• Current Optics and Photonics
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• v.5 no.6
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• pp.641-651
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• 2021

To handle the servo delay in a real-time adaptive optics system, a linear subspace system identification algorithm was employed to model the system, and the accuracy of the system identification was verified by numerical calculation. Experimental verification was conducted in a real test bed system. Through analysis and comparison of the experimental results, the convergence can be achieved only 200 times with prediction and 300 times without prediction. After the wavefront peak-to-valley value converges, its mean values are 0.27, 4.27, and 10.14 ㎛ when the communication distances are 1.2, 4.5, and 10.2 km, respectively. The prediction algorithm can effectively improve the convergence speed of the peak-to-valley value and improve the free-space optical communication performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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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.

• Journal of the Korean Society for Railway
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• v.18 no.4
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• pp.325-334
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• 2015

This paper deals with a control method for a hybrid energy storage system (ESS) with a battery and super capacitor for grid-connected operation. To improve the grid regulation and to minimize the depth of discharge of the battery, control algorithms for the hybrid ESS are proposed. A p-q control with predictive current control is used for the inverters for the super-capacitor and the battery. To verify the effectiveness of the proposed control method, simulation studies using the PSIM and RTDS are carried out for the actual grid model.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.2
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• pp.23-30
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• 2012

This paper proposes a novel adaptive header-based power gating structure to compensate for the performance loss and the increased wake-up time of the power gating structures induced by the negative bias temperature instability (NBTI) effect. The proposed structure consists of variable width footers based on the two-pass power gating and a new NBTI sensing circuit for an adaptive control. The simulation results of the proposed structure are compared to those of power gating without the adaptive control and show that both the circuit-delay and wake-up time dependence of the power gating structure on the NBTI stress is minimized with only 3% and 4% increase, respectively while keeping small leakage power and rush-current. In this paper, a 45 nm CMOS technology and predictive NBTI model have been used to implement the proposed circuits.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• Journal of Advanced Navigation Technology
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• v.18 no.3
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• pp.215-222
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• 2014

In this paper, parafoil airdrop system has been designed and analyzed. 6-degrees of freedom (6-DOF) model of the parafoil system is set up. Nonlinear model predictive control (NMPC) and Proportion integration differentiation (PID) methods were separately applied to adjust the flap yaw angle. Compared the results of setting time and overshoot time of yaw angle, it is found that the of yaw angle is more stable by using PID method. Then, trajectory following controller was designed based on the simulation results of trajectory following effects, which was carried out by using MATLAB. The lateral offset error of parafoil trajectory can be eliminated by its lateral deviation control. The later offset deviation reference was obtained by the interpolation of the current planning path. Moreover, using the designed trajectory, the trajectory following system was simulated by adding the wind disturbances. It is found that the simulation result is highly agreed with the designed trajectory, which means that wind disturbances have been eliminated with the change of yaw angle controlled by PID method.

• Korean Journal of Culture and Social Issue
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• v.28 no.4
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• pp.661-690
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• 2022

Adolescent delinquency is a substantial social problem that occurs in both offline and online domains. The current study utilized random forest algorithms to identify predictors of adolescents' online and offline delinquency. Further, we explored the applicability of classic delinquency theories (social learning, strain, social control, routine activities, and labeling theory). We used the first-grade and fourth-grade elementary school panels as well as the first-grade middle school panel (N=4,137) among the sixth wave of the nationally-representative Korean Children and Youth Panel Survey 2010 for analysis. Random forest algorithms were used instead of the conventional regression analysis to improve the predictive performance of the model and possibly consider many predictors in the model. Random forest algorithm results showed that classic delinquency theories designed to explain offline delinquency were also applicable to online delinquency. Specifically, salient predictors of online delinquency were closely related to individual factors(routine activities and labeling theory). Social factors(social control and social learning theory) were particularly important for understanding offline delinquency. General strain theory was the commonly important theoretical framework that predicted both offline and online delinquency. Findings may provide evidence for more tailored prevention and intervention strategies against offline and online adolescent delinquency.