• The Pure and Applied Mathematics
• /
• v.18 no.2
• /
• pp.97-111
• /
• 2011

We provide a semilocal convergence result for approximating a solution of a singular system with constant rank derivatives, using Newton's method in an Euclidean space setting. Our approach uses more precise estimates and a combination of two Lipschitz-type conditions leading to the following advantages over earlier works [13], [16], [17], [29]: tighter bounds on the distances involved, and a more precise information on the location of the solution. Numerical examples are also provided in this study.

• Convergence Security Journal
• /
• v.12 no.3
• /
• pp.45-52
• /
• 2012

CC does not Contain detailed instructions about evaluation document. So, we must develop document schema to make CC-based evaluation system. In this report, we developed document schema that can be used in CC-based evaluation system. We devloped document schema and DTD that applying Weakest precondition function, reduction rules about amount of document and dependancy analysis document from assurance class within CC. Approach of this study can be applied to develop document and DTD that can be used in evaluation system of software quality.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.6
• /
• pp.446-452
• /
• 2004

Under the condition of a common narrowband target signal and interference signals from several directions, the linearly constrained minimum variance (LCMV) method using the generalized sidelobe canceller (GSC) for adaptive beamforming has been exploited successfully However, in the case of wideband signals, the length of the adaptive filter must be extended. As a result, the complexity of the beamformer increases, which makes real-time implementation difficult. In this paper, we improve the convergence characteristics of the adaptive filter using the transform domain normalized least mean square (NLMS) approach based on the subband GSC structure without the increase of complexity. Besides, the M-MAX algorithm, which is one of various selective coefficient updating methods, is employed in order to remarkably reduce the computational cost without decreasing the convergence quality. With the combination of these methods, we propose a computationally efficient wideband adaptive beamformer and verify its efficiency through a series of simulations.

• Journal of the Institute of Convergence Signal Processing
• /
• v.3 no.1
• /
• pp.48-53
• /
• 2002

In this paper, we presented a new approach to the recognition of unconstrained handwritten numerals using an improved RBF(Radial Basis Function) Neural Networks. The RBF Neural Networks used Raised Cosine as a basis function to improve discrimination and reduce processing time. The performance of Raised Cosine RBF Neural Networks classifier was evaluated using totally unconstrained handwritten numeral database of Concordia University, Montreal, Canada, and the experimental results showed the recognition rate of 98.05%.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.36 no.6
• /
• pp.558-564
• /
• 2008

This paper presents an approach to combine adaptive controller with feedback linearization, which extends the applicability of the adaptive controller to a wider class of systems. The adaptive controller guarantees the asymptotic tracking convergence and the transient performance of the tracking error. The feedback linearization transforms a nonlinear plant into a linear time invariant form. The asymptotic tracking convergence is shown by the use of Lyapunov stability analysis and Barbalat's lemma.

• Journal of information and communication convergence engineering
• /
• v.9 no.4
• /
• pp.401-404
• /
• 2011

Considerations for green house gas (GHG) monitoring over next generation network (NGN) are regarded as a green convergence service for the successful reduction of GHG emission leading to resolve global warming issue in that NGN is expected to provide secure connections to fixed-and-mobile converged (FMC) features. Model-based scenario approach is an appropriate way to standardize and actualize the desired service. This paper first describes the service scenario of GHG monitoring service over NGN.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.1
• /
• pp.9-15
• /
• 2019

As the human population increases in the world, the ratio of health doctors is rapidly decreasing. Therefore, it is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies. Usually this happens due to stressful situations during everyday activities including work. This paper presents a machine learning approach to reliably estimating the level of human mental stress using wearable physiological sensors. And also, this paper presents an Android- and Arduino-based stress monitoring and relief system.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.1
• /
• pp.26-32
• /
• 2011

This paper proposes a new method to process panoramic image stitching using SURF(Speeded Up Robust Features). Panoramic image stitching is considered a problem of the correspondence matching. In computer vision, it is difficult to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. However, SURF algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform). In this work, we also describe an efficient approach to decreasing computation time through the homography estimation using RANSAC(random sample consensus). RANSAC is a robust estimation procedure that uses a minimal set of randomly sampled correspondences to estimate image transformation parameters. Experimental results show that our method is robust to rotation, zoom, Gaussian noise and illumination change of the input images and computation time is greatly reduced.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.11a
• /
• pp.894-897
• /
• 2014

The speed of processes and the extremely large amount of data to be used in Intelligence Transportations System (ITS) cannot be handling by humans without considerable automation. However, it is difficult to develop software with conventional fixed algorithms (hard-wired logic on decision making level) for effectively manipulate dynamically evolving real time transportation environment. This situation can be resolved by applying methods of artificial intelligence and data mining that provide flexibility and learning capability. This paper presents a brief introduction of data mining and artificial intelligence (AI) applications in Intelligence Transportation System (ITS), analyzing the prospects of enhancing the capabilities by means of knowledge discovery and accumulating intelligence to support in decision making.

• Communications for Statistical Applications and Methods
• /
• v.6 no.3
• /
• pp.849-860
• /
• 1999

This paper addresses the issue of estimating regression function with errors in variables using wavelets. We adopt a nonparametric approach in assuming that the regression function has no specific parametric form, To account for errors in covariates deconvolution is involved in the construction of a new class of linear wavelet estimators. using the wavelet characterization of Besov spaces the question of regression estimation with Besov constraint can be reduced to a problem in a space of sequences. Rates of convergence are studied over Besov function classes $B_{spq}$ using $L_2$ error measure. It is shown that the rates of convergence depend on the smoothness s of the regression function and the decay rate of characteristic function of the contaminating error.