• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Journal of Gifted/Talented Education
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• v.26 no.2
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• pp.319-346
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• 2016

In this study, by examining the characteristics of boys and girls which would appear in the performance process of Goldberg machine activities, it would be attempted to provide the implications for the development and teaching methods of gifted and talented programs. The object of study was organized into separate 2 groups of boys and girls by each, composed of a total of 16 people among 5th graders of the gifted class in elementary school, located in Gyeonggi province. The final assignment was to make the Goldberg machine in order to have the beads get to the target spot latest, in which the analysis was implemented qualitatively by participating in and observing the performance process of students. After dividing the Goldberg machine activities into the steps of planning, production, outcome, assessment and reflection, their analysis results are as follows: First, in the planning stage, the girls explained minutely the process of Goldberg machine in writing, whereas the boys represented it visually simply. Second, in the production stage, the boys showed the task commitment by trying to realize the machine as designed initially, but the girls showed their appearance to represent it simply and easily. Third, in the sophistication and efficiency of the machine production, the boys were superior to the girls, and in the creativity and diversity of the use of materials, the girls were more excellent. Fourth, in the assessment and reflection, the boys evaluated it individually, and the girls showed their appearance to evaluate it by reflecting others'thinking. Hence, when developing and teaching the gifted and talented programs, it would be required that the teaching and learning contents be recomposed by considering the gender, or that the various class strategies be sought. Further, the broader and more systematic studies, on the performance process of gifted students based on the gender, should be carried out.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• Journal of the Korean Society for information Management
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• v.37 no.1
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• pp.51-78
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• 2020

Recently, the emphasis on self-directed learning and lifelong education is increasing the importance of school libraries in the curriculum. Accordingly, various studies have been conducted mainly from a structural, institutional and operational point of view. However, more research is necessary on the micro topics such as school library users' autonomous intrinsic motivations in the sense that school libraries play key roles in autonomy-based self-directed education. This study aims at finding out what types of school library use motivations are more important and the degree to which the use motivations affect the school library service quality based on the self-determination theory. In addition, this study examines how the use motivations and the perceived service quality vary depending on the school grade of the library users. Based on a focus-group-interview pilot study, a questionnaire survey was administered on the effects of school library motivations on perceived library service quality to 588 students from 5 high schools and 2 middle schools in Seoul. When the service quality and its components, service affect, information control, and library as place were set as dependent variables, in all these four cases, intrinsic motivations were more significant than extrinsic motivations. In addition, when middle school students and high school students were selected as separate analysis target groups, the results of both analyses show that the higher the intrinsic motivations were, the higher the perceived service quality was. The contribution of this study is that it applies the self-determination theory to school library service, measures the influence of motivation type based on the theoretical basis, and focuses on micro aspects to improve school library services.

• Journal of the Korean School Mathematics Society
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• v.18 no.2
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• pp.187-201
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• 2015

The purpose of this study was to examine the effects of using process-based writing poems in the elementary mathematics classrooms. For this study, we chose 128 elementary school students to examine their mathematical achievements and attitude towards mathematics when using process-centered writing poems in the elementary mathematics classrooms. Process-based mathematics and writing programs developed mainly on the geometry units were composed of four levels, idea generation, idea selection, use and idea organization grouped into similar sections in order to separate into two sections. The results of the practice of this study's problem can be summarized as follows. First, the process-based mathematics and writing activity of geometry had a positive impact on academic achievement in mathematics. Although there was not a significant difference in the fourth and fifth grades, significant differences in the fifth and sixth grade were found. Second, in regards to attitudes in mathematics, process-based mathematics and writing activities had a positive impact. In particular, the improvement of mathematical attitudes was evident in all grades. It confirmed the effective facilitation of interest and enjoyment towards learning mathematics by 4th, 5th and 6th graders who had undertaken these mathematics classes.

• Journal of Hospice and Palliative Care
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• v.22 no.4
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• pp.207-218
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• 2019

Purpose: Physician competency in end-of-life (EOL) care is becoming increasingly important. This study investigated the EOL care curricula in Korean medical schools. Methods: Questionnaires were issued to the faculty members responsible for the EOL care curricula at each of the medical schools. These included questions on the structure and content of the curricula, teaching methods, and faculty members' attitudes to the curricula. Results: Characteristics of the EOL care curricula were compiled from 27 (66%) of the 41 medical schools. All of the medical schools taught essential aspects of the EOL care curriculum either as a separate course or embedded within other medical education courses. The mean time spent on EOL care teaching was 10 hrs (range, 2~32 hrs). The most frequently taught topics were delivering bad news (100%) and symptom management (74%). When the palliative care education assessment tool (PEAT) was used to evaluate the curricula, a median of 11 PEAT objectives was met (range, 2~26; maximum, 83). More than two teaching methods were used in most of the curricula. However, lectures were the only teaching method used by three medical schools. 78% of faculty members who were responsible for curriculum reported dissatisfaction with it, whereas 18% believed that the time allotted to it was adequate. Only 7% of these faculty members believed that their students were adequately prepared to practice EOL care. Conclusion: There is a need to improve EOL care education in basic medical curricula and to take a more systematic approach to achieving learning outcomes.

• Korean Journal of Occupational Health Nursing
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• v.17 no.1
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• pp.23-33
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• 2008

Purpose: The purpose of the study was to identify attitudes of middle managers toward employees with psychological problems and to determine factors affecting their attitudes. Methods: A questionnaire with Community Attitudes Toward Mentally Ill (CAMI) scales was administered to 161 middle managers working in small and medium-sized enterprises based in Seoul and Gyeonggi Province. Results: There are four separate subscales on the CAMI. Mean score for authoritarianism was $35.0{\pm}4.4$, benevolence $23.0{\pm}4.8$, social restrictiveness $32.3{\pm}4.9$ and community mental health ideology $27.2{\pm}5.1$ According to multiple regression analysis, middle managers with no experience of learning mental illness through mass media or higher levels of depression symptom were more authoritative and less benevolent towards employees with psychological problems. The experience of meeting a patient with mental problem contribute to positive attitudes toward people with mental illnesses in social restrictiveness subscale and community mental health ideology subscale among CAMI. Conclusion: This study suggests that experience of having patients with mental problems and information on psychological problems will have great influence on attitudes of middle managers toward employees with psychological problems. It might be important to help middle manager manage their depression because their depression also affects their attitudes toward employees with psychological problems.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.246-252
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• 2018

Hand gestures are attracting attention as a NUI (Natural User Interface) of wearable devices such as smart glasses. Recently, to support efficient media consumption in IoT (Internet of Things) and wearable environments, the standardization of IoMT (Internet of Media Things) is in the progress in MPEG. In IoMT, it is assumed that hand gesture detection and recognition are performed on a separate device, and thus provides an interoperable interface between these modules. Meanwhile, deep learning based hand gesture recognition techniques have been recently actively studied to improve the recognition performance. In this paper, we propose a method of hand gesture recognition based on CNN (Convolutional Neural Network) for various applications such as media consumption in wearable devices which is one of the use cases of IoMT. The proposed method detects hand contour from stereo images acquisitioned by smart glasses using depth information and color information, constructs data sets to learn CNN, and then recognizes gestures from input hand contour images. Experimental results show that the proposed method achieves the average 95% hand gesture recognition rate.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.