• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.2
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• pp.71-83
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• Earthquakes and Structures
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• v.18 no.1
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• pp.27-44
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• 2020

This work presents a comparison of three performance-based seismic design methods (PBSD) as applied to plane steel frames having eccentric braces (EBFs) and buckling restrained braces (BRBFs). The first method uses equivalent modal damping ratios (ξ_k), referring to an equivalent multi-degree-of-freedom (MDOF) linear system, which retains the mass, the elastic stiffness and responds in the same way as the original non-linear MDOF system. The second method employs modal strength reduction factors (${\bar{q}}_k$) resulting from the corresponding modal damping ratios. Contrary to the behavior factors of code based design methods, both ξ_k and ${\bar{q}}_k$ account for the first few modes of significance and incorporate target deformation metrics like inter-storey drift ratio (IDR) and local ductility as well as structural characteristics like structural natural period, and soil types. Explicit empirical expressions of ξ_k and ${\bar{q}}_k$, recently presented by the present authors elsewhere, are also provided here for reasons of completeness and easy reference. The third method, developed here by the authors, is based on a hybrid force/displacement (HFD) seismic design scheme, since it combines the force-base design (FBD) method with the displacement-based design (DBD) method. According to this method, seismic design is accomplished by using a behavior factor (q_h), empirically expressed in terms of the global ductility of the frame, which takes into account both non-structural and structural deformation metrics. These expressions for q_h are obtained through extensive parametric studies involving non-linear dynamic analysis (NLDA) of 98 frames, subjected to 100 far-fault ground motions that correspond to four soil types of Eurocode 8. Furthermore, these factors can be used in conjunction with an elastic acceleration design spectrum for seismic design purposes. Finally, a comparison among the above three seismic design methods and the Eurocode 8 method is conducted with the aid of non-linear dynamic analyses via representative numerical examples, involving plane steel EBFs and BRBFs.

• Proceedings of the Korean Geotechical Society Conference
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• 2007.09a
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• pp.317-333
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• 2007

The Round-Robin Test (RRT) for ground response analysis was performed by Division of Geotechnical Earthquake Engineering of Korean Geotechnical Society. This research analyzed the influence of analysis methods on variation of ground response by using the results of this RRT. The analysis methods include equivalent linear analysis, non-linear analysis and effective stress analysis. A total of 5 teams among 12 teams applied two kinds of analysis methods. This research compared the results of these 5 teams and analyzed the variation of the results according to analysis methods. The compared results were shear stress-shear strain relation, transfer function, time history and the response spectrum of ground surface acceleration, peak ground acceleration, peak shear strain and maximum excess pore pressure ratio.

• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1365-1372
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• 2011

This paper proposes a cognitive contrast enhancement algorithm based on the non-linear masking to advance low cognitive contrast in dark regions of images. In order to improve brightness in dark regions of an image, we propose a new contrast enhancement algorithm based on the non-linear masking using regional adaptive parameters of an image. For performance evaluation of the proposed method, chromaticity and saturation comparison as a quantitative assessment and z-score comparison as a qualitative assessment were executed between test images and their simulated images by SSR, MSR, a conventional non-linear masking and the proposed method, respectively. As a result, the proposed method showed low chromaticity and saturation difference and improved cognitive contrast for the three methods.

• Computers and Concrete
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• v.25 no.4
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• pp.327-341
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• 2020

Advanced forms of structural design (e.g., displacement-based methods) require knowledge of the non-linear force-displacement behavior of both the overall building and individual lateral load resisting elements, i.e., walls or building cores. Similarly, understanding the non-linear behaviour of the elements in a structure can also allow for a less conservative structural response to be calculated by better understanding the cracked (i.e., effective) properties of the various RC elements. Calculating the non-linear response of an RC section typically involves using 'black box' analysis packages, wherein the user may not be in complete control nor be aware of all the intricate settings and/or decisions behind the scenes. This paper introduces a user-friendly and transparent analysis program for predicting the back-bone force displacement behavior of slender (i.e., flexure controlled) RC walls, building cores or columns. The program has been validated and benchmarked theoretically against both commonly available and widely used analysis packages and experimentally against a database of 16 large-scale RC wall test specimens. The program, which is called WHAM, is written using Microsoft Excel spreadsheets to promote transparency and allow users to further develop or modify to suit individual requirements. The program is available free-of-charge and is intended to be used as an educational tool for structural designers, researchers or students.

• Journal of Navigation and Port Research
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• v.41 no.1
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• pp.1-8
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• 2017

The estimation of non-linear ship motion is one of the most important issues in recent studies of ship stability. In this paper, bispectral analysis and bicoherence analysis were introduced in order to analyze the non-linear ship motion. In addition to the previously observed non-linear pitching motion in following seas, this study observed the non-linear phase coupling of pitching motion in following & quartering seas, and starboard beam seas. By comparing phase coupling between each frequency quantitatively via the bicoherence analysis, it was confirmed that non-linear phase coupling was much stronger in frequency regions other than the peak frequencies of a power spectrum. Furthermore, it was found out that the results of bicoherence calculation were analagous to each other, although the different normalization methods were applied.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.15-22
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• 2011

Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction while maintaining most of the variation in data. Although PCA has been applied in many areas successfully, it is sensitive to outliers and only valid for Gaussian distributions. Several variants of PCA have been proposed to resolve noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA, however, is still a linear algorithm that cannot accommodate non-Gaussian distributions. In this paper, a non-linear algorithm that combines RF-PCA2 and kernel PCA (K-PCA), called improved robust kernel fuzzy PCA (RKF-PCA2), is introduced. The kernel methods make it to accommodate non-Gaussian distributions. RKF-PCA2 inherits noise robustness from RF-PCA2 and non-linearity from K-PCA. RKF-PCA2 outperforms previous methods in handling non-Gaussian distributions in a noise robust way. Experimental results also support this.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.185-191
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• 2004

Partial least squares(PLS) is one of multiplicate statistical process methods and has been developed in various algorithms with the characteristics of principal component analysis, dimensionality reduction, and analysis of the relationship between input variables and output variables. But it has been limited somewhat by their dependency on linear mathematics. The algorithm is proposed to classify for the non-linear data using PLS and the residual compensator(RC) based on radial basis function network (RBFN). It compensates for the error of the non-linear data using the RC based on RBFN. The experimental result is given to verify its efficiency compared with those of previous works.

• 전기의세계
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• v.31 no.1
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• pp.59-67
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• 1982

This paper suggests easy stabilization methods for linear time-varying systems with delay in the state. While existing methods employ the function space concept, the methods introduced in this paper transform the delay systems into the non-delay systems so that the well known methods for finite dimensional systems can be utilized. Particularly the intervalwise predictor is introduced and shown to satisfy an ordinary system. Control laws stabilizing the non-delay systems satisfied by this predictor will be shown to at least pointwise stabilize the delay systems with the additional strong possibility of true stabilization. In order to combine two steps of the predictor method, first transformation and then stabilization, an intervalwise regulator problem is suggested whose optimal control laws incorporate the intervalwise predictor as an integral part and also at least pointwise stabilize the delay systems. Since the above mentioned methods render the periodic feedback gains for time invariant systems the pointwise predictor and regulator are introduced in order to obtain the constant feedback gains, with additional stability properties. The control laws given in this paper are perhaps simplest and easiest to implement.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.189-204
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• 2021

In a regression analysis, a single best model is usually selected among several candidate models. However, it is often useful to combine several candidate models to achieve better performance, especially, in the prediction viewpoint. Model combining methods such as stacking and Bayesian model averaging (BMA) have been suggested from the perspective of averaging candidate models. When the candidate models include a true model, it is expected that BMA generally gives better performance than stacking. On the other hand, when candidate models do not include the true model, it is known that stacking outperforms BMA. Since stacking and BMA approaches have different properties, it is difficult to determine which method is more appropriate under other situations. In particular, it is not easy to find research papers that compare stacking and BMA when regression model assumptions are violated. Therefore, in the paper, we compare the performance among model averaging methods as well as a single best model in the linear regression analysis when standard linear regression assumptions are violated. Simulations were conducted to compare model averaging methods with the linear regression when data include outliers and data do not include them. We also compared them when data include errors from a non-normal distribution. The model averaging methods were applied to the water pollution data, which have a strong multicollinearity among variables. Simulation studies showed that the stacking method tends to give better performance than BMA or standard linear regression analysis (including the stepwise selection method) in the sense of risks (see (3.1)) or prediction error (see (3.2)) when typical linear regression assumptions are violated.