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Real-Time Joint Animation Production and Expression System using Deep Learning Model and Kinect Camera (딥러닝 모델과 Kinect 카메라를 이용한 실시간 관절 애니메이션 제작 및 표출 시스템 구축에 관한 연구)

  • Kim, Sang-Joon;Lee, Yu-Jin;Park, Goo-man
    • Journal of Broadcast Engineering
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    • v.26 no.3
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    • pp.269-282
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    • 2021
  • As the distribution of 3D content such as augmented reality and virtual reality increases, the importance of real-time computer animation technology is increasing. However, the computer animation process consists mostly of manual or marker-attaching motion capture, which requires a very long time for experienced professionals to obtain realistic images. To solve these problems, animation production systems and algorithms based on deep learning model and sensors have recently emerged. Thus, in this paper, we study four methods of implementing natural human movement in deep learning model and kinect camera-based animation production systems. Each method is chosen considering its environmental characteristics and accuracy. The first method uses a Kinect camera. The second method uses a Kinect camera and a calibration algorithm. The third method uses deep learning model. The fourth method uses deep learning model and kinect. Experiments with the proposed method showed that the fourth method of deep learning model and using the Kinect simultaneously showed the best results compared to other methods.

Comparison of Multi-angle TerraSAR-X Staring Mode Image Registration Method through Coarse to Fine Step (Coarse to Fine 단계를 통한 TerraSAR-X Staring Mode 다중 관측각 영상 정합기법 비교 분석)

  • Lee, Dongjun;Kim, Sang-Wan
    • Korean Journal of Remote Sensing
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    • v.37 no.3
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    • pp.475-491
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    • 2021
  • With the recent increase in available high-resolution (< ~1 m) satellite SAR images, the demand for precise registration of SAR images is increasing in various fields including change detection. The registration between high-resolution SAR images acquired in different look angle is difficult due to speckle noise and geometric distortion caused by the characteristics of SAR images. In this study, registration is performed in two stages, coarse and fine, using the x-band SAR data imaged at staring spotlight mode of TerraSAR-X. For the coarse registration, a method combining the adaptive sampling method and SAR-SIFT (Scale Invariant Feature Transform) is applied, and three rigid methods (NCC: Normalized Cross Correlation, Phase Congruency-NCC, MI: Mutual Information) and one non-rigid (Gefolki: Geoscience extended Flow Optical Flow Lucas-Kanade Iterative), for the fine registration stage, was performed for performance comparison. The results were compared by using RMSE (Root Mean Square Error) and FSIM (Feature Similarity) index, and all rigid models showed poor results in all image combinations. It is confirmed that the rigid models have a large registration error in the rugged terrain area. As a result of applying the Gefolki algorithm, it was confirmed that the RMSE of Gefolki showed the best result as a 1~3 pixels, and the FSIM index also obtained a higher value than 0.02~0.03 compared to other rigid methods. It was confirmed that the mis-registration due to terrain effect could be sufficiently reduced by the Gefolki algorithm.

Scale Revalidation Study for Online Use of the Learning Strategy Diagnostic Scale for Junior College (전문대학생용 학습전략 진단 척도의 온라인 활용을 위한 재타당화 연구)

  • Hwang, Jae Gyu
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.1
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    • pp.349-359
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    • 2022
  • The purpose of this study is to add and revalidate items of learning cognition and learning emotion factors for online use of the K-LSS for junior college. It is important for self-reflection and improvement of academic achievement to specifically explore and analyze the sub-factors of learning cognition, learning behavior, and learning emotion for each item that can affect the learning strategy of junior college students. The added items are two items for diagnosing the concentration of attention in the learning information processing process of the learning cognitive factor and two questions about the interpersonal anxiety factor for diagnosing the level of anxiety about others in the learning emotional factor. The study area was conducted in 5 areas nationwide, and the subjects of the study were 923 junior college students excluding 327 respondents who answered insincerity. The K-LSS_r scale is a learning strategy diagnosis scale of 52 questions composed of three sub-elements of learning cognition (18 questions), learning emotion (15 questions), and learning behavior (19 questions), and reliability for generalization in this study. As a result of the verification, Cronbach's α coefficient of the entire scale was .896, and Cronbach's α coefficient of the three factors ranged from .876 to .910. The half-segment reliability coefficient of the scale was .858 in total, and the half-segment reliability coefficients of the three factors ranged from .792 to .843. The test-retest reliability verification result for 3 weeks for 350 Junior college Students in 5 regions was .884, and the validity test for generalization also confirmed that the recruitment validity is significant.

Research of Water-related Disaster Monitoring Using Satellite Bigdata Based on Google Earth Engine Cloud Computing Platform (구글어스엔진 클라우드 컴퓨팅 플랫폼 기반 위성 빅데이터를 활용한 수재해 모니터링 연구)

  • Park, Jongsoo;Kang, Ki-mook
    • Korean Journal of Remote Sensing
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    • v.38 no.6_3
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    • pp.1761-1775
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    • 2022
  • Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

A study on the correction of the connection part of the underground facility 3D model and the correction of irregularities (지하시설물 3차원 모델 연결부 보정 및 요철보정에 관한 연구)

  • Kim, Sung Su;Han, Kyu Won;Heo, Sung Seo;Han, Sang Hoon
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.39 no.6
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    • pp.429-435
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    • 2021
  • The integrated underground space map shows the underground facility(water supply, sewage, gas, electric power, communication, heating), underground structures (subway, underpass, underground walkway, underground parking lot, underground shopping mall, common ward), ground(drilling, coffin, geology) refers to a map constructed so that a total of 15 types of underground information can be checked at a glance on a three-dimensional basis. The purpose of this study is to develop a technology to correct the problem of curved surface processing and the unevenness of underground facility pipelines that occur in converting 2D underground facility data into 3D-based underground space integrated map(3D underground facility model). do it with. To this end, we first investigated and reviewed the domestic and foreign status of technologies that generate data on underground facilities based on three dimensions, and developed a surface correction algorithm and an unevenness correction algorithm to solve practical problems. Algorithms to verify the developed algorithm This applied correction program was developed. Based on the above process, the three-dimensional model of the underground facility could be produced identically to reality. This study is judged to have significance as a basic study to improve the utilization of the underground spatial integration map.

Integrated receptive field diversification method for improving speaker verification performance for variable-length utterances (가변 길이 입력 발성에서의 화자 인증 성능 향상을 위한 통합된 수용 영역 다양화 기법)

  • Shin, Hyun-seo;Kim, Ju-ho;Heo, Jungwoo;Shim, Hye-jin;Yu, Ha-Jin
    • The Journal of the Acoustical Society of Korea
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    • v.41 no.3
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    • pp.319-325
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    • 2022
  • The variation of utterance lengths is a representative factor that can degrade the performance of speaker verification systems. To handle this issue, previous studies had attempted to extract speaker features from various branches or to use convolution layers with different receptive fields. Combining the advantages of the previous two approaches for variable-length input, this paper proposes integrated receptive field diversification that extracts speaker features through more diverse receptive field. The proposed method processes the input features by convolutional layers with different receptive fields at multiple time-axis branches, and extracts speaker embedding by dynamically aggregating the processed features according to the lengths of input utterances. The deep neural networks in this study were trained on the VoxCeleb2 dataset and tested on the VoxCeleb1 evaluation dataset that divided into 1 s, 2 s, 5 s, and full-length. Experimental results demonstrated that the proposed method reduces the equal error rate by 19.7 % compared to the baseline.

Cognitive-Motor Interaction-Based Instrument Playing for Improving Early Social Skills of Children With Autism Spectrum Disorder (ASD 아동의 초기 사회기술 향상을 위한 인지-운동 통합 기반 악기연주 중재)

  • Yu, Hyun Kyung
    • Journal of Music and Human Behavior
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    • v.20 no.1
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    • pp.75-97
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    • 2023
  • This study aimed to investigate the effect of cognitive-motor interaction-based instrument playing on the early social skills of children with autism spectrum disorder (ASD). Nine children with ASD, averaging 5.6 years of age, participated in twelve individual sessions lasting 30 minutes each. The intervention comprised five stages: self-regulation, motivation to engage in social interaction, acceptance of a partner in co-playing activities, interpersonal coordination with a partner, and engagement in joint music playing. To evaluate changes in early social skills, joint attention and social interaction behaviors were observed, and the Social Responsiveness Scale (SRS-2) was administered pre- and post-intervention. Social synchronization was measured through a dyadic drum task, measuring synchronization accuracy, duration, and timing. Following the intervention, all nine children demonstrated increased early social behaviors, although there were no significant differences in SRS scores. Moreover, synchronized movement improved significantly in accuracy and maintained duration but not in reaction time. This study highlights the significance of recognizing the cognitive-motor interplay as crucial element in facilitating early social skills development in children with ASD.

SAR(Synthetic Aperture Radar) 3-Dimensional Scatterers Point Cloud Target Model and Experiments on Bridge Area (영상레이더(SAR)용 3차원 산란점 점구름 표적모델의 교량 지역에 대한 적용)

  • Jong Hoo Park;Sang Chul Park
    • Journal of the Korea Society for Simulation
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    • v.32 no.3
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    • pp.1-8
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    • 2023
  • Modeling of artificial targets in Synthetic Aperture radar (SAR) mainly simulates radar signals reflected from the faces and edges of the 3D Computer Aided Design (CAD) model with a ray-tracing method, and modeling of the clutter on the Earth's surface uses a method of distinguishing types with similar distribution characteristics through statistical analysis of the SAR image itself. In this paper, man-made targets on the surface and background clutter on the terrain are integrated and made into a three-dimensional (3D) point cloud scatterer model, and SAR image were created through computational signal processing. The results of the SAR Stripmap image generation of the actual automobile based SAR radar system and the results analyzed using EM modeling or statistical distribution models are compared with this 3D point cloud scatterer model. The modeling target is selected as an bridge because it has the characteristic of having both water surface and ground terrain around the bridge and is also a target of great interest in both military and civilian use.

An Educational Needs Analysis of Home Economics Teachers for Food Literacy Education in Secondary School Home Economics (중등 가정과 푸드리터러시 함양 식생활교육에 대한 가정과 교사의 교육 요구도 분석)

  • Song, Yunmi;Lee, Kyung Won
    • Journal of Korean Home Economics Education Association
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    • v.35 no.2
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    • pp.41-59
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    • 2023
  • This study aimed at analyzing the educational needs of home economics teachers for food literacy cultivation education in secondary school home economics. A total of 192 home economics teachers were surveyed about their perceptions of importance and performance of food literacy education content categories and elements, and their priorities were identified. Among the 38 content elements presented, home economics teachers recognized that the current performance level was significantly lower than that of the importance over 34 content elements. Based on the Borich educational needs assessment and the Locus for Focus Model, we identified five content elements with 'very high' educational needs ('food security/food sovereignty', 'reducing food-related waste', 'food waste management and recycling', 'food-related waste recycling', and 'country of origin information'), and four content elements with 'high' educational needs ('genetically modified food', 'ethical food consumption', 'food additives', and 'eco-friendly agricultural products'). These results indicate that dietary education in secondary school home economics should emphasize both the production and disposal of food. The findings of this study suggest implications for the needs for a new dietary education in secondary school home economics that allows students to learn all stages of food system. These results can also be used as a basis for dietary education for cultivating food literacy in secondary school home economics education.

Shielding Performance of PLA and Tungsten Mixture using Research Extruder (연구용 압출기를 활용한 PLA와 텅스텐 혼합물의 차폐 성능)

  • Do-Seong Kim;Tae-Hyung Kim;Myeong-Seong Yoon;Sang-Hyun Kim
    • Journal of the Korean Society of Radiology
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    • v.17 no.4
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    • pp.557-564
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    • 2023
  • In this study, 3D printing technology was used to compensate for the shortcomings of the use of lead, which has proven to have excellent shielding performance, and to control unnecessary human exposure. 3D printers can implement three-dimensional shapes and can immediately apply individual ideas, which has great advantages in maintaining technology supplementation while reducing the cost and duration of prototyping. Among the various special 3D printers, the FDM method was adopted, and the filament used for output was manufactured using a research extruder by mixing two materials, PLA (Poly-Lactic-Acid) and tungsten. The purpose was to verify the validity through dose evaluation and to provide basic information on the production of chapezones of various materials. The mixed filament was implemented as a morphological shield. Filaments made of a research extruder by mixing PLA and tungsten were divided into 10 %, 20 %, 30 %, 40 %, and 50 % according to the tungsten content ratio. Through the process of 3D Modeling, STL File storage, G-code generation, and output, 10 cm × 10 cm × 0.5 cm was manufactured, respectively, and dose and shielding ability were evaluated under the conditions of tube voltages of 60 kVp, 80 kVp, 100 kVp, 120 kVp, and tube currents of 20 mAs and 40 mAs.