• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.253-259
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• 2004

The sheetwise cadastral map data needs to become a Serial Cadastral Map (SCM) database for the promotion of the reliability of cadastral surveying, for the efficient operation of the Parcel Based Land Information System, and for the convenient use of land information as well. A large amount of money and time are required for the editing process of producing SCM DB in accordance with the $\ulcorner$Guideline for the Production of Serial Cadastral Maps$\lrcorner$ by the Ministry of Construction & Transportation if any of field surveying techniques is accompanied by. In addition, a boundary line that extends to a neat line does not meet the counterpart of the neighboring map sheet at a point. Such cases frequently occur and are much dependent upon the decisions of individuals in charge of editing or inspecting. The core processes of the research, firstly overlay SCM produced by the edition of the sheetwise cadastral maps with Autodesk Map on orthophoto images, secondly adjust the parcel boundaries which are delineated over more than one map sheet, and lastly compare the original boundary coordinates and areas with the corresponding adjusted ones and calculate root mean square errors (RMSEs). The research aims at promoting the quality of SCM by minimizing the inconsistency of parcel boundaries by means of the comparative analysis of the calculated RMSEs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.525-534
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• 2016

In South Korea, paddy rice has been consumed over the entire region and it is the main source of income for farmers, thus mathematical model for the estimation of rice yield is required for such decision-making processes in agriculture. The objectives of our study are to: (1) develop rice yield estimation model using Convolutional Neural Networks(CNN), (2) choose hyper-parameters for the model which show the best performance and (3) investigate whether CNN model can effectively predict the rice yield by the comparison with the model using Artificial Neural Networks(ANN). Weather and MODIS(The MOderate Resolution Imaging Spectroradiometer) products from April to September in year 2000~2013 were used for the rice yield estimation models and cross-validation was implemented for the accuracy assessment. The CNN and ANN models showed Root Mean Square Error(RMSE) of 36.10kg/10a, 48.61kg/10a based on rice points, respectively and 31.30kg/10a, 39.31kg/10a based on 'Si-Gun-Gu' districts, respectively. The CNN models outperformed ANN models and its possibility of application for the field of rice yield estimation in South Korea was proved.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.295-303
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• 2018

The stereo camera system has a fixed baseline and therefore has a constant scale. However, it is difficult to measure the actual three-dimensional coordinate since the scale is not fixed when relative orientation parameters are determined through the key-point matching in the stereo image each time. Therefore, the purpose of this study was to perform the stereo camera calibration that simultaneously determines the internal characteristics of the left and right cameras and the camera relationship between them using the modified collinearity equation and compared it with the two independent single cameras calibration. In the experiment using the images taken at close range, the RMSE (Root Mean Square Error) of ${\pm}0.014m$ was occurred when the three dimensional distances were compared in the single calibration results. On the other hand, the accuracy of the three-dimensional distance of the stereo camera calibration was better because the stereo camera results were almost no error compared to the results from two single cameras. In the comparison of the epipolar images, the RMSE of the stereo camera was 0.3 pixel more than that of the two single cameras, but the effect was not significant.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.223-233
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• 2018

In this study was classified the cadastral control points and parcel boundary points into 40m, 100m by flight altitude of UAV (Unmanned Aerial Vehicle) which compared the coordinates extracted from the orthophoto with the parcel boundary point coordinates by GNSS (Global Navigation Satellite System) ground survey. As a results of this study, first, in the spatial resolution analysis that the average error of the orthoimage by flight altitude were 0.024m at 40m, and 0.034m at 100m which were higher 40m than 100m for spatial resolution of orthophotos and position accuracy. Second, in order to analyze the accuracy of image recognition by airmark of flight altitude that was divided into three cases of nothing, green, and red of RMSE (Root Mean Square Error) were X=0.039m, Y=0.019m and Z=0.055m, the highest accuracy. Third, the result of the comparison between orthophotos and field survey results that showed the total RMSE error of the cadastral control points were X=0.029m, Y=0.028m, H=0.051m, and the parcel boundary points were X=0.041m, Y=0.030m. In conclusion, based on the results of this study, it is expected that if the average error of flight altitude is limited to less than 0.05m in the legal regulations related to orthophotos for cadastral surveying, it will be an economical and efficient method for cadastral survey as well as spatial information acquisition.

• Journal of the Korean earth science society
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• v.29 no.3
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• pp.280-289
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• 2008

An extensive set of both in-situ and satellite data regarding oceanic sea surface temperatures in Northeast Asian seas, collected over a 10-year period, was collocated and surveyed to assess the accuracy of satellite-observed sea surface temperatures (SST) and investigate the characteristics of satellite measured SST errors. This was done by subtracting insitu SST measurements from multi-channel SST (MCSST) measurements. 845 pieces of collocated data revealed that MCSST measurements had a root-mean-square error of about 0.89$^{\circ}C$ and a bias error of about 0.18$^{\circ}C$. The SST errors revealed a large latitudinal dependency with a range of $\pm3^{\circ}C$ around 40$^{\circ}N$, which was related to high spatial and temporal variability from smaller eddies, oceanic currents, and thermal fronts at higher latitudes. The MCSST measurements tended to be underestimated in winter and overestimated in summer when compared to in-situ measurements. This seasonal dependency was discovered from shipboard and moored buoy measurements, not satellite-tracked surface drifters, and revealed the existence of a strong vertical temperature gradient within a few meters of the upper ocean. This study emphasizes the need for an effort to consider and correct the significant skin-bulk SST difference which arises when calculating SST from satellite data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.204-208
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• 2018

In this paper, we proposed and evaluated the time series deep learning prediction model for learning fluctuation pattern of stock price. Recurrent neural networks, which can store previous information in the hidden layer, are suitable for the stock price prediction model, which is time series data. In order to maintain the long - term dependency by solving the gradient vanish problem in the recurrent neural network, we use LSTM with small memory inside the recurrent neural network. Furthermore, we proposed the stock price prediction model using bidirectional LSTM recurrent neural network in which the hidden layer is added in the reverse direction of the data flow for solving the limitation of the tendency of learning only based on the immediately preceding pattern of the recurrent neural network. In this experiment, we used the Tensorflow to learn the proposed stock price prediction model with stock price and trading volume input. In order to evaluate the performance of the stock price prediction, the mean square root error between the real stock price and the predicted stock price was obtained. As a result, the stock price prediction model using bidirectional LSTM recurrent neural network has improved prediction accuracy compared with unidirectional LSTM recurrent neural network.

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.357-364
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• 2016

Purpose: This study was performed to define the drying characteristics of sorghum by developing thin layer drying equations and evaluating various grain drying equations. Thin layer drying equations lay the foundation characteristics to establish the thick layer drying equations, which can be adopted to determine the design conditions for an agricultural dryer. Methods: The drying rate of sorghum was measured under three levels of drying temperature ($40^{\circ}C$, $50^{\circ}C$, and $60^{\circ}C$) and relative humidity (30%, 40%, and 50%) to analyze the drying process and investigate the drying conditions. The drying experiment was performed until the weight of sorghum became constant. The experimental constants of four thin layer drying models were determined by developing a non-linear regression model along with the drying experiment results. Result: The half response time (moisture ratio = 0.5) of drying, which is an index of the drying rate, was increased as the drying temperature was high and relative humidity was low. When the drying temperature was $40^{\circ}C$ at a relative humidity (RH) of 50%, the maximum half response time of drying was 2.8 h. Contrastingly, the maximum half response time of drying was 1.2 h when the drying temperature was $60^{\circ}C$ at 30% RH. The coefficient of determination for the Lewis model, simplified diffusion model, Page model, and Thompson model was respectively 0.9976, 0.9977, 0.9340, and 0.9783. The Lewis model and the simplified diffusion model satisfied the drying conditions by showing the average coefficient of determination of the experimental constants and predicted values of the model as 0.9976 and Root Mean Square Error (RMSE) of 0.0236. Conclusion: The simplified diffusion model was the most suitable for every drying condition of drying temperature and relative humidity, and the model for the thin layer drying is expected to be useful to develop the thick layer drying model.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.5
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• pp.135-147
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• 2018

The objective of this study was to analyze the characteristics of combined 1D/2D inundation simulation of riverside farmland using the Hydrologic Engineering Center - River Analysis System (HEC-RAS). We compared and analyzed inundation simulation results between 1D and combined 1D/2D hydraulic simulation using HEC-RAS. Calibration and validation of stream stage were performed using three rainfall events. The coefficient of determination ($R^2$) and root mean square error (RMSE) between simulated and observed stream stage were 0.935 - 0.957 and 0.250 m - 0.283 m in calibration and validation, respectively. The inundation area showed no significant difference in 1D and combined 1D/2D simulation ($8.48km^2$ in 1D simulation, $8.75km^2$ in combined 1D/2D simulation). The average inundation depth by 1D simulation was 1.4 m deeper than combined 1D/2D simulation. In the lower inundation depth, the inundation area by combined 1D/2D simulation was larger than inundation area by 1D simulation. As the inundation depth increased, the inundation area by 1D simulation became wider. In the case of the 1D/2D combined simulation, low elevation areas along the river bank were inundated widely. Compared to 1D/2D combined simulation, the flood radius in some sections was longer in 1D simulation. In the 1D analysis, because the low altitude riverside farmlands are also assumed to stream, it is calculated that riverside farmlands have the same stage as the mainstream when the stream is overflowed. Therefore, the inundation area seems to be overestimated in those sections. In other regions, the inundation areas tend to be broken depending on overflow by each stream cross-section. In the case of river flooding, the overflow is expected to flow to the lower area depending on the terrain, such as the results of the combined 1D/2D simulation. It is concluded that the results of combined 1D/2D inundation simulation reflected the topographical characteristics of low-lying farmland.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.275-285
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• 2017

National Meteorological Satellite Center(NMSC) has produced Sea Surface Temperature (SST) using Communication, Ocean, and Meteorological Satellite(COMS) data since April 2011. In this study, we have developed a new regional COMS SST algorithm optimized within the North-West Pacific Ocean area based on the Multi-Channel SST(MCSST) method and made a composite SST using polar orbit satellites as well as the COMS data. In order to retrieve the optimized SST at Northwest Pacific, we carried out a colocation process of COMS and in-situ buoy data to make coefficients of the MCSST algorithm through the new cloud masking including contaminant pixels and quality control processes of buoy data. And then, we have estimated the composite SST through the optimal interpolation method developed by National Institute of Meteorological Science(NIMS). We used four satellites SST data including COMS, NOAA-18/19(National Oceanic and Atmospheric Administration-18/19), and GCOM-W1(Global Change Observation Mission-Water 1). As a result, the root mean square error ofthe composite SST for the period of July 2012 to June 2013 was $0.95^{\circ}C$ in comparison with in-situ buoy data.

• Korean Journal of Applied Biomechanics
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• v.26 no.4
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• pp.397-405
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• 2016

Objective: Pilates is a low/mid-intensity exercise that can be easily performed by elderly individuals as it is an individual body-oriented exercise. It is also a cardio workout that can be performed anywhere to develop strength and flexibility. Therefore, we investigated the effects of 8 week Pilates program on the balancing ability of elderly individuals. Method: The research participants were selected from elderly residents in B city. Ten individuals voluntarily signed an agreement to undergo free measurements as well as to participate in the workout program. (Height: $157.1{\pm}11.9cm$, Weight: $61.7{\pm}8.0kg$). The Pilates exercise was performed 60 minutes a day, three times a week for a total of eight weeks. The measurement variables used to test balance were the vestibular test, 5 m habitual and maximum walk test and 3 m tandem walk test. A series of paired t-test were conducted using IBM SPSS Statistics 23.0 to analyze all the research data collected in order to determine the balance ability of the participants before and after the Pilates program. Additionally, the statistically significant level for all analysis was set to ${\alpha}=.05$. Results: In the vestibular test, some meaningful changes were observed in the length envelope area (ENV) while standing on one foot, but there were no significant differences in the ENV, rectangle(REC), root mean square, and total length. Results also revealed that statistically significant differences existed in the 5 m habitual and maximum walk test, as well as the 3 m tandem walk test. Conclusion: To summarize the findings, the 8 week Pilates program employed in this study significantly improved the dynamic balance of the elderly participants. Thus, elderly individuals that frequent perform Pilates are expected to enjoy positive benefits such as increased balance and fewer falling accidents.