• Journal of Power System Engineering
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• v.2 no.1
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• pp.80-85
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• 1998

Engineering plastics have been magnified its usability due to its outstanding mechanic al, electrical and chemical properties, for example, in the area of computer, electricity, electronics, automobile, camera industry. In recent, automobile speedometer system is changing from manual operation to motor operation. All plastic gears inserted by metal shaft are used In motor operated speedometer system. Therefore, in this research, experimental investigation of the shrinkage phenomenon was executed according to various inserted depth and injection conditions. In experiments, the inserted depth was controlled as 30% and 90% of the total thickness of the plastic gear. The main parameters of injection process were selected as injection pressure, holding pressure, melt temperature, injection rate. As main results, free shrinkage rate of the test part is increased about 4 times to restricted shrinkage rate and shrinkage phenomenon against all injection conditions have a trivial effect on the test parts as conventional parts.

• Journal of Institute of Convergence Technology
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• v.7 no.2
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• pp.13-16
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• 2017

The design of sealing mechanisms of a manual pressure valve was analyzed with FE analysis for a hydrogen fuels charge and discharge system of FCEV. The damage prediction of the O-ring with respect to the material models of rubbers was calculated by the gap analysis of the backup ring and O-ring according to the internal pressure. Two kinds of the rubber material characteristic models were adopted to the O-ring. One was the linear elastic and the other was hyperelastic of Ogden $3^{rd}$ order model. The experimental data of urethane of Shore hardness 90 was utilized to the curve fitting of hyperelastic properties. It was found that the contact pattern of the backup ring was different in two models and the sealing mechanism was better in the case of the hyperelastic characteristic model.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.629-644
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• 2010

A real-time image georeferencing system requires all inputs to be determined in real-time. The intrinsic camera parameters can be identified in advance from a camera calibration process while other control information can be derived instantaneously from real-time GPS/INS data. The bottleneck process is tie point acquisition since manual operations will be definitely obstacles for real-time system while the existing extraction methods are not fast enough. In this paper, we present a fast-and-automated image matching technique based on the KLT tracker to obtain a set of tie-points in real-time. The proposed work accelerates the KLT tracker by supplying the initial guessed tie-points computed using the GPS/INS data. Originally, the KLT only works effectively when the displacement between tie-points is small. To drive an automated solution, this paper suggests an appropriate number of depth levels for multi-resolution tracking under large displacement using the knowledge of uncertainties the GPS/INS data measurements. The experimental results show that our suggested depth levels is promising and the proposed work can obtain tie-points faster than the ordinary KLT by 13% with no less accuracy. This promising result suggests that our proposed algorithm can be effectively integrated into the real-time image georeferencing for further developing a real-time surveillance application.

• Journal of the Korean Wood Science and Technology
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• v.49 no.5
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• pp.491-503
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• 2021

Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.55-62
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• 2019

The stereo geometry establishment based on the precise sensor modeling is prerequisite for accurate stereo data processing. Ground control points are generally required for the accurate sensor modeling though it is not possible over the area where the accessibility is limited or reference data is not available. For the areas, the relative orientation should be carried out to improve the geometric consistency between the stereo data though it does not improve the absolute positional accuracy. The relative orientation requires conjugate points that are well distributed over the entire image region. Therefore the automatic conjugate point extraction is required because the manual operation is labor-intensive. In this study, we applied the method consisting of the key point extraction, the search space minimization based on the epipolar line, and the rigorous outlier detection based on the RPCs (Rational Polynomial Coefficients) bias compensation modeling. We tested different parameters of window sizes for Kompsat-2 across track stereo data and analyzed the RPCs precision after the bias compensation for the cases whether the epipolar line information is used or not. The experimental results showed that matching outliers were inevitable for the different matching parameterization but they were successfully detected and removed with the rigorous method for sub-pixel level of stereo RPCs precision.

• Journal of The Korean Society of Integrative Medicine
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• v.7 no.2
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• pp.69-76
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• 2019

Purpose : This study examined the effects of a virtual reality rehabilitation program on stroke patients' upper extremity functions and activities of daily living (ADL). Methods : The subjects were equally and randomly divided into an experimental group (n=16) to whom a virtual reality rehabilitation program was applied and a control group (n=16) who received traditional occupational therapy. The intervention was applied five times per week, 30 minutes per each time, for six weeks. Jebsen-Taylor hand function test was conducted and the subjects' Manual Function Test was measured to examine their upper extremity functions before and after the treatment intervention, and a Korean version of modified Barthel index was calculated to look at their activities of daily living. Results : After the intervention, the upper extremity functions and activities of daily living of the participants in both groups significantly improved (p<.05). However, the improvements in these parameters among the participants in the virtual reality rehabilitation program were significantly greater than those in the control group (p>.05). Conclusion : The virtual reality rehabilitation program is a stable and reliable intervention method for enhancing the upper limb functions and activities of daily living of stroke patients.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1156-1170
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• 2019

In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and reliably find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become an important research topic. This paper analyzes the data composition of the smart grid, and explains the power model in two smart grid applications, followed by an analysis on the application of each parameter in density-based spatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out for the processing effects of the boxplot method, probability weight analysis method and DBSCAN clustering algorithm on the big data driven power grid. According to the comparison results, the performance of the DBSCAN algorithm outperforming other methods in processing effect. The experimental verification shows that the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantly improving the accuracy and reliability of the calculation result of the main grid's theoretical line loss.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.442-444
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• 2019

Fabric defect classification is an important issue in fabric quality control. However, automated classification is difficult because it is hard to identify various types of defects in images. classification of fabric defects mostly rely on human ability. In this paper, to solve this problem we apply Convolutional Neural Networks (CNN) for fabric defect classification. To make training CNN easier, we propose a method that is manually attentive defect regions in images. we compare the proposed method with the original image and confirm that the proposed method is effective for learning.

• English & American cultural studies
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• v.14 no.2
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• pp.77-96
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• 2014

As the cyberspace several decades ago created a cyber fiction fever, the augmented reality as the future of imagination can generate another kind of literary genre and new social ambiance where books tend to come to life more realistically. This newly created "smart fiction," "smart movies," and "smart environment" will be full of fun, hopes and conveniences. But addiction to smart kinds will create unwanted dangerous plethora like ghost-like avatars, wild animals and Farid due to the limitations of human control over hi-technology. If so, the adventures we plan to take will turn fantasy into horror in no time. Instead of loving new scientific things blindly, the emphasis hereafter must be put rather on the potentially negative aftermaths of the new innovative technology. Some viewers after watching the film Avatar are still suffering from the syndrome called "avatar blues," a homesick for Pandora. After their experiencing of the experimental 3D effects in books and media, audience and readers are required to actively deal with the increased lack of the darker cave which the comparatively unsatisfactory present can never fill with fixity and limit. Like the prevention against the addictive online game or the manual of 3D television or 3D printer, the extreme off-limits and safety zone for this virtually and expendably subverting technology must be seriously reviewed by community before using and adopting it. Also, these technologically expanded and augmented environments must be prudently criticized by the in-depth study of literature just as cyber space begun by Gibson's cyber fiction and its criticism.

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.1-10
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• 2022

China is a big country in animal fur industry. The total production and consumption of fur are increasing year by year. However, the recognition of fur in the fur production process still mainly relies on the visual identification of skilled workers, and the stability and consistency of products cannot be guaranteed. In response to this problem, this paper proposes a feature fusion-based animal fur recognition network on the basis of typical convolutional neural network structure, relying on rapidly developing deep learning techniques. This network superimposes texture feature - the most prominent feature of fur image - into the channel dimension of input image. The output feature map of the first layer convolution is inverted to obtain the inverted feature map and concat it into the original output feature map, then Leaky ReLU is used for activation, which makes full use of the texture information of fur image and the inverted feature information. Experimental results show that the algorithm improves the recognition accuracy by 9.08% on Fur_Recognition dataset and 6.41% on CIFAR-10 dataset. The algorithm in this paper can change the current situation that fur recognition relies on manual visual method to classify, and can lay foundation for improving the efficiency of fur production technology.