• Journal of Music and Human Behavior
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• 제18권1호
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• pp.79-102
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• 2021

The purpose of this study was to identify the teaching methods used in inclusive music classes at elementary schools by of music in elementary school inclusive classes through the application of understanding by design and differentiated instruction, and to explore the feasibility of inclusive education. To this end, based on the 2.0 version of the backward design template, a unit for music lessons for 3rd and 4th grade inclusive classes was developed. The unit presented elements of differentiated instruction that considered students with intellectual disabilities at each stage. In the first stage, goals and essential questions were presented by analyzing the curriculum's achievement standards. In the second stage, a performance task was developed using the GRASPS technique, guidelines and examples were presented. Various evaluation methods based on students' readiness, interest, and learning type were suggested. In the third stage, the unit's seven lessons were planned using the WHERETO model. Examples of differentiated instruction for students with intellectual disabilities were presented by flexibly using classroom elements. This study indicated that understanding by design and differentiated instruction can be applied to inclusive education. Future studies on more diversified educational design and strategies are needed for promoting inclusive education.

• KIPS Transactions on Software and Data Engineering
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• 제11권3호
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• pp.133-140
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• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.

• Korean Journal of Remote Sensing
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• 제38권6_1호
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

• The Journal of the Korea Contents Association
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• 제22권10호
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• pp.426-436
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• 2022

This study aimed to explore the role of models and reformulations as feedback to English writing in L2 learners' noticing and their writing. 92 participants were placed into three groups; a models group (MG), a reformulations group (RG), a control group (CG), involved in a three-stage writing task. In stage 1, they were asked to perform a 1st draft of writing, while taking notes on the problems they experienced. In stage 2, the MG was asked to compare their writing with a model text and the RG with a reformulated version of it. They were instructed to write down whatever they noticed in their comparison. The CG was asked to just read their writing. In stage 3, all the participants attempted subsequent revisions. The results indicated that all the participants noticed problematic linguistic features the most in a lexical category, and models and reformulations led to higher rate of noticing the problematic linguistic features reported in stage 1 and contributed to subsequent revisions. It was also revealed that the MG and RG significantly improved with their writings of MG and RG on the post-writing test. The findings imply that models and reformulations result in better performance in L2 writing and should be promoted in an English writing class.

• The Journal of the Korea Contents Association
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• 제21권11호
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Korean journal of applied entomology
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• 제61권1호
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• pp.249-254
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• 2022

Silkworms, which have for long been used as an insect resource for industrialization, have recently attracted attention as potential bio-factories for the production of novel biomaterials. In this regard, material production is typically achieved based on transformation technology, mediated via microinjection, in which a target gene is inserted into eggs containing an embryo. However, an essential step in the microinjection procedure is egg fixation, which can be a time-consuming and laborious task. Therefore, in this study, using the 3DCADian program, we adopted a 3D printing approach to model egg liners and glue drawers, which can contribute to facilitating egg alignment and fixation, thereby enhancing transformation efficiency by reducing time consumption and fatigue. After rendering using Fusion 360, the two supplementary tools were produced by printing with nylon resin (PA12) and Sinterit Lisa Pro. Subsequent analysis of the time required to fix eggs on glass slides using the two manufactured tools, revealed that the processing time was reduced by approximately 18.6% when the two tools were used compared with when these tools were not used. These innovations not only reduced fatigue but also contributed to more effective use of the microscope and manipulator for microinjection. Consequently, we believe that with additional research and refinement, the egg liner and glue drawer developed in this study could be used to enhance silkworm transformation efficiency and study similar transformation systems in other industrial insects.

• Research in Community and Public Health Nursing
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• 제33권1호
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• pp.13-31
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• 2022

Purpose: This study conducted a job analysis of visiting nurses in the process of change. Methods: Participants were the visiting nurses working for the Seoul Metropolitan city. On the basis of the Public Health Intervention Wheel model, two times of the focus group interview (FGI) with seven visiting nurses and one time of the Developing a Curriculum (DACUM) with 34 visiting nurses were performed. A questionnaire survey of 380 visiting nurses was conducted to examine the frequency, importance and difficulty levels of the tasks created by using the FGI and DACUM. Results: Visiting nurses' job was derived as the theme of present versus transitional roles. The present role was categorized as 'providing individual- and group-focused services' and 'conducting organization management', while the transitional role was categorized as 'providing district-focused services' and 'responding to new health issues'. The job generated 13 duties, 28 tasks, and 73task elements. The tasks showed the levels of frequency (3.65 scores), importance (4.27 scores), and difficulty (3.81 scores). All the tasks were determined as important, exceeding the average 4.00 scores. The group- and district-focused services of the tasks were recognized as more difficult but less frequent tasks. Conclusion: The visiting nurses exert both present and transitional roles. The transitional roles identified in the present study should be recognized as an extended role of visiting nurses in accordance with the current changing healthcare needs in South Korea. Finally, the educational curriculum for visiting nurses that reflects the transitional roles from the present study is needed.

• KIPS Transactions on Software and Data Engineering
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• 제12권5호
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• pp.217-228
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• 2023

Video instance segmentation is an intelligent visual task with high complexity because it not only requires object instance segmentation for each image frame constituting a video, but also requires accurate tracking of instances throughout the frame sequence of the video. In special, human instance segmentation in drama videos has an unique characteristic that requires accurate tracking of several main characters interacting in various places and times. Also, it is also characterized by a kind of the class imbalance problem because there is a significant difference between the frequency of main characters and that of supporting or auxiliary characters in drama videos. In this paper, we introduce a new human instance datatset called MHIS, which is built upon drama videos, Miseang, and then propose a novel video data augmentation method, CDVA, in order to overcome the data imbalance problem between character classes. Different from the previous video data augmentation methods, the proposed CDVA generates more realistic augmented videos by deciding the optimal location within the background clip for a target human instance to be inserted with taking rich spatio-temporal context embedded in videos into account. Therefore, the proposed augmentation method, CDVA, can improve the performance of a deep neural network model for video instance segmentation. Conducting both quantitative and qualitative experiments using the MHIS dataset, we prove the usefulness and effectiveness of the proposed video data augmentation method.

• KIPS Transactions on Software and Data Engineering
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• 제12권7호
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• pp.295-302
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• 2023

Software requirements written in natural language may have different meanings from the stakeholders' viewpoint. When designing an architecture based on quality attributes, it is necessary to accurately classify quality attribute requirements because the efficient design is possible only when appropriate architectural tactics for each quality attribute are selected. As a result, although many natural language processing models have been studied for the classification of requirements, which is a high-cost task, few topics improve classification performance with the imbalanced quality attribute datasets. In this study, we first show that the classification model can automatically classify the Korean requirement dataset through experiments. Based on these results, we explain that data augmentation through EDA(Easy Data Augmentation) techniques and undersampling strategies can improve the imbalance of quality attribute datasets, and show that they are effective in classifying requirements. The results improved by 5.24%p on F1-score, indicating that handling imbalanced data helps classify Korean requirements of classification models. Furthermore, detailed experiments of EDA illustrate operations that help improve classification performance.

• KSCE Journal of Civil and Environmental Engineering Research
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• 제28권6D호
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• pp.839-847
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• 2008

No proper system exists for private investment projects, and efficient project management is not being achieved due to entanglements of management. Recognizing these circumstances, this paper has diagnosed the hard facts that project management organizations and systems are facing, and presented solutions to the factors that are obstructing the establishment of efficient project management system. This paper carried out focus group interviews on the experts who had participated in the Incheon International Airport Railway construction project, using the methodology of an exploratory case study. The results were systematically analyzed according to organizational behavior and causes corresponding to each of the problems were deduced. Private investment projects were divided into task environments and project organizations based on social science methodology and analyzed, and a final improvement plan for each participating organization was presented. An improvement plan was presented, and it was compared with the case study of Incheon bridge construction project, which is recognized as a model of successful project management, and its appropriateness evaluated.