• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.4 no.1
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• pp.37-42
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• 1986

This paper deals with comparison of simultaneous geodetic networks between with geographical coordinates and with plane coordinates. The adjustment computation is performed by variation of coordinates. Provisional values for observation equations are computed by extended Guass mid-latitude formula using, official coordinates ($\varphi,\lambda$) in geographical network abjustment, measurements are reduced to plane by origin scale factor (＝1.0000) Bessel ellipsoid and unit weight are adopted, and geographical coordinates are projected by Guass conformal double projection. The processing results of a test-network by distances yield the average root mean square error of position 6ㆍ2cm for adjustment with $\varphi,\lambda$ and 5.8cm for adjustment with X, Y. RMSE of discrepancy between two methods is 1.7cm. This result conform to required accracy.

• Korean Journal of Remote Sensing
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• v.19 no.5
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• pp.341-349
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• 2003

The so-called B-method, a simplified surface energy budget, permits calculation of daily actual evapotranspiration (ET) using remotely sensed data, such as NOAA-AVHRR. Even if the use of satellite data allows estimation of the albedo and surface temperature, this model requires meteorological data measured at ground-level to obtain the other inputs. In addition, a difficulty may be occurred by the difference of temporal scales between the net radiation in daily scale and instantaneous measurement at midday of the surface and air temperatures because the data covered whole day are necessary to obtain accumulated daily net radiation. In order to solve these problems, this study attempted a modification of B-method through an extension of hourly ET value calculated using a complete instantaneous inputs. The estimation of the daily apparent ET from newly proposed system showed a root mean square error of 0.26 mm/day as compared the output obtained from the classical model. It is evident that this may offer more rapid estimation and reduced data volume.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.194-194
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• 2017

Flooding is one of the most serious and frequently occurred natural disaster at many regions around the world. Especially, under the climate change impact, it is more and more increasingly trend. To reduce the flood damage, flood forecast and its accuracy analysis are required. This study is conducted to analyze the accuracy of the real-time flood forecasting of a coupled meteo-hydrological model for the Han River basin, South Korea. The LDAPS (Local Data Assimilation and Prediction System) products with the spatial resolution of 1.5km and lead time of 36 hours are extracted and used as inputs for the SURR (Sejong University Rainfall-Runoff) model. Three statistical criteria consisting of CC (Corelation Coefficient), RMSE (Root Mean Square Error) and ME (Model Efficiency) are used to evaluate the performance of this couple. The results are expected that the accuracy of the flood forecasting reduces following the increase of lead time corresponding to the accuracy reduction of LDAPS rainfall. Further study is planed to improve the accuracy of the real-time flood forecasting.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.341-341
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• 2019

물 순환과정의 구성요소 중 증발산(Evapotranspiration)은 수자원개발을 위한 계획의 수립과 수자원 시스템 운영적 측면에서 대단히 중요한 부분이다. 증발산량을 산정하기 위해서는 온도, 바람, 상대습도, 대기압, 수질 및 수표면의 성질과 형상 등을 산정하여야 하는데 이러한 기상자료들을 확보하기란 매우 어려운 실정이다. 본 연구에서는 기온자료만을 이용하여 기준증발산량을 산정할 수 있는 Hargreaves 공식의 경험적 매개변수 및 온도 매개변수를 수정하여 경안천유역의 기준증발산량을 산정하였다. 수정된 공식의 성능평가를 위해 현재 널리 사용되고 있는 Penman-Monteith 방법을 이용하여 산정된 기준증발산량을 정해로 가정하여 Root Mean Square Error와 Nash Sutcliffe Model Efficiency Coefficient분석을 수행하여 검증하였다. 또한 기온 및 Hargreaves 경험적 매개변수와의 상관관계를 이용한 회귀식에 대한 검증을 수행함으로써 본 연구에서 제안한 수정된 공식의 적용가능성을 확인하였으며, 향후 수자원 시스템 운영 측면에 도움이 될 것으로 판단된다.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• Computers and Concrete
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• v.30 no.1
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• pp.33-42
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• 2022

Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a time-consuming and laborious process. The present study aims to propose surrogate models based on Support Vector Machine (SVM) and Gaussian Process Regression (GPR) machine learning techniques, which can predict the CS of concrete containing nano-silica. Content of cement, aggregates, nano-silica and its fineness, water-binder ratio, and the days at which strength has to be predicted are the input variables. The efficiency of the models is compared in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE), Variance Account For (VAF), Nash-Sutcliffe Efficiency (NSE), and RMSE to observation's standard deviation ratio (RSR). It has been observed that the SVM outperforms GPR in predicting the CS of the concrete containing nano-silica.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.241-247
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• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.129-133
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• 2023

Image compression is a fundamental technique in the field of digital image processing, which will help to decrease the storage space and to transmit the files efficiently. Recently many deep learning techniques have been proposed to promise results on image compression field. Since many image compression techniques have artifact problems, this paper has compared two deep learning approaches to verify their performance experimentally to solve the problems. One of the approaches is a deep autoencoder technique, and another is a deep convolutional neural network (CNN). For those results in the performance of peak signal-to-noise and root mean square error, this paper shows that deep autoencoder method has more advantages than deep CNN approach.

• Journal of the Korean Society of Safety
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• v.38 no.4
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• pp.1-7
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• 2023

The steel wheels of urban railway vehicles gather a lot of data through regular measurements during maintenance. However, limited research has been carried out utilizing this data, resulting in difficulties predicting the maintenance period. This paper studied a machine learning model suitable for mileage prediction by studying the characteristics of mileage change according to diameter and flange thickness changes. The results of this study indicate that the larger the diameter, the longer the travel distance, and the longest flange thickness is at 30 mm, which gradually shortened at other times. As a result of research on the machine learning prediction model, it was confirmed that the random forest model is the optimal model with a high coefficient of determination and a low root mean square error.