• Journal of Sensor Science and Technology
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• v.18 no.6
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• pp.423-431
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• 2009

Standard uroflowmetry measures the urine weight using single load cell to evaluate the urinary flow rate. Impact noise should be introduced due to gravity when the urine stream falls down into the container upon the load cell. The present study placed three load cells on the three vertices of a regular triangle and the three signals were ensemble averaged to enhance the signal-to-noise ratio(SNR) regardless of how the urination was made. Simulated urination experiment was performed with three different urine collection methods. In all three methods, SNR of the averaged signal was much higher than each load cell signals. With no urine collection device, the present signal averaging technique resulted in SNR values higher by 10~15 dB than when dual funnels or upper funnel were used to guide the urine stream. Therefore, it was demonstrated that the three point measurement followed by with ensemble averaging could enable accurate uroflowmetric test without any specially made urine collection devices.

• Atmosphere
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• v.26 no.1
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• pp.35-45
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• 2016

Initialization of the global seasonal forecast system is as much important as the quality of the embedded climate model for the climate prediction in sub-seasonal time scale. Recent studies have emphasized the important role of soil moisture initialization, suggesting a significant increase in the prediction skill particularly in the mid-latitude land area where the influence of sea surface temperature in the tropics is less crucial and the potential predictability is supplemented by land-atmosphere interaction. This study developed a new soil moisture initialization method applicable to the KMA operational seasonal forecasting system. The method includes first the long-term integration of the offline land surface model driven by observed atmospheric forcing and precipitation. This soil moisture reanalysis is given for the initial state in the ensemble seasonal forecasts through a simple anomaly initialization technique to avoid the simulation drift caused by the systematic model bias. To evaluate the impact of the soil moisture initialization, two sets of long-term, 10-member ensemble experiment runs have been conducted for 1996~2009. As a result, the soil moisture initialization improves the prediction skill of surface air temperature significantly at the zero to one month forecast lead (up to ~60 days forecast lead), although the skill increase in precipitation is less significant. This study suggests that improvements of the prediction in the sub-seasonal timescale require the improvement in the quality of initial data as well as the adequate treatment of the model systematic bias.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.57-72
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• 2020

The price of iron ore has continued to fluctuate with high demand and supply from many countries and companies. In this business environment, forecasting the price of iron ore has become important. This study developed the machine learning model forecasting the price of iron ore a one month after the trading events. The forecasting model used distributed lag model and deep learning models such as MLP (Multi-layer perceptron), RNN (Recurrent neural network) and LSTM (Long short-term memory). According to the results of comparing individual models through metrics, LSTM showed the lowest predictive error. Also, as a result of comparing the models using the ensemble technique, the distributed lag and LSTM ensemble model showed the lowest prediction.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.83-90
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• 2019

People constantly strive for better choices. For this reason, recommender system has been developed since the early 1990s. In particular, collaborative filtering technique has shown excellent performance in the field of recommender systems, and research of recommender system using machine learning has been actively conducted. This study constructs recommender system using collaborative filtering and machine learning based on stacking model which is one of ensemble methods. The results of this study confirm that the recommender system with the stacking model is useful in aspects of recommender performance. In the future, the model proposed in this study is expected to help individuals or firms to make better choices.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.57-64
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• 2021

This study presents a method for realtime fuel consumption prediction using real data collected from OBDII. With the advent of the era of self-driving cars, electronic control units(ECU) are getting more complex, and various studies are being attempted to extract and analyze more accurate data from vehicles. But since ECU is getting more complex, it is getting harder to get the data from ECU. To solve this problem, the firmware was developed for acquiring accurate vehicle data in this study, which extracted 53,580 actual driving data sets from vehicles from January to February 2019. Using these data, the ensemble stacking technique was used to increase the accuracy of the realtime fuel consumption prediction model. In this study, Ridge, Lasso, XGBoost, and LightGBM were used as base models, and Ridge was used for meta model, and the predicted performance was MAE 0.011, RMSE 0.017.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.1-8
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• 2023

Monitoring and prediction of water quality are essential for effective river pollution prevention and water quality management. In this study, a multi-classification model was developed to predict chlorophyll-a (Chl-a) level in rivers. A model was developed using CatBoost, a novel ensemble machine learning algorithm. The model was developed using hourly field monitoring data collected from January 1 to December 31, 2015. For model development, chl-a was classified into class 1 (Chl-a≤10 ㎍/L), class 2 (10<Chl-a≤50 ㎍/L), and class 3 (Chl-a>50 ㎍/L), where the number of data used for the model training were 27,192, 11,031, and 511, respectively. The macro averages of precision, recall, and F1-score for the three classes were 0.58, 0.58, and 0.58, respectively, while the weighted averages were 0.89, 0.90, and 0.89, for precision, recall, and F1-score, respectively. The model showed relatively poor performance for class 3 where the number of observations was much smaller compared to the other two classes. The imbalance of data distribution among the three classes was resolved by using the synthetic minority over-sampling technique (SMOTE) algorithm, where the number of data used for model training was evenly distributed as 26,868 for each class. The model performance was improved with the macro averages of precision, rcall, and F1-score of the three classes as 0.58, 0.70, and 0.59, respectively, while the weighted averages were 0.88, 0.84, and 0.86 after SMOTE application.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.29-37
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• 2023

Due to climate change, interest in crop production and distribution is increasing, and attempts are being made to use bigdata and AI to predict production volume and control shipments and distribution stages. Prediction of agricultural product imports not only affects prices, but also controls shipments of farms and distributions of distribution companies, so it is important information for establishing marketing strategies. In this paper, we create an artificial intelligence prediction model that predicts the future import volume based on the wholesale market melon import volume data disclosed by the agricultural statistics information system and evaluate its accuracy. We create prediction models using three models: the Neural Prophet technique, the Ensembled Neural Prophet model, and the GRU model. As a result of evaluating the performance of the model by comparing two major indicators, MAE and RMSE, the Ensembled Neural Prophet model predicted the most accurately, and the GRU model also showed similar performance to the ensemble model. The model developed in this study is published on the web and used in the field for 1 year and 6 months, and is used to predict melon production in the near future and to establish marketing and distribution strategies.

• Information Systems Review
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• v.21 no.4
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• pp.31-45
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• 2019

Despite the expansion of the market of performing arts and culture, small and medium size theaters are still experiencing difficulties due to poor accessibility of information by consumers. This study proposes a machine learning based genre recommendation system as an alternative to enhance the marketing capability of small and medium sized theaters. We developed five recommendation systems that recommend three genres per customer using customer master DB and transaction history DB of domestic venues. We propose an optimal recommendation system by comparing performances of recommendation system. As a result, the recommendation system based on the ensemble model showed better performance than the single predictive model. This study applied the personalized recommendation technique which was scarce in the field of performing arts and culture, and suggests that it is worthy enough to use it in the field of performing arts and culture.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.12
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• pp.1716-1723
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• 2003

A stereoscopic PIV(Particle Image Velocimetry) technique was employed to measure the 3 dimensional flow structure of turbulent wake behind a marine propeller with 5 blades. The out-of-plane velocity component was determined using two CCD cameras with the angular displacement configuration. Four hundred instantaneous velocity fields were measured for each of four different blade phases and ensemble averaged to investigate the spatial evolution of the propeller wake in the near-wake region from the trailing edge to one propeller diameter(D) downstream. The phase-averaged velocity fields show the potential wake and the viscous wake developed along the blade surfaces. Tip vortices were generated periodically and the slipstream contraction occurs in the near-wake region. The out-of-plane velocity component and strain rate have large values at the locations of tip and trailing vortices. As the flow goes downstream, the turbulence intensity, the strength of tip vortices and the magnitude of out-of-plane velocity component at trailing vortices are decreased due to viscous dissipation, turbulence diffusion and blade-to-blade interaction.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.479-482
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• 2002

Riblets with longitudinal grooves along the streamwise direction have been used as an effective flow control technique for drag reduction. A flexible micro-riblet with v-grooves of peak-to-peak spacing of $300{\mu}m$ was made using a MEMS fabrication process of PDMS replica. The flexible micro-riblet was attached on the whole surface of a NACA0012 airfoil with which grooves are aligned with the streamwise direction. The riblet surface reduces drag coefficient about $7.9{\%}\;at\;U_o=3.3m/s$, however, it increases drag about $8{\%}\;at\;U_o=7.0m/s$, compared with the smooth airfoil without riblets. The near wake has been investigated experimentally far the cases of drag reduction ($U_o\;=\;3.3 m/s$) and drag increase ($U_o\;=\;7 m/s$). Five hundred instantaneous velocity fields were measured for each experimental condition using the cross-correlation PIV velocity field measurement technique. The instantaneous velocity fields were ensemble averaged to get spatial distribution of turbulent statistics such as turbulent kinetic energy. The experimental results were compared with those of a smooth airfoil under the same flow condition. The micro-riblet surface influences the near wake flow structure largely, especially in the region near the body surface