• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.291-302
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• 2020

Korean popular music industry, which is lead by "Idol group", has forsaken their fandom all over the world. Therefore, idol groups has become not only an artist but also the most influential people in the Korean economy. A global idol group with a strong fandom can earn more than a trillion-dollar by attracting their global fan's interest in Korea. In other words, it is considerably important to carry the idol to a successful conclusion. This study tries to expect whether the idols can be survived or not at a certain point after their debut by ANN, Decision Tree, Random Forest. We decide that certain point as the three-year and eight-year after their debut, because it is their break-even point year and the year after their average renewal of the contract. In addition, this study also explains which feature is the most important to their survival by feature importance and Logistic regression. In conclusion, features like the number of idol competitors, the number of debut members and the number of the genre are significant. These results shed light on the efficient management of K-Pop idol to improve industrial competitiveness.

• Journal of the Korean Society of Hazard Mitigation
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• v.3 no.4 s.11
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• pp.119-129
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• 2003

Concrete and steel are essential structural materials in the construction. But, concrete, different from steel, consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructors. Concrete have two kinds of properties, immediately knowing properties such as slump, air contents and time dependent one like strength. Therefore, concrete mixes depend on experiences of experts. However, at point of time using High Performance Concrete, new method is wanted because of more ingredients like mineral and chemical admixtures and lack of data. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network ate used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength, slump, and air contents are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.424-424
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• 2018

인공신경망(Artificial Neural Network; ANN)은 뇌에 존재하는 생물학적 신경세포와 이들의 신호처리 과정을 수학적으로 묘사하여 뇌가 나타내는 지능적 형태의 반응을 구현한 것이다. 인공신경망은 학습(training)을 통해 입력과 출력으로 구성되는 하나의 시스템을 병렬적이고 비선형적으로 구축할 수 있으며, 유연한 모델링 특성으로 인하여 시스템 예측, 패턴인식, 분류 및 공정제어 등의 다양한 분야에서 활용되고 있다. 인공신경망에 대한 최초의 이론은 Muculloch and Pitts(1943)가 제안한 Perceptron에서 시작 되었으며, 기본적인 학습기법인 오차역전파 기법(back-propagation Algorithm) 이 1980년대에 들어 수학적으로 정립된 이후 여러 분야에서 활용되기 시작하였다). 본 연구에서는 하도추적, 구체적으로는 상류단의 복수의 수위관측을 이용하여 하류단의 수위를 예측하기 위하여 인공신경망 모델을 구성하였다. 대상하도는 금강유역의 용담댐과 대청댐 사이의 본류이며, 상류단 입력자료로써 본류에 있는 수통, 호탄 관측소 관측수위와 지류인 송천 관측소 관측수위를 고려하였다. 출력 값으로는 하류단의 옥천 관측소 수위를 3시간 및 6시간의 선행시간으로 예측하도록 인공신경망 모형을 구성하였다. 인공신경망의 학습(testing), 시험(testing), 검증(validation)을 위해 2000년부터 2012년까지 13년간의 시수위자료를 이용하여 학습을 진행하였으며, 2013년부터 2014년의 2년간의 수위자료를 이용한 시험을 통해 최적의 모형을 선정하였다. 또한 선정된 최적의 모형을 이용하여 2015년부터 2016년까지의 수위예측을 수행하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.570-573
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• 2022

This paper presents the identification of void diameters for a cast-resin transformer using an artificial neural network (ANN) model. A PD signal was measured by the Rogowski coil sensor which has the planar and thin structures fabricated on a printed circuit board (PCB), and the PD electrode system was fabricated to simulate a PD defect by a void. In addition, void samples with different diameters were fabricated by injecting air in a cylindrical aluminum frame using a syringe during the epoxy curing process. To identify the diameter of void defects, PD characteristics such as the discharge magnitude, pulse count, and phase angle were extracted and back propagation algorithm (BPA) was designed using virtual instrument (VI) based on the Labview program. From the experimental results, the BPA algorithm proposed in this paper has over 90% accurate rate to identify the diameter of void defects and is expected to use reference data of maintenance and replacement of insulation for cast-resin transformers in the on-site PD measurement.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.3
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• pp.112-117
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• 2017

Ion exchange fiber, PADD was synthesized by the reaction between PAN based acrylic fiber and DETA with $AlCl_3{\cdot}6H_2O$, and was analyzed by FT-IR and SEM to investigate its characteristics. The experimental results of Cr(VI) removal by PADD were better fitted with Langmuir adsorption isotherm, and the maximum uptake value ($Q_{max}$) was calculated to be 6.93 mmol/g. The kinetic data can be well described by Lagergen pseudo-second order rate model. The Cr(VI) adsorption capacity of PADD was 4.11 mmol/g at pH 2, which shows the effect of pH changes on the removal of Cr(VI). The adsorption selectivity of Cr(VI) was higher than phosphate and As(V). Total ion exchange capacity of PADD was 4.70 mmol/g, which was measured by acid-base back titration.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.5
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• pp.397-403
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• 2016

During the rapid economic growth in Korea since the 1970s, many underground facilities were constructed such as under passes and railways. Seismic design has been mandated in 1988, but the structures built before 1988 were not reflected on the seismic design. Accordingly, these underground structures require effective seismic reinforcing methods to ensure safety when the earthquake happens. By these reasons, in this study, using the proposed pre-flexed members, RC box structure was analyzed for seismic reinforcement of the corner. This method is based on a principle that enlarging the resistance against the external force by installing the pre-flexed member to the box structure corner. To evaluate validity, a newly developed member with CornerSafe was compared with traditional type reinforcement using experiments and finite element analysis. In finite element mode, nonlinearity of steel was modeled based on J2 plasticity model and concrete was based on CEB FIP MODEL CODE 1990. Also, composite ratios of box and pre-flexed member were computed for design application. The reinforcement and box structure were analyzed under the bond condition completely attached by the tie, and the results of experiment and finite element analysis were same in the force-displacement curve.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.245-252
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• 2016

The purpose of this study is to make up for missing of weather informations from ASOS and AWS using artificial neural networks. We collected temperature, relative humidity and wind velocity for August during 5-yr (2011-2015) and sample designed artificial neural networks, assuming the Seoul weather station was missing. The result of sensitivity study on number of epoch shows that early stopping appeared at 2,000 epochs. Correlation between observation and prediction was higher than 0.6, especially temperature and humidity was higher than 0.9, 0.8 respectively. RMSE decreased gradually and training time increased exponentially with respect to increase of number of epochs. The predictability at 40 epoch was more than 80% effect on of improved results by the time the early stopping. It is expected to make it possible to use more detailed weather information via the rapid missing complemented by quick learning time within 2 seconds.

• Journal of the Korean Society of Propulsion Engineers
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• v.21 no.6
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• pp.64-72
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• 2017

The longitudinal dynamic instability which can occur in the fueling process of a space launch vehicle is called pogo. It is caused by coupling between the fuselage and propulsion system and they would be formed as a closed-loop system. so that the amplitude of the response may increase or decrease. In this paper, a mathematical model which is applicable to the systematic pogo analysis of a general launch vehicle is developed for an example of space shuttle. The formulations are composed of the linearized second-order differential equation for the propulsion system, and of the pressure, weight displacement, and generalized displacement. Those are important parameters for pogo analysis, are derived through eigenvalue analysis. By the formulation suggested in this paper, it is expected that mathematical modeling method of the pogo system can be obtained and systematic pogo stability analysis for any launch vehicle will be enabled.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.93-98
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• 2023

In this paper, an artificial neural network based on simulated annealing was constructed. The mapping relationship between the parameters of micro-scaled flow channels electrochemical machining and the channel shape was established by training the samples. The depth and width of micro-scaled flow channels electrochemical machining on stainless steel surface were predicted, and the flow channels experiment was carried out with pulse power supply in NaNO3 solution to verify the established network model. The results show that the depth and width of the channel predicted by the simulated annealing artificial neural network with "4-7-2" structure are very close to the experimental values, and the error is less than 5.3%. The predicted and experimental data show that the etching degree in the process of channels electrochemical machining is closely related to voltage and current density. When the voltage is less than 5V, a "small island" is formed in the channel; When the voltage is greater than 40V, the lateral etching of the channel is relatively large, and the "dam" between the channels disappears. When the voltage is 25V, the machining morphology of the channel is the best.