• IEIE Transactions on Smart Processing and Computing
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• v.6 no.2
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• pp.85-92
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• 2017

This paper presents a dehazing method based on a fuzzy membership function and variational method. The proposed algorithm consists of three steps: i) estimate transmission through a pixel-based operation using a fuzzy membership function, ii) refine the transmission using an L1-norm-based regularization method, and iii) obtain the result of haze removal based on a hazy image formation model using the refined transmission. In order to prevent color distortion of the sky region seen in conventional methods, we use a trapezoid-type fuzzy membership function. The proposed method acquires high-quality images without halo artifacts and loss of color contrast.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1403-1417
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• 2019

With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of $L_{2,q}$ norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1103-1120
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• 2015

A self-sensing magnetorheological (MR) damper with embedded piezoelectric force sensor has recently been devised to facilitate real-time close-looped control of structural vibration in a simple and reliable manner. The development and characterization of the self-sensing MR damper are presented based on experimental work, which demonstrates its reliable force sensing and controllable damping capabilities. With the use of experimental data acquired under harmonic loading, a nonparametric dynamic model is formulated to portray the nonlinear behaviors of the self-sensing MR damper based on NARX modeling and neural network techniques. The Bayesian regularization is adopted in the network training procedure to eschew overfitting problem and enhance generalization. Verification results indicate that the developed NARX network model accurately describes the forward dynamics of the self-sensing MR damper and has superior prediction performance and generalization capability over a Bouc-Wen parametric model.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.26 no.3
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• pp.156-184
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• 2022

In this article, we propose a novel variational model for restoring color images corrupted by mixed multiplicative Gamma noise and additive Gaussian noise. The model involves a data-fidelity term that characterizes the mixed noise as an infimal convolution of two noise distributions and the saturation-value total variation (SVTV) regularization. The data-fidelity term facilitates suitable separation of the multiplicative Gamma and Gaussian noise components, promoting simultaneous elimination of the mixed noise. Furthermore, the SVTV regularization enables adequate denoising of homogeneous regions, while maintaining edges and details and diminishing the color artifacts induced by noise. To solve the proposed nonconvex model, we exploit an alternating minimization approach, and then the alternating direction method of multipliers is adopted for solving subproblems. This contributes to an efficient iterative algorithm. The experimental results demonstrate the superior performance of the proposed model compared to other existing or related models, with regard to visual inspection and image quality measurements.

• International Commerce and Information Review
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• v.1 no.2
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• pp.205-228
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• 1999

Intercompany online businesses can offer digital information to each company, and yet without legal verification business activities are less efficient. Just one single country cannot control this problem with its own EC law and now international cooperations are being required. Currently, International Regularization is the main agenda among the international organizations such as UNCITRAL, OECD and WTO and so on. Furthermore, most of the advanced nations, including the USA and EU, announce their fundamental strategies for the multilateral regularization in their favor. At the present stage, South Korea's Electronic Commerce law and Digital Signature law went into effect as from July 1, 1999, indicating that they can strike the keynote of the systematic infrastructure for the electronic commerce transactions in this country.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.9
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• pp.1270-1275
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• 2013

A lot of research about camera calibration and lens distortion for wide-angle lens has been made. Especially, calibration for fish-eye lens which has 180 degree FOV(field of view) or above is more tricky, so existing research employed a huge calibration pattern or even 3D pattern. And it is important that calibration parameters (such as distortion coefficients) are suitably initialized to get accurate calibration results. It can be achieved by using manufacturer information or lease-square method for relatively narrow FOV(135, 150 degree) lens. In this paper, without any previous manufacturer information, camera calibration and barrel undistortion for fish-eye lens with over 180 degree FOV are achieved by only using one calibration pattern image. We applied QR decomposition for initialization and Regularization for optimization. With the result of experiment, we verified that our algorithm can achieve camera calibration and image undistortion successfully.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.18 no.2
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• pp.143-156
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• 2014

This paper aims to give a quick view on denoising without comprehensive details. Denoising can be understood as removing unwanted parts in signals and images. Noise incorporates intrinsic random fluctuations in the data. Since noise is ubiquitous, denoising methods and models are diverse. Starting from what noise means, we briefly discuss a denoising model as maximum a posteriori estimation and relate it with a variational form or energy model. After that we present a few major branches in image and signal processing; filtering, shrinkage or thresholding, regularization and data adapted methods, although it may not be a general way of classifying denoising methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.196-201
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• 2008

Statistical learning theory has three analytical tools which are support vector machine, support vector regression, and support vector clustering for classification, regression, and clustering respectively. In general, their performances are good because they are constructed by convex optimization. But, there are some problems in the methods. One of the problems is the subjective determination of the parameters for kernel function and regularization by the arts of researchers. Also, the results of the learning machines are depended on the selected parameters. In this paper, we propose an efficient method for objective determination of the parameters of support vector clustering which is the clustering method of statistical learning theory. Using evolutionary algorithm and bootstrap method, we select the parameters of kernel function and regularization constant objectively. To verify improved performances of proposed research, we compare our method with established learning algorithms using the data sets form ucr machine learning repository and synthetic data.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.6
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• pp.972-981
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• 1990

Many researchers have been interested in whether complex computational problems can be solved by the neural net or not. Especially, problems of early vision are integrated by Tikhonov's regularization theory. Regularization technique can be realized in resistive net. In this paper, we suggest the resistive net with upper and lower thresholder to be able to compute shape from shading and to solve its discontinuous problem. We simulate three algorithms-Horn's algorithm, resistive net and up-low thrwsholding net -with respect to three cases-fully boundary, boundary losing partly and noisy image. As being able to cope with crease and discontinuous parts, we get the good 3D shape from shading.