaper and the upper part was made by replacing it with UITexture. For UI Widget Wizard, I made Label, Buttom, ScrollView, and Sprite. In this study, it is possible to provide workers with realistic educational content, enable self-directed education, and educate with 3D stereoscopic images based on reality to provide interesting and immersive education. Through the images provided in Mixed Reality, the learner can directly operate things through the interaction between the real world and the Virtual Reality, and the learner's learning efficiency can be improved. In addition, mixed reality education can play a major role in non-face-to-face learning content in the corona era, where time and place are not disturbed.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.521-535
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• 2023

When converting rice fields into fields,sorghum (sorghum bicolor L. Moench) has excellent moisture resistance, enabling stable production along with soybeans. Therefore, it is a crop that is expected to improve the self-sufficiency rate of domestic food crops and solve the rice supply-demand imbalance problem. However, there is a lack of fundamental statistics,such as cultivation fields required for estimating yields, due to the traditional survey method, which takes a long time even with a large manpower. In this study, U-Net was applied to RGB images based on unmanned aerial vehicle to confirm the possibility of non-destructive segmentation of sorghum cultivation fields. RGB images were acquired on July 28, August 13, and August 25, 2022. On each image acquisition date, datasets were divided into 6,000 training datasets and 1,000 validation datasets with a size of 512 × 512 images. Classification models were developed based on three classes consisting of Sorghum fields(sorghum), rice and soybean fields(others), and non-agricultural fields(background), and two classes consisting of sorghum and non-sorghum (others+background). The classification accuracy of sorghum cultivation fields was higher than 0.91 in the three class-based models at all acquisition dates, but learning confusion occurred in the other classes in the August dataset. In contrast, the two-class-based model showed an accuracy of 0.95 or better in all classes, with stable learning on the August dataset. As a result, two class-based models in August will be advantageous for calculating the cultivation fields of sorghum.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Journal of Christian Education in Korea
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• v.66
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• pp.113-145
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• 2021

As a contemporary exponent of Bauderillard's Simulation and Simulacra, this paper aims to reflect on the 'consumer culture' criticized by Baudrillard from the lens of Christian Education in reading the Drama, Penthouse related to the notions of the consumption-ideology, the desire and violence of image in the post-Covid 19 era. As Baudrillard begins to realize that the concept of simulation rooted from mass media in the modern society, he explains mass media as the emerging of Simulation or the process of Simulation will lead to the impulsion of reality, which ends up with vanishing the original reality. Baudrillard is explaining in his argument that the process of Simulation proceeds among various areas of the contemporary society being manipulated by mass media. While Simulation is the process of producing the hyperreality characterized by the excess of images that seems more real than the original reality, Simulation brought about Simulacra as excess reality or consequently exploding reality. Christian educators in the post-Covid 19 must know how to deal with critical theory by considering positive ways of avoiding questioning of how to articulate what the norm of universal consensus is in the specific situation. In other words, it should be noted that the nature of the ruling ideology and the ideology of consumption has been influenced or manipulated by mass media. Christian educators especially have to help young people in seeing the messages from the images of the screens, television, soap-opera, and commercial advertising making reality as Simulacre which is more real than the original reality. When the medium becomes the message, the power of medium makes the consumer not reach communication with it. This is the main reason in the controversy about the images on television drama, Penthouse and the impact of images on people's mind. As an exponent of McLuhan's belief that "the medium is the message", Baudrillard argues although the message and a subject of Simulacra(excessive reality) is unexpectedly disappearing, the medium itself is vanished through the silence of image. However, the task of Christian education has to fuel how we teach, learn, share and pass on the Word of God as the Message. Furthermore, it is worth noting that the Message of God cannot be vanished or burst with the impulsion of it, but exists forever. With Baudrillard's ideas of Simulation and Simulacra in mind, the work of Christian education as an observation platform can better engage the reflection on a consumer society of consumerism that makes Church community and a consumer irresistible against the Fake world.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.295-304
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• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.

• Journal of The Korean Association For Science Education
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• v.28 no.1
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• pp.1-14
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• 2008

The image of science is one of the recurrent topics in science education research. In particular, we believe that students' images of Doing-Science-Well could be used for identifying not only students' perceived goals of science learning, but also practical guidelines of effective science teaching. In this study, the students' images of Doing-Science-Well were investigated with the following two research questions: (i) what are student's images of Doing-Science-Well?; (ii) in what contexts do students perceive that someone is doing science well? Thirty seven students in a high school in Seoul, Korea were asked to write their personal experiences by which they realized that someone was doing science well. The main results of the study are the following: Firstly, the images of Doing-Science-Well could be categorized into 'Einstein type', 'Socrates type', 'MacGyver type' and six more types. Secondly, with regard to contexts, students tended to realize that somebody is doing science well in terms of two kinds of contexts: 4 physical contexts and 6 psychological contexts. The findings led us to develop a frame of judging Doing-Science-Well, which combines the types and two kinds of contexts. The frame illustrates the multiplicity of the images of Doing-Science-Well.