• Communications of Mathematical Education
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• v.37 no.3
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• pp.517-544
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• 2023

This study presents the process and outcomes of developing mathematical-informatics linkage·convergence class materials, based on previous research findings that indicate a lack of such materials in high schools despite the increasing need for development of interdisciplinary linkage·convergence class materials In particular, this research provides insights into the discussions of six teachers who participated in the same professional learning community program, aiming to create materials that are suitable for linkage·convergence class materials and highly practical for classroom implementation. Following the material development process, a theme-based design model was applied to create the materials. In alignment with prior research and consensus among teacher learning community members, mathematics and informatics teachers developed instructional materials that can be utilized together during a 100-minute block lesson. The developed materials utilize societal issue contexts to establish links between the two subjects, enabling students to engage in problem-solving through mathematical modeling and coding. To increase the validity and practicality of the developed resources during their field application, CVR verification was conducted involving field teachers. Incorporating the results of the CVR verification, the finalized instructional materials were presented in the form of a teaching guide. Furthermore, we aimed to provide insights into the trial-and-error experiences and deliberations of the developers throughout the material development process, with the intention of offering valuable information that can serve as a foundation for conducting related research by field researchers. These research findings hold value as empirical evidence that can explore the applicability of teaching material development models in fields. The accumulation of such materials is expected to facilitate a cyclical relationship between theoretical teaching models and practical classroom applications.

• Journal of Korean Home Economics Education Association
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• v.19 no.4
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• pp.91-117
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• 2007

This study was to compare the contents and practical problems addressed, the process of teaching-learning method, and evaluation method of Korean Home Economics curriculum and of the Oregon and Ohio's Practical Problem Focused Family & Consumer Sciences Curricula. The results are as follows. First, contents of Korean curriculum are organized by major sub-concepts of Home Economics academic discipline whereas curricular of both Oregon and Ohio states are organized by practical problems. Oregon uses the practical problems which integrate multi-subjects and Ohio uses ones which are good for the contents of the module by integrating concerns or interests which are lower or detailed level (related interests). Since it differentiates interest and module and used them based on the basic concept of Family and Consumer Science, Ohio's approach could be easier for Korean teachers and students to adopt. Second, the teaching-learning process in Korean home economics classroom is mostly teacher-centered which hinders students to develop higher order thinking skills. It is recommended to use student-centered learning activities. State of Oregon and Ohio's teaching-learning process brings up the ability of problem-solving by letting students clearly analyze practical problems proposed, solve problems by themselves through group discussions and various activities, and apply what they learn to other problems. Third, Korean evaluation system is heavily rely on summative evaluation such as written tests. It is highly recommended to facilitate various performance assessment tools. Since state of Oregon and Ohio both use practical problems, they evaluate students mainly based on their activity rather than written tests. The tools for evaluation include project documents, reports of learning activity, self-evaluation, evaluation of discussion activity, peer evaluation in a group for each students for their performance, assessment about module, and written tests as well.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Sleep Medicine and Psychophysiology
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• v.10 no.2
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• pp.93-99
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• 2003

Objectives：Sleep loss and excessive daytime sleepiness may have serious consequences, including traffic and industrial accidents, decreased productivity, learning disabilities and interpersonal problems. Yet despite these adverse effects, there are few epidemiological studies on sleep loss and daytime sleepiness in the general population of Korea. This study investigates the number of people who suffer from sleep insufficiency, how much recovery sleep occurs on weekends, and the relationship between the amount of recovery sleep and daytime sleepiness. Methods：A total 164 volunteers, aged 20 and over, were recruited by advertisement. The subjects were workers and college students living in Seoul, Korea. Subjects were excluded if they were aged over 60；if they had medical, neurological, psychiatric or sleep disorders that could cause insomnia or daytime sleepiness；if they were not following a regular sleep schedule；if they traveled abroad during the study；or if they did not leave home to work or were shift workers. They were interviewed and given a sleep log to complete on each of 14 consecutive mornings. They also completed the Epworth Sleepiness Scale (ESS) at noontime on the last day of the second week. All statistical data were analyzed by t-test, $X^2$-test or ANOVA, using SPSS/PC+. Results：The results showed that the subjects woke up at 6：50 (${\pm}1$：16) on weekdays, 7：09 (${\pm}1$：29) on Saturdays, and 8：12 (${\pm}1$：39) on Sundays and holidays. They took more frequent and longer naps on Sundays than on weekdays and Saturdays. The mean sleep duration was 6h 35 min. on week nights, with a mean increase of about 1h on weekends. Only 9.1% of the subjects spent more than 8h in bed on week nights, with 67% spending less than 7h, and 49.4% reported recovery sleep of more than 1h on Sundays. The subjects who reported recovery sleep of more than 2h on Sundays, showed significantly more excessive daytime sleepiness than those who reported less than 30 min (F=2.62, p<.05). Conclusions：These findings suggest that sleep insufficiency and excessive daytime sleepiness are relatively common in Korea, and that the people who get insufficient sleep on weekdays try to compensate for sleep loss with oversleeping and daytime napping on Sundays and holidays. It appeared that daily sleep insufficiency had a cumulative effect and increased daytime sleepiness.

• Journal of Music and Human Behavior
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• v.1 no.1
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• pp.11-32
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• 2004

According to Edwin Gordon(1987, 1997, 2003), music aptitude is a product of interaction of innate potential and early environmental experiences. He referred to music aptitude of children up to nine years of age as developmental music aptitude which fluctuates due to musical environment. Music aptitude stabilizes at age nine, and the music aptitude after age nine is called "stabilized music aptitude". This research is to examine Gorden's hypothesis that the younger a child receives music education, the higher music aptitude. Also, this research is to experiment the effect of Audiation activities developed in Audie Music Curriculum on music aptitude. The researcher and another Audie teacher as a co-teacher guided children together for 30 minutes once a week. The pedagogy guidelines for informal guidance in music learning theory were kept throughout the classes. Also, Audie's teaching method which had been developed for Korean Kindergarten educational environment was also applied. Five-year-old subjects in Experimental group 1 experienced the Audie Music Curriculum of one year; five-year-old subjects in Experimental group 2 experienced it for two years. Primary Measures of Music Audiation was administered three times during their last year of Kindergarten. Subjects in the Control groups, one examined at the beginning and the other at the end of their last year in Kindergarten, received no Audie instruction. There was no significant difference in tonal aptitude, but there was significant difference in rhythmic aptitude(p< .05) among the experiemental groups. Because both Experimental groups showed statistical significance (p< .001) in the music aptitude increase during their academic years, the significant differences of the year-end music aptitude between control group and experimental groups were the expected result.

• Journal of Gastric Cancer
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• v.8 no.1
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• pp.27-34
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• 2008

Purpose: Many recent studies have reported on the feasibility and usefulness of laparoscopy assisted distal gastrectomy (LADG) for treating early gastric cancer. On the other hand, there has been few reports about laparoscopy assisted total gastrectomy (LATG) because upper located gastric cancer is relatively rare and the surgical technique is more difficult than that for LADG, We now present our procedure and results of performing LATG for the gastric cancer located in the upper or middle portion of the stomach. Materials and Methods: From Jan 2005 to Sep 2007, 96 patients underwent LATG by four surgeons at the Asan Medical Center, Seoul, Korea. Among them, 48 consecutive patients who were operated on by asingle surgeon were analyzed with respect to the clinicopathological features, the surgical results and the postoperative courses with using the prospectively collected laparoscopy surgery data. Results: There was no conversion to open surgery during LATG. For all the reconstructions, Roux-en Y esophago-jejunostomy and D1+beta lymphadenectomy were the standard procedures. The mean operation time was $212{\pm}67$ minutes. The mean total number of retrieved lymph nodes was $28.9{\pm}10.54$ (range: $12{\sim}64$) and all the patients had a clear proximal resection margin in their final pathologic reports. The mean time to passing gas, first oral feeding and discharge from the hospital was 2.98, 3.67 and 7.08 days, respectively. There were 5 surgical complications and 2 non-surgical complications for 5 (10.4%) patients, and there was no mortality. None of the patients needed operation because of complications and they recovered with conservative treatments. The mean operation time remained constant after 20 cases and so a learning curve was present. The morbidity rate was not different between the two periods, but the postoperative course was significantly better after the learning curve. Analysis of the factors contributing to the postoperative morbidity, with using logistic regression analysis, showed that the 8MI is the only contributing factor forpostoperative complications (P=0.029, HR=2.513, 95% CI=1.097-5.755). Conclusions: LATG with regional lymph node dissection for upper and middle early gastric cancer is considered to be a safe, feasible method that showed an excellent postoperative course and acceptable morbidity. BMI should be considered in the patient selection at the beginning period because of the impact of the BMI on the postoperative morbidity.

• Journal of Korea Entertainment Industry Association
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• v.13 no.6
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• pp.247-256
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• 2019

The importance of the convergence education is getting more and more emphasized. But the university convergence education has not yet met today's needs. So this study is focused on showing effective practice methods and finding development directions of the university convergence criticism education. One of a significant trend of the contemporary academic education of liberal arts is the tendency of the convergent and integrative studies. What is essential in the convergent and integrative studies creativity. The zeitgeist of knowledge-based society is change and innovation. In response to its request, college education is being asked to do convergence education, And general education as the basis of convergence education is strengthened.

• 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 Broadcast Engineering
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• v.19 no.5
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• pp.578-589
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• 2014

Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.

• The Transactions of the Korea Information Processing Society
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• v.2 no.6
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• pp.937-946
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• 1995

We proposed and efficient block segmentation and classification method for the document analysis using SGLDM(spatial gray level dependence matrix) and BP (back Propagation) neural network. Seven texture features are extracted directly from the SGLDM of each gray-level block image, and by using the nonlinear classifier of neural network BP, we can classify document blocks into 9 categories. The proposed method classifies the equation block, the table block and the flow chart block, which are mostly composed of the characters, out of the blocks that are conventionally classified as non-character blocks. By applying Sobel operator on the gray-level document image beforebinarization, we can reduce the effect of the background noises, and by using the additional horizontal-vertical smoothing as well as the vertical-horizontal smoothing of images, we can obtain an effective block segmentation that does not lead to the segmentation into small pieces. The result of experiment shows that a document can be segmented and classified into the character blocks of large fonts, small fonts, the character recognigible candidates of tables, flow charts, equations, and the non-character blocks of photos, figures, and graphs.