• Journal of Information Processing Systems
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• v.18 no.5
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• pp.701-710
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• 2022

With the development of artificial intelligence technology, various methods have been developed for recognizing objects in images using machine learning. Image segmentation is the most effective among these methods for recognizing objects within an image. Conventionally, image datasets of various classes are trained simultaneously. In situations where several classes require segmentation, all datasets have to be trained thoroughly. Such repeated training results in low training efficiency because most of the classes have already been trained. In addition, the number of classes that appear in the datasets affects training. Some classes appear in datasets in remarkably smaller numbers than others, and hence, the training errors will not be properly reflected when all the classes are trained simultaneously. Therefore, a new method that separates some classes from the dataset is proposed to improve efficiency during training. In addition, the accuracies of the conventional and proposed methods are compared.

• Smart Media Journal
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• v.12 no.3
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• pp.112-119
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• 2023

Shrimp farming has been becoming a new source of income for fishermen in South Korea. It is often necessary for fishers to measure the size of the shrimp for the purpose to understand the growth rate of the shrimp and to determine the amount of food put into the breeding pond. Traditional methods rely on humans, which has huge time and labor costs. This paper proposes a deep learning-based method for calculating the size of shrimps automatically. Firstly, we use fine-tuning techniques to update the Mask RCNN model with our farm data, enabling it to segment shrimps and generate shrimp masks. We then use skeletonizing method and maximum inscribed circle to calculate the length and width of shrimp, respectively. Our method is simple yet effective, and most importantly, it requires a small hardware resource and is easy to deploy to shrimp farms.

• Smart Media Journal
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• v.11 no.11
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• pp.63-74
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• 2022

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.821-824
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• 2022

최근 딥러닝(Deep Learning) 기술이 다양한 분야에서 활용되고 있으며, 사전 학습된 딥러닝 모델에 대한 압축과 전송 방안에 관한 연구 또한 활발히 진행되고 있다. 이와 관련하여, 국제 표준화 기구인 ISO/IEC 산하 MPEG(Moving Picture Expert Group)에서는 인공신경망 모델을 다양한 딥러닝 프레임워크(Deep Learning Framework)에서 상호운용 가능한 포맷으로 압축 표현할 수 있는 NNC(Compression of Neural Network for Multimedia Content Description nd Analysis) 표준화를 진행하고 있다. 압축된 딥러닝 모델의 데이터를 효과적으로 저장하여 전송 및 사용하기 위해서는 ISOBMFF(ISO based Media File Format) 캡슐화 과정이 필요하다. 본 논문에서는 MPEG의 NNC 표준에 따라 사전 학습된 딥러닝 모델을 압축한 후 이를 통해 생성된 비트스트림(bitstream)을 ISOBMFF로 캡슐화하기 위한 기술을 제안 및 실험한다. 또한, 실험에 대한 검증을 위하여 생성된 ISOBMFF 데이터를 비트스트림으로 복원한 뒤 복호화하여 입력 비트스트림과 차이가 없음을 확인한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.184-185
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• 2022

인터넷 환경의 변화에 따라 텍스트 기반의 정보 전달에서 멀티미디어 기반의 스트리밍 방식으로 바뀌어가고 있다. 또한 대용량의 동영상 데이터뿐 아니라 Shorts, Clip Reels 또는 등 다양한 방식의 동영상 형태로 배포되고 있으며 서비스 플랫폼에서는 손쉽게 편집할 수 있도록 기능을 제공하고 있다. 대용량 콘텐츠, TV, Youtue 콘텐츠를 포함하여 소용량 동영상 편집에 필요한 영상 제작 기술에서 가장 인력과 시간이 많이 소요되는 부분은 편집 단계로 딥러닝 기반 인공지능 기술을 활용하여 자동화하고 있으며 영상편집에서 가장 기본이 되는 단위인 씬검출 기법을 개발하였다. 키프레임 검출 기법과 유사도 기법을 이용하여 씬을 추출하였으며 블록 Cost Function을 이용하여 최적화하여 0.5214의 정확도를 도출하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.45-46
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• 2023

In order to improve and promote immersive learning experiences for English as a Second Language (ESL) students, the deployment of a YOLOv5 model for object identification in videos is proposed. The procedure includes collecting annotated datasets, preparing the data, and then fine-tuning a model using the YOLOv5 framework. The study's major objective is to integrate a well-trained model into ESL instruction in order to analyze the effectiveness of AI application in the field.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.87-88
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• 2023

본 논문에서는 COVID-19 팬데믹으로 인해 사회적 거리두기 및 규제조치가 시행되면서 다양한 분야에서 큰 영향을 가져왔다. 변화된 홈트레이닝 분야는 운동기구를 구비하여 개인운동을 통해 건강을 유지하고 있으나 전문적인 교육을 받지 않은 홈트레닝으로 부상 위험에 노출 되고 있다. 요가는 호흡운동과 명상을 지향하는 운동으로 요가의 효과를 얻기 위해 올바른 움직임과 자세가 중요 하다. 본 논문에서는 실시간으로 입력된 영상 프레임을 OpenCV와 MediaPipe를 통해 추출된 주요좌표 값을 벡터 내적공식을 대입, 코사인2법칙을 통해 요가의 올바른 자세를 분석하여 종합적인 정보를 제공하는 요가교정 모델이다.

• The Journal of Korean Association of Computer Education
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• v.10 no.4
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• pp.73-82
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• 2007

The purpose of the study was to develope e-Learning multimedia resource management system to support developing of e-Learning contents. This system is to create and manage multimedia resources like video, audio, and image more easily. We analyzed and redesigned media asset management system that would be used in broadcast, press, and industry. This system has SCORM metadata and is consisted of Windows 2003 server and RDBMS. The survey was conducted to investigate usability and satisfaction for the developed system.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1102-1112
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• 2017

The purpose of this study is to design and application of term project model for Game Math in flipped learning environment. In the term project self study model, students interacts with multi-instruction materials and multi-tutors on flipped learning. We develop a case for game update term project and implement it to a real Game Math classroom. As a result, we show the positive learning experiences focused on effects of technology and human relation through survey.

• Journal of the Korean School Mathematics Society
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• v.3 no.2
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• pp.123-131
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• 2000

In order to educate future leaders of the new age, we should help students to increase their basic knowledge, thinking and problem solving ability. It is necessary that we should use multi-media, computer as well as old teaching-learning material to improve students' basic knowledge and to motivate their interest in mathematics in the small-sized Middle School situated on the agricultural and fishery village. In solving this problem, it is ultimately necessary that we should utilize CAI program on the learning achievement in mathematics for the students to understand basic concept, principle, law and to promote teaching-learning process considered on individual different abilities. Therefore, this study is on the effect of students' interest and learning achievement in mathematics when we develop CAI program focusing on the lesson statistics in the 3rd Grade Middle School Mathematics Textbook and explain the concept and principle of statistics through using exact and various techniques of computers