• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.840-855
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• 2011

In the context of additive noise steganography model, we propose a method to detect least significant bit (LSB) matching steganography in grayscale images. Images are decomposed into detail sub-bands with local linear transform (LLT) masks which are sensitive to embedding. Novel normalized characteristic function features weighted by a bank of band-pass filters are extracted from the detail sub-bands. A suboptimal feature set is searched by using a threshold selection algorithm. Extensive experiments are performed on four diverse uncompressed image databases. In comparison with other well-known feature sets, the proposed feature set performs the best under most circumstances.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1854-1855
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• 2006

In this paper, the Takagi-Sugeno (TS) fuzzy state estimation scheme, which is suggested for a steady state estimator using standard Kalman filter theory with uncertainties. In that case, the steady state with uncertain can be represented by the TS fuzzy model structure, which is further rearranged to give a set of uncertain linear model using standard Kalman filter theory. And then the unknown uncertainty is regarded as an additive process noise. To optimize fuzzy system, we utilize the genetic algorithm. The steady state solutions can be found for proposed linear model then the linear combination is used to derive a global model. The proposed state estimator is demonstrated on a truck-trailer.

• Computers and Concrete
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• v.9 no.5
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• pp.375-387
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• 2012

The objective of this investigation was to develop rules for automatic categorization of concrete quality using selected artificial intelligence methods based on machine learning. The range of tested materials included concrete containing a new waste material - solid residue from coal combustion in fluidized bed boilers (CFBC fly ash) used as additive. The rapid chloride permeability test - Nordtest Method BUILD 492 method was used for determining chloride ions penetration in concrete. Performed experimental tests on obtained chloride migration provided data for learning and testing of rules discovered by machine learning techniques. It has been found that machine learning is a tool which can be applied to determine concrete durability. The rules generated by computer programs AQ21 and WEKA using J48 algorithm provided means for adequate categorization of plain concrete and concrete modified with CFBC fly ash as materials of good and acceptable resistance to chloride penetration.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2001-2002
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• 2006

시각센서로부터 가까운 물체들을 다루게 되는 경우 원근효과를 통해 로봇주변의 물체들을 인지하게 된다. 영역추적기법은 컴퓨터비전에서 가장 널리 사용되는 기술 중 하나이며, 실시간 추적을 위해 많은 효율적 추적기법들이 개발되어 왔다. Hager와 Belhumeur는 Kanade 추적기를 수정한 효율적인 Hager-Belhumeur 알고리즘을 제안하였으며, 어파인(affine) 움직임까지 추적가능함을 보였다. 본 연구에서는 Hager-Belhumeur 알고리즘을 원근 움직임에 적용할 수 있도록 확장하였으며, 기존의 원근 움직임 영역추적기법들과 수렴테스트를 통해 성능비교를 하였으며, 실시간 추적을 수행하였다.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.350-353
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• 1994

This paper presents a method to calculate the characteristic root areas and loci band of control systems with uncertainties. First, equations of boundary curves of root areas in the case of additive and multiplicative perturbation are derived. Then, an algorithm for the calculation of the array of closed curves is presented. When the upper bound of the absolute values of frequency responses for the uncertain part, is also frequency-dependent, the frequency-dependent, terms are included in the characteristic equation of the nominal system. This lead to the boundary equations of the root, areas for control systems with frequency-dependent uncertainty. Numerical examples of the control systems with multiplicative perturbations including frequency-dependent terms are presented to verify this calculation method. Finally, its applications to the design of robust control systems, e.g., passive adaptive control systems are also discussed.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.7
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• pp.44-54
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• 1996

In DS/SS systems it is necessary to synchronize the locally generated despreading signal with the received spreading signal to demodulate the received signal. The synch process between the two signals is usually accomplished in two steps : first acquisition then tracking. In this paper, an acquisition system aided by the neural network is proposed for the rapid and exact acquisition in DS/SS. the neural netowrk is composed o fthree-layered perpecptrons and trained by the backpropagation algorithm. The performance of the proposed system is analyzed and compared with ones of conventional systems using the sequential estimation technique under an additive while gaussian noisy channel. In all of th econsidered simulations, the proposed system outperforms conventional systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.559-562
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• 2011

In this research, We derived Recursive Least Squares(RLS) algorithm with adaptive maximum-likelihood channel estimate for digital pulse amplitude modulated sequence in the presence of intersymbol interference and additive white Gaussian noise. RLS algorithms have better convergence characteristics than conventional algorithms, LMS (Least Mean Squares) algorithms.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.171-177
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• 2003

In this paper, we investigate a robust $H_{\infty}$ state feedback control technique for continuous time bilinear systems with an additive disturbance input. The nonlinear robust $H_{\infty}$control for bilinear systems requires a solution to the state dependent algebraic Riccati equation (SDARE). We present a new robust $H_{\infty}$control technique based on the successive approximation method for solving the SDARE by converting bilinear systems into time-varying linear systems. The proposed control method guarantees robust stability for closed loop bilinear systems. The proposed algorithm is verified by numerical examples.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1272-1275
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• 2002

Watermark detection plays a crucial role in multimedia copyright protection and has traditionally been tackled using correlation-based algorithms. However, correlation-based detection is not actually the best choice, as it does not utilize the distributional characteristics of the image being marked. Accordingly, an efficient watermark detection scheme for DWT coefficients is proposed as optimal for non-additive schemes. Based on the statistical decision theory, the proposed method is derived according to Bayes' decision theory, the Neyman-Pearson criterion, and the distribution of the DWT coefficients, thereby minimizing the missed detection probability subject to a given false alarm probability. The proposed method was tested in the context of robustness, and the results confirmed the superiority of the proposed technique over conventional correlation-based detection method.

• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.445-454
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• 2006

We describe a solution to the Ornstein-Uhlenbeck equation $\frac{dI}{dt}-\frac{1}{\tau}$I(t)=cV(t) where V(t) is a constant multiple of a Gaussian white noise. Our solution is based on a discrete set of Gaussian white noise obtained by taking sample points from a sum of single frequency harmonics that have random amplitudes, random frequencies, and random phases. Hence, it is different from the solution by the standard random walk using random numbers generated by the Box-Mueller algorithm. We prove that the power of the signal has the additive property, from which we derive that the Lyapunov characteristic exponent for our solution is positive. This compares with the solution by other methods where the noise is kept to be in an error range so that its Lyapunov exponent is negative.