• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2488-2497
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• 1997

In this paper, a computer simulation of the Lornz discrete chaotic system is described. The chotic behavior closely matches the results predicted by numerical simulations. Using the concept of discrete synchronized chaotic systems, the possibility of a secure communication is proved by simulating the Lorenz system in both the transmitter and receiver. In the proposed approach, at first, a chaotic modulating signal is multiplied with the message, and these are transmitted with adding a chaotic modulating signals, and then at the receiver, the chaotic modulating signal is regenerated and divided from the receiver signal. Varying a smapling time interval to calibrate the robustness of the Lorenz discrete synchronized chaotic system as a nonlinear state estimator, we measured the performance of the Lorenz discrete syncrhonized chaotic system by comparing the synchronization error and the error between transmitted signal and received signal.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.44-45
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• 2007

This paper deals with error compensation in current transformers. Since the exciting current can be considered as the main error source, its evaluation can allow the compensation of its detrimental effects to be obtained. The exciting current required by the transformer in every king of steady state operation can be determined by simply acquiring the secondary current, provided that the examined CT has been preliminarily identified. This paper also proposed a new approach to the model of the exciting branch. The exciting branch can be divided into a non-linear core loss resistor, and a non-linear magnetizing inductor whose flux and current characteristic is not the same as the characteristic shown by the joined tips of the first quadrant of a family of hysteresis loops. The performance of the proposed algorithm was validated under various conditions using EMTP generated data. Test result show, in all cases an improvement in primary current reproduction accuracy, compared with that achieved using CT's ratio.

• Smart Structures and Systems
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• v.30 no.4
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• pp.339-351
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• 2022

Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.

• Journal of the Korean Ceramic Society
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• v.53 no.3
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• pp.317-324
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• 2016

Continued miniaturization and increasingly exact requirements for thin film deposition in the semiconductor industry is driving the search for new effective, efficient, selective precursors and processes. The requirements of defect-free, conformal films, and precise thickness control have focused attention on atomic layer deposition (ALD). ALD precursors so far have been developed through a trial-and-error experimental approach, leveraging the expertise and tribal knowledge of individual research groups. Precursors can show significant variation in performance, depending on specific choice of co-reactant, deposition stage, and processing conditions. The chemical design space for reactive thin film precursors is enormous and there is urgent need for the development of computational approaches to help identify new ligand-metal architectures and functional co-reactants that deliver the required surface activity for next-generation thin-film deposition processes. In this paper we discuss quantum mechanical simulation (e.g. density functional theory, DFT) applied to ALD precursor reactivity and state-of-the-art automated screening approaches to assist experimental efforts leading toward optimized precursors for next-generation ALD processes.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.657-665
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• 1991

Although orthogonal function is introduced in control theory in early 1970's, it is not perfect. Since the concept of integral operator by Chen and Hsiao in mid 1970's, orthogonal function (for example Walsh, Block-pulse, Haar, Laguerre, Legendre, Chebychev etc) has been widely applied In system's analysis and identification, model reduction, state estimation, optimal control, signal processing, image processing, EEG, and ECG etc. The reason why Walsh Functions introduces in control theory is that as integral of Walsh function is also developed in Walsh orthogonal function, if we transfer give system into integral equation and introduce Walsh function. We can know that system's characteristic by algebraical expression. This approach is based on least square error and that result is expressed as computer calculation and partly continuous constant value which is easy to apply. Such a Walsh function has been actively studied in USA, TAIWAN, INDO, CHINA, EUROPE etc and in domestic, author has studied it for 10 years since it was is introduced in 1982. This paper is consider the that author has studied for 10 years and Walsh function's efficiency.

• Advances in environmental research
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• v.4 no.2
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• pp.83-104
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• 2015

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.129-136
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• 2004

Anisotropic characteristics of deformation are important to understand the particular behavior in the pre-failure state of soils. Recent experiments shows that cross-anisotropic moduli of granular soils can be expressed by functions of normal stresses in the corresponding directions, which is closely linked to micromechanical characteristics of particles. Granular soils are composed of a number of particles so that the force-displacement relationship at each contact point governs the macroscopic stress-strain relationship. Therefore, the micromechanical approach in which the deformation of granular soils is regarded as a mutual interaction between particle contacts is one of the best ways to investigate the anisotropic deformation of soils. In this study, a numerical program based on the theory of micromechanics is developed. Modified Hertz-Mindlin model is adopted to represent the force-displacement relationship in each contact point for the realistic prediction of anisotropic moduli. To evaluate the model parameters, a set of analytical solutions of anisotropic moduli is derived in the isotropic stress condition. By comparing the analytical solutions with exact values, we confirm that the analytical solutions can be utilized to evaluate model parameters within the acceptable range of error of 10%.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.2
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• pp.8-15
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• 2010

This paper proposes a robust high-gain observer design scheme for nonlinear systems and its stability is analyzed based on Lyapunov theory. It is assumed that their states are unmeasurable. The proposed high-gain observer has the integrator of the estimation error in dynamics. It improves the performance of high-gain observers and makes the proposed observer robust to noisy measurements, uncertainties and peaking phenomenon as well. Its stability is analyzed by the Lyapunov approach. In order to verify the effectiveness of the proposed scheme, it is applied to output feedback controllers and some comparative simulation result with the existed observer based output feedback controllers and state feedback controllers is given.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4967-4986
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• 2015

Multi-hop relaying communications have great potentials in improving transmission performance by deploying relay nodes. The benefit is critically dependent on the accuracy of the channel state information (CSI) of all the transmitting links. However, the CSI has to be estimated. In this paper, we investigate the channel estimation problem in one-way multi-hop MIMO amplify-and-forward (AF) relay system, where both the two-hop and three-hop communication link exist. Traditional point-to-point MIMO channel estimation methods will result in error propagation in estimating relay links, and separately tackling the channel estimation issue of each link will lose the gain as part of channel matrices involved in multiple communication links. In order to exploit all the available gains, we develop a novel channel estimation model by structuring different communication links using the PARAFAC and PARATUCK2 tensor analysis. Furthermore, a two-stage fitting algorithm is derived to estimate all the channel matrices involved in the communication process. In particular, essential uniqueness is further discussed. Simulation results demonstrate the advantage and effectiveness of the proposed channel estimator.