• Smart Media Journal
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• v.11 no.11
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• pp.17-24
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• 2022

With the spread of smart speakers based on voice recognition technology and deep learning technology, not only non-disabled people, but also the blind or physically handicapped can easily control home appliances such as lights and TVs through voice by linking home network services. This has greatly improved the quality of life. However, in the case of speech-impaired people, it is impossible to use the useful services of the smart speaker because they have inaccurate pronunciation due to articulation or speech disorders. In this paper, we propose a personalized voice classification technique for the speech-impaired to use for some of the functions provided by the smart speaker. The goal of this paper is to increase the recognition rate and accuracy of sentences spoken by speech-impaired people even with a small amount of data and a short learning time so that the service provided by the smart speaker can be actually used. In this paper, data augmentation and one cycle learning rate optimization technique were applied while fine-tuning ResNet18 model. Through an experiment, after recording 10 times for each 30 smart speaker commands, and learning within 3 minutes, the speech classification recognition rate was about 95.2%.

• Education of Primary School Mathematics
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• v.24 no.4
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• pp.217-231
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• 2021

The purpose of this study was to investigate the effects of flipped learning through EBSmath on Students' 'rate and ratio' learning. By increasing demands for change in education, an innovative teaching and learning paradigm, 'Flipped Learning', has been presented and drawing attentions. In South Korea, Flipped Learning is also highly recognized for its effectiveness by many scholars and various media. However, this innovative learning model has limitations in application and expansion due to the excessive burden of class preparation of teachers. As remote learning becomes more active, it would be possible to overcome the limitations of Filliped learning by using the platform provided by the Korea Educational Broadcasting System (EBS). EBSmath is an online learning module that is designed to assist students' self-directed learning. Thus, EBSmath would reduce teachers' burden to prepare mathematics classes for the application of Flipped Learning; and led to students' better understanding of mathematical concepts and problem solving. In this study, the effect of Flipped Learning through EBSmath on learning 'rate and ratio' was investigated. In order to scrutinize the effects of flipped learning, students' achievement and mathematical disposition were examined and analyzed. Students' achievement, specifically, was divided into two subcategories: concept understanding and problem solving. As a result, Flipped learning through EBSmath had a positive effect on students' 'rate and ratio' problem solving. In addition, a statistically significant change was identified in the 'willingness', which is subdomain of students' mathematical disposition.

• Journal of Korea Game Society
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• v.9 no.4
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• pp.67-80
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• 2009

In this paper, we described the development of a Korean Manual Alphabet (KMA) learning game with avatar. KMA letters correspond to the vocabulary of Korean Sign Language (KSL) when spelling a word. Each KMA letter corresponds to a letter of the Korean Alphabet (KA) and KA is represented as hand shapes by sign language user. We developed a KMA learning game for a beginner to learn KMA letters from sign language avatar and practice KMA presentation easily. The system composed of sign language teacher avatar GUI popup window based on OpenGL, KMA letter recognition module, KA letter raining game module and USB camera. A user learns a KMA letter with expressing KA syllabic from avatar and inputs a KMA letter to the system using USB camera. We evaluated the efficiency of the developed system through the verification of users.

• Smart Media Journal
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• v.9 no.3
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• pp.122-129
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• 2020

The possibility that an asymptotic coronavirus-19 infected person around the world is not aware of his infection and can spread it to people around him is still a very important issue in that the public is not free from anxiety and fear over the spread of the epidemic. In this paper, the K-means clustering algorithm and deep learning-based crowd aggregation were proposed to determine the possibility of contact with confirmed cases of infectious diseases. As a result of 300 iterations of all input learning images, the PSNR value was 21.51, and the final MAE value for the entire data set was 67.984. This means the average absolute error between observations and the average absolute error of fewer than 4,000 people in each CCTV scene, including the calculation of the distance and infection rate from the confirmed patient and the surrounding persons, the net group of potential patient movements, and the prediction of the infection rate.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.485-491
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• 2014

The aim of this study is to suggest development guidelines of educational smart application for the students with development disability based on Universal Design for Learning(UDL). The basic guideline set were organized by analyzing UDL studies on different media. The totally 35 preliminary guidelines which consist of 14 items of contents presentation, 12 items of education strategy and 9 items of education engagement were settled through review of smart contents expert developer and professors in special education. Based on the preliminary guideline, a survey was conducted to determine effective guidelines among end users which consist of the teachers who educate students with disability, parents with disabled children and professors in special education major. 3 categories and 20 guidelines are presented by analyzing survey result through excluding items whose importance factor were lower than average of importance.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.529-539
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• 2017

The purpose of this study is to categorize and analyze the communication competency of engineering undergraduates and to develop educational content in order to improve that. In this study, communication competency of engineering undergraduates was categorized into three areas: critical thinking, scientific communication, and media literacy. As a means to improve communication competency, the experience with and perception of writing were investigated. The communication competency of undergraduates needs to be improved overall. There is a high need for writing programs that enhance critical thinking oriented around practice. It suggests flipped learning based on smart education, E-community, problem-solving programs based on action learning, cooperative learning programs, reflection journals & portfolio, and collaborative writing programs as educational content. The results of this study can be used as basic data to design competency-based communication curriculum and practical applications for engineering undergraduates.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.145-150
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• 2021

The trend of learning as an online study platform continues, and motivation of learners for continued use of the platform is also becoming important. In addition to learning, various services use gamification techniques that excite users for continued use. This study analyzes successful service cases and proposes guidelines applicable to learning platforms to present ways to increase learners' interest and efficiency in online study platforms using reward systems and storytelling techniques among gamification elements. Based on the three guidelines drawn through this study, it can contribute to the digitization of educational infrastructure by increasing the immersion of learners within the online study platform that will be more commercialized in the future.

• Smart Media Journal
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• v.7 no.4
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• pp.17-23
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• 2018

We proposed a LSTM-based RNN model that can effectively perform the automatic spacing characteristics. For those long or noisy sentences which are known to be difficult to handle within Neural Network Learning, we defined a proper input data format and decoding data format, and added dropout, bidirectional multi-layer LSTM, layer normalization, and attention mechanism to improve the performance. Despite of the fact that Sejong corpus contains some spacing errors, a noise-robust learning model developed in this study with no overfitting through a dropout method helped training and returned meaningful results of Korean word spacing and its patterns. The experimental results showed that the performance of LSTM sequence-to-sequence model is 0.94 in F1-measure, which is better than the rule-based deep-learning method of GRU-CRF.

• Smart Media Journal
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• v.10 no.2
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• pp.16-21
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• 2021

Financial time series analysis plays a very important role economically and socially in modern society and is an important task affecting global development, but due to difficulties such as a lot of noise and uncertainty, financial time series analysis prediction is a difficult research topic. In this paper, we propose a market prediction method (MPIL) by converting unstructured data and structured data into images. For market prediction, it analyzes SNS and news data, which is unstructured data for n days, and converts the market data, which is structured data, to an image with the GADF algorithm, and predicts an ultra-short market that predicts the price of n+1 days through image learning. MPIL has an average accuracy of 56%, which is higher than the 50% average accuracy of the model that predicts the market with LSTM by using sentiment analysis used for existing market forecasting.

• Smart Media Journal
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• v.9 no.4
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• pp.118-125
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• 2020

This paper is conducted for automatically recommending and providing information services desired by users on websites of local governments and public institutions with vast amounts of information, In this system, we defined a method of collecting data based on the SiiRU CMS system that collects and preprocesses data, and a study that provides curation services (contents and menus) to users through a collaborative filtering algorithm based on machine learning. Also, the data used in the paper is conducted based on about 1 million data collected in 2019. The analyzed data can provide important information that cannot be easily accessed by providing a cloud tag service or recommended menu for users to conveniently view, and the environment configuration that can realize this service to local governments and public institutions is also provided.