• Journal of Music and Human Behavior
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• v.18 no.1
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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.

• Journal of Environmental Impact Assessment
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• v.30 no.1
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• pp.49-60
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• 2021

Environmental impact assessment (EIA) practitioners play a very pivotal role in establishing EIA policies, and when implementing EIA environmental conflicts can be prevented and resolved by sharing information with stakeholders and coordinating opinions. For this reason, grasping the perceptions of stakeholders including practitioners about the overall system, such as EIA policies and implementation, can be helpful in setting improvement directions and policy directions for EIA. However, there is an insufficient information on the perception and understanding of stakeholders about the EIA system and operation currently in effect in Korea. Therefore, this study diagnoses operational and procedural problems for the EIA system, which is a decision-making tool and a precautionary technique that can minimize adverse effects through the environmental information analysis method, and improvement points and systems of the EIA system in the future. We tried to find a complement of an online survey of 37 questions,responses from 95 responses from stakeholders of EIA were summarized. Stakeholders were aware of the problems of the operation of the current system and the preparation of the evaluation form, and this was reflected. Period and cost of preparation of EIS (49%), the introduction of a new method (26%) and the items of collecting opinions and conflict management (41%), which showed high negative response rates (dissatisfied and very dissatisfied), are considered to be areas that we need to supplement further in the future. As society develops rapidly, the system needs to be supplemented accordingly, and policy improvement efforts are needed for items with high negative responses as a result of the survey.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.112-125
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• 2021

Marine spatial planning(MSP) designates the marine as nine kinds of use zones for the systematic and rational management of marine spaces. One of them is the fishery protection zone, which is necessary for the sustainable production of fishery products, including the protection and fosterage of fishing activities. This study intends to quantitatively identify the fishing activity space, one of the elements necessary for the designation of fisheries protection zones, by mapping of fishery activities using V-Pass data and deriving the fishery activity concentrated zone. To this end, pre-processing of V-Pass data was performed, such as constructing a dataset that combines static and dynamic information, calculating the speed of fishing vessels, extracting fishing activity points, and removing data in non-fishing activity zone. Finally, using the selected V-Pass point data, a fishery activity map was made by kernel density estimation, and the concentrated space of fishery activity was analyzed. In addition, it was confirmed that there is a difference in the spatial distribution of fishing activities according to the type of fishing vessel and the season. The pre-processing technique of large volume V-Pass data and the mapping method of fishing activities performed through this study are expected to contribute to the study of spatial characteristics evaluation of fishing activities in the future.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.125-132
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• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.

• Journal of the Korean Geotechnical Society
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• v.37 no.5
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• pp.47-63
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• 2021

Recently, the demand for three-dimensional (3D) underground maps from the perspective of digital twins and the demand for linkage utilization are increasing. However, the vastness of national geotechnical survey data and the uncertainty in applying geostatistical techniques pose challenges in modeling underground regional geotechnical characteristics. In this study, an optimal learning model based on multi-layer perceptron (MLP) was constructed for 3D subsurface lithological and geotechnical classification in Seoul, South Korea. First, the geotechnical layer and 3D spatial coordinates of each borehole dataset in the Seoul area were constructed as a geotechnical database according to a standardized format, and data pre-processing such as correction and normalization of missing values for machine learning was performed. An optimal fitting model was designed through hyperparameter optimization of the MLP model and model performance evaluation, such as precision and accuracy tests. Then, a 3D grid network locally assigning geotechnical layer classification was constructed by applying an MLP-based bet-fitting model for each unit lattice. The constructed 3D geotechnical layer map was evaluated by comparing the results of a geostatistical interpolation technique and the topsoil properties of the geological map.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.234-246
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• 2021

The purpose of this study is to develop the main subjects of the job-based curriculum by deriving the job analysis results of general security job workers in the physical protection field responsible for responding to threats to nuclear materials and nuclear facilities. In the job analysis stage, FGI was conducted on 7 content experts to derive 8 duties and 55 tasks. In addition, knowledge and skills were drawn for each task. In the analysis of educational needs, surveys were conducted for workers in general security jobs to derive the top 25 educational priorities through t-test and Borich needs assess model. At the stage of selecting core tasks and organizing required/optional contents, 42 tasks, both above average or at least one of them, were derived as core tasks based on the result of evaluation of importance and difficulty ratings of 55 tasks through a questionaire. In addition, tasks applied to the top 25 rankings derived from Borich needs assess model were applied as the required contents when designing courses, and tasks which applied only one of them were selected as optional contents. At the stage of required/optional modules and educational contents, four required modules and five optional modules were derived by drifting similar tasks between the required and optional contents. Based on the above results, the study suggested academic and practical implications and future suggestions.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.216-227
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• 2022

The MPEG-I (Immersive) group is working on a standardization project for immersive video that provides 6 degrees of freedom (6DoF). The MPEG Immersion Video (MIV) standard technology is intended to provide limited 6DoF based on depth map-based image rendering (DIBR) technique. Many efficient coding methods have been suggested for MIV, but efficient transmission strategies have received little attention in MPEG-I. This paper proposes group-based adaptive rendering method for immersive video streaming. Each group can be transmitted independently using group-based encoding, enabling adaptive transmission depending on the user's viewport. In the rendering process, the proposed method derives weights of group for view synthesis and allocate high quality bitstream according to a given viewport. The proposed method is implemented through the Test Model for Immersive Video (TMIV) test model. The proposed method demonstrates 17.0% Bjontegaard-delta rate (BD-rate) savings on the peak signalto-noise ratio (PSNR) and 14.6% on the Immersive Video PSNR(IV-PSNR) in terms of various end-to-end evaluation metrics in the experiment.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.118-129
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• 2022

The purpose of this report is to study a strategic model of promotion activities through various analysis and sales forecasting by selecting wearable products for domestic online companies and collecting sales data. For data analysis, various algorithms are used for analysis and the results are selected as the optimal model. The gradation boosting model, which is selected as the best result, will allow nine independent variables to be entered, including promotion type, price, amount, gender, model, company, grade, sales date, and region, when predicting dependent variables through supervised learning. In this study, the review values set as dependent variables for each type of sales promotion were studied in more detail through the ensemble analysis technique, and the main purpose is to analyze and predict them. The purpose of this study is to study the grades. As a result of the analysis, the evaluation result is 95% of AUC, and F1 is about 93%. In the end, it was confirmed that among the types of sales promotion activities, value-added benefits affected the number of reviews and review grades, and that major variables affected the review and review grades.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.115-124
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• 2022

The purpose of this study is to analyze and present keywords, trends, and language networks of keywords for each year of the pedagogical exam of the secondary teacher's employment exam for the 2019~2022 school year. The main research methods were text mining technique and language network analysis method, and analysis programs were KrKwic, Wordcloud Maker, Ucinet6, NetDraw, etc. The research results are as follows; First, keywords such as teacher, student, curriculum, class, and evaluation appeared in the top rankings, and keywords (online, wiki, discussion ceremony, information, etc.) that reflect the recent online class progress in the current COVID-19 situation also tended to appear. The keywords with high frequency of occurrence in the four-year integrated text were student(44), teacher(39), class(27), school(18), curriculum(16), online(10), and discussion method(8). Second, the overall language network of the keywords with high frequency of 4 years showed a significant level of density(0.566), total number of links(492), and average degree of links(16.4). The degree centrality was found in the order of teacher(199.0), class(197.0), student(185.0), and school(150.0). Betweenness centrality was found in the order of teacher(30.859), class(18.956), student(16.054), and school (15.745). It is expected that the results of this study will serve as data to be considered for preparatory teachers, institutions and related persons, and teachers and administrators of secondary school teacher training institutions.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.