• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.358-358
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• 2023

본 연구에서는 그동안 수자원분야 강우유출 해석분야에 활용되었던 대표적인 머신러닝&딥러닝(ML&DL) 모델을 활용하여 모델의 하이퍼파라미터 튜닝뿐만 아니라 모델의 특성을 고려한 기상 및 수문데이터의 조합과 전처리(lag-time, 이동평균 등)를 통하여 데이터 특성과 ML&DL모델의 조합시나리오에 따른 일 유입량 예측성능을 비교 검토하는 연구를 수행하였다. 이를 위해 소양강댐 유역을 대상으로 1974년에서 2021년까지 축적된 기상 및 수문데이터를 활용하여 1) 강우, 2) 유입량, 3) 기상자료를 주요 영향변수(독립변수)로 고려하고, 이에 a) 지체시간(lag-time), b) 이동평균, c) 유입량의 성분분리조건을 적용하여 총 36가지 시나리오 조합을 ML&DL의 입력자료로 활용하였다. ML&DL 모델은 1) Linear Regression(LR), 2) Lasso, 3) Ridge, 4) SVR(Support Vector Regression), 5) Random Forest(RF), 6) LGBM(Light Gradient Boosting Model), 7) XGBoost의 7가지 ML방법과 8) LSTM(Long Short-Term Memory models), 9) TCN(Temporal Convolutional Network), 10) LSTM-TCN의 3가지 DL 방법, 총 10가지 ML&DL모델을 비교 검토하여 일유입량 예측을 위한 가장 적합한 데이터 조합 특성과 ML&DL모델을 성능평가와 함께 제시하였다. 학습된 모형의 유입량 예측 결과를 비교·분석한 결과, 소양강댐 유역에서는 딥러닝 중에서는 TCN모형이 가장 우수한 성능을 보였고(TCN>TCN-LSTM>LSTM), 트리기반 머신러닝중에서는 Random Forest와 LGBM이 우수한 성능을 보였으며(RF, LGBM>XGB), SVR도 LGBM수준의 우수한 성능을 나타내었다. LR, Lasso, Ridge 세가지 Regression모형은 상대적으로 낮은 성능을 보였다. 또한 소양강댐 댐유입량 예측에 대하여 강우, 유입량, 기상계열을 36가지로 조합한 결과, 입력자료에 lag-time이 적용된 강우계열의 조합 분석에서 세가지 Regression모델을 제외한 모든 모형에서 NSE(Nash-Sutcliffe Efficiency) 0.8이상(최대 0.867)의 성능을 보였으며, lag-time이 적용된 강우와 유입량계열을 조합했을 경우 NSE 0.85이상(최대 0.901)의 더 우수한 성능을 보였다.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.465-470
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• 2024

The advancement of AI-based image generation technology has resulted in the creation of various images, emphasizing the need for technology capable of accurately discerning them. The amount of generated image data is limited, and to achieve high performance with a limited dataset, this study proposes a model for discriminating generated images using transfer learning. Applying pre-trained models from the ImageNet dataset directly to the CIFAKE input dataset, we reduce training time cost followed by adding three hidden layers and one output layer to fine-tune the model. The modeling results revealed an improvement in the performance of the model when adjusting the final layer. Using transfer learning and then adjusting layers close to the output layer, small image data-related accuracy issues can be reduced and generated images can be classified.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.8-15
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• 2020

A forecasting method using deep learning does not have consistent results due to the differences in the characteristics of the dataset, even though they have the same forecasting models and parameters. For example, the forecasting model X optimized with dataset A would not produce the optimized result with another dataset B. The forecasting model with the characteristics of the dataset needs to be optimized to increase the accuracy of the forecasting model. Therefore, this paper proposes novel optimization steps for outlier removal, dataset classification, and a CNN-LSTM-based hyperparameter tuning process to forecast the daily power usage of a university campus based on the hourly interval. The proposing model produces high forecasting accuracy with a 2% of MAPE with a single power input variable. The proposing model can be used in EMS to suggest improved strategies to users and consequently to improve the power efficiency.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.605-614
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• 2002

Amount of the web traffic web server handles are explosively increasing, which requires that the performance of the web server should be improved for the various web services. Although the analysis for the HTTP traffic with the proper tuning for the web server is essential, the research relevant to the subject are insignificant. In particular, although most of applications are implemented over HTTP 1.1 protocol, the researches mostly deal with the performance evaluation of the HTTP 1.0 protocol. Consequently, the modeling approach and the performance evaluation over HTTP 1.1 protocol have not been well formed. Therefore, basing on the HTTP 1.1 protocol supporting persistent connection, we present an analytical end-to-end tandem queueing model for web server to consider the specific hardware configuration inside web server beginning at accepting the user request until completing the service. we compare various performances between HTTP 1.0 and HTTP 1.1 under the overloading condition, and then analyze the characteristics of the HTTP traffic that include file size requested to web server, the OFF time between file transfers, the frequency of requests, and the temporal locality of requests. Presented model is verified through the comparing the server throughput according to varying requests rate with the real web server. Thereafter, we analyze the performance evaluation of the web server, according to the interrelation between TCP Listen queue size, the number of HTTP threads and the size of the network buffers.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.1
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• pp.71-80
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• 2013

According to design specifications for structural safety, a bridge in initial design step has been modelled to have larger self-weight, external loads and less stiffness than those of real one in service. Thereby measured buffeting responses of existing bridge show different distributions from those of the design model in design step. In order to obtain accurate buffeting responses of the in-site bridge, the analysis model needs to be modified by considering the measured natural frequencies. Until now, a Manual Tuning Method (MTM) has been widely used to obtain the Measurement-based Model(MBM) that has equal natural frequencies to the real bridge. However, since state variables can be selected randomly and its result is not apt to converge exact rapidly, MTM takes a lot of effort and elapsed time. This study presents Buffeting Response Correction Method (BRCM) to obtain more exact buffeting response above MTM. The BRCM is based on the idea the commonly used frequency domain buffeting analysis does not need all structural properties except mode shapes, natural frequencies and damping ratio. BRCM is used to improve each modal buffeting responses of the design model by substituting measured natural frequencies. The measured natural frequencies are determined from acceleration time-history in ordinary vibration of the real bridge. As illustrated examples, simple beam is applied to compare the results of BRCM with those of a assumed MBM by numerical simulation. Buffeting responses of BRCM are shown to be appropriate for those of in-site bridge and the difference is less than 3% between the responses of BRCM and MTM. Therefore, BRCM can calculate easily and conveniently the buffeting responses and improve effectively maintenance and management of in-site bridge than MTM.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.