• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4476-4491
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• 2021

Video service providers tend to face user network problems in the process of transmitting video streams. They strive to provide user with superior video quality in a limited bitrate environment. It is necessary to accurately determine the target bitrate range of the video under different quality requirements. Recently, several schemes have been proposed to meet this requirement. However, they do not take the impact of visual influence into account. In this paper, we propose a new multi-category model to accurately predict the target bitrate range with target visual quality by machine learning. Firstly, a dataset is constructed to generate multi-category models by machine learning. The quality score ladders and the corresponding bitrate-interval categories are defined in the dataset. Secondly, several types of spatial-temporal features related to VMAF evaluation metrics and visual factors are extracted and processed statistically for classification. Finally, bitrate prediction models trained on the dataset by RandomForest classifier can be used to accurately predict the target bitrate of the input videos with target video quality. The classification prediction accuracy of the model reaches 0.705 and the encoded video which is compressed by the bitrate predicted by the model can achieve the target perceptual quality.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.321-326
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• 2019

As AI technology is recently introduced into various fields, it is being applied to the fashion field. This paper proposes a system for recommending cody clothes suitable for a user's selected clothes. The proposed system consists of user app, cody recommendation module, and server interworking of each module and managing database data. Cody recommendation system classifies clothing images into 80 categories composed of feature combinations, selects multiple representative reference images for each category, and selects 3 full body cordy images for each representative reference image. Cody images of the representative reference image were determined by analyzing the user's preference using Google survey app. The proposed algorithm classifies categories the clothing image selected by the user into a category, recognizes the most similar image among the classification category reference images, and transmits the linked cody images to the user's app. The proposed system uses the ResNet-50 model to categorize the input image and measures similarity using ORB and HOG features to select a reference image in the category. We test the proposed algorithm in the Android app, and the result shows that the recommended system runs well.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.178-179
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• 2018

Recently, methods of using machine learning in artificial intelligence such as autonomous navigation and speech recognition have been actively studied. Classical image processing methods such as classical boundary detection and pattern recognition have many limitations in order to recognize a specific object or area in a digital image. However, when a machine learning method such as deep-learning is used, Can be obtained. However, basically, a large amount of learning data must be secured for machine learning such as deep-learning. Therefore, it is difficult to apply the machine learning for area classification when the amount of data is very small, such as aerial photographs for environmental analysis. In this study, we apply a transfer-learning technique that can be used when the dataset size of the input image is small and the shape of the input image is not included in the category of the training dataset.

• Korean Journal of Cognitive Science
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• v.11 no.1
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• pp.47-65
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• 2000

Categorization systems of homepages in search engines are likely to be constructed considering system's efficiency only but not user-centered. This study I investigated user's mental model of superordinate and subordinate categories using category terms of major Korean search engines. From this result, we constructed two kinds of categorization system; redundant system and singular system. In the redundant system, for example, a subordinate category can belong to a number of superordinate categories, but in the singular system to only one superordinate category Three prototype categorization systems, with 'Simmani', were designed and search performances of each system were observed repetitively Overall results, with frequency of correct a answers, number of steps and time taken in solution taken into account, showed the redundant system was superior to the other two systems. This indicates that categorization search could be improved with appropriate categorizaton system. However. l in recognition test score in singular system was the best, which indicates that search performance and recognition memory of categorization reveal different aspects of categorization system learning. Issues of category organization. ways of interface, prior knowledge, exploratory learning, and application areas are discussed further.

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.479-488
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• 2009

This paper shows a reinforced construction scheme of training data for improvement of text classification by automatic search of boundary category. The documents laid on boundary area are usually misclassified as they are including multiple topics and features. which is the main factor that we focus on. In this paper, we propose an automated exploring methodology of optimal boundary category based on previous research. We consider the boundary area among target categories to new category to be required training, which are then added to the target category sementically. In experiments, we applied our method to complex documents by intentionally making errors in training process. The experimental results show that our system has high accuracy and reliability in noisy environment.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.65-72
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• 2020

This study conducted to investigate the core competency of dental hygiene based on learning outcomes for establishing an educational evaluation and certification system for dental hygiene. The sub-categories of core competencies, the overall score of professional behavior and ethical decision-making ability (category 1) and the ability to apply scientific and professional clinical hygiene and knowledge and skills of clinical dental work (category 2) was highest. and the level of communication skills (category 3) was 4.48 points, and the level of evidence-based integrated thinking and problem solving skills (category 5) was 4.35 points. Furthermore, there was a strong correlation between the core competency sub-categories, and it is necessary to identify the core competency between health care service occupations and to qualitatively study the core competency of dental hygienists working in clinical practice.

• Journal of The Korean Association For Science Education
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• v.15 no.4
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• pp.452-458
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• 1995

Science textbooks are very important materials in order to know elementary science learning in Japan and Korea. In this research the 5th grade science textbooks in Japan and Korea are analyzed by an analyzing category. The analyzing category is consisted of knowledge and scientific inquiry. Knowledge is divided by fact, concept, and rule. Scientific inquiry is divided by problem cognition, variable control, experiment planning, observing, measuring, categorizing, inferring, data transformation, predicting, correlation, cause and effect, result, communication, which are 13 subcategories. Analyzing methods are counting the frequency of each subcategory and tabulating the data. The results of this study are: 1. The frequency of scientific inquiry appeared in Korean 5th grade science textbooks is three times more than that in Japanese textbooks. 2. In scientific inquiry category, Japanese science textbooks emphasized observing, predicting, measuring and problem cognition; Korean science textbooks emphasized experiment planning, observing and problem cognition. 3. In knowledge category, fact subcategory is mostly emphasized in both countries.

• Journal of Korean Elementary Science Education
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• v.23 no.4
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• pp.273-278
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• 2004

According to Piaget, children aged 11 are in the middle of concrete operation period and formal operation period. So, it is necessary to adopt the Learning Cycle Model (LCM) which helps students improve their cognitive development. After determining the test for the Science Concept of Matter (SCOM), the experimental group showed higher average than the comparative group in the post-test. In the sound understanding, the experimental group showed higher ratio than the comparative group. And in the ratio of imperfect, wrong understanding and no response, the experimental group was lower than the comparative group. On the questions that were needed the complicated inquiry, many students of both groups still couldn't find the fundamental cause. In forming the scientific conceptualization, there was a meaningful difference (p < .001) after post-test Analysis of Covariance (ANCOVA) with pre-test result. After determining the test for the Test Inquiry Science Process (TISP), the experimental group showed higher average than the comparative group in the post-test. In the category of basic inquiry process which is needed in concrete operation, there was a meaningful difference (p < .05). In the category of unified inquiry process which is needed in formal operation, they showed no meaningful difference (p > .05). Therefore, applying the LCM to the chapter of 'Solution and Dissolving' is more effective on improving the scientific conceptualization and on helping the concrete operation abilities than the teacher centered learning.

• Journal of Korean Library and Information Science Society
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• v.39 no.4
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• pp.283-304
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• 2008

This is to develop Intelligent Retrieval System which can automatically present early query's category terms(association terms connected with knowledge structure of relevant terminology) through learning function and it changes searching form automatically and runs it with association terms. For the reason, this theoretical study of Intelligent Automatic Indexing System abstracts expert's index term through learning and clustering algorism about automatic classification, text mining(categorization), and document category representation. It also demonstrates a good capacity in the aspects of expense, time, recall ratio, and precision ratio.

• Journal of Korean Home Economics Education Association
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• v.31 no.2
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• pp.113-135
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• 2019

The purpose of this study was to analyze the characteristics reflected in the 2015 revised home economics curriculum for the key competencies presented in the OECD education 2030 project. The results indicate that first, in general, about 46.5% of the competencies could be classified into the skill, attitude and value category; 17% into the learning concept framework category; 24.2% into the competency development cycle category; and 12.5% into the complex competency category. Overall, the competencies of the OECD learning framework are found to be reflected primarily in the achievement standards(59%), followed by characteristics(16.1%), teaching-learning and assessments orientation(9.4%), content system(8%), and goals(7.6%). Second, the key competencies were reflected in the middle school curriculum, more often in the descending order of action, problem-solving, communication, respect, creative thinking, conflict resolution, empathy, critical thinking, self-regulation, and student agency. In the high school curriculum, the competencies were reflected more often in the descending order of action, empathy, problem-solving, anticipation, global competence, self-regulation, student agency, literacy for sustainable development, reflection, and critical thinking. Third, the heat map shows that the competencies corresponding to the third and fourth levels are most frequently reflected in the curriculum. Therefore, it is advisable to develop effective plans to execute and support the reflection of key competencies in the curriculum. Through this study, home economics educators are expected to understand the inter-connectivity between the key competencies emphasized by the OECD learning framework and the competencies of home economics as a practical subject, and to scrutinize how to help individual students develop their overall competencies and be prepared for the future.