• Food Science of Animal Resources
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• v.38 no.5
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• pp.1109-1119
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• 2018

In this paper, we report the development of a nondestructive prediction model for lean meat percentage (LMP) in Korean pig carcasses and in the major cuts using a machine vision technique. A popular vision system in the meat industry, the VCS2000 was installed in a modern Korean slaughterhouse, and the images of half carcasses were captured using three cameras from 175 selected pork carcasses (86 castrated males and 89 females). The imaged carcasses were divided into calibration (n=135) and validation (n=39) sets and a multilinear regression (MLR) analysis was utilized to develop the prediction equation from the calibration set. The efficiency of the prediction equation was then evaluated by an independent validation set. We found that the prediction equation - developed to estimate LMP in whole carcasses based on six variables - was characterized by a coefficient of determination ($R^2_v$) value of 0.77 (root-mean square error [RMSEV] of 2.12%). In addition, the predicted LMP values for the major cuts: ham, belly, and shoulder exhibited $R^2_v$ values${\geq}0.8$ (0.73 for loin parts) with low RMSEV values. However, lower accuracy ($R^2_v=0.67$) was achieved for tenderloin cuts. These results indicate that the LMP in Korean pig carcasses and major cuts can be predicted successfully using the VCS2000-based prediction equation developed here. The ultimate advantages of this technique are compatibility and speed, as the VCS2000 imaging system can be installed in any slaughterhouse with minor modifications to facilitate the on-line and real-time prediction of LMP in pig carcasses.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.535-544
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• 2017

UAVs (Unmanned Aerial Vehicle) are generally classified into fixed-wing and rotary-wing type, and both have very different flight characteristics each other during photographing. These can greatly effect on the quality of images and their productions. In this paper, the change of the camera rotation angle at the moment of photographing was compared and analyzed by calculating orientation angles of each image taken by both types of payload. Study materials were acquired at an altitude of 130m and 260m with fixed-wing, and at an altitude of 130m with rotary-wing UAV over an agricultural land. In addition, an accuracy comparison of boundary surveying methods between UAV photogrammetry and terrestrial cadastral surveying was conducted in two parcels of the study area. The study results are summarized as follows. The differences at rotation angles of images acquired with between two types of UAVs at the same flight height of 130m were significantly very large. On the other hand, the distance errors of parcel boundary surveying were not significant between them, but almost the same, about within ${\pm}0.075m$ in RMSE (Root Mean Square Error). The accuracy of boundary surveying with a fixed-wing UAV at 260m altitude was quite variable, $0.099{\sim}0.136m$ in RMSE. In addition, the error of area extracted from UAV-orthoimages was less than 0.2% compared with the results of the cadastral survey in the same two parcels used for the boundary surveying, In conclusion, UAV photogrammetry can be highly utilized in the field of cadastral surveying.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.395-401
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• 2018

Recently, most of mobile devices are equipped with GNSS (Global Navigation Satellite System). When the GNSS signal is available, it is easy to obtain position information. However, GNSS is not suitable solution for indoor localization, since the signals are normally not reachable inside buildings. A wide varieties of technology have been developed as a solution for indoor localization such as Wi-Fi, beacons, and inertial sensor. With the increased sensor combinations in mobile devices, mobile devices also became feasible to provide a solution, which based on PDR (Pedestrian Dead Reckoning) method. In this study, we utilized the combination of three sensors equipped in mobile devices including accelerometer, digital compass, and gyroscope and applied three representative PDR methods. The proposed methods are done in three stages; step detection, step length estimation, and heading determination and the final indoor localization result was evaluated with terrestrial LiDAR (Light Detection And Ranging) data obtained in the same test site. By using terrestrial LiDAR data as reference ground truth for PDR in two differently designed experiments, the inaccuracy of PDR methods that could not be found by existing evaluation method could be revealed. The firstexperiment included extreme direction change and combined with similar pace size. Second experiment included smooth direction change and irregular step length. In using existing evaluation method which only checks traveled distance, The results of two experiments showed the mean percentage error of traveled distance estimation resulted from three different algorithms ranging from 0.028 % to 2.825% in the first experiment and 0.035% to 2.282% in second experiment, which makes it to be seen accurately estimated. However, by using the evaluation method utilizing terrestrial LiDAR data, the performance of PDR methods emerged to be inaccurate. In the firstexperiment, the RMSEs (Root Mean Square Errors) of x direction and y direction were 0.48 m and 0.41 m with combination of the best available algorithm. However, the RMSEs of x direction and y direction were 1.29 m and 3.13 m in the second experiment. The new evaluation result reveals that the PDR methods were not effective enough to find out exact pedestrian position information opposed to the result from existing evaluation method.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.638-642
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• 2005

For gas hydrate exploration, long offset multichannel seismic data acquired using by the 4km streamer length in Ulleung basin of the East Sea. The dataset was processed to define the BSRs (Bottom Simulating Reflectors) and to estimate the amount of gas hydrates. Confirmation of the presence of Bottom Simulating reflectors (BSR) and investigation of its physical properties from seismic section are important for gas hydrate detection. Specially, faster interval velocity overlying slower interval velocity indicates the likely presences of gas hydrate above BSR and free gas underneath BSR. In consequence, estimation of correct interval velocities and analysis of their spatial variations are critical processes for gas hydrate detection using seismic reflection data. Using Dix's equation, Root Mean Square (RMS) velocities can be converted into interval velocities. However, it is not a proper way to investigate interval velocities above and below BSR considering the fact that RMS velocities have poor resolution and correctness and the assumption that interval velocities increase along the depth. Therefore, we incorporated Migration Velocity Analysis (MVA) software produced by Landmark CO. to estimate correct interval velocities in detail. MVA is a process to yield velocities of sediments between layers using Common Mid Point (CMP) gathered seismic data. The CMP gathered data for MVA should be produced after basic processing steps to enhance the signal to noise ratio of the first reflections. Prestack depth migrated section is produced using interval velocities and interval velocities are key parameters governing qualities of prestack depth migration section. Correctness of interval velocities can be examined by the presence of Residual Move Out (RMO) on CMP gathered data. If there is no RMO, peaks of primary reflection events are flat in horizontal direction for all offsets of Common Reflection Point (CRP) gathers and it proves that prestack depth migration is done with correct velocity field. Used method in this study, Tomographic inversion needs two initial input data. One is the dataset obtained from the results of preprocessing by removing multiples and noise and stacked partially. The other is the depth domain velocity model build by smoothing and editing the interval velocity converted from RMS velocity. After the three times iteration of tomography inversion, Optimum interval velocity field can be fixed. The conclusion of this study as follow, the final Interval velocity around the BSR decreased to 1400 m/s from 2500 m/s abruptly. BSR is showed about 200m depth under the seabottom

• Journal of radiological science and technology
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• v.41 no.3
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• pp.201-207
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• 2018

The purpose of this study is to evaluate the performance of a "stealth chamber" as a novel reference chamber for measuring percentage depth dose (PDD) and profile of 6, 8 and 10 MV photon energies. The PDD curves and dose profiles with fields ranging from $3{\times}3$ to $25{\times}25cm^2$ were acquired from measurements by using the stealth chamber and CC 13 chamber as reference chamber. All measurements were performed with Varian VitalBeam linear accelerator. In order to assess the performance of stealth chamber, PDD curves and profiles measured with stealth chamber were compared with measurement data using CC13 chamber. For PPDs measured with both chambers, the dosimetric parameters such as $d_{max}$ (depth of maximum dose), $D_{50}$ (PDD at 50 mm depth), and $D_{100}$ (PDD at 100 mm depth) were analyzed. Moreover, root mean square error (RMSE) values for profiles at $d_{max}$ and 100 mm depth were evaluated. The measured PDDs and profiles between the stealth chamber and CC13 chamber as reference detector had almost comparable. For PDDs, the evaluated dosimetric parameters were observed small difference (<1%) for all energies and field sizes, except for $d_{max}$ less than 2 mm. In addition, the difference of RMSEs for profiles at $d_{max}$ and 100 mm depth was similar for both chambers. This study confirmed that the use of stealth chamber for measuring commission beam data is a feasible as reference chamber for fields ranging from $3{\times}3$ to $20{\times}20cm^2$. Furthermore, it has an advantage with respect to measurement of the small fields (less than $3{\times}3cm^2$ field) although not performed in this study.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.2
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• pp.115-126
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• 2017

There is an increasing concern over arsenic (As) contamination in rice. This study was conducted to develope a prediction model for As uptake by rice based on the physico-chemical properties of soil. Soil and brown rice samples were collected from 46 sites in paddy fields near three different areas of closed mines and industrial complexes. Total As concentration, soil pH, Al oxide, available phosphorus (avail-P), organic matter (OM) content, and clay content in the soil samples were determined. Also, 1.0 N HCl, 1.0 M $NH_4NO_3$, 0.01 M $Ca(NO_3)_2$, and Mehlich 3 extractable-As in the soils were measured as phytoavailable As concentration in soil. Total As concentration in brown rice samples was also determined. Relationships among As concentrations in brown rice, total As concentrations in soils, and selected soil properties were as follows: As concentration in brown rice was negatively correlated with soil pH value, where as it was positively correlated with Al oxide concentration, avail-P concentration, and OM content in soil. In addition, the concentration of As in brown rice was statistically correlated only with 1.0 N HCl-extractable As in soil. Also, using multiple stepwise regression analysis, a modelling equation was created to predict As concentration in brown rice as affected by selected soil properties including soil As concentration. Prediction of As uptake by rice was delineated by the model [As in brown rice = 0.352 + $0.00109^*$ HCl extractable As in soil + $0.00002^*$ Al oxide + $0.0097^*$ OM + $0.00061^*$ avail-P - $0.0332^*$ soil pH] ($R=0.714^{***}$). The concentrations of As in brown rice estimated by the modelling equation were statistically acceptable because normalized mean error (NME) and normalized root mean square error (NRMSE) values were -0.055 and 0.2229, respectively, when compared with measured As concentration in the plant.

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.71-79
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• 2005

Existing arterial link travel time estimation methods relying on either aggregate point-based or individual section-based traffic data have their inherent limitations. This paper demonstrates the utility of data fusion for improving arterial link travel time estimation. If the data describe traffic conditions, an operator wants to know whether the situations are going better or worse. In addition, some traffic information providing strategies require predictions of what would be the values of traffic variables during the next time period. In such situations, it is necessary to use a prediction algorithm in order to extract the average trends in traffic data or make short-term predictions of the control variables. In this research. a multi-step ahead prediction algorithm using Data fusion was developed to predict a link travel time. The algorithm performance were tested in terms of performance measures such as MAE (Mean Absolute Error), MARE(mean absolute relative error), RMSE (Root Mean Square Error), EC(equality coefficient). The performance of the proposed algorithm was superior to the current one-step ahead prediction algorithm.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.547-565
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• 2011

Different types of schemes have been used in stage prediction involving conceptual and physical models. Nevertheless, none of these schemes can be considered as a single superior model. To overcome disadvantages of existing physics based rainfall-runoff models for stage predicting because of the complexity of the hydrological process, recently the data-derived models has been widely adopted for predicting flood stage. The objective of this study is to evaluate model performance for stage prediction of the Neuro-Fuzzy and regression analysis stage prediction models in these data-derived methods. The proposed models are applied to the Wangsukcheon in Han river watershed. To evaluate the performance of the proposed models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient(NSEC), mean absolute error(MAE), adjusted coefficient of determination($R^{*2}$). The results show that the Neuro-Fuzzy stage prediction model can carry out the river flood stage prediction more accurately than the regression analysis stage prediction model. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1698-1702
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• 2009

효과적인 저수지 수질관리를 위해서는 신뢰도 높은 수리 및 수질모델이 필요하며, 이러한 모델의 성능은 다양한 수문사상에 대하여 적용함으로써 검증할 수 있다. CE-QUAL-W2 모델(이후 W2)은 횡방향 평균 2차원 수리 수질 해석 모델로써 수체의 길이에 비해 폭이 상대적으로 좁고 수심이 깊은 우리나라 대부분의 저수지 지형에 적합한 모델이다. 본 연구의 목적은 기존 연구에서 가뭄년인 2001년과 평수년인 2004년 수문사상에 대하여 보정한 대청호 W2 부영양화모델을 최근 평수년인 2006년과 가뭄년인 2008년을 대상으로 검증하는데 있다. 모델의 검증은 물수지, 수온성층 구조 변화, 부영양화 해석에 중점을 두었으며, 실측자료와 모의결과의 적합성 비교 평가는 결정계수값$(R^2)$, AME(absolute mean error)와 RMSE(root mean square error)를 이용하였다. 저수지 물수지의 적합성을 검증하기 위하여 모의수위와 실측수위를 비교한 결과, $R^2$값이 2006년과 2008년에 각각 0.9945, 0.9972로 나타나 신뢰도가 높은 것으로 확인되었다. 계절별 성층구조 변화 모의 성능을 검증하기 위해 회남수역과 댐 앞 지점에서 수심별 수온의 모의값과 실측값을 비교하였다. 2006년의 경우 모델은 홍수기 동안 안정적으로 수온 성층현상을 모의하였으나, 댐 앞 지점에서 수온 약층이 형성된 구간에서 실측값과 다소 편차를 보였으며, 오차크기는 AME가 $0.561\sim2.088^{\circ}C$, RMSE는 $0.797\sim2.762^{\circ}C$범위였다. 반면, 가뭄년인 2008년에는 전 기간에 걸쳐 모두 안정적으로 저수지 수온 성층현상을 모의하였으며, 오차크기는 AME $0.413\sim1.162^{\circ}C$, RMSE $0.546\sim1.415^{\circ}C$ 범위였다. 조류의 생산성이 높은 표층에서 T-N, T-P 및 Chl-a 농도 모의결과를 장계교, 대정리, 회남대교, 댐 앞, 추동취수탑 및 문의취수탑에서 시계열로 실측값과 비교 검증한 결과, T-N과 T-P는 2006년과 2008년 모두 모든 비교 지점에서 모의값과 실측값의 시계열 변동이 매우 잘 일치하였으며, 홍수기 이외 기간에는 큰 변동 폭을 보이지 않았다. 그러나 기존 연구에서 확인된 바와 같이 7월 이후부터 T-P 모의값이 실측값을 과대 산정하는 경향을 보였는데, 그 원인은 산소 결핍상태에서 저니층에서 용출되는 철(Fe) 또는 망간(Mn)과 같은 이온 성분이 인과 흡착하여 침전되는 기작이 모의과정에 적절히 반영되지 않은 것이 원인으로 판단된다. 조류(Chl-a)농도의 경우, 2006년과 2008년에 모든 지점에서 모델은 조류의 발생과 시계열 변화를 적절히 모의하였으나, 2008년 1월부터 8월까지 댐 앞과 추동 및 문의취수탑에서는 모의값이 실측값을 과대 산정하는 경향을 보였다. 이는 해마다 그리고 계절별로 우점하는 조류 종이 상이한 반면, 모델에서는 이에 대한 매개변수를 적절히 고려하지 못한 것이 원인으로 판단된다.