• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.05a
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• pp.240-243
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• 2003

Combustion instability phenomena have been observed in various different combustion systems. For each specific combustion system, pressure fluctuations measured during high frequency combustion instability presented many different characteristics. High frequency instability occurring in a lean premixed gas turbine combustor mar be dominantly affected by a nonlinear relation between pressure oscillations and heat release rate fluctuations, and gas dynamics plays a crucial role in determining an amplitude of a limit cycle for a liquid rocket thrust chamber. Combustion instability phenomena manifest their inherent nonlinear characteristics. One is a limit cycle and the other bifurcation described by nonlinear time series analysis.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.769-777
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• 1998

In this study, various time series are analyzed to check nonlinearities of the data. The nonlinearity of a system can be investigated by testing the randomness of the time series data. To test the randomness, four nonparametric test statistics and a new test statistic, called the BDS statistic are used and the results and the results are compared. The Brock, Dechert, and Scheinkman (BDS) statistic is originated from the statistical properties of the correlation integral which is used for searching for chaos and has been shown very effective in distinguishing nonlinear structures in dynamic systems from random structures. As a result of application to linear and nonlinear models which are well known, the BDS statistic is found to be more effective than nonparametric test statistics in identifying nonlinear structure in the time series. Hydrologic time series data are fitted to ARMA type models and the statistics are applied to the residuals. The results show that the BDS statistic can distinguish chaotic nonlinearity from randomness and that the BDS statistic can also be used for verifying the validity of the fitted model.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.451-454
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• 1997

It has been reported that sudden cardiac death and ventricular tachycardia occur after treatment of tetralogy of fallot(TOF). It is regarded that ventricular arrythmia is the main source or the sudden cardiac death, but it is not verified. It is likely that TOF has effect on the heart rate variability because of the ventricular arrythmia. We study how complex and periodic heart rate dynamics change in the normal children (n=13) and TOF children (n=13) throughout 24 hours. We recorded 24-hour holter ECG, and segmented each ECG data into 1 hour length. We analyze each HR time series, and quantify the overall complexity of each HR time series by its correlation dimension. We also calculate the power spectrum of HR, and obtain low-frequency component (0.03-0.15Hz) and high-frequency component (0.15-0.4Hz). We compare the results between normal and TOF groups throughout 24 hours. TOF group have lower correlation dimension ($4.055{\pm}0.4134$ vs. $4.9310{\pm}0.2054$, p<0.05) than the normal group, even though there are no significant differences in the low($0.9864{\pm}0.5598$ vs. $1.5560{\pm}0.8325$, p<0.05) and high($1.1168{\pm}0.1.1448$ vs. $0.9271{\pm}0.6528$, p<0.05) frequency components. It can be concluded that HR time series of TOF group are more regular than that of normal group, and that correlation dimension reveals a nonlinear characteristics of HR time series which is not determined in the frequency domain.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.484-492
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• 2010

Magnetic switch is a kind of saturable inductor, which utilizes nonlinearity of the magnetization curve of ferromagnetic materials. The right understanding of the saturation phenomena, magnetic properties, voltage-time product, and switching characteristics of the magnetic switch is essential in designing the magnetic pulse compressor (MPC). In this paper, the historical background of research on the MPC, fundamental physical properties of the magnetic switches, and application fields of the MPC are presented. Further, an in-depth analysis of pulse compression in series and parallel MPCs is incorporated. As practical application examples, a series MPC used for water treatments and a parallel MPC used for pulsed electric field (PEF) inactivation of bacteria are cited.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.4
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• pp.84-92
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• 1998

A program was developed to analyze the electrical characteristics and harmonic distrotion in a two-dimensional silicon BJT. The finite difference equations of the small signal and its second and thired harmonics for basic semiconductor equations are formulated treating the nonlinearity and time dependence with Volterra series and Taylor series. The soluations for three sets of simultaneous equations were obtained sequantially by a decoupled iteration method and each set was solved by a modified Stone's algorithm. Distortion magins and ac parameters such as input impedance and current gains are calculated with frequency and load resistance as parameters. The distortion margin vs. load resistancecurves show cancellation minima when the pahse of output voltage shifts. It is shown that the distortionof small signal characteristics can be reduced by reducing the base width, increasing the emitter stripe length and reducing the collector epitaxial layer doping concentration in the silicon BJT structure. The simulation program called TRADAP can be used for the design and optimization of transistors and circuits as well as for the calculation of small signal and distortion solutions.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Smart Structures and Systems
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• v.26 no.1
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.401-406
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• 2004

In this paper, a new time-domain adaptive predistortion scheme is proposed to compensate for the nonlinearity of high power amplifiers (HPA) in OFDM systems. A complex Wiener-Hammerstein model (WHM) is adopted to describe the input-output relationship of unknown HPA with linear dynamics, and a power series model with memory (PSMWM) is used to approximate the HPA expressed by WHM. By using the PSMWM, the compensation input to HPA is calculated in a real-time manner so that the linearization from the predistorter input to the HPA output can be attained even if the nonlinear input-output relation of HPA is uncertain and changeable. In numerical example, the effectiveness of the proposed method is confirmed and compared with the identification method based on PSMWM.

• Structural Engineering and Mechanics
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• v.41 no.5
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• pp.675-689
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• 2012

Most connections of steel structures exhibit flexible behaviour under cyclic loading. The flexible connections can be assumed as nonlinear rotational springs attached to the ends of each beam. The nonlinear behaviour of the connections can be considered by suitable moment-rotation relationship. Time-history analysis by direct integration method can be used as a powerful technique to determine the nonlinear dynamic response of the structure. In conventional numerical integration, the response is evaluated for a series of short time increments. The limitations on the size of time intervals can be removed by using Chen and Robinson improved time history analysis method, in which the integrated displacements are used as the new variables in integrated equation of motion. The proposed method permits longer time intervals and reduces the computational works. In this paper the nonlinearity behaviour of the structure is summarized on the connections, and the step by step improved time-history analysis is used to calculate the dynamic response of the structure. Several numerical calculations which indicate the applicability and advantages of the proposed methodology are presented. These calculations illustrate the importance of the effect of the nonlinear behaviour of the flexible connections in the calculation of the dynamic response of steel frames.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.525-537
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• 2016

The paper considers a hybrid model to analyze and forecast time series data based on an empirical mode decomposition (EMD) that accommodates complex characteristics of time series such as nonstationarity and nonlinearity. We aggregate IMFs using the concept of cumulative energy to improve the interpretability of intrinsic mode functions (IMFs) from EMD. We forecast aggregated IMFs and residue with a hybrid model that combines the ARIMA model and an exponential smoothing method (ETS). The proposed method is applied to forecast KOSPI time series and is compared to traditional forecast models. Aggregated IMFs and residue provide a convenience to interpret the short, medium and long term dynamics of the KOSPI. It is also observed that the hybrid model with ARIMA and ETS is superior to traditional and other types of hybrid models.