• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.187-195
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• 2024

This paper presents two-dimensional boundary value problems of the stress-based gradient elasticity within the smoothed finite element method (S-FEM) framework. Gradient elasticity is introduced to address the limitations of classical elasticity, particularly its struggle to capture size-dependent mechanical behavior at the micro/nano scale. The Ru-Aifantis theorem is employed to overcome the challenges of high-order differential equations in gradient elasticity. This theorem effectively splits the original equation into two solvable second-order differential equations, enabling its incorporation into the S-FEM framework. The present method utilizes a staggered scheme to solve the boundary value problems. This approach efficiently separates the calculation of the local displacement field (obtained over each smoothing domain) from the non-local stress field (computed element-wise). A series of numerical tests are conducted to investigate the influence of the internal length scale, a key parameter in gradient elasticity. The results demonstrate the effectiveness of the proposed approach in smoothing stress concentrations typically observed at crack tips and dislocation lines.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.578-589
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• 2014

Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.2
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• pp.177-187
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• 1999

Up to date, in many application fields of GSIS, we usually have used vector-based spatial overlay or grid-based spatial algebra for extraction and analysis of spatial data. But, because these methods are based on traditional crisp set, concept which is used these methods. shows that many kinds of spatial data are partitioned with sharp boundary. That is not agree with spatial distribution pattern of data in the real world. Therefore, it has a error that a region or object is restricted within only one attribution (One-Entity-one-value). In this study, for improving previous methods that deal with spatial data based on crisp set, we are suggested to apply into spatial overlay process the concept of fuzzy set which is good for expressing the boundary vagueness or ambiguity of spatial data. two methods be given. First method is a fuzzy interval partition by fuzzy subsets in case of spatially continuous data, and second method is fuzzy boundary set applied on categorical data. with a case study to get a land suitability map for the development site selection of new town, we compared results between Boolean analysis method and fuzzy spatial overlay method. And as a result, we could find out that suitability map using fuzzy spatial overlay method provide more reasonable information about development site of new town, and is more adequate type in the aspect of presentation.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.79-90
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• 2017

Urban environment represent one of the most dynamic regions on earth. As in other countries, forests, green areas, agricultural lands are rapidly changing into residential or industrial areas in South Korea. Monitoring such rapid changes in land use requires rapid data acquisition, and satellite imagery can be an effective method to this demand. In general, SAR(Synthetic Aperture Radar) satellites acquire images with an active system, so the brightness of the image is determined by the surface roughness. Therefore, the water areas appears dark due to low reflection intensity, In the residential area where the artificial structures are distributed, the brightness value is higher than other areas due to the strong reflection intensity. If we use these characteristics of SAR images, settlement areas can be extracted efficiently. In this study, extraction of settlement areas was performed using TerraSAR-X of German high-resolution X-band SAR satellite and KOMPSAT-5 of South Korea, and object-oriented image classification method using the image segmentation technique is applied for extraction. In addition, to improve the accuracy of image segmentation, the speckle divergence was first calculated to adjust the reflection intensity of settlement areas. In order to evaluate the accuracy of the two satellite images, settlement areas are classified by applying a pixel-based K-means image classification method. As a result, in the case of TerraSAR-X, the accuracy of the object-oriented image classification technique was 88.5%, that of the pixel-based image classification was 75.9%, and that of KOMPSAT-5 was 87.3% and 74.4%, respectively.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.99-132
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• 2022

The purpose of this study is to propose AET (Augmented and Extended Topics), a novel method of synthesizing both LDA and BERTopic results, and to analyze the recently published LIS articles as an experimental approach. To achieve the purpose of this study, 55,442 abstracts from 85 LIS journals within the WoS database, which spans from January 2001 to October 2021, were analyzed. AET first constructs a WORD2VEC-based cosine similarity matrix between LDA and BERTopic results, extracts AT (Augmented Topics) by repeating the matrix reordering and segmentation procedures as long as their semantic relations are still valid, and finally determines ET (Extended Topics) by removing any LDA related residual subtopics from the matrix and ordering the rest of them by F₁ (BERTopic topic size rank, Inverse cosine similarity rank). AET, by comparing with the baseline LDA result, shows that AT has effectively concretized the original LDA topic model and ET has discovered new meaningful topics that LDA didn't. When it comes to the qualitative performance evaluation, AT performs better than LDA while ET shows similar performances except in a few cases.

• Progress in Medical Physics
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• v.26 no.4
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• pp.258-266
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• 2015

The modern radiotherapy technique which delivers a large amount of dose to patients asks to confirm the positions of patients or tumors more accurately by using X-ray projection images of high-definition. However, a rapid increase in patient's exposure and image information for CT image acquisition may be additional burden on the patient. In this study, by introducing structural similarity (SSIM) index that can effectively extract the structural information of the image, we analyze the differences between daily acquired x-ray images of a patient to verify the accuracy of patient positioning. First, for simulating a moving target, the spherical computational phantoms changing the sizes and positions were created to acquire projected images. Differences between the images were automatically detected and analyzed by extracting their SSIM values. In addition, as a clinical test, differences between daily acquired x-ray images of a patient for 12 days were detected in the same way. As a result, we confirmed that the SSIM index was changed in the range of 0.85~1 (0.006~1 when a region of interest (ROI) was applied) as the sizes or positions of the phantom changed. The SSIM was more sensitive to the change of the phantom when the ROI was limited to the phantom itself. In the clinical test, the daily change of patient positions was 0.799~0.853 in SSIM values, those well described differences among images. Therefore, we expect that SSIM index can provide an objective and quantitative technique to verify the patient position using simple x-ray images, instead of time and cost intensive three-dimensional x-ray images.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.6
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• pp.423-430
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• 2013

In this research, a paramteric study to account for the effect of interfacial strength and nanotube agglomeration on the elastoplastic behavior of carbon nanotube reinforced polypropylene composites is performed. At first, the elastoplastic behavior of nanocomposites is predicted from molecular dynamics(MD) simulations. By combining the MD simulation results with the nonlinear micromechanics model based on the Mori-Tanaka model, a two-step domain decomposition method is applied to inversely identify the elastoplastic behavior of adsorption interphase zone inside nanocomposites. In nonlinear micromechanics model, the secant moduli method combined with field fluctuation method is used to predict the elastoplastic behavior of nanocomposites. To account for the imperfect material interface between nanotube and matrix polymer, displacement discontinuity condition is applied to the micromechanics model. Using the elastoplastic behavior of the adsorption interphase zone obtained from the present study, stress-strain relation of nanocomposites at various interfacial bonding condition and local nanotube agglomeration is predicted from nonlinear micromechanics model with and without the adsorption interphase zone. As a result, it has been found that local nanotube agglomeration is the most important design factor to maximize reinforcing effect of nanotube in elastic and plastic behavior.

• Journal of Sensor Science and Technology
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• v.11 no.4
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• pp.209-217
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• 2002

The developing of buried fiber optic sensor with high sensitivity and broad-area detecting intruders is carried out using fiber optic ROTDR(Rayleigh Optical Time Domain Reflectometry). The sensing part was designed to be able to broad-area detect intrusion effect per optical fiber length under ground. The bending light losses in optical fibers are investigated by commercial mini ROTDR with wavelength $1.55{\mu}m$, distance range 5km, pulse width 20ns, SNR=5.7. The sensing fibers are selected as the common telecommunication fibers are the 1.5mm, 3.5 mm outer diameter, 4km each length fiber products. Experiments were investigate the characteristics of signal sensitivity according to applied intrusion weight. The relation between the applied weight and the bending loss was almost linear, and broad-area detect intrusion effects are the 2m resolution and $1.3m^2$ per optical fiber length respectively. The light loss by the applied weight on fiber was 0.17 dB/kg. that the sensitivity of the optical fiber sensor was sufficient to detect intruders passing over the buried optical fiber.

• Bulletin of the Society of Naval Architects of Korea
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• v.27 no.4
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• pp.58-66
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• 1990

A series of test was performed measuring the failure strength and failure mode of Gr/Pi, $[0^{\circ}/45^{\circ}/90^{\circ}/-45^{\circ}]_s$ laminate containing a single pin loaded hole. The finite element method is applied to calculate the stress distribution in the laminates, then the failure load and the failure mode were predicted by means of the characteristic length. 12 different geometric variations were developed to analyze the effects of the ratio of specimen width to hole diameter (W/d) and ratio of edge distance to hole diameter (L/d). X-Ray of NDE methods were utilized in finding out the initial defects, damage and the fracture mechanism, and SEM(Scanning Electron Microscopes) was used the evaluation of the fracture mechanism and crack propagation around hole under tension pin loading. $[0^{\circ}/45^{\circ}/90^{\circ}/-45^{\circ}]_s$ laminate are found to be most sensitive to W/d but not so influenced by L/d. The failure mode and tensile strength predicted by the model show agreement with experiment data for pin loading bolted jointed test except range of $L/d{\leqq}3$.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.25-33
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• 2021

With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.