• Title/Summary/Keyword: accuracy estimation

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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.

A methodology for Identification of an Air Cavity Underground Using its Natural Poles (물체의 고유 Pole을 이용한 지하 속의 빈 공간 식별 방안)

  • Lee, Woojin
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.6
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    • pp.566-572
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    • 2021
  • A methodology for the identification and coordinates estimation of air cavities under urban ground or sandy soil using its natural poles and natural resonant frequencies is presented. The potential of this methodology was analyzed. Simulation models of PEC (Perfect Electric Conductor)s with various shapes and dimensions were developed using an EM (Electromagnetic) simulator. The Cauchy method was applied to the obtained EM scattering response of various objects from EM simulation models. The natural poles of objects corresponding to its instinct characterization were then extracted. Thus, a library of poles can be generated using their natural poles. The generated library of poles provided the possibility of identifying a target by comparing them with the computed natural poles from a target. The simulation models were made assuming that there is an air cavity under urban ground or sandy soil. The response of the desired target was extracted from the electromagnetic wave scattering data from its simulation model. The coordinates of the target were estimated using the time delay of the impulse response (peak of the impulse response) in the time domain. The MP (Matrix Pencil) method was applied to extract the natural poles of a target. Finally, a 0.2-m-diameter spherical air cavity underground could be estimated by comparing both the pole library of the objects and the calculated natural poles and the natural resonant frequency of the target. The computed location (depth) of a target showed an accuracy of approximately 84 to 93%.

Estimation of Shear Wave Velocity of Weathered Granite Layer Using Nonlinear Multiple Regression Analysis; A Case Study in South Korea (비선형 다중회귀분석을 통한 국내 화강 풍화대 전단파 속도 평가에 대한 사례 연구)

  • Lee, Seung-Hwan;Baek, Sung-Ha;Chung, Choong-Ki;Kwak, Tae-Young
    • Journal of the Korean Geotechnical Society
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    • v.37 no.6
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    • pp.29-37
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    • 2021
  • Since many geotechnical structures are constructed on a weathered granite layer, it is important to evaluate their characteristics. As a seismic design is the more important nowadays, the demands to estimate a shear wave velocity (VS) based on acceptable methods are increasing. In this study, an empirical equation predicting VS of the weathered granite layer is suggested based on the nonlinear multiple variable regression analysis whose independent variables are both SPT (Standard penetration test)-N60 and chemical weathering index. It is concluded that the accuracy of the empirical equation estimating VS of the weathered granite layer increases when it considers the chemical weathering index as an additional independent variable compared to the result of simple regression analysis using only N60.

Effect of the Learning Image Combinations and Weather Parameters in the PM Estimation from CCTV Images (CCTV 영상으로부터 미세먼지 추정에서 학습영상조합, 기상변수 적용이 결과에 미치는 영향)

  • Won, Taeyeon;Eo, Yang Dam;Sung, Hong ki;Chong, Kyu soo;Youn, Junhee
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.38 no.6
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    • pp.573-581
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    • 2020
  • Using CCTV images and weather parameters, a method for estimating PM (Particulate Matter) index was proposed, and an experiment was conducted. For CCTV images, we proposed a method of estimating the PM index by applying a deep learning technique based on a CNN (Convolutional Neural Network) with ROI(Region Of Interest) image including a specific spot and an full area image. In addition, after combining the predicted result values by deep learning with the two weather parameters of humidity and wind speed, a post-processing experiment was also conducted to calculate the modified PM index using the learned regression model. As a result of the experiment, the estimated value of the PM index from the CCTV image was R2(R-Squared) 0.58~0.89, and the result of learning the ROI image and the full area image with the measuring device was the best. The result of post-processing using weather parameters did not always show improvement in accuracy in all cases in the experimental area.

A study on the estimation of onion's bulb weight using multi-level model (다층모형을 활용한 양파 구중 추정 연구)

  • Kim, Junki;Choi, Seung-cheon;Kim, Jaehwi;Seo, Hong-Seok
    • The Korean Journal of Applied Statistics
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    • v.33 no.6
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    • pp.763-776
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    • 2020
  • Onions show severe volatility in production and price because crop conditions highly depend on the weather. The government has designated onions as a sensitive agricultural product, and prepared various measures to stabilize the supply and demand. First of all, preemptive and reliable information on predicting onion production is essential to implement appropriate and effective measures. This study aims to contribute to improving the accuracy of production forecasting by developing a model to estimate the final weight of onions bulb. For the analysis, multi-level model is used to reflect the hierarchical data characteristics consisting of above-ground growth data in individual units and meteorological data in parcel units. The result shows that as the number of leaf, stem diameter, and plant height in early May increase, the bulb weight increases. The amount of precipitation as well as the number of days beyond a certain temperature inhibiting carbon assimilation have negative effects on bulb weight, However, the daily range of temperature and more precipitation near the harvest season are statistically significant as positive effects. Also, it is confirmed that the fitness and explanatory power of the model is improved by considering the interaction terms between level-1 and level-2 variables.

A study on estimation of lowflow indices in ungauged basin using multiple regression (다중회귀분석을 이용한 미계측 유역의 갈수지수 산정에 관한 연구)

  • Lim, Ga Kyun;Jeung, Se Jin;Kim, Byung Sik;Chae, Soo Kwon
    • Journal of Korea Water Resources Association
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    • v.53 no.12
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    • pp.1193-1201
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    • 2020
  • This study aims to develop a regression model that estimates a low-flow index that can be applied to ungauged basins. A total of 30 midsized basins in South Korea use long-term runoff data provided by the National Integrated Water Management System (NIWMS) to calculate average low-flow, average minimum streamflow, and low-flow index duration and frequency. This information is used in the correlation analysis with 18 basin factors and 3 climate change factors to identify the basin area, average basin altitude, average basin slope, water system density, runoff curve number, annual evapotranspiration, and annual precipitation in the low-flow index regression model. This study evaluates the model's accuracy by using the root-mean-square error (RMSE) and the mean absolute error (MAE) for 10 ungauged, verified basins and compares them with the previous model's low-flow calculations to determine the effectiveness of the newly developed model. Comparative analysis indicates that the new regression model produces average low-flow, attributed to the consideration of varied basin and hydrologic factors during the new model's development.

The Effects of Metacognitive Training in Math Problem Solving Using Smart Learning System (스마트 러닝 시스템을 활용한 수학 문제 풀이 맥락에서 메타인지 훈련의 효과)

  • Kim, Sungtae;Kang, Hyunmin
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.1
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    • pp.441-452
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    • 2022
  • Training using metacognition in a learning environment is one of the topics that have been continuously studied since the 1990s. Metacognition can be broadly divided into declarative metacognitive knowledge and procedural metacognitive knowledge (metacognitive skills). Accordingly, metacognitive training has also been studied focusing on one of the two metacognitive knowledge. The purpose of this study was to examine the role of metacognitive skills training in the context of mathematical problem solving. Specifically, the learner performed the prediction of problem difficulty, estimation of problem solving time, and prediction of accuracy in the context of a test in which problems of various difficulty levels were mixed within a set, and this was repeated 5 times over a total of 5 weeks. As a result of the analysis, we found that there was a significant difference in all three predictive indicators after training than before training, and we revealed that training can help learners in problem-solving strategies. In addition, we analyzed whether there was a difference between the experiment group and control group in the degree of test anxiety and math achievement. As a result, we found that learners in the experiment group showed less emotional and relationship anxiety at 5 weeks. This effect through metacognitive skill training is expected to help learners improve learning strategies needed for test situations.

A Study on Estimation of Road and Transportation Facility Improvement Direction Using Random Forest (랜덤 포레스트를 활용한 도로 및 교통시설 개선방향 추정 연구)

  • Hwang, Jae-seong;Kim, Do-kyeong;Kim, Nam-sun;Lee, Choul-ki
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.20 no.6
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    • pp.37-46
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    • 2021
  • Government agencies, such as police and local governments, strive to prevent traffic hazards and create a comfortable road environment by pormoting transportation and road facilities. To this end, roads and transportation facilities are enhanced and adjusted, and improvement projects in areas with frequent traffic accidents are carried out. Usually, improvement projects in areas with frequent traffic accidents vary by projects and region. Moreover, these projects are carried out under the supervision of a person in charge and related parties. Hence, civil complaints and subjectivity are reflected in deriving priorities for the improvement projects, limiting the efficiency of the project. To this end, a study was conducted to estimate the direction of improvement of the project target site. This study comprehensively considered road, traffic, and accident conditions of representative projects with high effectiveness in handling traffic accidents. The results of the study state that the accuracy of estimating the improvement project was around 88%. In addition, the study found that there was a strong relationship between traffic volume, accident rate, and accident severity in estimating the improvement direction.

Detection of Wildfire Smoke Plumes Using GEMS Images and Machine Learning (GEMS 영상과 기계학습을 이용한 산불 연기 탐지)

  • Jeong, Yemin;Kim, Seoyeon;Kim, Seung-Yeon;Yu, Jeong-Ah;Lee, Dong-Won;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.5_3
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    • pp.967-977
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    • 2022
  • The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO2), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

Derivation of Suitable-Site Environmental Factors in Robinia pseudoacacia Stands Using Type I Quantification Theory (수량화이론 I방법에 의한 아까시나무 임분의 적지 환경인자 도출)

  • Kim, Sora;Song, Jungeun;Park, Chunhee;Min, Suhui;Hong, Sunghee;Lim, Jongsoo;Son, Yeongmo
    • Journal of Korean Society of Forest Science
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    • v.111 no.3
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    • pp.428-434
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    • 2022
  • This study was conducted to derive the site index of forest productivity of Robinia pseudoacacia (honey plant) to characterize suitable planting sites and to investigate the effect of the site environmental factors on the site index using the quantification theory I method. The data used in the analysis were growth factors (stand age, dominant height, etc.) of the 6th national forest resources survey and various site environmental factors of a forest soil map (1:5,000). The average site index value of the R. pseudoacacia stand in Korea was 14 (range, 8 to 18). The environmental factors affecting the site index were parent rock, climatic zone, soil texture, local topography, and altitude. The accuracy of the estimation model using quantification theory I was only 33%. However, the correlation between the site index and the site environmental factors was statistically significant at the 1% level. Results of quantification analysis between site index and site environmental factors revealed that metamorphic and igneous rocks received high grades as parent rocks, climate zones received higher grades than central temperate zone, clay loam and silt loam received high grades in soil texture, and hillside received a high grade in local topography. Analysis of the partial correlation between site topographical factors and forest productivity (site index) found that soil class and altitude were partially correlated to x by 0.4129 and 0.4023, respectively, indicating that these factors are the most influential variables.