• Tunnel and Underground Space
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• v.15 no.2 s.55
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• pp.129-136
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• 2005

This paper describes the implementation of rock mass rating evaluation based on genetic algorithm(GA) and conditional simulation technique to estimate RMR in the area without sufficient borehole data RMR were estimated by GA and conditional simulation technique with reflecting distribution feature and spatial correlation. And RMR determined by GA were compared with the results from kriging. Through the analysis of the results from 30 simulations, the uncertainty of estimation could be quantified.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.99-110
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• 2021

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

• Journal of IKEEE
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• v.25 no.4
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• pp.613-618
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• 2021

Facial expression recognition has long been established as a subject of continuous research in various fields. In this paper, the relationship between each landmark is analyzed using the features obtained by calculating the distance between the facial landmarks in the image, and five facial expressions are classified. We increased data and label reliability based on our labeling work with multiple observers. In addition, faces were recognized from the original data and landmark coordinates were extracted and used as features. A genetic algorithm was used to select features that are relatively more helpful for classification. We performed facial recognition classification and analysis with the method proposed in this paper, which shows the validity and effectiveness of the proposed method.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.705-708
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• 2010

An eigen color co-occurrence approach is proposed that exploits the correlation between color channels to identify the degree of image similarity. This method is based on traditional co-occurrence matrix method and histogram equalization. On the purpose of feature extraction, eigen color co-occurrence matrices are computed for extracting the statistical relationships embedded in color images by applying Principal Component Analysis (PCA) on a set of color co-occurrence matrices, which are computed on the histogram equalized images. That eigen space is created with a set of orthogonal axes to gain the essential structures of color co-occurrence matrices, which is used to identify the degree of similarity to classify an input image to be tested for various purposes. In this paper RGB, Gaussian color space are compared with grayscale image in terms of PCA eigen features embedded in histogram equalized co-occurrence features. The experimental results are presented.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.139-146
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• 2024

Surface-enhanced Raman scattering (SERS) enables the detection of various types of π-conjugated biological and chemical molecules owing to its exceptional sensitivity in obtaining unique spectra, offering nondestructive classification capabilities for target analytes. Herein, we demonstrate an innovative strategy that provides significant machine learning (ML)-enabled predictive SERS platforms through surface-engineered graphene via complementary hybridization with Au nanoparticles (NPs). The hybridized Au NPs/graphene SERS platforms showed exceptional sensitivity (10^-7 M) due to the collaborative strong correlation between the localized electromagnetic effect and the enhanced chemical bonding reactivity. The chemical and physical properties of the demonstrated SERS platform were systematically investigated using microscopy and spectroscopic analysis. Furthermore, an innovative strategy employing ML is proposed to predict various analytes based on a featured Raman spectral database. Using a customized data-preprocessing algorithm, the feature data for ML were extracted from the Raman peak characteristic information, such as intensity, position, and width, from the SERS spectrum data. Additionally, sophisticated evaluations of various types of ML classification models were conducted using k-fold cross-validation (k = 5), showing 99% prediction accuracy.

• Journal of Astronomy and Space Sciences
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• v.30 no.2
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• pp.79-82
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• 2013

We present the Hilbert-Huang transform (HHT) analysis on the quasi-periodic modulation of SMC X-1. SMC X-1, consisting of a neutron star and a massive companion, exhibits superorbital modulation with a period varying between ~40 d and ~65 d. We applied the HHT on the light curve observed by the All-Sky Monitor onboard Rossi X-ray Timing Explorer (RXTE) to obtain the instantaneous frequency of the superorbital modulation of SMC X-1. The resultant Hilbert spectrum is consistent with the dynamic power spectrum while it shows more detailed information in both the time and frequency domains. According to the instantaneous frequency, we found a correlation between the superorbital period and the modulation amplitude. Combining the spectral observation made by the Proportional Counter Array onboard RXTE and the superorbital phase derived in the HHT, we performed a superorbital phase-resolved spectral analysis of SMC X-1. An analysis of the spectral parameters versus the orbital phase for different superorbital states revealed that the diversity of $n_H$ has an orbital dependence. Furthermore, we obtained the variation in the eclipse profiles by folding the All Sky Monitor light curve with orbital period for different superorbital states. A dip feature, similar to the pre-eclipse dip of Her X-1, can be observed only in the superorbital ascending and descending states, while the width is anti-correlated with the X-ray flux.

• Journal of the Korean Society for Railway
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• v.11 no.3
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• pp.257-262
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• 2008

Design and analysis of road subgrade system, which is exposed to repetitive loading condition, uses resilient modulus. The behavior of railway subgrade system will not be quite different from that of road system. Following this phenomenological feature of the subgrade system, this paper introduces the implementation of the resilient modulus based constitutive model on a commercial finite element software. The implementation of the resilient modulus models such as K-${\theta}$ and Uzan on a FE program has been conducted previously. These model assumes that the material state reaches to the nonlinear elastic condition and with further application of repetitive loads, the response of material is completed in elastic condition. According to the recent test results performed on cohesive subgrade soils, however, permanent deformation occurs with repetitive loads. With aids of previously suggested models the permanent deformation cannot be modeled. To overcome such limitation a plastic potential derived from the test results and simple failure criterion based constitutive model is developed. The comparison between the analysis and test results shows a good correlation.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.221-232
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• 2021

This study analyzed co-registration results according to the geometric processing level of reference image, which are Level 1R and Level 1G provided from KOMPSAT-3 and KOMPSAT-3A images. We performed co-registration using each Level 1R and Level 1G image as a reference image, and Level 1R image as a sensed image. For constructing the experimental dataset, seven Level 1R and 1G images of KOMPSAT-3 and KOMPSAT-3A acquired from Daejeon, South Korea, were used. To coarsely align the geometric position of the two images, SURF (Speeded-Up Robust Feature) and PC (Phase Correlation) methods were combined and then repeatedly applied to the overlapping region of the images. Then, we extracted tie-points using the SURF method from coarsely aligned images and performed fine co-registration through affine transformation and piecewise Linear transformation, respectively, constructed with the tie-points. As a result of the experiment, when Level 1G image was used as a reference image, a relatively large number of tie-points were extracted than Level 1R image. Also, in the case where the reference image is Level 1G image, the root mean square error of co-registration was 5 pixels less than the case of Level 1R image on average. We have shown from the experimental results that the co-registration performance can be affected by the geometric processing level related to the initial geometric relationship between the two images. Moreover, we confirmed that the better geometric quality of the reference image achieved the more stable co-registration performance.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.57-77
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• 2016

This paper analysis spatial correlation applying commercial activating factor and categories clusters among have homogeneity in garosu street which are rising commercial issue in residential district. Based on this research we can draw several implications. Firstly, Garosu street are forming unique space around fassion feature like clothes and food and Beverage stores are supporting main functions. secondly, in terms of utilization of semi-public space in individual buildings, main Street are using display goods and put product.Also restaurants and cafes are using public space as terrace seats. These results mean principal road emphasizes displaying and passing but inner road emphasizes taking a break and staying. Third, repetitive action between high rising vacancy and new building cause negative effects city decline and lossing identity. So residents and merchants should cooperate and make communities for sustainable district.