• Title/Summary/Keyword: outlier removal

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Background Concentration and Contamination Assessment of Heavy Metals in Korean Coastal Sediments (한반도 연안 퇴적물의 중금속 배경농도 및 오염도 평가)

  • WOO, JUNSIK;LEE, HYOJIN;PARK, JONGKYU;PARK, KYOUNGKYU;CHO, DONGJIN;JANG, DONGJUN;PARK, SOJUNG;CHOI, MANSIK;YOO, JEONGKYU
    • The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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    • v.24 no.1
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    • pp.64-78
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    • 2019
  • The background concentrations of heavy metals in Korean coastal sediments were estimated using heavy metal data for 495 sediments obtained from 'National Marine Ecosystem Survey (Coastal ecosystem) in 2016-2017' and the extent of contamination was assessed. Al, Cs, and Li are chosen as appropriate indicators for sediment grain size. In the relationships between heavy metal and indicators concentrations, the lowest slope data were selected through the outlier removal and residual analysis, and the background concentrations were presented as a linear regression line between metal and indicator. Comparing the previous studies for the background concentrations of heavy metals in Korean coastal sediments, concentration levels were generally consistent but those for As and Cd were presented for the first time, and the background concentration using Li as the indicator was presented for the first time.

Design and Implementation of System for Estimating Diameter at Breast Height and Tree Height using LiDAR point cloud data

  • Jong-Su, Yim;Dong-Hyeon, Kim;Chi-Ung, Ko;Dong-Geun, Kim;Hyung-Ju, Cho
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.2
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    • pp.99-110
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    • 2023
  • In this paper, we propose a system termed ForestLi that can accurately estimate the diameter at breast height (DBH) and tree height using LiDAR point cloud data. The ForestLi system processes LiDAR point cloud data through the following steps: downsampling, outlier removal, ground segmentation, ground height normalization, stem extraction, individual tree segmentation, and DBH and tree height measurement. A commercial system, such as LiDAR360, for processing LiDAR point cloud data requires the user to directly correct errors in lower vegetation and individual tree segmentation. In contrast, the ForestLi system can automatically remove LiDAR point cloud data that correspond to lower vegetation in order to improve the accuracy of estimating DBH and tree height. This enables the ForestLi system to reduce the total processing time as well as enhance the accuracy of accuracy of measuring DBH and tree height compared to the LiDAR360 system. We performed an empirical study to confirm that the ForestLi system outperforms the LiDAR360 system in terms of the total processing time and accuracy of measuring DBH and tree height.

Estimation of Image-based Damage Location and Generation of Exterior Damage Map for Port Structures (영상 기반 항만시설물 손상 위치 추정 및 외관조사망도 작성)

  • Banghyeon Kim;Sangyoon So;Soojin Cho
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.27 no.5
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    • pp.49-56
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    • 2023
  • This study proposed a damage location estimation method for automated image-based port infrastructure inspection. Memory efficiency was improved by calculating the homography matrix using feature detection technology and outlier removal technology, without going through the 3D modeling process and storing only damage information. To develop an algorithm specialized for port infrastructure, the algorithm was optimized through ground-truth coordinate pairs created using images of port infrastructure. The location errors obtained by applying this to the sample and concrete wall were (X: 6.5cm, Y: 1.3cm) and (X: 12.7cm, Y: 6.4cm), respectively. In addition, by applying the algorithm to the concrete wall and displaying it in the form of an exterior damage map, the possibility of field application was demonstrated.

Proposal of a Step-by-Step Optimized Campus Power Forecast Model using CNN-LSTM Deep Learning (CNN-LSTM 딥러닝 기반 캠퍼스 전력 예측 모델 최적화 단계 제시)

  • Kim, Yein;Lee, Seeun;Kwon, Youngsung
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.10
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    • pp.8-15
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    • 2020
  • A forecasting method using deep learning does not have consistent results due to the differences in the characteristics of the dataset, even though they have the same forecasting models and parameters. For example, the forecasting model X optimized with dataset A would not produce the optimized result with another dataset B. The forecasting model with the characteristics of the dataset needs to be optimized to increase the accuracy of the forecasting model. Therefore, this paper proposes novel optimization steps for outlier removal, dataset classification, and a CNN-LSTM-based hyperparameter tuning process to forecast the daily power usage of a university campus based on the hourly interval. The proposing model produces high forecasting accuracy with a 2% of MAPE with a single power input variable. The proposing model can be used in EMS to suggest improved strategies to users and consequently to improve the power efficiency.

Estimation of Consolidation Characteristics of Soft Ground in Major River Mouth (주요 강하구 연약지반의 압밀 특성 평가)

  • Lee, JunDae;Kwon, YoungChul;Bae, WooSeok
    • Journal of the Korean GEO-environmental Society
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    • v.20 no.2
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    • pp.69-79
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    • 2019
  • The coastal area forms various sedimentary layers according to the environmental conditions such as the topography and geological features of the upper region of the river, ocean currents, and river mouth. Therefore, identifying the characteristics of the marine clay deposited in the coastal area plays a key role in the investigation of the formation of soft ground. In general, alluvial grounds are formed by a variety of factors such as changes in topography and natural environment, they have very diverse qualities depending on the deposited region or sedimentation conditions. The most important thing for the construction of social infrastructures in soft ground areas is economical and efficient treatment of soft ground. In this study, the author collected data from diverse laboratory and field tests on five areas in western and southern offshore with relatively high reliability, and then statistically analyzed them, thereby presenting standard constants for construction design. Correlation between design parameters such as over consolidation ratio, preconsolidation pressure was analyzed using linear and non-linear regression analyses. Also, proposed distribution characteristics of design parameters in consideration of each region's uncertainty through statistical analyses such as normality verification, outlier removal.

Pupil Data Measurement and Social Emotion Inference Technology by using Smart Glasses (스마트 글래스를 활용한 동공 데이터 수집과 사회 감성 추정 기술)

  • Lee, Dong Won;Mun, Sungchul;Park, Sangin;Kim, Hwan-jin;Whang, Mincheol
    • Journal of Broadcast Engineering
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    • v.25 no.6
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    • pp.973-979
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    • 2020
  • This study aims to objectively and quantitatively determine the social emotion of empathy by collecting pupillary response. 52 subjects (26 men and 26 women) voluntarily participated in the experiment. After the measurement of the reference of 30 seconds, the experiment was divided into the task of imitation and spontaneously self-expression. The two subjects were interacted through facial expressions, and the pupil images were recorded. The pupil data was processed through binarization and circular edge detection algorithm, and outlier detection and removal technique was used to reject eye-blinking. The pupil size according to the empathy was confirmed for statistical significance with test of normality and independent sample t-test. Statistical analysis results, the pupil size was significantly different between empathy (M ± SD = 0.050 ± 1.817)) and non-empathy (M ± SD = 1.659 ± 1.514) condition (t(92) = -4.629, p = 0.000). The rule of empathy according to the pupil size was defined through discriminant analysis, and the rule was verified (Estimation accuracy: 75%) new 12 subjects (6 men and 6 women, mean age ± SD = 22.84 ± 1.57 years). The method proposed in this study is non-contact camera technology and is expected to be utilized in various virtual reality with smart glasses.

A Real-time Correction of the Underestimation Noise for GK2A Daily NDVI (GK2A 일단위 NDVI의 과소추정 노이즈 실시간 보정)

  • Lee, Soo-Jin;Youn, Youjeong;Sohn, Eunha;Kim, Mija;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1301-1314
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    • 2022
  • Normalized Difference Vegetation Index (NDVI) is utilized as an indicator to represent the vegetation condition on the land surface in various applications such as land cover, crop yield, agricultural drought, soil moisture, and forest disaster. However, satellite optical sensors for visible and infrared rays cannot see through the clouds, so the NDVI of the cloud pixel is not a valid value for the land surface. This study proposed a real-time correction of the underestimation noise for GEO-KOMPSAT-2A (GK2A) daily NDVI and made sure its feasibility through the quantitative comparisons with Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI and the qualitative interpretation of time-series changes. The underestimation noise was effectively corrected by the procedures such as the time-series correction considering vegetation phenology, the outlier removal using long-term climatology, and the gap filling using rigorous statistical methods. The correlation with MODIS NDVI was higher, and the difference was lower, showing a 32.7% improvement compared to the original NDVI product. The proposed method has an extensibility for use in other satellite products with some modification.