• Title/Summary/Keyword: Learning media

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Deep Learning-based Gaze Direction Vector Estimation Network Integrated with Eye Landmark Localization (딥 러닝 기반의 눈 랜드마크 위치 검출이 통합된 시선 방향 벡터 추정 네트워크)

  • Joo, Heeyoung;Ko, Min-Soo;Song, Hyok
    • Journal of Broadcast Engineering
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    • v.26 no.6
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    • pp.748-757
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    • 2021
  • In this paper, we propose a gaze estimation network in which eye landmark position detection and gaze direction vector estimation are integrated into one deep learning network. The proposed network uses the Stacked Hourglass Network as a backbone structure and is largely composed of three parts: a landmark detector, a feature map extractor, and a gaze direction estimator. The landmark detector estimates the coordinates of 50 eye landmarks, and the feature map extractor generates a feature map of the eye image for estimating the gaze direction. And the gaze direction estimator estimates the final gaze direction vector by combining each output result. The proposed network was trained using virtual synthetic eye images and landmark coordinate data generated through the UnityEyes dataset, and the MPIIGaze dataset consisting of real human eye images was used for performance evaluation. Through the experiment, the gaze estimation error showed a performance of 3.9, and the estimation speed of the network was 42 FPS (Frames per second).

Window Attention Module Based Transformer for Image Classification (윈도우 주의 모듈 기반 트랜스포머를 활용한 이미지 분류 방법)

  • Kim, Sanghoon;Kim, Wonjun
    • Journal of Broadcast Engineering
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    • v.27 no.4
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    • pp.538-547
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    • 2022
  • Recently introduced image classification methods using Transformers show remarkable performance improvements over conventional neural network-based methods. In order to effectively consider regional features, research has been actively conducted on how to apply transformers by dividing image areas into multiple window areas, but learning of inter-window relationships is still insufficient. In this paper, to overcome this problem, we propose a transformer structure that can reflect the relationship between windows in learning. The proposed method computes the importance of each window region through compression and a fully connected layer based on self-attention operations for each window region. The calculated importance is scaled to each window area as a learned weight of the relationship between the window areas to re-calibrate the feature value. Experimental results show that the proposed method can effectively improve the performance of existing transformer-based methods.

Facial Age Classification and Synthesis using Feature Decomposition (특징 분해를 이용한 얼굴 나이 분류 및 합성)

  • Chanho Kim;In Kyu Park
    • Journal of Broadcast Engineering
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    • v.28 no.2
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    • pp.238-241
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    • 2023
  • Recently deep learning models are widely used for various tasks such as facial recognition and face editing. Their training process often involves a dataset with imbalanced age distribution. It is because some age groups (teenagers and middle age) are more socially active and tends to have more data compared to the less socially active age groups (children and elderly). This imbalanced age distribution may negatively impact the deep learning training process or the model performance when tested against those age groups with less data. To this end, we propose an age-controllable face synthesis technique using a feature decomposition to classify age from facial images which can be utilized to synthesize novel data to balance out the age distribution. We perform extensive qualitative and quantitative evaluation on our proposed technique using the FFHQ dataset and we show that our method has better performance than existing method.

A Study on Lightweight Transformer Based Super Resolution Model Using Knowledge Distillation (지식 증류 기법을 사용한 트랜스포머 기반 초해상화 모델 경량화 연구)

  • Dong-hyun Kim;Dong-hun Lee;Aro Kim;Vani Priyanka Galia;Sang-hyo Park
    • Journal of Broadcast Engineering
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    • v.28 no.3
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    • pp.333-336
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    • 2023
  • Recently, the transformer model used in natural language processing is also applied to the image super resolution field, showing good performance. However, these transformer based models have a disadvantage that they are difficult to use in small mobile devices because they are complex and have many learning parameters and require high hardware resources. Therefore, in this paper, we propose a knowledge distillation technique that can effectively reduce the size of a transformer based super resolution model. As a result of the experiment, it was confirmed that by applying the proposed technique to the student model with reduced number of transformer blocks, performance similar to or higher than that of the teacher model could be obtained.

The Risk Factors for Musculoskeletal Symptoms During Work From Home Due to the Covid-19 Pandemic

  • Sjahrul Meizar Nasri;Indri Hapsari Susilowati;Bonardo Prayogo Hasiholan;Akbar Nugroho Sitanggang;Ida Ayu Gede Jyotidiwy;Nurrachmat Satria;Magda Sabrina Theofany Simanjuntak
    • Safety and Health at Work
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    • v.14 no.1
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    • pp.66-70
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    • 2023
  • Background: Online teaching and learning extend the duration of using gadgets such as mobile phones and tablets. A prolonged usage of these gadgets in a static position can lead to musculoskeletal disorders (MSD). Therefore, this study aims to identify the risk factors related to musculoskeletal symptoms while using gadgets during work from home due to the COVID-19 pandemic. Method: A cross-sectional survey with online-based questionnaires was collected from the University of Indonesia, consisting of lecturers, students, and managerial staff. The minimum number of respondents was 1,080 and was defined by stratified random sampling. Furthermore, the dependent variable was musculoskeletal symptoms, while the independent were age, gender, job position, duration, activity when using gadgets, and how to hold them. Result: Most of the respondents had mobile phones but only 16% had tablets. Furthermore, about 56.7% have used a mobile phone for more than 10 years, while about 89.7% have used a tablet for less than 10 years. A multivariate analysis found factors that were significantly associated with MSD symptoms while using a mobile phone, such as age, gender, web browsing activity, work, or college activities. These activities include doing assignments and holding the phone with two hands with two thumbs actively operating. The factors that were significantly associated with MSD symptoms when using tablets were gender, academic position, social media activity, and placing the tablet on a table with two actively working index fingers. Conclusion: Therefore, from the results of this study it is necessary to have WFH and e-learning policies to reduce MSD symptoms and enhance productivity at work.

Panorama Image Stitching Using Sythetic Fisheye Image (Synthetic fisheye 이미지를 이용한 360° 파노라마 이미지 스티칭)

  • Kweon, Hyeok-Joon;Cho, Donghyeon
    • Journal of Broadcast Engineering
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    • v.27 no.1
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    • pp.20-30
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    • 2022
  • Recently, as VR (Virtual Reality) technology has been in the spotlight, 360° panoramic images that can view lively VR contents are attracting a lot of attention. Image stitching technology is a major technology for producing 360° panorama images, and many studies are being actively conducted. Typical stitching algorithms are based on feature point-based image stitching. However, conventional feature point-based image stitching methods have a problem that stitching results are intensely affected by feature points. To solve this problem, deep learning-based image stitching technologies have recently been studied, but there are still many problems when there are few overlapping areas between images or large parallax. In addition, there is a limit to complete supervised learning because labeled ground-truth panorama images cannot be obtained in a real environment. Therefore, we produced three fisheye images with different camera centers and corresponding ground truth image through carla simulator that is widely used in the autonomous driving field. We propose image stitching model that creates a 360° panorama image with the produced fisheye image. The final experimental results are virtual datasets configured similar to the actual environment, verifying stitching results that are strong against various environments and large parallax.

In-Loop Filtering with a Deep Network in HEVC (깊은 신경망을 사용한 HEVC의 루프 내 필터링)

  • Kim, Dongsin;Lee, So Yoon;Yang, Yoonmo;Oh, Byung Tae
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2020.11a
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    • pp.145-147
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    • 2020
  • As deep learning technology advances, there have been many attempts to improve video codecs such as High-Efficiency-Video-Coding (HEVC) using deep learning technology. One of the most researched approaches is improving filters inside codecs through image restoration researches. In this paper, we propose a method 01 replacing the sample adaptive offset (SAO) filtering with a deep neural network. The proposed method uses the deep neural network to find the optimal offset value. The proposed network consists of two subnetworks to find the offset value and its type of the signal, which can restore nonlinear and complex type of error. Experimental results show that the performance is better than the conventional HEVC in low delay P and random access mode.

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Personal Information Protection Recommendation System using Deep Learning in POI (POI 에서 딥러닝을 이용한 개인정보 보호 추천 시스템)

  • Peng, Sony;Park, Doo-Soon;Kim, Daeyoung;Yang, Yixuan;Lee, HyeJung;Siet, Sophort
    • Proceedings of the Korea Information Processing Society Conference
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    • 2022.11a
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    • pp.377-379
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    • 2022
  • POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

The Higher Education Possibility of Sound Art in Korea - Focusing on the Proposal of Creative Fusion Liberal Arts Learning (사운드아트의 국내 고등교육 가능성 - 창의적 융복합 교양교과 제안을 중심으로)

  • Irene Eunyoung Lee
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.6
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    • pp.443-451
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    • 2023
  • Sound Art (Sonic Art) is a branch of contemporary art that has been practiced dominantly in Europe and the Americas since the mid-20th century; and in Korea, it tends to be regarded as a multiple art field or as a subgenre of contemporary music or media art. Since the 2000s, some leading universities in North America and Europe have been opened sound art majors, producing talented people who specialize in this field or work as practical artists, yet it is still considered a non-mainstream art field. It is difficult to find schools that have opened sound arts as their major program in domestic universities. Along with the introduction of a liberal arts curriculum model and teaching methods used in the <Sound Art of Modern Society> course operated in a four-year university in South Korea, this paper discusses the possibility of using sound art as a main subject in liberal arts learning in higher education as a creative fusion liberal arts subject.

Design of Hardware(Hacker Board) for IoT Security Education Utilizing Dual MCUs (이중 MCU를 활용한 IoT 보안 교육용 하드웨어(해커보드) 설계)

  • Dong-Won Kim
    • Convergence Security Journal
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    • v.24 no.1
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    • pp.43-49
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    • 2024
  • The convergence of education and technology has been emphasized, leading to the application of educational technology (EdTech) in the field of education. EdTech provides learner-centered, customized learning environments through various media and learning situations. In this paper, we designed hardware for EdTech-based educational tools for IoT security education in the field of cybersecurity education. The hardware is based on a dual microcontroller unit (MCU) within a single board, allowing for both attack and defense to be performed. To leverage various sensors in the Internet of Things (IoT), the hardware is modularly designed. From an educational perspective, utilizing EdTech in cybersecurity education enhances engagement by incorporating tangible physical teaching aids. The proposed research suggests that the design of IoT security education hardware can serve as a reference for simplifying the creation of a security education environment for embedded hardware, software, sensor networks, and other areas that are challenging to address in traditional education..