• Journal of Korean Society of Coastal and Ocean Engineers
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• v.14 no.2
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• pp.95-107
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• 2002

A reliability analysis on sliding of monolithic vertical caisson of composite breakwaters is extensively carried out in order to make the basis for the applicability of reliability-based design method. The required width of caisson of composite breakwaters is determined by the deterministic design method including the effect of impulsive breaking waves as a function of water depth, also studied interactively with the results of reliability analyses. It is found that the safety factor applied in current design may be a little over-weighted magnitude for the sliding of caisson. The reliability index/failure probability is also seen to slowly decrease as the water depth increases for a given wave condition and a safety factor. In addition, optimal safety factor can roughly be evaluated by using the concept of target reliability index for several incident waves. The variations of optimal safety factor may be resulted from the different wave conditions. Finally, it may be concluded from the sensitivity studies that the reliability index may be more depended on the incident wave angles and the wave periodsrather than on the bottom slopes and the thickness of rubble mound.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.73-76
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• 2008

We present a area-based method for stereo matching using varying weights. A central problem in a area-based stereo matching is different result from selecting a window size. Most of the previous window-based methods iteratively update windows. However, the iterative methods very sensitive the initial disparity estimation and are computationally expensive. To resolve this problem, we proposed a new function to assign weights to pixels using features. To begin with, we extract features in a given stereo images based on edge. We adjust the weights of the pixels in a given window based on correlation of the stereo images. Then, we match pixels in a given window between the reference and target images of a stereo pair. The proposed method is compared to existing matching strategies using both synthetic and real images. The experimental results show the improved accuracy of the proposed method.

• Journal of KIISE:Software and Applications
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• v.32 no.1
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• pp.1-9
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• 2005

In this paper we propose a modified fuzzy min-max neural network model for pattern classification and discuss the usefulness of the model. We define a new hypercube membership function which has a weight factor to each of the feature within a hyperbox. The weight factor makes it possible to consider the degree of relevance of each feature to a class during the classification process. Based on the proposed model, a knowledge extraction method is presented. In this method, a list of relevant features for a given class is extracted from the trained network using the hyperbox membership functions and connection weights. Ft)r this purpose we define a Relevance Factor that represents a degree of relevance of a feature to the given class and a similarity measure between fuzzy membership functions of the hyperboxes. Experimental results for the proposed methods and discussions are presented for the evaluation of the effectiveness and feasibility of the proposed methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Progress in Medical Physics
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• v.30 no.1
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• pp.22-31
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• 2019

Purpose: To develop a new diffusion-based functional MRI (fMRI) sequence to generate apparent diffusion coefficient (ADC) maps in single excitation and evaluate the contribution of b0 signal on neuronal changes. Materials and Methods: A diffusion-based fMRI sequence was designed with single measurement that can acquire images of three directions at a time, obtaining $b=0s/mm^2$ during the first baseline condition (b0_b), followed by 107 diffusion-weighted imaging (DWI) with $b=600s/mm^2$ during the baseline and visual stimulation conditions, and another $b=0s/mm^2$ during the last activation condition (b0_a). ADC was mapped in three different ways: 1) using b0_b (ADC_b) for all time points, 2) using b0_a (ADC_a) for all time points, and 3) using b0_b and b0_a (ADC_ba) for baseline and stimulation scans, respectively. The fMRI studies were conducted on the brains of 16 young healthy volunteers using visual stimulations in a 3T MRI system. In addition, the blood oxygen level dependent (BOLD) fMRI was also acquired to compare it with diffusion-based fMRI. A sample t-test was used to investigate the voxel-wise average between the subjects. Results: The BOLD data consisted of only activated voxels. However, ADC_ba data was observed in both deactivated and activated voxels. There were no statistically significant activated or deactivated voxels for DWI, ADC_b, and ADC_a. Conclusions: With the new sequence, neuronal activations can be mapped with visual stimulation as compared to the baseline condition in several areas in the brain. We showed that ADC should be mapped using both DWI and b0 images acquired with the same conditions.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.4
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• pp.369-379
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• 2022

In weighted k-nearest neighbor (WkNN)-based Fingerprinting positioning step, a process of comparing the requested positioning signal with signal information for each reference point stored in the fingerprint DB is performed. At this time, the higher the number of matched base station identifiers, the higher the possibility that the terminal exists in the corresponding location, and in fact, an additional weight is added to the location in proportion to the number of matching base stations. On the other hand, if the matching number of base stations is small, the selected candidate reference point has high dependence on the similarity value of the signal. But one problem arises here. The positioning signal can be compared with the repeater signal in the signal information stored on the DB, and the corresponding reference point can be selected as a candidate location. The selected reference point is likely to be an outlier, and if a certain weight is applied to the corresponding location, the error of the estimated location information increases. In order to solve this problem, this paper proposes a WkNN technique including an outlier removal function. To this end, it is first determined whether the repeater signal is included in the DB information of the matched base station. If the reference point for the repeater signal is selected as the candidate position, the reference position corresponding to the outlier is removed based on the clustering technique. The performance of the proposed technique is verified through data acquired in Seocho 1 and 2 dongs in Seoul.

• Investigative Magnetic Resonance Imaging
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• v.25 no.2
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• pp.93-100
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• 2021

Purpose: (1) To evaluate the trabecular pattern at the femoral attachment of the posterior cruciate ligament (PCL) in patients with a PCL injury; (2) to analyze bone microarchitecture by applying gray level co-occurrence matrix (GLCM)-based texture analysis; and (3) to determine if there is a significant relationship between bone microarchitecture and posterior instability. Materials and Methods: The study included 96 patients with PCL tears. Trabecular patterns were evaluated on T2-weighted MRI qualitatively, and were evaluated by GLCM texture analysis quantitatively. The grades of posterior drawer test (PDT) and the degrees of posterior displacement on stress radiographs were recorded. The 96 patients were classified into two groups: acute and chronic injury. And 27 patients with no PCL injury were enrolled for control. Pearson's correlation coefficient and one-way ANOVA with Bonferroni test were conducted for statistical analyses. This protocol was approved by the Institutional Review Board. Results: A thick and anisotropic trabecular bone pattern was apparent in normal or acute injury (n = 57/61;93.4%), but was not prominent in chronic injury and posterior instability (n = 31/35;88.6%). Grades of PDT and degrees of posterior displacement on stress radiograph were not correlated with texture parameters. However, the texture analysis parameters of chronic injury were significantly different from those of acute injury and control groups (P < 0.05). Conclusion: The trabecular pattern and texture analysis parameters are useful in predicting posterior instability in patients with PCL injury. Evaluation of the bone microarchitecture resulting from altered biomechanics could advance the understanding of PCL function and improve the detection of PCL injury.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1846-1852
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• 2021

In modern society, various digital devices are being distributed in a wide range of fields due to the fourth industrial revolution and the development of IoT technology. However, noise is generated in the process of acquiring or transmitting an image, and not only damages the information, but also affects the system, causing errors and incorrect operation. AWGN is a representative noise among image noise. As a method for removing noise, prior research has been conducted, and among them, AF, A-TMF, and MF are the representative methods. Existing filters have a disadvantage that smoothing occurs in areas with high frequency components because it is difficult to consider the characteristics of images. Therefore, the proposed algorithm calculates the standard deviation distribution to effectively eliminate noise even in the high frequency domain, and then calculates the final output by applying the probability density function weight of the Laplace distribution using the curve fitting method.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1021-1033
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• 2021

Objective: To assess the expression of vascular normalization genes in different molecular subtypes of breast cancer and to determine whether molecular subtypes with a higher vascular normalization gene expression can be identified using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI). Materials and Methods: This prospective study evaluated 306 female (mean age ± standard deviation, 50 ± 10 years), recruited between January 2014 and August 2017, who had de novo breast cancer larger than 1 cm in diameter (308 tumors). DCE MRI followed by IVIM DWI studies using 11 different b-values (0 to 1200 s/mm²) were performed on a 1.5T MRI system. The Tofts model and segmented biexponential IVIM analysis were used. For each tumor, the molecular subtype (according to six [I-VI] subtypes and PAM50 subtypes), expression profile of genes for vascular normalization, pericytes, and normal vascular signatures were determined using freshly frozen tissue. Statistical associations between imaging parameters and molecular subtypes were examined using logistic regression or linear regression with a significance level of p = 0.05. Results: Breast cancer subtypes III and VI and PAM50 subtypes luminal A and normal-like exhibited a higher expression of genes for vascular normalization, pericyte markers, and normal vessel function signature (p < 0.001 for all) compared to other subtypes. Subtypes III and VI and PAM50 subtypes luminal A and normal-like, versus the remaining subtypes, showed significant associations with K^trans, k_ep, v_p, and IAUGC_BN90 on DEC MRI, with relatively smaller values in the former. The subtype grouping was significantly associated with D, with relatively less restricted diffusion in subtypes III and VI and PAM50 subtypes luminal A and normal-like. Conclusion: DCE MRI and IVIM parameters may identify molecular subtypes of breast cancers with a different vascular normalization gene expression.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.96-104
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• 2009

In this paper, we propose a no-reference objective Quality assessment metrics of image. The blockiness and blurring of edge areas which are sensitive to the human visual system are modeled as step functions. Blocking and blur metrics are obtained by estimating local visibility of blockiness and edge width, For the blocking metric, horizontal and vertical blocking lines are first determined by accumulating weighted differences of adjacent pixels and then the local visibility of blockiness at the intersection of blocking lines is obtained from the total difference of amplitudes of the 2-D step function which is modelled as a blocking region. The blurred input image is first re-blurred by a Gaussian blur kernel and an edge mask image is generated. In edge blocks, the local edge width is calculated from four directional projections (horizontal, vertical and two diagonal directions) using local extrema positions. In addition, the kurtosis and SSIM are used to compute the blur metric. The final no-reference objective metric is computed after those values are combined using an appropriate function. Experimental results show that the proposed objective metrics are highly correlated to the subjective data.