• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.47-55
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• 2022

Recently, researches on the prediction of sea water temperature using artificial intelligence models has been actively conducted in Korea. However, most researches in the sea around the Korean peninsula mainly focus on predicting sea surface temperatures. Unlike previous researches, this research predicted the vertical sea water temperature profile of the East Sea, which is very important in submarine operations and anti-submarine warfare, using XBT(eXpendable Bathythermograph) data and machine learning models(RandomForest, XGBoost, LightGBM). The model was trained using XBT data measured from sea surface to depth of 200m in a specific area of the East Sea, and the prediction accuracy was evaluated through MAE(Mean Absolute Error) and vertical sea water temperature profile graphs.

• Annual Conference of KIPS
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• 2000.10b
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• pp.931-934
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• 2000

움직임 추정 및 보상을 위한 방법 중 가장 많이 사용하는 블록 정합 방법은 어떤 평가 함수와 탐색방법(Search Procedure)을 사용했느냐에 따라 그 성능이 달라지게 된다. 본 논문에서는 평가 함수로써 평균 제곱 오차(Mean Squared Error; MSE), 평균 절대값 오차(Mean Absolute Error; MAE), 화소 차분류(Pel Difference Classification: PDC)을, 탐색 방법으로써 전체 탐색 방법(Full Search Method : FSM), 3단계 탐색 방법(Three Step Search : TSS), 대각 탐색 방법(Cross Search Algorithm ;CSA)을 사용하여 이들의 성능을 각각 비교 분석하여 봄으로써 블록 정합 방법을 이용한 움직임 추정에 대한 전반적인 이해를 도모하고자 한다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.145-153
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• 2015

This study aims to accurately estimate population distribution more specifically than administrative unites using a RK (Regression-Kriging) model. The RK model is the areal interpolation technique that involves linear regression and the Kriging model. In order to estimate a population’s distribution using a sample region, four different models were used, namely; a regression model, RK model, OK (Ordinary Kriging) model and CK (Co-Kriging) model. The results were then compared with each other. Evaluation of the accuracy and validity of evaluation analysis results were the basis RMSE (Root Mean Square Error), MAE (Mean Absolute Error), G statistic and correlation coefficient (ρ). In the sample regions, every statistic value of the RK model showed better results than other models. The results of this comparative study will be useful to estimate a population distribution of the metropolitan areas with high population density

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.141-148
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• 2023

During the medical data transmissions, the protection of the patient information is vital. Hence this work proposes a spatial domain watermarking algorithm that enhances the data payload (capacity) while maintaining the authentication and data hiding. The code is distributed at every pixel of the digital image and not only in the regions of non-interest pixels. But the image details are still preserved. The performance of the proposed algorithm is evaluated using several performance measures such as the mean square error (MSE), the mean absolute error (MAE), and the peak signal to noise Ratio (PSNR), the universal image quality index (UIQI) and the structural similarity index (SSIM).

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.236-241
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• 2024

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.125-134
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• 2010

It is occurred bus arrival time errors when a bus arrives at a bus stop because of a variety of traffic condition such as traffic signal cycle, the time to get on and off a bus, a bus-only lane and so on. In this paper, bus delay time which is occurred as the result of traffic condition was estimated with Markov Chain process and bus arrival time at each bus stop was predicted with it. As the result of the study, it is confirmed to improve accuracy than the method of bus arrival time prediction with existing method (weighed moving average method) in case predicting bus arrival time using 7 by 7 and 9 by 9 matrixes.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.885-893
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• 2015

It is important to improve the forecasting accuracy of one-year-ahead seasonal factors in order to produce seasonally adjusted series of the following year. In this paper, seasonal factors of 8 monthly Korean economic time series are examined and forecast based on the functional principal component regression. One-year-ahead forecasts of seasonal factors from the functional principal component regression are compared with other forecasting methods based on mean absolute error (MAE) and mean absolute percentage error (MAPE). Forecasting seasonal factors via the functional principal component regression performs better than other comparable methods.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.43-46
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• 2000

In this paper, we propose a fast adaptive diamond search algorithm(FADS) for block matching motion estimation. Fast motion estimation algorithms reduce the computational complexity by using the UESA (Unimodal Error Search Assumption) that the matching error monotonically increases as the search moves away from the global minimum error. Recently many fast BMAs(Block Matching Algorithms) make use of the fact that the global minimum points in real world video sequences are centered at the position of zero motion. But these BMAs, especially in large motion, are easily trapped into the local minima and result in poor matching accuracy. So, we propose a new motion estimation algorithm using the spatial correlation among the adjacent blocks. We change the origin of search window according to the spatially adjacent motion vectors and their MAE(Mean Absolute Error). The computer simulation shows that the proposed algorithm has almost the same computational complexity with UCBDS(Unrestricted Center-Biased Diamond Search)〔1〕, but enhance PSNR. Moreover, the proposed algorithm gives almost the same PSNR as that of FS(Full Search), even for the large motion case, with half the computational load.

• Geomechanics and Engineering
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• v.39 no.2
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• pp.129-142
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• 2024

The most significant soil parameters that are utilized in geotechnical engineering projects' design and implementations are soil strength parameters including friction (ϕ), cohesion (c), and uniaxial compressive strength (UCS). Understanding soil shear strength parameters can be guaranteed the design success and stability of structures. In this regard, professionals always looking for ways to get more accurate estimations. The presented study attempted to investigate soil shear strength parameters by using multivariate regression and multilayer perceptron predictive models which were implemented on 100 specimens' data collected from the Tabriz region (NW of Iran). The uniaxial (UCS), liquid limit (LL), plasticity index (PI), density (γ), percentage of fine-grains (pass #200), and sand (pass #4) which are used as input parameters of analysis and shear strength parameters predictions. A confusion matrix was used to validate the testing and training data which is controlled by the coefficient of determination (R²), mean absolute (MAE), mean squared (MSE), and root mean square (RMSE) errors. The results of this study indicated that MLP is able to predict the soil shear strength parameters with an accuracy of about 93.00% and precision of about 93.5%. In the meantime, the estimated error rate is MAE = 2.0231, MSE = 2.0131, and RMSE = 2.2030. Additionally, R² is evaluated for predicted and measured values correlation for friction angle, cohesion, and UCS are 0.914, 0.975, and 0.964 in the training dataset which is considerable.

• Korea Trade Review
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• v.47 no.3
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• pp.211-232
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• 2022

Due to the impact of the public health event COVID-19 epidemic, the Chinese futures market showed "Black Swan". This has brought the unpredictable into the economic environment with many commodities falling by the daily limit, while gold performed well and closed in the sunshine(Yan-Li and Rui Qian-Wang, 2020). Volatility is integral part of financial market. As an emerging market and a special precious metal, it is important to forecast return of gold futures price. This study selected data of the SHFE gold futures returns and conducted an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. Comparing the statistics of AIC, SC and H-QC, ARMA (12,9) model was selected as the best model. But serial correlation in the squared returns suggests conditional heteroskedasticity. Next part we established the autoregressive moving average ARMA-GARCH-type model to analysis whether Volatility Clustering and the leverage effect exist in the Chinese gold futures market. we consider three different distributions of innovation to explain fat-tailed features of financial returns. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE), Theil inequality coefficient(TIC) and root mean-squared error (RMSE). The results show that the ARMA(12,9)-TGARCH(2,2) model under Student's t-distribution outperforms other models when predicting the Chinese gold futures return series.