• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권5호
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• pp.153-160
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• 2020

산업용 에너지 소비 예측은 에너지 수요와 공급에 동적이고 계절적인 변화가 있기 때문에 에너지 관리 및 제어 시스템에서 중요한 위치를 차지한다. 본 논문은 철강 산업의 에너지 소비 예측 모델을 제시하고 논의한다. 사용되는 데이터에는 후행 및 선도적인 전류 반응 전력, 후행 및 선도적인 전류 동력 계수, 이산화탄소(TCO2) 배출 및 부하 유형이 포함된다. 테스트 세트에서는 (a) 선형 회귀(LR), (b) 방사형 커널(SVM RBF), (c) Gradient Boosting Machine (GBM), (d) 무작위 포리스트(RF). 평균 제곱 오차(RMSE), 평균 절대 오차(MAE) 및 평균 절대 백분율 오차(ME)의 네 가지 통계 모델을 사용하여 예측하고 평가한다. 회귀 설계의 효율성 모든 예측 변수를 사용할 때 최상의 모델 RF는 테스트 세트에서 RMSE 값 7.33을 제공할 수 있다.

• Structural Engineering and Mechanics
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• 제84권2호
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• pp.143-154
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• 2022

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R², and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R² and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

• 한국전자통신학회논문지
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• 제15권6호
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• pp.987-994
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• 2020

본 논문은 음향잡음감쇠기에서 CNN(: Convolutional Neural Network) 계층의 커널 사이즈가 성능에 미치는 영향을 위한 연구하였다 이 시스템은 기존의 적응필터를 이용하는 대신 신경망 적응예측필터를 이용한 심층학습 알고리즘으로 잡음감쇠 성능을 개선한다. 100-neuron, 16-filter CNN 필터와 오차 역전파(back propagation) 알고리즘을 이용하여 잡음이 포함된 단일입력 음성신호로부터 음성을 추정한다. 이는 음성신호가 갖는 유성음 구간에서의 준주기적 성질을 이용하는 것이다. 본 연구에서 커널 사이즈에 대한 잡음감쇠기의 성능을 검증하기 위하여 Tensorflow와 Keras 라이브러리를 사용한 시뮬레이션 프로그램을 작성하고 모의실험을 수행하였다. 모의실험 결과, 커널 사이즈가 16 정도일 때 평균자승오차(MSE: Mean Square Error) 및 평균절대값오차(MAE: Mean Absolute Error) 값이 가장 작은 것으로 나타났으며 사이즈가 이보다 더 작거나 커지면 MSE 및 MAE 값이 증가하는 것을 볼 수 있다. 이는 음성신호의 경우 커널 사이즈가 16 정도일 때 특성을 가장 잘 포집할 수 있음을 알 수 있다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 추계학술발표대회
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.440-449
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• 2022

In recent years, the air pollution and Air Quality Index (AQI) has been a pivotal point for researchers due to its effect on human health. Various research has been done in predicting the AQI but most of these studies, either lack dense temporal data or cover one or two air pollutant elements. In this paper, a hybrid Convolutional Neural approach integrated with recurrent neural network architecture (CNN-LSTM), is presented to find air pollution inference using a multivariate air pollutant elements dataset. The aim of this research is to design a robust and real-time air pollutant forecasting system by exploiting a neural network. The proposed approach is implemented on a 24-month dataset from Seoul, Republic of Korea. The predicted results are cross-validated with the real dataset and compared with the state-of-the-art techniques to evaluate its robustness and performance. The proposed model outperforms SVM, SVM-Polynomial, ANN, and RF models with 60.17%, 68.99%, 14.6%, and 6.29%, respectively. The model performs SVM and SVM-Polynomial in predicting O3 by 78.04% and 83.79%, respectively. Overall performance of the model is measured in terms of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE).

• Structural Engineering and Mechanics
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• 제86권5호
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• pp.621-633
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• 2023

Stability analysis and support system estimation of the Beheshtabad water transmission tunnel is investigated in this research. A combination approach based on the rock mass rating (RMR) and rock mass quality index (Q) is used for this purpose. In the first step, 40 datasets related to the petrological, structural, hydrological, physical, and mechanical properties of tunnel host rocks are measured in the field and laboratory. Then, RMR, Q, and height of influenced zone above the tunnel roof are computed and sorted into five general groups to analyze the tunnel stability and determine its support system. Accordingly, tunnel stand-up time, rock load, and required support system are estimated for five sorted rock groups. In addition, various empirical relations between RMR and Q i.e., linear, exponential, logarithmic, and power functions are developed using the analysis of variance (ANOVA). Based on the significance level (sig.), determination coefficient (R²) and Fisher-test (F) indices, power and logarithmic equations are proposed as the optimum relations between RMR and Q. To validate the proposed relations, their results are compared with the results of previous similar equations by using the variance account for (VAF), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) indices. Comparison results showed that the accuracy of proposed RMR-Q relations is better than the previous similar relations and their outputs are more consistent with actual data. Therefore, they can be practically utilized in designing the tunneling projects with an acceptable level of accuracy and reliability.

• Advances in materials Research
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• 제13권1호
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• pp.63-86
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• 2024

Asphalt concrete (AC), is a mixture of bitumen and aggregates, which is very sensitive in the design of flexible pavement. In this study, the Marshall stability of the glass and carbon fiber bituminous concrete was predicted by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and M5P Tree machine learning algorithms. To predict the Marshall stability, nine inputs parameters i.e., Bitumen, Glass and Carbon fibers mixed in 100:0, 75:25, 50:50, 25:75, 0:100 percentage (designated as 100GF:0CF, 75GF:25CF, 50GF:50 CF, 25GF:75CF, 0GF:100CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD) were utilized from the experimental and literary data. Seven statistical indices i.e., coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Scattering index (SI), and BIAS were applied to assess the effectiveness of the developed models. According to the performance evaluation results, Artificial neural network (ANN) was outperforming among other models with CC values as 0.9147 and 0.8648, MAE values as 1.3757 and 1.978, RMSE values as 1.843 and 2.6951, RAE values as 39.88 and 49.31, RRSE values as 40.62 and 50.50, SI values as 0.1379 and 0.2027 and BIAS value as -0.1 290 and -0.2357 in training and testing stage respectively. The Taylor diagram (testing stage) also confirmed that the ANN-based model outperforms the other models. Results of sensitivity analysis showed that the fiber length is the most influential in all nine input parameters whereas the fiber combination of 25GF:75CF was the most effective among all the fiber mixes in Marshall stability.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2000년도 추계학술대회
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• pp.64-68
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• 2000

장기간 관측된 지하수위 자료를 시계열분석 중의 하나인 전이함수 모형(Transfer Function - Noise model)을 이용하여 분석하였다. 일반적으로 전이함수 모형은 입력 변수와 출력변수와의 관계가 선형적일 때 적용이 가능하며, 자료가 시간에 대해 연속적으로 존재해야 하는 제한이 있다. 강수량과 지하수위의 변동은 비선형적인 관계를 가지고 있어 이러한 전이함수 모형을 직접 적용하는데는 어려움이 있다. 이러한 비선형성의 정도를 감소시키기 위해 물리모형(HYDRUS)을 이용하여 침투량을 계산하고 이를 입력변수로 사용하여 전이함수 모형을 적용하였다. 침투량을 입력변수로 모형을 추정하였을 때, 강수량을 직접 입력자료로 사용했을 경우보다 ME(mean error), RMSE(root-mean-squre error), MAE(mean absolute error)에서 상대적으로 작은 값을 보여주고 있다. TFN 모형의 모수를 추정하기 위해서 Kalman 필터 알고리즘과 최우추정법(Maximum Likelihood Estimation)을 이용하였다. Kalman 필터 알고리즘을 이용하여 불규칙한 관측주기를 갖는 시계열이나 결측값이 있는 시계열에 대해서도 전이함수 모형을 구하였으며, 이를 통해 결측값에 대한 추정이 가능하였다.

• 응용통계연구
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• 제34권5호
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• pp.723-734
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• 2021

빠르게 발전하고 있는 재생에너지 중 하나인 풍력에너지는 기후변화 대응에 맞추어 개발 및 투자가 이루어지고있다. 신재생에너지 정책과 발전소 설치가 추진됨에 따라 국내 풍력 보급이 점차 확대되어 수요를 정확히 예측하기 위한 시도들이 확대되고 있다. 본 논문에서는 전남지역과 경북지역의 풍력 발전량 예측을 위하여 시계열 기법인 ARIMA, ARIMAX 모형과 기계학습 모형인 SVR, Random Forest, XGBoost 모형들을 비교 분석하였다. 모형의 예측 결과를 비교하기 위한 지표로서 mean absolute error (MAE)와 mean absolute percentage error (MAPE)를 사용하였다. 2018년 1월 1일부터 2020년 10월 24일까지의 시간별 원 데이터를 차분한 후 모형을 훈련시켜 2020년 10월 25일부터 2020년 10월 31일까지의 168시간에 대한 풍력 발전량을 예측하였다. 모형의 예측력 비교 결과, Random Forest와 XGBoost 모형이 전남지역, 경북지역 순으로 가장 우수한 성능을 보였다. 향후 연구에서는 기계학습뿐 아니라 최근 활발한 연구가 이루어지는 데이터 마이닝 기법 기반의 풍력 발전량 예측을 시도할 것이다.

• Advances in Computational Design
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• 제7권3호
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• pp.253-279
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• 2022

This study investigated and predicted the Marshall stability of glass-fiber asphalt mix, carbon-fiber asphalt mix and glass-carbon-fiber asphalt (hybrid) mix by using machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest(RF), The data was obtained from the experiments and the research articles. Assessment of results indicated that performance of the Artificial Neural Network (ANN) based model outperformed applied models in training and testing datasets with values of indices as; coefficient of correlation (CC) 0.8492 and 0.8234, mean absolute error (MAE) 2.0999 and 2.5408, root mean squared error (RMSE) 2.8541 and 3.3165, relative absolute error (RAE) 48.16% and 54.05%, relative squared error (RRSE) 53.14% and 57.39%, Willmott's index (WI) 0.7490 and 0.7011, Scattering index (SI) 0.4134 and 0.3702 and BIAS 0.3020 and 0.4300 for both training and testing stages respectively. The Taylor diagram also confirms that the ANN-based model outperforms the other models. Results of sensitivity analysis show that Carbon fiber has a major influence in predicting the Marshall stability. However, the carbon fiber (CF) followed by glass-carbon fiber (50GF:50CF) and the optimal combination CF + (50GF:50CF) are found to be most sensitive in predicting the Marshall stability of fibrous asphalt concrete.