• Journal of the Korea Institute of Building Construction
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• v.16 no.4
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• pp.297-303
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• 2016

Shrinkage Reducing Agent(SRA) was developed in order to control drying shrinkage cracks in concrete, and the use of SRA is increasing since it can control drying shrinkage cracks and improve the quality of concrete structures. Although there are many types of prediction equations of drying shrinkage strain, there is no prediction method which can consider the effect of SRA up to the present. Therefore, it is impossible to predict the tensile stress generated by drying shrinkage of SRA concrete, and to investigate the quantitative serviceability limit state of SRA concrete. In this study, the drying shrinkage of SRA concrete was investigated by experiment and analysis in order to suggest the predictability of drying shrinkage of SRA concrete. As a result, AIJ model, ACI model, GL2000 model showed there was a correlation between the predicted values and the experimental values within the error range of ${\pm}10%$. However, CEB-FIP model and B3 model underestimated the experimental values.

• Bulletin of the Korean Chemical Society
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• v.33 no.4
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• pp.1123-1127
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• 2012

P-gp (P-glycoprotein) is a member of the ATP binding cassette (ABC) family of transporters. It transports many kinds of anticancer drugs out of the cell. It plays a major role as a cause of multidrug resistance (MDR). MDR function may be a cause of the failure of chemotherapy in cancer and influence pharmacokinetic properties of many drugs. Hence classification of candidate drugs as substrates or nonsubstrate of the P-gp is important in drug development. Therefore to identify whether a compound is a P-gp substrate or not, in silico method is promising. Recursive Partitioning (RP) method was explored for prediction of P-gp substrate. A set of 261 compounds, including 146 substrates and 115 nonsubstrates of P-gp, was used to training and validation. Using molecular descriptors that we can interpret their own meaning, we have established two models for prediction of P-gp substrates. In the first model, we chose only 6 descriptors which have simple physical meaning. In the training set, the overall predictability of our model is 78.95%. In case of test set, overall predictability is 69.23%. Second model with 2D and 3D descriptors shows a little better predictability (overall predictability of training set is 79.29%, test set is 79.37%), the second model with 2D and 3D descriptors shows better discriminating power than first model with only 2D descriptors. This approach will be used to reduce the number of compounds required to be run in the P-gp efflux assay.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.2
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• pp.69-74
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• 2016

Most hot water heating valves for apartments are constant-flow types, which limit the flow rate through an individual household for even distribution of heating water to other households. The constant-flow type is implemented by an on-off control. As a result, heating water is supplied intermittently and hence, indoor air temperature also fluctuates. Returning water temperature is also high, which reduces energy efficiency. To implement continuous feedback control, the indoor temperature dynamics was simulated to fit a measured temperature history by a state-of-the-art physical model. From the model, it was found that the most important disturbance is outdoor temperature and its effect on indoor temperature lasts about an hour. To cope with the slow response and the significant disturbance, a prediction control with proportional feedback is proposed. The control was found to be successful in implementing continuous heating water flow and improved indoor temperature control.

• Journal of Power Electronics
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• v.19 no.2
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• pp.497-508
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• 2019

The predictive speed control (PSC) strategy can realize the simultaneous control of speed and current by using one cost function. As a model-based control method, the performance of the PSC is vulnerable to model mismatches such as load torque disturbances and parameter uncertainties. To solve this problem, this paper presents a robust predictive speed control (RPSC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed RPSC uses extended state observers (ESOs) to estimate the lumped disturbances caused by load torque changes and parameter mismatches. The observer-based prediction model is then compensated by using the estimated disturbances. The introduction of ESOs can achieve robustness against predictive model uncertainties. In addition, a modified cost function is designed to further suppress load torque disturbances. The performance of the proposed RPSC scheme has been corroborated by experimental results under the condition of load torque changes and parameter mismatches.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1711-1716
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• 2004

This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial Neural Networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model for control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.664-667
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• 1997

For higher component density per chip, it is necessary to identify and control the semiconductor manufacturing process more stringently. Recently, neural networks have been identified as one of the most promising techniques for modeling and control of complicated processes such as plasma etching process. Since wafer states after each run using identical recipe may differ from each other, conventional neural network models utilizing input factors only cannot represent the actual state of process and equipment. In this paper, in addition to the input factors of the recipe, real-time tool data are utilized for modeling of 64M DRAM s-poly plasma etching process to reflect the actual state of process and equipment. For real-time tool data, we collect optical emission spectroscopy (OES) data. Through principal component analysis (PCA), we extract principal components from entire OES data. And then these principal components are included to input parameters of neural network model. Finally neural network model is trained using feed forward error back propagation (FFEBP) algorithm. As a results, simulation results exhibit good wafer state prediction capability after plasma etching process.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.2
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• pp.118-128
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• 2012

The goal of this paper is to design the target tracking controller for a quadrotor micro UAV using a vision sensor. First of all, the mathematical model of the quadrotor was estimated through the Prediction Error Method(PEM) using experimental input/output flight data, and then the estimated model was validated via the comparison with new experimental flight data. Next, the target tracking controller was designed using LQR(Linear Quadratic Regulator) method based on the estimated model. The relative distance between an object and the quadrotor was obtained by a vision sensor, and the altitude was obtained by a ultra sonic sensor. Finally, the performance of the designed target tracking controller was evaluated through flight tests.

• Structural Engineering and Mechanics
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• v.87 no.6
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• pp.601-614
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• 2023

In this study, the uniaxial ratcheting effect of Z2CND18.12N austenitic stainless steel at room and elevated temperatures is firstly simulated based on the Ahmadzadeh-Varvani hardening rule (A-V model), which is embedded into the finite element software ABAQUS by writing the user material subroutine UMAT. The results show that the predicted results of A-V model are lower than the experimental data, and the A-V model is difficult to control ratcheting strain rate. In order to improve the predictive ability of the A-V model, the parameter γ2 of the A-V model is modified using the isotropic hardening criterion, and the extended A-V model is proposed. Comparing the predicted results of the above two models with the experimental data, it is shown that the prediction results of the extended A-V model are in good agreement with the experimental data.

• 한국전산유체공학회:학술대회논문집
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• 2004.10a
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• pp.47-51
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• 2004

Accurate Prediction of a supersonic missile base drag continues to defy even well-rounded CFD codes. In an effort to address the accuracy and predictability of the base drags, the influence of grid system and competitive turbulence models on the base drag is analyzed. Characteristics of some turbulence models is reviewed through incompressible turbulent flow over a flat plate, and performance for the base drag prediction of several turbulence models such as Baldwin-Lomax(B-L), Spalart-Allmaras(S-A), $\kappa-\epsilon$, $\kappa-\omega$ model is assessed. When compressibility correction is injected into the S-A model, prediction accuracy of the base drag is enhanced. The NSWC wind tunnel test data are utilized for comparison of CFD and semi-empirical codes on the accuracy of base drag predictability: they are about equal, but CFD tends to perform better. It is also found that, as angle of attack of a missile with control (ins increases, even the best CFD analysis tool we have lacks the accuracy needed for the base drag prediction.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.6
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• pp.310-316
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• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.