• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1773-1778
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• 2012

Nowadays, the high quality of image is required with the demand for digital image processing devices is rapidly increasing. But image always damaged by many kinds of noises and it is necessary to remove noise and the denoising becomes one of the most important fields. In many cases image is corrupted by AWGN(additive white Gaussian noise). In this paper, we proposed an improved denoising algorithm with edge preservation. The proposed algorithm averages values processed by spatial weighted filter and self adaptive weighted filter. Then we add the value which is computed by the equation considering variance of mask and the estimated noise variance. Through the experience, the proposed filter performs well on noise suppression and edge preservation properties and improves the image visual quality.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.936-939
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• 2015

Recently, the image processing is utilized in various fields and many studies on the image restoration have been carried out in order to remove the noise occurring in the process of data transmission, processing and storage. There are many types of noises added to the image according to the cause and shape, and AWGN(additive white Gaussian noise) is one of typical noises. This paper proposed an algorithm which applies the weighting of filter differently according to the standard deviation in order to alleviate AWGN added to the image, and compared this algorithm with the current methods using PSNR(peak signal to noise ratio) as a criterion of judgment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.724-729
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• 2013

Recently, with the popularization of digital devices, the requirements of image quality is becoming higher and higher. However, the images are frequently corrupted in the image data processing, there are several reasons for this and the noise is considered as the main reason. Therefore, in order to alleviate the influence of AWGN(additive white Gaussian noise) in image, this paper puts forward the spatial weighted filtering algorithm. The algorithm set the weighted value according to the spatial distance, compared with the existing methods. The algorithm not only alleviated the influence of AWGN effectively but also reserved image details.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.782-784
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• 2013

As digital image processing technologies are developing, edges are being utilized in various areas. In the existing edge detection methods, there are mask methods which utilize Sobel, Prewitt, Roberts, Laplacian operator etc. To realize these existing edge detection methods is simple. But, in case that AWGN(additive white Gaussian noise) added images are processed, edge detection characteristics are slightly insufficient. Therefore, the edge detection algorithm using the standard deviation of local mask was suggested in this paper to compensate for the drawbacks in the existing detection methods and the suggested algorithm in AWGN environments showed excellent edge detection characteristics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.735-741
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• 2014

Edge in images appears when a great difference shows up in light and shade between pixels and includes data of the subject's size, location direction and etc. The edge is generally detected by the methods such as Sobel, Roberts, Laplacian, LoG(Laplacian of Gaussian) and etc. However, in AWGN(additive white Gaussian noise) added images, quality of the edge becomes slightly uncertain. Therefore, this paper proposed edge detection algorithm using modified mask of weighting to improve the quality of the existing methods. And in order to verify the performance efficiency of the proposed method, processed image and PFOM(Pratt's figure of merit) has been used as valuation standard for a comparison with the existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.315-321
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• 2011

Since the MB-OFDM(Multi-Band Orthogonal Frequency Division Multiplexing) is an ultra wideband communication system operated on ISM(Industrial, Scientific and Medical) band, DAA(Detect-And-Avoid) is required for co-existence with the other communication service. In this paper we propose the new detection algorithm based on decision-feedback, which shows faster convergence time and less complexity than previous works. The proposed algorithm detects interference above -20dB in AWGN(Additive White Gaussian Noise) and LOS(Line-Of-Sight) channel, and close to AWGN in non-LOS channel under appropriate channel clipping.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.207-218
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• 2010

Generalized additive model(GAM) is the statistical model that resolves most of the problems existing in the traditional linear regression model. However, overfitting phenomenon can be aroused without applying any method to reduce the number of independent variables. Therefore, variable selection methods in generalized additive model are needed. Recently, Lasso related methods are popular for variable selection in regression analysis. In this research, we consider Group Lasso and Elastic net models for variable selection in GAM and propose an algorithm for finding solutions. We compare the proposed methods via Monte Carlo simulation and applying auto insurance data in the fiscal year 2005. lt is shown that the proposed methods result in the better performance.

• Journal of Ocean Engineering and Technology
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• v.22 no.3
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• pp.71-75
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• 2008

This paper illustrates haw $Y_2O_3$ contributes to crack-healing strengths as a function of crack-healing temperature and the additive amount. In investigating mechanical properties, the indentation fracture method is very simple and useful, but careful attention must be paid to the statistical data processing because data may be scattered excessively, especially for brittle materials. To estimate accurate AE signal properties we applied the useful time-frequency method with a discrete wavelet analysis algorithm. In experiments, three kinds of specimens were prepared. After the specimens were indented by a Vickers indentor, they were heat-treated and crack-healed to evaluate bending strength and the AE signal. With higher amounts of the additive powder, as 1, 3, or 5% wt. of $Y_2O_3$, the concentrative tendency of dominant frequency trended toward lower frequency groups. The $Al_2O_3$ ceramic with 3% wt. of $Y_2O_3$ was judged most suitable because it demonstrated superior crack-healing ability and relative concentration on the highest frequency group.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.649-654
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• 2008

In the definition of non additive path, the sum of travel costs of links making up the path is not equal to the path cost. There are a variety of cases that non-additivity assumption does not hold in transportation fields. Nonetheless, traffic equilibrium models are generally built up on the fundamental hypothesis of additivity assumption. In this case traffic equilibrium models are only applicable within restrictive conditions of the path cost being linear functions of link cost. Area-wide road pricing is known as an example of realistic transportation situations, which violates such additivity assumption. Because travel fare is charged at the moment of driver's passing by exit gate while identified at entry gate, it may not be added linearly proportional to link costs. This research proposes a novel Wordrop type of traffic equilibrium model in terms of area-wide road pricing schemes. It introduces binary indicator variable for the sake of transforming non-additive path cost to additive. Since conventional shortest path and Frank-Wolfe algorithm can be applied without route enumeration and network representation is not required, it can be recognized more generalized model compared to the pre-proposed approaches. Theoretical proofs and case studies are demonstrated.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.93-100
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• 2003

Accuracy in maneuvering target tracking using multiple models relies upon the suit-ability of each target motion model to be used. To construct multiple models, the interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require predefined sub-models and predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers. To solve these problems, this paper proposes the GA-based IMM method as an intelligent tracking method for a maneuvering target. In the proposed method, the acceleration input is regarded as an additive process noise, a sub-model is represented as a fuzzy system to compute the time-varying variance of the overall process noise, and, to optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. The simulation results show that the proposed method has a better tracking performance than the AIMM algorithm.