• 한국멀티미디어학회논문지
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• 제23권5호
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• pp.650-657
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• 2020

The rapid growth of internet users and faster network speed are driving the new ICT services. ICT Technology has improved our way of thinking and style of life, but it has created security problems such as malware, ransomware, and so on. Therefore, we should research against the increase of malware and the emergence of malicious code. For this, it is necessary to accurately and quickly detect and classify malware family. In this paper, we analyzed and classified visualization technology, which is a preprocessing technology used for deep learning-based malware classification. The first method is to convert each byte into one pixel of the image to produce a grayscale image. The second method is to convert 2bytes of the binary to create a pair of coordinates. The third method is the method using LSH. We proposed improving the technique of using the entire existing malicious code file for visualization, extracting only the areas where important information is expected to exist and then visualizing it. As a result of experimenting in the method we proposed, it shows that selecting and visualizing important information and then classifying it, rather than containing all the information in malicious code, can produce better learning results.

• Genomics & Informatics
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• 제16권4호
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• pp.31.1-31.7
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• 2018

The prevalence of metabolic syndrome (MS) in the nonobese population is not low. However, the identification and risk mitigation of MS are not easy in this population. We aimed to develop an MS prediction model using genetic and clinical factors of nonobese Koreans through machine learning methods. A prediction model for MS was designed for a nonobese population using clinical and genetic polymorphism information with five machine learning algorithms, including naïve Bayes classification (NB). The analysis was performed in two stages (training and test sets). Model A was designed with only clinical information (age, sex, body mass index, smoking status, alcohol consumption status, and exercise status), and for model B, genetic information (for 10 polymorphisms) was added to model A. Of the 7,502 nonobese participants, 647 (8.6%) had MS. In the test set analysis, for the maximum sensitivity criterion, NB showed the highest sensitivity: 0.38 for model A and 0.42 for model B. The specificity of NB was 0.79 for model A and 0.80 for model B. In a comparison of the performances of models A and B by NB, model B (area under the receiver operating characteristic curve [AUC] = 0.69, clinical and genetic information input) showed better performance than model A (AUC = 0.65, clinical information only input). We designed a prediction model for MS in a nonobese population using clinical and genetic information. With this model, we might convince nonobese MS individuals to undergo health checks and adopt behaviors associated with a preventive lifestyle.

• 대한건축학회논문집:계획계
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• 제34권1호
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• pp.83-94
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• 2018

UNESCO has continued to work in Africa, especially in the six southern sub-Saharan countries, and Asia, where international cooperation is needed. The CLC (Rwanda Community Learning Center) covered in this study aims to create a regional learning center in Rwanda and to recover local communities and provide learning environment. During the course of this study, we conducted field trips for actual planning and reviewed the current state of educational and cultural facilities that recently opened and are operated, and found implications. In consultation with the Rwandan Educational Commission, the site for CLC was decided, the building was designed, and the construction is about to start. The results of this study are as follows. First, in addition to the efforts of the activists in the village, which can be considered the smallest unit of a local community, the approach for establishing an architectural space and active education and community environment can be evaluated as a result of experimental efforts. Second, we can pay attention to the attempts to realize local communities. The bridge business is based on the multi-purposes such as early childhood education, technical education for adults, and community restoration of local residents and it reflects space and program plans for this purposes. It also reflects detailed plans such as differentiating the flow planning depending on users' time of use. Third, we can explain the characteristics of architectural planning considering local characteristics such as active use of local materials. Due to the characteristics of a developing country, there were significant considerations on maintenance, and to this end, the plan included plans for the environment and use of materials that are easily maintained. In addition, the participation of local residents in the process of establishment was suggested as a possibility to serve an educational role.

• 사상체질의학회지
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• 제33권4호
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• pp.1-9
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• 2021

Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.

• 한국산학기술학회논문지
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• 제14권2호
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• pp.681-687
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• 2013

본 연구는 학습 환경의 실내 온도와 학습재료 색채와의 조합이 학습수행에 어떠한 영향을 미치는 지를 살펴보고자 하였다. 이를 위해 학습활동 적정온도($22.5{\sim}24^{\circ}C$)를 중심으로(중립 실내 온도 조건), 그 이상인 조건(고온 실내온도 조건), 그리고 그 이하인 조건(저온 실내 온도 조건)으로 각각 실내 온도 조건을 구분하였으며, 난색계열인 빨간색과 한색계열인 파란색, 그리고 중성인 검은색과 연두색으로 각각 색채 조건을 구분하였다. 학습과 관련된 과제로는 음운 작업기억 과제를 사용하여 집단 간 실내 온도 조건에 따른 색채 조건에서의 과제 수행을 살펴보았다. 그 결과, 학습과제의 반응시간에서는 각 독립변수들에 의한 차이가 유의하지 않은 반면, 정확률에서는 색채 조건 중 빨간색과 검은색 조건에서 보다 정확한 수행이 나타났다. 이는 빨간색이 가진 현저성과 색채 온도감 및 검정색이 가진 친숙성과 다른 색에 비해 유일하게 현저성을 가지지 않는 특이성이 존재하기 때문에 나타난 결과로 해석할 수 있다.

• 한국항행학회논문지
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• 제16권1호
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• pp.76-81
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• 2012

다양한 모바일 기기의 보급 확산으로 u러닝 기반의 학습 관리 시스템의 연구가 활발히 진행 되고 있다. u-러닝 기반의 학습 관리 시스템은 콘텐츠 사용자의 접근 시간과 장소 그리고 다양한 접근 기기에 대한 제약이 없다는 점에서 매우 편리하다. 그러나 사용자에 대한 접근의 인증과 학습에 대한 집중 여부에 대한 판단이 매우 어렵다. 본 논문은 일반적인 사용자 이벤트 중심의 인터페이스가 아닌 음성과 사용자 안면 캡춰 인터페이스를 학습 관리 시스템에 적용 하였다. 사용자가 학습 관리 시스템에 접근 시 등록된 본인의 패스워드를 음성 입력하여 로그인 하고, 사용자가 콘텐츠를 통해 학습이 진행 되는 과정에서도 간단한 단어의 응답 발화를 통해 사용자의 학습 태도 및 학습 성과를 판단하게 한다. 제안된 학습 관리 시스템의 평가 결과 사용자의 학습 성취도와 집중도가 향상 되었으며 이에 따른 사용자의 비정상적인 학습태도에 대한 관리자의 모니터링을 가능 하게 했다.

• 공학교육연구
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• 제24권5호
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• pp.3-14
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• 2021

This study deals with the case of online-based project learning, which was designed for the purpose of university educational innovation and enhancing learners' competencies required by society, operated during the COVID-19 pandemic. The course was applied Python programming language, team-based project learning, and intensive course system, which is required by our society and companies in the era of the 4th industrial revolution. Also it was operated as a non-face-to-face online class, which would have been operated in an offline class if it had not been for Covid 19 pandemic, to explore the possibilities and educational effects of online learning. To do this, 32 university students participated in online-based project learning during 8 weeks, and then conducted a survey. The survey results were analyzed in terms of i) non-face-to-face online learning, ii) team-based project learning, and iii) application of the intensive course system. Results say that most of the learners were satisfied with the online learning, team-based project learning, and the intensive semester system applied in this course at a high level, and also they clearly presented the reasons. Thereby, it has been confirmed that the learners were already well aware of the pros and cons of each learning method. Based on these results, the implications were discussed.

• 한국산업정보학회논문지
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• 제26권4호
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• pp.11-18
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• 2021

강화 학습을 통한 실내 자율주행을 위해 가상 환경과 실제 환경에서 학습을 병행하는 방법을 제안한다. 실제 환경에서만 학습을 진행했을 경우 80시간 정도의 소요 시간이 필요하지만, 실제 환경과 가상 환경을 병행하며 학습을 진행했을 경우 50시간의 소요 시간이 필요하다. 가상 환경과 실제 환경에서 학습을 병행하면서 빠른 학습으로 다양한 실험을 거쳐 최적화된 파라미터를 얻을 수 있는 이점이 있다. 실내복도 이미지를 이용하여 가상 환경을 구성한 후 데스크톱으로 선행학습을 진행하였고 실제 환경에서의 학습은 Jetson Xavier를 기반으로 다양한 센서와 연결하여 학습을 진행하였다. 또한, 실내복도 환경의 반복되는 텍스처에 따른 정확도 문제를 해결하기 위해 복도 벽의 아랫선을 강조하는 특징점 검출을 학습하여 복도 벽 객체를 판단하고 정확도를 높일 수 있었다. 학습을 진행할수록 실험 차량은 실내복도 환경에서 복도 중앙을 기준으로 주행하며 평균 70회의 조향명령을 통해 움직인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• 대한지구과학교육학회지
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• 제10권1호
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• pp.26-37
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• 2017

이 연구의 목적은 중등과학 예비교사들의 조석 개념에 대한 오개념을 조사하고, 오개념을 과학개념으로 변화시킬 수 있는 학습모듈을 개발하여 개념변화에 미치는 학습모듈의 효과를 검사하는 것이다. 이에 연구자는 연구의 목적을 달성하기 위해 조석 개념 검사지와 인지갈등 전략을 적용한 조석 개념 학습모듈을 개발하여 적용하였다. 연구대상은 G 광역시에 소재한 사범대학 과학교육 전공 1학년 학생 40명을 대상으로 하였다. 연구 결과는 다음과 같다. 첫째, 중등과학 예비교사들은 조석 현상에 대하여 다양한 오개념을 갖고 있었다. 둘째, 개발한 학습모듈은 중등과학 예비교사들의 조석에 대한 오개념을 과학개념으로 변화시키는데 효과적이었다. 그러나 일부 학생들은 모듈의 학습 후에도 조석에 대한 오개념을 갖고 있었다. 그중 대표적인 오개념은 지구와 달이 공통질량 중심을 회전 중심으로 공전할 때 발생하는 원심력을 지구 자전에 의한 원심력과 구별하지 못하는 것과 지구가 공통질량 중심을 회전 중심으로 공전하는 동안 지구의 자전을 고려하지 않아야 한다는 사실을 알지 못하는 것이었다.