• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.439-441
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• 2018

딥 러닝의 높은 성능으로 여러 분야에 사용되며 기침 탐지에서도 수행된다. 이 때 기침과 유사한 재채기, 큰 소리는 단일 데이터만으로는 구분하기에 한계가 있다. 본 논문에서는 기존의 오디오 데이터와 오디오 데이터를 인코딩 한 스펙트로그램 이미지 데이터를 함께 학습하는 멀티 모달 딥 러닝을 적용하는 방법을 사용한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.64-67
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• 2020

최근 다양한 분야에서 딥 러닝 기반의 많은 연구가 진행되고 있으며 이에 따라 딥 러닝 모델의 경량화를 통해 제한된 메모리를 가진 하드웨어에 올릴 수 있는 경량화 된 딥 뉴럴 네트워크(DNN)를 개발하는 연구도 활발해졌다. 이에 본 논문은 주파수 영역에서의 군집화 기반 계층별 딥 뉴럴 네트워크 압축을 제안한다. 이산 코사인 변환, 양자화, 군집화, 적응적 엔트로피 코딩 과정을 각 모델의 계층에 순차적으로 적용하여 DNN이 차지하는 메모리를 줄인다. 제안한 알고리즘을 통해 VGG16을 손실률은 1% 미만의 손실에서 전체 가중치를 3.98%까지 압축, 약 25배가량 경량화 할 수 있었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.297-298
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• 2020

본 논문에서는 머신러닝 학습에 있어 데이터 전처리의 중요성과 기존 데이터 전처리 기능을 가진 교육용 실습 플랫폼 서비스의 단점은 개선할 수 있는 데이터 전처리 학습을 위한 교육용 블록코딩 기반 실습 플랫폼을 제안한다. 머신러닝 모델의 학습데이터는 데이터 전처리에 따라 모델의 정확도에 큰 영향을 미치므로 데이터를 다양하게 활용하기 위해서는 전처리의 필요성을 깨닫고 과정을 정확하게 이해해야 한다. 따라서 데이터를 처리하는 과정을 이해하고 전처리를 직접 실행해 볼 수 있는 교육용 프로그래밍 언어 기반 D.I.Y 실습 플랫폼을 구현한다.

• Smart Media Journal
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• v.8 no.2
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• pp.58-65
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• 2019

Automatic text summarization is a process of shortening a text document by either extraction or abstraction. The abstraction approach inspired by deep learning methods scaling to a large amount of document is applied in recent work. Abstractive text summarization involves utilizing pre-generated word embedding information. Low-frequent but salient words such as terminologies are seldom included to dictionaries, that are so called, out-of-vocabulary(OOV) problems. OOV deteriorates the performance of Encoder-Decoder model in neural network. In order to address OOV words in abstractive text summarization, we propose a copy mechanism to facilitate copying new words in the target document and generating summary sentences. Different from the previous studies, the proposed approach combines accurate pointing information and selective copy mechanism based on bidirectional RNN and bidirectional LSTM. In addition, neural network gate model to estimate the generation probability and the loss function to optimize the entire abstraction model has been applied. The dataset has been constructed from the collection of abstractions and titles of journal articles. Experimental results demonstrate that both ROUGE-1 (based on word recall) and ROUGE-L (employed longest common subsequence) of the proposed Encoding-Decoding model have been improved to 47.01 and 29.55, respectively.

• Journal of The Korean Association of Information Education
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• v.22 no.3
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• pp.325-333
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• 2018

This study analyzes students' motivation for learning when applying SW-STEAM education using Flipped Learning. As a main content of the study, we developed a program that combines SW education with existing subjects and utilized flipped learning as a method. Elementary school students were given an education program. The data were collected through pre and post-test of learning motivation. As a result of analysis, the SW-STEAM program based on flipped learning has improved attention, relevance and confidence. We expect that the results of the SW-STEAM education program developed in this study and the learning motivation analysis will help in the direction of the SW-STEAM education and be useful as a basic resources for settling in class.

• The Journal of Bigdata
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• v.8 no.1
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• pp.157-165
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• 2023

Seafood is a major source of protein in many countries and its consumption is increasing. In Korea, consumption of seafood is increasing, but self-sufficiency rate is decreasing, and the importance of safety management is increasing as the amount of imported seafood increases. There are hundreds of species of aquatic products imported into Korea from over 110 countries, and there is a limit to relying only on the experience of inspectors for safety management of imported aquatic products. Based on the data, a model that can predict the customs inspection results of imported aquatic products is developed, and a machine learning classification model that determines the non-conformity of aquatic products when an import declaration is submitted is created. As a result of customs inspection of imported marine products, the nonconformity rate is less than 1%, which is very low imbalanced data. Therefore, a sampling method that can complement these characteristics was comparatively studied, and a preprocessing method that can interpret the classification result was applied. Among various machine learning-based classification models, Random Forest and XGBoost showed good performance. The model that predicts both compliance and non-conformance well as a result of the clearance inspection is the basic random forest model to which ADASYN and one-hot encoding are applied, and has an accuracy of 99.88%, precision of 99.87%, recall of 99.89%, and AUC of 99.88%. XGBoost is the most stable model with all indicators exceeding 90% regardless of oversampling and encoding type.

• Journal of KIISE
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• v.45 no.2
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• pp.157-164
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• 2018

Due to the revolutionary advances in deep learning, performance of pattern recognition has increased significantly in many applications like speech recognition and image recognition, and some systems outperform human-level intelligence in specific domains. Unlike pattern recognition, in this paper, we focus on generating Korean sentences based on a few Korean sentences. We apply variational recurrent auto-encoder (VRAE) and modify the model considering some characteristics of Korean sentences. To reduce the number of words in the model, we apply a word spacing model. Also, there are many Korean sentences which have the same meaning but different word order, even without subjects or objects; therefore we change the unidirectional encoder of VRAE into a bidirectional encoder. In addition, we apply an interpolation method on the encoded vectors from the given sentences, so that we can generate new sentences which are similar to the given sentences. In experiments, we confirm that our proposed method generates better sentences which are semantically more similar to the given sentences.

• Journal of IKEEE
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• v.23 no.3
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• pp.852-857
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• 2019

In order to manage the demand resources of project participants and to provide appropriate strategies in the virtual power plant's power trading platform for consumers or operators who want to participate in the distributed resource collective trading market, it is very important to forecast the next day's demand of individual participants and the overall system's electricity demand. This paper developed a power demand forecasting model for the next day. For the model, we used LSTM algorithm of deep learning technique in consideration of time series characteristics of power demand forecasting data, and new scheme is applied by applying one-hot encoding method to input/output values such as power demand. In the performance evaluation for comparing the general DNN with our LSTM forecasting model, both model showed 4.50 and 1.89 of root mean square error, respectively, and our LSTM model showed high prediction accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.693-695
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• 2018

In this paper, we propose the improvement of Hangul OCR accuracy through deep learning. OCR is a program that senses printed and handwritten characters in an optical way and encodes them digitally. In the case of the most commonly used Tesseract OCR, the accuracy of English recognition is high. However, Hangul has lower accuracy because it has less learning data for a complex structure. Therefore, in this study, we propose a method to improve the accuracy of Hangul OCR by extracting the character region from the desired image through image processing and using deep learning using it as learning data. It is expected that OCR, which has been developed only by existing alphanumeric and several languages, can be applied to various languages.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.19-29
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• 2020

The study aims to examine the effectiveness of flipped learning teaching methods by using learning analytics to enable effective programming learning for non-major students. After designing a flipped learning programming class model applied with the ADDIE model, learning-related data of the lecture support system operated by the school was processed with crawling. By providing data processed with crawling through a dashboard so that the instructor can understand it easily, the instructor can design classes more efficiently and provide individually tailored learning based on this. As a result of analysis based on the learning-related data collected through one semester class, it was found that the department, academic year, attendance, assignment submission, and preliminary/review attendance had an effect on academic achievement. As a result of survey analysis, they responded that the individualized feedback of instructors through learning analysis was very helpful in self-directed learning. It is expected that it will serve as an opportunity for instructors to provide a foundation for enhancing teaching activities. In the future, the contents of social network services related to learners' learning will be processed with crawling to analyze learners' learning situations.