• International Journal of Highway Engineering
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• v.9 no.4
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• pp.1-9
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• 2007

To decide the number of road lane is very important and related to the 30th design hourly factor in the design of transportation facilities. But, as the quantitative division of road types is difficult, most planner and designer for deciding the 30th design hourly factors have used the fixed values in our country. In this study, we have analyzed the time series property of the design hourly factors in national highways and developed the model capable of estimating the 30th design hourly factors using real data. The presented model is a simple regression model(DHV = K*AADT), which is applied to the division of road lanes(2 or 4 lanes) and the level of AADT(3 levels). As a results, the simple regression model have better performance than the existing method with respect to MAPE and $R^2$. Also, the variations of the 30th design hourly factors are small. The more traffic volume increase, the more the factors decrease. But, the limitation of this study is to use the exiting method estimating the values of the factors, it is subject to study hereafter.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.70-70
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• 2015

하천에서의 수문/수리적 특성은 주수로의 다양한 지형학적 형태로 나타난다. 특히 흐름과 수반된 유사량의 변화는 하천의 지형학적, 수리기하적 특성을 지배하며, 하도의 물리적 시스템을 변화시켜 동적 평형에 이르게 된다. 하천에서 하도 형성에 지배적인 역할을 하는 수리특성은 하도형성유량으로 지배유량이며, 보통 만제유량을 사용한다. Dunne and Leopold(1978)는 만제유량을 유사의 이송, 하천의 사행, 유선형의 변화 등 하천의 일반적인 형태를 변화시키며, 주수로의 특성을 형성하는 유량으로 정의하였다. 이와 같이 수리 지형학적 특성을 반영하는 만제유량은 하천의 특성을 나타내는 중요한 요소이다. 따라서 본 연구에서는 한강 수계 20개 하천, 27개 수위 관측소의 최소 10년 이상의 실측 자료를 기반으로 다년간의 연평균 실측유량을 산정하였으며, McCandless(2003)가 제시한 지형학적 만제지표를 이용하여 추정한 만제유량과의 상관성을 분석하였다. 추정된 만제유량은 HEC-RAS model을 이용하여 만제하폭, 만제수심, 만제 시 평균유속을 산정하였다. 27개 지점의 실측유량과 만제유량의 상관성 분석결과 만제유량은 연평균 일유량의 7.7배로 나타났다. 따라서 만제유량을 7일 평균유량(1 week mean discharge)으로 정의하였으며, 수정된 7일 유량과 만제유량의 RMSE는 13.90 m/s, MAPE는 9.94 %로 상관성이 매우 높게 나타났다. 또한 만제유량과 만제하폭, 만제수심, 평균유량, 구간경사와 상관성 분석결과 개별적으로의 상관성은 나타나지 않았으나, 만제하폭, 수심, 평균유량과 만제유량에 대한 회귀 분석을 실시한 결과 $R^2$는 0.911로 매우 높게 나타났으며, 구간경사를 추가하여 분석한 결과 $R^2$가 0.914로 증가하였다. 따라서 만제유량은 수리 기하학적 특성이 모두 반영된 하천 특성을 나타내는 복합적인 지표임을 확인하였다. 아울러 만제유량을 통해 추정된 연평균 유량($48{\cdot}Q_{bf}$)을 우리나라의 월간 유출량 분포 비율을 이용하여 일유량으로 배분/생성하였으며, 생성된 일유량을 통해 CCHE2D model을 이용하여 하상변동 모의를 수행하였다. 대상 구간은 병성천 최하류로부터 상류로 7 km 구간이며, 2013년 1월과 12월 측량 자료를 통한 1년간의 실제 하상 변동 자료와 2013년 실측 유량자료에 따른 하상변동 모의 결과 및 만제유량에 의해 배분/생성된 일유량에 따른 하상변동 모의 결과를 비교하였다. 비교 분석 결과 7일 평균 유량으로 정의된 만제유량을 통해 배분/생성된 유량의 수치모의 결과는 실제 측량자료 및 실측유량자료에 따른 하상변동 결과와 매우 일치하는 것을 확인하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.580-580
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• 2015

하천에서의 수문/수리적 특성은 주수로의 다양한 지형학적 형태로 나타난다. 특히 흐름과 수반된 유사량의 변화는 하천의 지형학적, 수리기하적 특성을 지배하며, 하도의 물리적 시스템을 변화시켜 동적 평형에 이르게 된다. 하천에서 하도 형성에 지배적인 역할을 하는 수리특성은 하도형성 유량으로 지배유량이며, 보통 만제유량을 사용한다. Dunne and Leopold(1978)는 만제유량을 유사의 이송, 하천의 사행, 유선형의 변화 등 하천의 일반적인 형태를 변화시키며, 주수로의 특성을 형성하는 유량으로 정의하였다. 이와 같이 수리 지형학적 특성을 반영하는 만제유량은 하천의 특성을 나타내는 중요한 요소이다. 따라서 본 연구에서는 한강 수계 20개 하천, 27개 수위 관측소의 최소 10년 이상의 실측 자료를 기반으로 다년간의 연평균 실측유량을 산정하였으며, McCandless(2003)가 제시한 지형학적 만제지표를 이용하여 추정한 만제유량과의 상관성을 분석하였다. 추정된 만제유량은 HEC-RAS model을 이용하여 만제하폭, 만제수심, 만제 시 평균유속을 산정하였다. 27개 지점의 실측유량과 만제유량의 상관성 분석결과 만제유량은 연평균 일유량의 7.7배로 나타났다. 따라서 만제유량을 7일 평균유량(1 week mean discharge)으로 정의하였으며, 수정된 7일 유량과 만제유량의 RMSE는 13.90m/s, MAPE는 9.94 %로 상관성이 매우 높게 나타났다. 또한 만제유량과 만제하폭, 만제수심, 평균유량, 구간경사와 상관성 분석결과 개별적으로의 상관성은 나타나지 않았으나, 만제하폭, 수심, 평균유량과 만제유량에 대한 회귀 분석을 실시한 결과 $R^2$는 0.911로 매우 높게 나타났으며, 구간경사를 추가하여 분석한 결과 $R^2$가 0.914로 증가하였다. 따라서 만제유량은 수리 기하학적 특성이 모두 반영된 하천 특성을 나타내는 복합적인 지표임을 확인하였다. 아울러 만제유량을 통해 추정된 연평균 유량($48{\cdot}Q_{bf}$)을 우리나라의 월간 유출량 분포 비율을 이용하여 일유량으로 배분/생성하였으며, 생성된 일유량을 통해 CCHE2D model을 이용하여 하상변동 모의를 수행하였다. 대상 구간은 병성천 최하류로부터 상류로 7 km 구간이며, 2013년 1월과 12월 측량 자료를 통한 1년간의 실제 하상 변동 자료와 2013년 실측 유량자료에 따른 하상변동모의 결과 및 만제유량에 의해 배분/생성된 일유량에 따른 하상변동 모의 결과를 비교하였다. 비교 분석 결과 7일 평균 유량으로 정의된 만제유량을 통해 배분/생성된 유량의 수치모의 결과는 실제 측량자료 및 실측유량자료에 따른 하상변동 결과와 매우 일치하는 것을 확인하였다.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.393-400
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• 2021

Body weight of livestock is a crucial indicator for assessing feed requirements and nutritional status. This study was performed to estimate the body weight of Korean cattle (Hanwoo) using body volume determined from three-dimensional (3-D) image. A TOF camera with a resolution of 640×480 pixels, a frame rate of 44 fps and a field of view of 47°(H)×37°(V) was used to capture the 3-D images for Hanwoo. A grid image of the body was obtained through preprocessing such as separating the body from background and removing outliers from the obtained 3-D image. The body volume was determined by numerical integration using depth information to individual grid. The coefficient of determination for a linear regression model of body weight and body volume for calibration dataset was 0.8725. On the other hand, the coefficient of determination was 0.9083 in a multiple regression model for estimating body weight, in which the age of Hanwoo was added to the body volume as an explanatory variable. Mean absolute percentage error and root mean square error in the multiple regression model to estimate the body weight for validation dataset were 8.2% and 24.5kg, respectively. The performance of the regression model for weight estimation was improved and the effort required for estimating body weight could be reduced as the body volume of Hanwoo was used. From these results obtained, it was concluded that the body volume determined from 3-D of Hanwoo could be used as an effective variable for estimating body weight.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.239-254
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• 2021

This paper studies time series analysis with estimation and forecasting for Korean COVID-19 confirmed cases, based on the approach of a heterogeneous autoregressive (HAR) model with two-piece t (TP-T) distributed errors. We consider HAR-TP-T time series models and suggest a step-by-step method to estimate HAR coefficients as well as TP-T distribution parameters. In our proposed step-by-step estimation, the ordinary least squares method is utilized to estimate the HAR coefficients while the maximum likelihood estimation (MLE) method is adopted to estimate the TP-T error parameters. A simulation study on the step-by-step method is conducted and it shows a good performance. For the empirical analysis on the Korean COVID-19 confirmed cases, estimates in the HAR-TP-T models of order p = 2, 3, 4 are computed along with a couple of selected lags, which include the optimal lags chosen by minimizing the mean squares errors of the models. The estimation results by our proposed method and the solely MLE are compared with some criteria rules. Our proposed step-by-step method outperforms the MLE in two aspects: mean squares error of the HAR model and mean squares difference between the TP-T residuals and their densities. Moreover, forecasting for the Korean COVID-19 confirmed cases is discussed with the optimally selected HAR-TP-T model. Mean absolute percentage error of one-step ahead out-of-sample forecasts is evaluated as 0.0953% in the proposed model. We conclude that our proposed HAR-TP-T time series model with optimally selected lags and its step-by-step estimation provide an accurate forecasting performance for the Korean COVID-19 confirmed cases.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.388-393
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• 2023

The analysis of engineering data using neural network based on supervised learning has been utilized in various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport phenomena system. The supervised learning requires training data, and the performance of the supervised learning is affected by the composition and the configurations of the given training data. Among the frequently observed engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the log-scaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction performance. The optimal loss functions and method for how to compose training data studied in this work would be applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of colloidal suspension.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.209-223
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• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.