• Progress in Medical Physics
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• v.20 no.3
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• pp.132-138
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• 2009

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

• Journal of the Korean Society for Railway
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• v.15 no.4
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• pp.413-421
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• 2012

The purpose of this study is to evaluate the structural dynamic behavior under hybrid control system. The hybrid test is to consider the interaction between the numerical and physical models. In this paper, single degree of freedom hybrid test was performed with one-bay, two-story steel frame structure. One column at the first floor was selected as a physical substructure and one actuator was used for applying the displacement load in horizontal direction. El Centro as earthquake waves was inputted and OpenSees was employed as the numerical analysis program for the hybrid real-time simulation. As a result, the total time of the hybrid test was about 9.6% of actual measured seismic period. The experimental results agreed well with the numerical one in terms of the maximum displacement. In nonlinear analysis, however, material nonlinearity made a difference of residual strain. Therefore, this hybrid dynamic test can be used to predict the structural dynamic performance more effectively than shaking table test, because of the spatial and economic limitations.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.6
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• pp.715-722
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• 2011

In this paper, the behavior of nuclear-power plant subjected to an aircraft impact is performed using the parallel analysis. In the erstwhile study of an aircraft impact to the nuclear-power plant, it has been used that the impact load is applied at the local area by using the impact load-time history function of Riera, and the target structures have been restricted to the simple RC(Reinforced Concrete) walls or RC buildings. However, in this paper, the analysis of an aircraft impact is performed by using a real aircraft model similar to the Boeing 767 and a fictitious nuclear-power plant similar to the real structure, and an aircraft model is verified by comparing the generated history of the aircraft crash against the rigid target with another history by using the Riera's function which is allowable in the impact evaluation guide, NEI07-13(2009). Also, in general, it is required too much time for the hypervelocity impact analysis due to the contact problems between two or more adjacent physical bodies and the high nonlinearity causing dynamic large deformation, so there is a limitation with a single CPU alone to deal with these problems effectively. Therefore, in this paper, Message-Passing MIMD type of parallel analysis is performed by using self-constructed Linux-Cluster system to improve the computational efficiency, and in order to evaluate the parallel performance, the four cases of analysis, i.e. plain concrete, reinforced concrete, reinforced concrete with bonded containment liner plate, steel-plate concrete structure, are performed and discussed.

• Journal of the Korean Geotechnical Society
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• v.32 no.4
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• pp.5-14
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• 2016

Numerical simulation of dynamic soil-pile-structure interaction embedded in a dry sand was carried out. 3D model of the dynamic centrifuge model tests was formulated in a time domain to consider nonlinear behavior of soil using the finite difference method program, FLAC3D. As a modeling methodology, Mohr-Coulomb criteria was adopted as soil constitutive model. Soil nonlinearity was considered by adopting the hysteretic damping model, and an interface model which can simulate separation and slip between soil and pile was adopted. Simplified continuum modeling (Kim et al., 2012) was used as boundary condition to reduce analysis time. Calibration process for numerical modeling results and test results was performed through the parametric study. Verification process was then performed by comparing numerical modeling results with another test results. Based on the calibration and validation procedure, it is identified that proposed modeling method can properly simulate dynamic behavior of soil-pile system in dry condition.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.486-491
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• 2008

This paper describes a model identification method that has been applied to a commercial 12-inch RTP (rapid thermal processing) equipment with an ultimate aim to develop a high-performance advanced controller. Seven thermocouples are attached on the wafer surface and twelve tungsten-halogen lamp groups are used to heat up the wafer. To obtain a MIMO balanced state space model, multiple SIMO (single-input multiple-output) identification with highorder ARX models have been conducted and the resulting models have been combined, transformed and reduced to a MIMO balanced state space model through a balanced truncation technique. The identification experiments were designed to minimize the wafer warpage and an output linearization block has been proposed for compensation of the nonlinearity from the radiation-dominant heat transfer. As a result from the identification at around 600, 700, and $800^{\circ}C$, respectively, it was found that $y=T(K)^2$ and the state dimension of 80-100 are most desirable. With this choice the root-mean-square value of the one-step-ahead temperature prediction error was found to be in the range of 0.125-0.135 K.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.487-496
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• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.6
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• pp.110-117
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• 2011

In order to study the growth models between the growth of Sedum takevimense and growth rate in soil with three types of mix ratios, this experiment was carried out on April 3rd, 2011. A nonlinearity regression analysis was performed using the Logistic and Gompertz models by SPSS. According to the study of growth models of Sedum takevimense, the process of growth and management methods after over-wintering were explicitly determined. According to the measured values, the growth in the soil of $P_1P_2V_1$ and $P_2P_1V_1$ was better than that of $P_1$. Particularly, the average length of Sedum takevimense in the soil of $P_1P_2V_1$ was about twice as great as that in the $P_1$. The fitness test of the two growth models was: The predicted value and measured value were separately compared and analysed, the average fitting precision $R^2$ of the Logistic models was 0.995, but the average $R^2$ of the Gompertz models was below 0.978, which showed that the Logistic models were better than the Gompertz models. The growth models also showed that the growth time of Sedum takevimense was divided into three: rapid, most rapid and slow. When managed in the rapid and the most rapid time, it will grow better.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.12
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• pp.1339-1349
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• 2008

In this paper, a nonlinear relationship between an input complex envelope and an output complex envelope of m-th harmonic zone is theoretically analyzed, and AM/$AM_m$ and AM/$PM_m$ are defined. A scheme to extract these characteristics from measured in-phase and quadrature-phase data is suggested. The proposed analysis is verified with a fundamental-fundamental and fundamental-third harmonic measurements for a InGaP power amplifier(PA). Based on the harmonic-band nonlinear analysis and extraction scheme, a new technique to send a signal in m-th harmonic band with a harmonic signal Linearization Digital Predistortion(DPD) scheme is presented. A numerical analysis and a Look-Up Table(LUT) based DPD algorithms to linearize output signal on m-th harmonic zone are developed. For a 16- and a 64-QAM input signals, a DPD for third harmonic signal linearization is implemented, and output spectrum and signal constellation are measured. The wholly distorted signals are linearized, and thus the measured Error Vector Magnitudes (EVM) are 6.4 % and 6.5 % respectively. The results show that a proposed scheme linearizes a nonlinearly distorted harmonic band signals. The proposed nonlinear analysis and predistortion scheme can be applied to multiband transmitter in next generation software defined radio(SDR)/cognitive radio(CR) wireless system.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.