• Title/Summary/Keyword: Data Accuracy

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Effect of Sample Preparations on Prediction of Chemical Composition for Corn Silage by Near Infrared Reflectance Spectroscopy (시료 전처리 방법이 근적외선분광법을 이용한 옥수수 사일리지의 화학적 조성분 평가에 미치는 영향)

  • Park Hyung-Soo;Lee Jong-Kyung;Lee Hyo-Won;Hwang Kyung-Jun;Jung Ha-Yeon;Ko Moon-Suck
    • Journal of The Korean Society of Grassland and Forage Science
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    • v.26 no.1
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    • pp.53-62
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    • 2006
  • Near infrared reflectance spectroscopy (NIRS) has been increasingly used as a rapid, accurate method of evaluating some chemical compositions in forages. Analysis of forage quality by NIRS usually involves dry ground samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations and spectral math treatments on prediction ability of chemical composition for corn silage by NIRS. A population of 112 corn silage representing a wide range in chemical parameters were used in this investigation. Samples of com silage were scanned at 2nm intervals over the wavelength range 400-2500nm and the optical data recorded as log l/Reflectance(log l/R) and scanned in overt-dried grinding(ODG), liquid nitrogen grinding(LNG) or intact fresh(IF) condition. Samples were analysed for neutral detergent fiber(NDF), acid detergent fiber(ADF), acid detergent lignin(ADL), crude protein(CP) and crude ash content were expressed on a dry-matter(DM) basis. The spectral data were regressed against a range of chemical parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with four spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation(SECV). The results of this study show that NIRS predicted the chemical parameters with very high degree of accuracy(the correlation coefficient of cross validation$(R^2cv)$ range from $0.70{\sim}0.95$) in ODG. The optimum equations were selected on the basis of minimizing the standard error of prediction(SEP). The Optimum sample preparation methods and spectral math treatment were for ADF, the ODG method using 2,10,5 math treatment(SEP = 0.99, $R^2v=0.93$), and for CP, the ODG method using 1,4,4 math treatment(SEP = 0.29. $R^2v=0.91$).

Performance Evaluation of Machine Learning and Deep Learning Algorithms in Crop Classification: Impact of Hyper-parameters and Training Sample Size (작물분류에서 기계학습 및 딥러닝 알고리즘의 분류 성능 평가: 하이퍼파라미터와 훈련자료 크기의 영향 분석)

  • Kim, Yeseul;Kwak, Geun-Ho;Lee, Kyung-Do;Na, Sang-Il;Park, Chan-Won;Park, No-Wook
    • Korean Journal of Remote Sensing
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    • v.34 no.5
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    • pp.811-827
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    • 2018
  • The purpose of this study is to compare machine learning algorithm and deep learning algorithm in crop classification using multi-temporal remote sensing data. For this, impacts of machine learning and deep learning algorithms on (a) hyper-parameter and (2) training sample size were compared and analyzed for Haenam-gun, Korea and Illinois State, USA. In the comparison experiment, support vector machine (SVM) was applied as machine learning algorithm and convolutional neural network (CNN) was applied as deep learning algorithm. In particular, 2D-CNN considering 2-dimensional spatial information and 3D-CNN with extended time dimension from 2D-CNN were applied as CNN. As a result of the experiment, it was found that the hyper-parameter values of CNN, considering various hyper-parameter, defined in the two study areas were similar compared with SVM. Based on this result, although it takes much time to optimize the model in CNN, it is considered that it is possible to apply transfer learning that can extend optimized CNN model to other regions. Then, in the experiment results with various training sample size, the impact of that on CNN was larger than SVM. In particular, this impact was exaggerated in Illinois State with heterogeneous spatial patterns. In addition, the lowest classification performance of 3D-CNN was presented in Illinois State, which is considered to be due to over-fitting as complexity of the model. That is, the classification performance was relatively degraded due to heterogeneous patterns and noise effect of input data, although the training accuracy of 3D-CNN model was high. This result simply that a proper classification algorithms should be selected considering spatial characteristics of study areas. Also, a large amount of training samples is necessary to guarantee higher classification performance in CNN, particularly in 3D-CNN.

A Study on Retrieval of Storage Heat Flux in Urban Area (우리나라 도심지에서의 저장열 산출에 관한 연구)

  • Lee, Darae;Kim, Honghee;Lee, Sang-Hyun;Lee, Doo-Il;Hong, Jinkyu;Hong, Je-Woo;Lee, Keunmin;Lee, Kyeong-sang;Seo, Minji;Han, Kyung-Soo
    • Korean Journal of Remote Sensing
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    • v.34 no.2_1
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    • pp.301-306
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    • 2018
  • Urbanization causes urban floods and urban heat island in the summer, so it is necessary to understanding the changes of the thermal environment through urban climate and energy balance. This can be explained by the energy balance, but in urban areas, unlike the typical energy balance, the storage heat flux saved in the building or artificial land cover should be considered. Since the environment of each city is different, there is a difficulty in applying the method of retrieving the storage heat flux of the previous research. Especially, most of the previous studies are focused on the overseas cities, so it is necessary to study the storage heat retrieval suitable for various land cover and building characteristics of the urban areas in Korea. Therefore, the object of this study, it is to derive the regression formula which can quantitatively retrieve the storage heat using the data of the area where various surface types exist. To this end, nonlinear regression analysis was performed using net radiation and surface temperature data as independent variables and flux tower based storage heat estimates as dependent variables. The retrieved regression coefficients were applied to each independent variable to derive the storage heat retrieval regression formula. As a result of time series analysis with flux tower based storage heat estimates, it was well simulated high peak at day time and the value at night. Moreover storage heat retrieved in this study was possible continuous retrieval than flux tower based storage heat estimates. As a result of scatter plot analysis, accuracy of retrieved storage heat was found to be significant at $50.14Wm^{-2}$ and bias $-0.94Wm^{-2}$.

Development of relative radiometric calibration system for in-situ measurement spectroradiometers (현장관측용 분광 광도계의 상대 검교정 시스템 개발)

  • Oh, Eunsong;Ahn, Ki-Beom;Kang, Hyukmo;Cho, Seong-Ick;Park, Young-Je
    • Korean Journal of Remote Sensing
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    • v.30 no.4
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    • pp.455-464
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    • 2014
  • After launching the Geostationary Ocean Color Imager (GOCI) on June 2010, field campaigns were performed routinely around Korean peninsula to collect in-situ data for calibration and validation. Key measurements in the campaigns are radiometric ones with field radiometers such as Analytical Spectral Devices FieldSpec3 or TriOS RAMSES. The field radiometers must be regularly calibrated. We, in the paper, introduce the optical laboratory built in KOSC and the relative calibration method for in-situ measurement spectroradiometer. The laboratory is equipped with a 20-inch integrating sphere (USS-2000S, LabSphere) in 98% uniformity, a reference spectrometer (MCPD9800, Photal) covering wavelengths from 360 nm to 1100 nm with 1.6 nm spectral resolution, and an optical table ($3600{\times}1500{\times}800mm^3$) having a flatness of ${\pm}0.1mm$. Under constant temperature and humidity maintainance in the room, the reference spectrometer and the in-situ measurement instrument are checked with the same light source in the same distance. From the test of FieldSpec3, we figured out a slight difference among in-situ instruments in blue band range, and also confirmed the sensor spectral performance was changed about 4.41% during 1 year. These results show that the regular calibrations are needed to maintain the field measurement accuracy and thus GOCI data reliability.

The Accuracy Evaluation according to Dose Delivery Interruption and Restart for Volumetric Modulated Arc Therapy (용적변조회전 방사선치료에서 선량전달의 중단 및 재시작에 따른 정확성 평가)

  • Lee, Dong Hyung;Bae, Sun Myung;Kwak, Jung Won;Kang, Tae Young;Back, Geum Mun
    • The Journal of Korean Society for Radiation Therapy
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    • v.25 no.1
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    • pp.77-85
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    • 2013
  • Purpose: The accurate movement of gantry rotation, collimator and correct application of dose rate are very important to approach the successful performance of Volumetric Modulated Arc Therapy (VMAT), because it is tightly interlocked with a complex treatment plan. The interruption and restart of dose delivery, however, are able to occur on treatment by various factors of a treatment machine and treatment plan. If unexpected problems of a treat machine or a patient interrupt the VMAT, the movement of treatment machine for delivering the remaining dose will be restarted at the start point. In this investigation, We would like to know the effect of interruptions and restart regarding dose delivery at VMAT. Materials and Methods: Treatment plans of 10 patients who had been treated at our center were used to measure and compare the dose distribution of each VMAT after converting to a form of digital image and communications in Medicine (DICOM) with treatment planning system (Eclipse V 10.0, Varian, USA). We selected the 6 MV photon energy of Trilogy (Varian, USA) and used OmniPro I'mRT system (V 1.7b, IBA dosimetry, Germany) to analyze the data that were acquired through this measurement with two types of interruptions four times for each case. The door interlock and the beam-off were used to stop and then to restart the dose delivery of VMAT. The gamma index in OmniPro I'mRT system and T-test in Microsoft Excel 2007 were used to evaluate the result of this investigation. Results: The deviations of average gamma index in cases with door interlock, beam-off and without interruption on VMAT are 0.141, 0.128 and 0.1. The standard deviations of acquired gamma values are 0.099, 0.091, 0.071 and The maximum gamma value in each case is 0.413, 0.379, 0.286, respectively. This analysis has a 95-percent confidence level and the P-value of T-test is under 0.05. Gamma pass rate (3%, 3 mm) is acceptable in all of measurements. Conclusion: As a result, We could make sure that the interruption of this investgation are not enough to seriously affect dose delivery of VMAT by analyzing the measured data. But this investigation did not reflect all cases about interruptions and errors regarding the movement of a gantry rotation, collimator and patient So, We should continuously maintain a treatment machine and program to deliver the accurate dose when we perform the VMAT for the many kinds of cancer patients.

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Prediction of Target Motion Using Neural Network for 4-dimensional Radiation Therapy (신경회로망을 이용한 4차원 방사선치료에서의 조사 표적 움직임 예측)

  • Lee, Sang-Kyung;Kim, Yong-Nam;Park, Kyung-Ran;Jeong, Kyeong-Keun;Lee, Chang-Geol;Lee, Ik-Jae;Seong, Jin-Sil;Choi, Won-Hoon;Chung, Yoon-Sun;Park, Sung-Ho
    • Progress in Medical Physics
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    • v.20 no.3
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    • pp.132-138
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    • 2009
  • Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

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Habitat Distribution Change Prediction of Asiatic Black Bears (Ursus thibetanus) Using Maxent Modeling Approach (Maxent 모델을 이용한 반달가슴곰의 서식지 분포변화 예측)

  • Kim, Tae-Geun;Yang, DooHa;Cho, YoungHo;Song, Kyo-Hong;Oh, Jang-Geun
    • Korean Journal of Ecology and Environment
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    • v.49 no.3
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    • pp.197-207
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    • 2016
  • This study aims at providing basic data to objectively evaluate the areas suitable for reintroduction of the species of Asiatic black bear (Ursus thibetanus) in order to effectively preserve the Asiatic black bears in the Korean protection areas including national parks, and for the species restoration success. To this end, this study predicted the potential habitats in East Asia, Southeast Asia and India, where there are the records of Asiatic black bears' appearances using the Maxent model and environmental variables related with climate, topography, road and land use. In addition, this study evaluated the effects of the relevant climate and environmental variables. This study also analyzed inhabitation range area suitable for Asiatic black and geographic change according to future climate change. As for the judgment accuracy of the Maxent model widely utilized for habitat distribution research of wildlife for preservation, AUC value was calculated as 0.893 (sd=0.121). This was useful in predicting Asiatic black bears' potential habitat and evaluate the habitat change characteristics according to future climate change. Compare to the distribution map of Asiatic black bears evaluated by IUCN, Habitat suitability by the Maxent model were regionally diverse in extant areas and low in the extinct areas from IUCN map. This can be the result reflecting the regional difference in the environmental conditions where Asiatic black bears inhabit. As for the environment affecting the potential habitat distribution of Asiatic black bears, inhabitation rate was the highest, according to land coverage type, compared to climate, topography and artificial factors like distance from road. Especially, the area of deciduous broadleaf forest was predicted to be preferred, in comparison with other land coverage types. Annual mean precipitation and the precipitation during the driest period were projected to affect more than temperature's annual range, and the inhabitation possibility was higher, as distance was farther from road. The reason is that Asiatic black bears are conjectured to prefer more stable area without human's intervention, as well as prey resource. The inhabitation range was predicted to be expanded gradually to the southern part of India, China's southeast coast and adjacent inland area, and Vietnam, Laos and Malaysia in the eastern coastal areas of Southeast Asia. The following areas are forecast to be the core areas, where Asiatic black bears can inhabit in the Asian region: Jeonnam, Jeonbuk and Gangwon areas in South Korea, Kyushu, Chugoku, Shikoku, Chubu, Kanto and Tohoku's border area in Japan, and Jiangxi, Zhejiang and Fujian border area in China. This study is expected to be used as basic data for the preservation and efficient management of Asiatic black bear's habitat, artificially introduced individual bear's release area selection, and the management of collision zones with humans.

Simulation Approach for the Tracing the Marine Pollution Using Multi-Remote Sensing Data (다중 원격탐사 자료를 활용한 해양 오염 추적 모의 실험 방안에 대한 연구)

  • Kim, Keunyong;Kim, Euihyun;Choi, Jun Myoung;Shin, Jisun;Kim, Wonkook;Lee, Kwang-Jae;Son, Young Baek;Ryu, Joo-Hyung
    • Korean Journal of Remote Sensing
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    • v.36 no.2_2
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    • pp.249-261
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    • 2020
  • Coastal monitoring using multiple platforms/sensors is a very important tools for accurately understanding the changes in offshore marine environment and disaster with high temporal and spatial resolutions. However, integrated observation studies using multiple platforms and sensors are insufficient, and none of them have been evaluated for efficiency and limitation of convergence. In this study, we aimed to suggest an integrated observation method with multi-remote sensing platform and sensors, and to diagnose the utility and limitation. Integrated in situ surveys were conducted using Rhodamine WT fluorescent dye to simulate various marine disasters. In September 2019, the distribution and movement of RWT dye patches were detected using satellite (Kompsat-2/3/3A, Landsat-8 OLI, Sentinel-3 OLCI and GOCI), unmanned aircraft (Mavic 2 pro and Inspire 2), and manned aircraft platforms after injecting fluorescent dye into the waters of the South Sea-Yeosu Sea. The initial patch size of the RWT dye was 2,600 ㎡ and spread to 62,000 ㎡ about 138 minutes later. The RWT patches gradually moved southwestward from the point where they were first released,similar to the pattern of tidal current flowing southwest as the tides gradually decreased. Unmanned Aerial Vehicles (UAVs) image showed highest resolution in terms of spatial and time resolution, but the coverage area was the narrowest. In the case of satellite images, the coverage area was wide, but there were some limitations compared to other platforms in terms of operability due to the long cycle of revisiting. For Sentinel-3 OLCI and GOCI, the spectral resolution and signal-to-noise ratio (SNR) were the highest, but small fluorescent dye detection was limited in terms of spatial resolution. In the case of hyperspectral sensor mounted on manned aircraft, the spectral resolution was the highest, but this was also somewhat limited in terms of operability. From this simulation approach, multi-platform integrated observation was able to confirm that time,space and spectral resolution could be significantly improved. In the future, if this study results are linked to coastal numerical models, it will be possible to predict the transport and diffusion of contaminants, and it is expected that it can contribute to improving model accuracy by using them as input and verification data of the numerical models.

Multi-Component Relaxation Study of Human Brain Using Relaxographic Analysis (Relaxographic 분석법을 이용한 뇌의 다중 자기이완특성에 관한 연구)

  • Yongmin Chang;Bong Soo Han;Bong Seok Kang;Kyungnyeo Jeon;Kyungsoo Bae;Yong-Sun Kim;Duk-Sik Kang
    • Investigative Magnetic Resonance Imaging
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    • v.6 no.2
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    • pp.120-128
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    • 2002
  • Purpose : To demonstrate that the relaxographic method provides additional information such as the distribution of relaxation times and water content which are poentially applicable to clinical medicine. Materials and Methods : First, the computer simulation was performed with the generated relaxation data to verify the accuracy and reliabilility of the relaxographic method (CONTINI). Secondly, in or der to see how well the CONTIN quantifies and resolves the two different ${T_1}$ environments, we calculated the oil to water peak area ratios and identified peak positions of ${T_1}-distribution$ curve of the phantom solutions, which consist of four centrifugal tubes (10 ml) filled with the compounds of 0, 10, 20, 30% of corn oil and distilled water, using CONTIN. Finally, inversion recovery MR images for a volunteer are acquired for each TI ranged from 40 to 1160 msec with TR/TE=2200/20 msec. From the 3 different ROIs (GM, WM, CSF), CONTIN analysis was performed to obtain the ${T_1}$-distribution curves, which gave peak positions and peak area of each ROI location. Results : The simulation result shows that the errors of peak positions were less in the higher peak (centered ${T_1}=600$ msec) than in the lower peak (centered ${T_1}=150$ msec) for all SNR but the errors of peak areas were larger in the higher peak than in the lower peak. The CONTIN analysis of the measured relaxation data of phantoms revealed two peaks between 20 and 60 msec and between 500 and 700 msec. The analysis gives the peak area ratio as oil 10%: oil 20%: oil 30% = 1:1.3:1.9, which is different from the exact ratio, 1:2:3. For human brain, in ROI 3 (CSF), only one component of -distributions was observed whereas in ROI 1(GM) and in ROI 2 (WM) we observed two components of ${T_1}-distribution$. For the WM and CSF there was great agreement between the observed ${T_1}-relaxation$ times and the reported values. Conclusion : we demonstrated that the relaxographic method provided additional information such as the distribution of relaxation times and water content, which were not available in the routine relaxometry and ${T_1}/{T_2}$ mapping techniques. In addition, these additional information provided by relaxographic analysis may have clinical importance.

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Net Primary Production Changes over Korea and Climate Factors (위성영상으로 분석한 장기간 남한지역 순 일차생산량 변화: 기후인자의 영향)

  • Hong, Ji-Youn;Shim, Chang-Sub;Lee, Moung-Jin;Baek, Gyoung-Hye;Song, Won-Kyong;Jeon, Seong-Woo;Park, Yong-Ha
    • Korean Journal of Remote Sensing
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    • v.27 no.4
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    • pp.467-480
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    • 2011
  • Spatial and temporal variabilities of NPP(Net Primary Production) retrieved from two satellite instruments, AVHRR(Advanced Very High Resolution Radiometer, 1981-2000) and MODIS(MODerate-resolution Imaging Spectroradiometer, 2000-2006), were investigated. The range of mean NPP from A VHRR and MODIS were estimated to be 894-1068 $g{\cdot}C/m^2$/yr and 610-694.90 $g{\cdot}C/m^2$/yr, respectively. The discrepancy of NPP between the two instruments is about 325 $g{\cdot}C/m^2$/yr, and MODIS product is generally closer to the ground measurement than AVHRR despite the limitation in direct comparison such as spatial resolution and vegetation classification. The higher NPP values over South Korea are related to the regions with higher biomass (e.g., mountains) and higher annual temperature. The interannual NPP trends from the two satellite products were computed, and both mean annual trends show continuous NPP increase; 2.14 $g{\cdot}C/m^2$/yr from AVHRR(1981-2000) and 6.08 $g{\cdot}C/m^2$/yr from MODIS (2000-2006) over South Korea. Specifically, the higher increasing trends over the Southwestern region are likely due to the increasing productivity of crop fields from sufficient irrigation and fertilizer use. The retrieved NPP shows a closer relationship between monthly temperature and precipitation, which results in maximum correlation during summer monsoons. The difference in the detection wavelength and model schemes during the retrieval can make a significant difference in the satellite products, and a better accuracy in the meterological and land use data and modeling applications will be necessary to improve the satellite-based NPP data.