• Journal of Korean language education
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• v.28 no.4
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• pp.39-62
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• 2017

This study is to examine grammar understanding with beginner English speakers according to presentational methods of grammatical content. 18 English-speaking Korean beginners were randomly assigned and divided into two groups. We first examined if there was a statistically significant difference between the two groups in grammar comprehension with and without illustrations. It was found that the group given the presentation illustrations showed greater understanding than the group without them. Second, no statistically significant difference in grammar understanding was found between the groups with and without materials marked for their awareness. Third, no statistically significant difference was found between the groups with the order of presenting content elements related to grammatical items. This study is to affirm the importance of illustrations as a component of Korean textbooks.

• Smart Media Journal
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• v.3 no.1
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• pp.22-27
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• 2014

Recently, domestic energy environment is facing new challenges owing to the depletion of fossil fuel such as oil. Renewable energy resources including solar and wind energy are attracting more interests than ever before. However, solar power system is costly in comparison with the conventional power generation systems and also the energy density is low. Furthermore, large area is required in order to install solar power system. Generally, performance of solar power system is affected by weather conditions and alignment of sun and the solar cell modules. In this study, a new type of sun tracking system for solar power system is proposed. To verify the feasibility of the proposed system, actual implementation of prototype system and experiments are carried out.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.368-373
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• 2007

In recent year s there have been increasing interests in real-time object tracking with image information. This dissertation presents a real-time object tracking method through the object recognition based on neural networks that have robust characteristics under various illuminations. This dissertation proposes a global search and a local search method to track the object in real-time. The global search recognizes a target object among the candidate objects through the entire image search, and the local search recognizes and track only the target object through the block search. This dissertation uses the object color and feature information to achieve fast object recognition. The experiment result shows the usefulness of the proposed method is verified.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.297-298
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• 2007

Repetitive systems stand for a kind of systems that perform a simple task on a fixed pattern repetitively and are widely spread in industrial fields. Hence, those systems have been of much interests by many researchers, especially in the field of iterative learning control (ILC). In this paper, we propose a finite-horizon tracking control scheme for linear time-varying repetitive systems with uncertain initial conditions. The scheme is derived both analytically and numerically for state-feedback systems and only numerically for output-feedback systems. Then, it is extended to stable systems with input constraints. All numerical schemes are developed in the forms of linear matrix inequalities. A distinguished feature of the proposed scheme from the existing iterative learning control is that the scheme guarantees the tracking performance exactly even under uncertain initial conditions. The simulation results demonstrate the good performance of the proposed scheme.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.146-154
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• 2021

Conspicuous consumption is often investigated in Korea and consumers purchase a product to publicly display economic power or social status. The existing studies argue that brand loyalty and luxury brand preferences escalate conspicuous consumption, and it is important to identify different visual perception to understand irrational consumers' behavior. Car emblem as brands' visual identity represents the brand's image, mission and message. Irrational consumers thus are expected have different visual perception to the emblem or logo. To understand the impact of irrational consumer behavior on visual perception, this study explored how brand loyalty and luxury brand preferences impact on the visual perception of car emblems between Korean domestic brand and imported brand. This study used eye tracking technology to measure the respondents' attention on an emblem of the cars. Eye tracking tools analyze fixations and measure the attention on a specific area of interests (AOI). This study explored the maintaining time and frequency on AOI, as well as explored the size of pupil to identify the respondents' attention on the AOI. In this experiment, the logos of domestic cars (i.e., Hyundai and Kia) and foreign cars (i.e., Mercedes-Benz and BMW) were swapped to see how irrational consumer behavior impact on visual perception of car emblem.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.274-282
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• 2018

Most occupancy sensors installed in buildings, households and so forth are pyroelectric infra-red (PIR) sensors. One of disadvantages is that PIR sensor can not detect the stationary person due to its functionality of detecting the variation of thermal temperature. In order to overcome this problem, the utilization of camera vision sensors has gained interests, where object tracking is used for detecting the stationary persons. However, the object tracking has an inherent problem such as tracking drift. Therefore, the recognition of humans in static trackers is an important task. In this paper, we propose a CNN-based human recognition to determine whether a static tracker contains humans. Experimental results validated that human and non-humans are classified with accuracy of about 88% and that the proposed method can be incorporated into practical vision occupancy sensors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.574-578
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• 2009

Real-time object detection for distinguishing a moving object of interests from the background image in still image or video image sequence is an essential step to a correct object tracking and recognition. Moving cast shadow can be misclassified as part of objects or moving objects because the shadow region is included in the moving object region after object segmentation. For this reason, an algorithm for shadow removal plays an important role in the results of accurate moving object detection and tracking systems. To handle with the problems, an accurate algorithm based on the features of moving object and shadow in color space is presented in this paper. Experimental results show that the proposed algorithm is effective to detect a moving object and to remove shadow in test video sequences.

• Transactions of Materials Processing
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• v.32 no.1
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• pp.20-27
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• 2023

In this study, the deformation of materials was measured using the video and tracking API of OpenCV. Circular markers attached to the material were selected the region of interests (ROIs). The position of the marker was measured from the area center of the circular marker. The position and displacement of the center point was measured along the image frames. For the verification, tensile tests were conducted. In the tensile test, four circular markers were attached along the longitudinal and transverse directions. The strain was calculated using the distance between markers both in the longitudinal and transverse direction. As a result, the stress-strain curve obtained using machine vision is compared to the stress-strain curve obtained from the DIC results. RMSE values of the strain from the machine vision and DIC were less than 0.005. In addition, as a measurement example, a bending angle and springback measurement according to bending deformation, and a moving position measurement of a punch, a blank holder, and a die by time change were performed. Using the proposed method, the deformation and displacement of the materials were measured precisely and easily.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.329-341
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• 2014

Kernel-based mean-shift object tracking has gained more interests nowadays, with the aid of its feasibility of reliable real-time implementation of object tracking. This algorithm calculates the best mean-shift vector based on the color histogram similarity between target model and target candidate models, where the color histograms are usually produced after uniform color-space quantization for the implementation of real-time tracker. However, when the image of target model has a reduced contrast, such uniform quantization produces the histogram model having large values only for a few histogram bins, resulting in a reduced accuracy of similarity comparison. To solve this problem, a non-uniform quantization algorithm has been proposed, but it is hard to apply to real-time tracking applications due to its high complexity. Therefore, this paper proposes a fast non-uniform color-space quantization method using the histogram equalization, providing an adjusted histogram distribution such that the bins of target model histogram have as many meaningful values as possible. Using the proposed method, the number of bins involved in similarity comparison has been increased, resulting in an enhanced accuracy of the proposed mean-shift tracker. Simulations with various test videos demonstrate the proposed algorithm provides similar or better tracking results to the previous non-uniform quantization scheme with significantly reduced computation complexity.

• Geomechanics and Engineering
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• v.12 no.5
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• pp.849-862
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• 2017

With growing interests in using bacterial biopolymers in geotechnical practices, identifying mechanical properties of soft gel-like biopolymers is important in predicting their efficacy in soil modification and treatment. As one of the promising candidates, dextran was found to be produced by Leuconostoc mesenteroides. The model bacteria utilize sucrose as working material and synthesize both soluble and insoluble dextran which forms a complex and inhomogeneous polymer network. However, the traditional rheometer has a limitation to capture in situ properties of inherently porous and inhomogeneous biopolymers. Therefore, we used the particle tracking microrheology to characterize the material properties of the dextran polymer. TEM images revealed a range of pore size mostly less than $20{\mu}m$, showing large pores > $2{\mu}m$ and small pores within the solid matrix whose sizes are less than $1{\mu}m$. Microrheology data showed two distinct regimes in the bacterial dextran, purely viscous pore region of soluble dextran and viscoelastic region of the solid part of insoluble dextran matrix. Diffusive beads represented the soluble dextran dissolved in an aqueous phase, of which viscosity was three times higher than the growth medium viscosity. The local properties of the insoluble dextran were extracted from the results of the minimally moving beads embedded in the dextran matrix or trapped in small pores. At high frequency (${\omega}>0.2Hz$), the insoluble dextran showed the elastic behavior with the storage modulus of ~0.1 Pa. As frequency decreased, the insoluble dextran matrix exhibited the viscoelastic behavior with the decreasing storage modulus in the range of ${\sim}0.1-10^{-3}Pa$ and the increasing loss modulus in the range of ${\sim}10^{-4}-1\;Pa$. The obtained results provide a compilation of frequency-dependent rheological or viscoelastic properties of soft gel-like porous biopolymers at the particular conditions where soil bacteria produce bacterial biopolymers in subsurface.