• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.517-529
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• 2009

Many satellite image fusion techniques have been developed in order to produce a high resolution multispectral (MS) image by combining a high resolution panchromatic (PAN) image and a low resolution MS image. Heretofore, most high resolution image fusion techniques have used IKONOS and QuickBird images. Recently, GeoEye-1, offering the highest resolution of any commercial imaging system, was launched. In this study, we have experimented with GeoEye-1 images in order to evaluate which fusion algorithms are suitable for these images. This paper presents compares and evaluates the efficiency of five image fusion techniques, the $\grave{a}$ trous algorithm based additive wavelet transformation (AWT) fusion techniques, the Principal Component analysis (PCA) fusion technique, Gram-Schmidt (GS) spectral sharpening, Pansharp, and the Smoothing Filter based Intensity Modulation (SFIM) fusion technique, for the fusion of a GeoEye-1 image. The results of the experiment show that the AWT fusion techniques maintain more spatial detail of the PAN image and spectral information of the MS image than other image fusion techniques. Also, the Pansharp technique maintains information of the original PAN and MS images as well as the AWT fusion technique.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.556-564
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• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Advances in Computational Design
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• v.9 no.1
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• pp.39-52
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• 2024

If the governing differential equation arising from engineering problems is treated as an analytic, continuous and derivable function, it can be expanded by one point as a series of finite numbers. For the function to be zero for each value of its domain, the coefficients of each term of the same power must be zero. This results in a recursive relationship which, after applying the natural conditions or the boundary conditions, makes it possible to obtain the values of the derivatives of the function with acceptable accuracy. The elastoplastic analysis of an inhomogeneous thick sphere of metallic materials with linear variation of the modulus of elasticity, yield stress and Poisson's ratio as a function of radius subjected to internal pressure is presented. The Beltrami-Michell equation is established by combining equilibrium, compatibility and constitutive equations. Assuming axisymmetric conditions, the spherical coordinate parameters can be used as principal stress axes. Since there is no analytical solution, the natural boundary conditions are applied and the governing equations are solved using a proposed new method. The maximum effective stress of the von Mises yield criterion occurs at the inner surface; therefore, the negative sign of the linear yield stress gradation parameter should be considered to calculate the optimal yield pressure. The numerical examples are performed and the plots of the numerical results are presented. The validation of the numerical results is observed by modeling the elastoplastic heterogeneous thick sphere as a pressurized multilayer composite reservoir in Abaqus software. The subroutine USDFLD was additionally written to model the continuous gradation of the material.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.299-308
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• 2009

AAM(Active Appearance Model) is an algorithm to extract face feature points with statistical models of shape and texture information based on PCA(Principal Component Analysis). This method is widely used for face recognition, face modeling and expression recognition. However, the detection performance of AAM algorithm is sensitive to initial value and the AAM method has the problem that detection error is increased when an input image is quite different from training data. Especially, the algorithm shows high accuracy in case of closed lips but the detection error is increased in case of opened lips and deformed lips according to the facial expression of user. To solve these problems, we propose the improved AAM algorithm using lip feature points which is extracted based on a new lip detection algorithm. In this paper, we select a searching region based on the face feature points which are detected by AAM algorithm. And lip corner points are extracted by using Canny edge detection and histogram projection method in the selected searching region. Then, lip region is accurately detected by combining color and edge information of lip in the searching region which is adjusted based on the position of the detected lip corners. Based on that, the accuracy and processing speed of lip detection are improved. Experimental results showed that the RMS(Root Mean Square) error of the proposed method was reduced as much as 4.21 pixels compared to that only using AAM algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.834-841
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• 2022

Recently, applying artificial intelligence technologies in various fields of production has drawn an upsurge of research interest due to the increase for smart factory and artificial intelligence technologies. A great deal of effort is being made to introduce artificial intelligence algorithms into the defect detection task. Particularly, detection of defects on the surface of metal has a higher level of research interest compared to other materials (wood, plastics, fibers, etc.). In this paper, we compare and analyze the speed and performance of defect classification by combining machine learning techniques (Support Vector Machine, Softmax Regression, Decision Tree) with dimensionality reduction algorithms (Principal Component Analysis, AutoEncoders) and two convolutional neural networks (proposed method, ResNet). To validate and compare the performance and speed of the algorithms, we have adopted two datasets ((i) public dataset, (ii) actual dataset), and on the basis of the results, the most efficient algorithm is determined.

• (The) Research of the performance art and culture
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• no.39
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• pp.133-165
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• 2019

This paper tried an approach of oral literature as the research subject of Soeburi song in Ulsan. First, UlsanSoeburi song is meaningful as materials collected in Ulsan such as Hansil, Dodoekgol, Dudong and Byeongueong. In addition, it is related to regional identity as song native to Ulsan, which has prototype and archetype. And it shows that Ulsan is the city as well as hometown of Soeburi(meaning ' iron manufacture'). The characteristics of lyrics are different between Hansil Soeburi song and Dodeokgol Soeburi song. Jeiman Choi is considered as a good oral literature poet, because he is a performer who is faithful in official structural principal and in original lyrics(archetype) of Soeburi song. Therefore, SoeburiBulmei song of Jeiman Choi signifies aesthetic meaning, having lyrics which make to feel labor's purity and sacred and melody which overcomes labor's difficulty through united action. On the other hand, SoeburiBulmei song of Dalo Kim in Doseokgol shows that he is a extemporaneous performer even though he performs based on official structural principal. In this paper, transmission and changes of UlsanSoeburi song are divided into basic type, frequent shift type and overall type. 'Basic type' originates from Jeiman Choi's Soeburi song in Hansil. 'Frequent shift type' was created by combining SoeburiBulmei song of Dalo Kim in Doseokgol and SoeburiGeumjul song in Ulsan. 'Overall type' is current Soeburi song, which was created by adding Bulmei song for lulling a baby in Byeongueong near Dalcheon region and Seoknyanggan(smithy) Bulmei song. UlsanSoeburi song is being passed down continuously, strengthening the identity as a representative folk song in Ulsan through endless process of transmission and changes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.101-110
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• 2015

Recently, nine $1397{\times}1397{\times}178mm$ RC panels were tested under in-plane pure-shear monotonic loading condition using the Panel Element Tester by Hsu (1997, ACI). By combining the equilibrium, compatibility, and the softened stress-strain relationship of concrete in biaxial state, Modern Truss Model (MCFT, RA-STM) are capable of producing the nonlinear analysis of RC membrane panel through the complicated trial-and-error method with double loop. In this paper, an efficient algorithm with one loop is proposed for the refined Mohr compatibility Method based on the strut-tie failure criteria. This algorithm can be speedy calculated to analyze the shear history of RC membrane element using the results of Hsu test. The results indicate that the response of shear deformation energy at Big Bang of shear strain significantly influenced by the principal compressive stress-strain (crushing failure).

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.1-7
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• 2016

Structural MRI(sMRI) imaging is used to extract morphometric features after Grey Matter (GM), White Matter (WM) for several univariate and multivariate method, and Cerebro-spinal Fluid (CSF) segmentation. A new approach is applied for the diagnosis of very mild to mild AD. We propose the classification method of Alzheimer disease patients from normal controls by combining morphometric features and Gaussian Mixture Models parameters along with MMSE (Mini Mental State Examination) score. The combined features are fed into Multi-kernel SVM classifier after getting rid of curse of dimensionality using principal component analysis. The experimenral results of the proposed diagnosis method yield up to 96% stratification accuracy with Multi-kernel SVM along with high sensitivity and specificity above 90%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.3
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• pp.428-451
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• 2010

Traffic matrix-based anomaly detection and DDoS attacks detection in networks are research focus in the network security and traffic measurement community. In this paper, firstly, a new type of unidirectional flow called IF flow is proposed. Merits and features of IF flows are analyzed in detail and then two efficient methods are introduced in our DDoS attacks detection and evaluation scheme. The first method uses residual variance ratio to detect DDoS attacks after Recursive Least Square (RLS) filter is applied to predict IF flows. The second method uses generalized likelihood ratio (GLR) statistical test to detect DDoS attacks after a Kalman filter is applied to estimate IF flows. Based on the two complementary methods, an evaluation formula is proposed to assess the seriousness of current DDoS attacks on router ports. Furthermore, the sensitivity of three types of traffic (IF flow, input link and output link) to DDoS attacks is analyzed and compared. Experiments show that IF flow has more power to expose anomaly than the other two types of traffic. Finally, two proposed methods are compared in terms of detection rate, processing speed, etc., and also compared in detail with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) methods. The results demonstrate that adaptive filter methods have higher detection rate, lower false alarm rate and smaller detection lag time.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.