• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.259-267
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• 2016

Most manufacturing processes, including welding, create residual stresses. Residual stresses can reduce material strength and cause fractures. For estimating the reliability and aging of a welded structure, residual stresses should be evaluated as precisely as possible. Optical techniques such as holographic interferometry, electronic speckle pattern interferometry (ESPI), Moire interferometry, and shearography are noncontact means of measuring residual stresses. Among optical techniques, ESPI is typically used as a nondestructive measurement technique of in-plane displacement, such as stress and strain, and out-of-plane displacement, such as vibration and bending. In this study, ESPI was used to measure the residual stress on the welded part of butt-welded American Society for Testing and Materials (ASTM) A36 specimens with $CO_2$ welding. Four types of specimens, base metal specimen (BSP), tensile specimen including welded part (TSP), compression specimen including welded part (CSP), and annealed tensile specimen including welded part (ATSP), were tested. BSP was used to obtain the elastic modulus of a base metal. TSP and CSP were used to compare residual stresses under tensile and compressive loading conditions. ATSP was used to confirm the effect of heat treatment. Residual stresses on the welded parts of specimens were obtained from the phase map images obtained by ESPI. The results confirmed that residual stresses of welded parts can be measured by ESPI.

• Journal of Korean Elementary Science Education
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• v.35 no.1
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• pp.54-64
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• 2016

The purpose of this study is to develop instructional strategy which utilizes thinking maps focused on drawing as a measure to enhance science learning motivation, self-directed learning activity and science academic achievement of learners, and to examine the effects of its application. The target unit for this study is 'life cycle of plants' in the fourth grade of elementary school. Two classes of 4th grades of elementary school were selected and divided into two groups. The learners of experimental group have completed thinking map by drawing a picture to express the results to be observed and measured, and used it to arrange the learning contents. The result of this study is as follows. First, it is proven that using thinking maps focused on drawing actually helped improving the motivation of learners to study science. Second, it is proven that this strategy was effective to change their self-directed learning ability in positive ways. Third, it contributed to the improvement of learners' science academic achievement. We found out that the application of this strategy enabled them to enjoy the mapping using drawing, to be immersed in learning, to better recognize the scientific concepts and the structure of learning contents, and to have a positive awareness of the usefulness of thinking maps focused on drawing.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.425-438
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• 2015

This study proposes a three-stage model of R&BD performance which captures commercialization outcomes as well as conventional R&D performance. The model is composed of three factors : inputs (R&D budgets and researchers), outputs (patents and papers), and outcomes (technical fees, products sales, and cost savings). Three stages are defined for each transformation process between the three factors : efficiency stage from input to output (stage 1), effectiveness stage from output to outcome (stage 2), and productivity stage from input to outcome (stage 3). The performance of each stage is measured by data envelopment analysis (DEA). DEA is a non-parametric efficiency measurement technique that has widely been used in R&D performance measurement. We measure the performance of 171 projects of 6 public R&BD programs managed by Seoul Business Agency using the proposed three-stage model. In order to provide a balanced and holistic view of R&BD performance, the R&BD performance map is also constructed based on performance of efficiency and productivity stages.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.3
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• pp.346-355
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• 2016

In this paper, we propose a method to analyze the clusters of RF threats emitting electrical signals based on collected signal variables in integrated electronic warfare environments. We first analyze the signal variables collected by an electronic warfare receiver, and construct a model based on variables showing the properties of threats. To visualize the distribution of RF threats and reversely identify them, we use k-means clustering algorithm and self-organizing map (SOM) algorithm, which are belonging to unsupervised learning techniques. Through the resulting model compiled by k-means clustering and SOM algorithms, the RF threats can be classified into one of the distribution of RF threats. In an experiment, we measure the accuracy of classification results using the algorithms, and verify the resulting model that could be used to visually recognize the distribution of RF threats.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.834-851
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• 2017

The amount of sources of information available on the web using ontologies as support continues to increase and is often heterogeneous and distributed. Ontology alignment is the solution to ensure semantic interoperability. In this paper, we describe a new ontology alignment approach, which consists of combining structure-based and reasoning-based approaches in order to discover new semantic correspondences between entities of different ontologies. We used the biblio test of the benchmark series and anatomy series of the Ontology Alignment Evaluation Initiative (OAEI) 2012 evaluation campaign to evaluate the performance of our approach. We compared our approach successively with LogMap and YAM++ systems. We also analyzed the contribution of our method compared to structural and semantic methods. The results obtained show that our performance provides good performance. Indeed, these results are better than those of the LogMap system in terms of precision, recall, and F-measure. Our approach has also been proven to be more relevant than YAM++ for certain types of ontologies and significantly improves the structure-based and reasoningbased methods.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.06a
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• pp.81-88
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• 2001

To study the influence of the shape of contacting bodies (especially the end profile) on slip regime, wear test is conducted in the case of the contact between tube and support. Two different end profiles of the support are used such as truncated wedge and rounded punch. During the test, 10, 30 and 50 N are applied as normal force and slip displacement varies between 10-200 $\mu\textrm{m}$. The tube and the support specimens are made of Zircaloy-4 and a specially designed wear tester is used. Tests are carried out in air at room temperature. Wear on the tube is examined by measuring microscope. Partial and gross slip regimes are classified from the observed wear shape. Surface roughness tester is also used to measure the wear depth and contour, from which wear volume is evaluated. The transition from partial to gross slip is also investigated by investigating the considerable increase of wear volume. From the result, the boundary between the partial and the gross slip is newly determined in the conventional fretting map for the present specific contact configuration. Since the transition is related with the amount of energy dissipation from the contact surface so is wear, it is regarded that wear can be restrained by designing a proper shape of support.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2287-2312
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• 2018

Extracting key visual information from images containing natural scene is a challenging task and an important step for the visually impaired to recognize information based on tactile graphics. In this study, a novel method is proposed for extracting salient regions based on global contrast enhancement and saliency cuts in order to improve the process of recognizing images for the visually impaired. To accomplish this, an image enhancement technique is applied to natural scene images, and a saliency map is acquired to measure the color contrast of homogeneous regions against other areas of the image. The saliency maps also help automatic salient region extraction, referred to as saliency cuts, and assist in obtaining a binary mask of high quality. Finally, outer boundaries and inner edges are detected in images with natural scene to identify edges that are visually significant. Experimental results indicate that the method we propose in this paper extracts salient objects effectively and achieves remarkable performance compared to conventional methods. Our method offers benefits in extracting salient objects and generating simple but important edges from images containing natural scene and for providing information to the visually impaired.

• Korean Journal of Remote Sensing
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• v.25 no.3
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• pp.295-309
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• 2009

In this study, an iterative maximum a posteriori (MAP) approach using a Bayesian model of Markovrandom field (MRF) was proposed for despeckling images that contains speckle. Image process is assumed to combine the random fields associated with the observed intensity process and the image texture process respectively. The objective measure for determining the optimal restoration of this "double compound stochastic" image process is based on Bayes' theorem, and the MAP estimation employs the Point-Jacobian iteration to obtain the optimal solution. In the proposed algorithm, MRF is used to quantify the spatial interaction probabilistically, that is, to provide a type of prior information on the image texture and the neighbor window of any size is defined for contextual information on a local region. However, the window of a certain size would result in using wrong information for the estimation from adjacent regions with different characteristics at the pixels close to or on boundary. To overcome this problem, the new method is designed to use less information from more distant neighbors as the pixel is closer to boundary. It can reduce the possibility to involve the pixel values of adjacent region with different characteristics. The proximity to boundary is estimated using a non-uniformity measurement based on standard deviation of local region. The new scheme has been extensively evaluated using simulation data, and the experimental results show a considerable improvement in despeckling the images that contain speckle.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.4
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• pp.16-25
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• 2000

In this paper, a new disparity detection algorithm which is robust to noise is presented. It detects the disparity of an arbitrary pixel through the iterative competition with neighbor pixels in the range of disparity. A diffusion process to improve stereo matching confidence is used prior to detecting disparity of an arbitrary pixel. It is used for aggregating initial matching measure of the difference map. If the image region for matching is too small, a wrong match might be found due to noise. On the contrary, the region is too big, it results in blurring of object boundaries. Therefore, we decide the image region for matching by using the diffusion process for aggregating matching measure, then detect the true disparity with proposed competition method to the distribution of matching measure. Through the proposed method we get the result of improving matching rate of 6.96% with real stereo imge. From the simulation with the stereo imge, the proposed disparity detection method significantly outperforms the conventional method to matching rate.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.115-122
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• 2016

This paper proposes a system to detect driver's cognitive state by internal and external information of vehicle. The proposed system can measure driver's eye gaze. This is done by concept of information delivery and mutual information measure. For this study, we set up two web-cameras at vehicles to obtain visual information of the driver and front of the vehicle. We propose Gestalt principle based selective attention model to define information quantity of road scene. The saliency map based on gestalt principle is prominently represented by stimulus such as traffic signals. The proposed system assumes driver's cognitive resource allocation on the front scene by gaze analysis and head pose direction information. Then we use several feature algorithms for detecting driver's characteristics in real time. Modified census transform (MCT) based Adaboost is used to detect driver's face and its component whereas POSIT algorithms are used for eye detection and 3D head pose estimation. Experimental results show that the proposed system works well in real environment and confirm its usability.