• Korean Journal of Medicinal Crop Science
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• v.3 no.1
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• pp.12-15
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• 1995

This experiment was carried out to elucidate the effect of planting depth on the Quality and yield in Rehmannia glutinosa. Planting depths were 3,6,9 and 12cm, and planted on April 18. Emergence rate was decreased in the order of 86% in 3cm, 67% in 6cm, 37% in 9cm and 21% in 12cm. Leaf length was longer about $0.3{\sim}0.8cm$ in planting depth 6-12cm than that of 15.6cm in planting depth 3cm. Yield of Rehmannia glutinosa were lower 5% in planting depth 6cm, 62% in 9cm and 68% in 12cm than that of l,154kg/10a in planting depth 3cm. And optimum planting depth was considered 6cm.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.25-32
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• 2014

Petroglyphs is an essential part of the worldwide cultural heritage since it plays a key role for the comprehension of prehistoric communities previous to writing. nowadays 3D data are a critical component to permanently record the form of important cultural heritage so that they might be passed down to future generations. Recent 3D scanning technologies allow the generation of very realistic 3D model that can be used for multimedia museum exhibitions to attract the users into the 3D world. In this paper, we develop the 3D petroglyph VR contents based on a novel gesture recognition method. The proposed gesture recognition method can recognizes the movements of the user using 3D depth sensor by comparing with the pre-defined movements. Also this paper presents new approaches for 3D petroglyphs data recording using 3D scanning technology as accurate and non-destructive tools.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.327-336
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• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.59-68
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• 2005

This paper proposes a face recognition under varying poses using local area obtained by side-view pose normalization. General normalization methods for face recognition under varying pose have a problem with the information about invisible area of face. Generally this problem is solved by compensation, but there are many cases where the image is distorted or features lost due to compensation .To solve this problem we normalize the face pose in side-view to reduce distortion that happens mainly in areas that have large depth variation. We only use undistorted area, removing the area that has been distorted by normalization. We consider two cases of yaw pose variation and pitch pose variation, and by experiments, we confirm the improvement of recognition performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.106-112
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• 2015

In this paper, we propose an on-line signature recognition algorithm using fingertip point in the air from the depth image acquired by Kinect. We use ten statistical features for each X, Y, Z axis to react to changes in Shifting and Scaling of the signature trajectories in three-dimensional space. Artificial Neural Network is a machine learning algorithm used as a tool to solve the complex classification problem in pattern recognition. We implement the proposed algorithm to actual on-line signature recognition system. In experiment, we verify the proposed method is successful to classify 4 different on-line signatures.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.33-40
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• 2021

Automatic Speech Recognition(ASR) is a technology that analyzes human speech sound into speech signals and then automatically converts them into character strings that can be understandable by human. Speech recognition technology has evolved from the basic level of recognizing a single word to the advanced level of recognizing sentences consisting of multiple words. In real-time voice conversation, the high recognition rate improves the convenience of natural information delivery and expands the scope of voice-based applications. On the other hand, with the active application of speech recognition technology, concerns about related cyber attacks and threats are also increasing. According to the existing studies, researches on the technology development itself, such as the design of the Automatic Speaker Verification(ASV) technique and improvement of accuracy, are being actively conducted. However, there are not many analysis studies of attacks and threats in depth and variety. In this study, we propose a cyber attack model that bypasses voice authentication by simply manipulating voice frequency and voice speed for AI voice recognition service equipped with automated identification technology and analyze cyber threats by conducting extensive experiments on the automated identification system of commercial smartphones. Through this, we intend to inform the seriousness of the related cyber threats and raise interests in research on effective countermeasures.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.72-86
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• 2023

The purpose of this study is to examine what epistemological beliefs pre-service teachers have about science depending on the situation, and to explore in-depth changes in epistemological beliefs through disciplinary reading. For this purpose, 77 essays written by pre-service elementary school teachers after reading Feynman's 'the meaning of it all' were analyzed using an inductive analysis method. As a result of the study, the epistemological beliefs of pre-service teachers were divided into two situations: 'science in subject learning' and 'science in daily life', and the epistemological beliefs formed in the 'science handled by scientists' situation were analyzed after reading the book. Each situation was divided into sub-categories of 'Impression of Knowledge', 'Source of Knowledge', 'Justification of Knowledge', 'Variability of Knowledge', 'Structure of Knowledge', and 'Value of Knowledge Acquisition' to reveal differences in sophisticated beliefs and naive belief levels. As a result, it was derived that Feynman's science lecture influenced pre-service teachers in terms of establishing new perspectives and recontextualizing existing epistemological beliefs. This study is meaningful in that pre-service teachers' scientific epistemological beliefs may vary depending on the situation, and that the scope and depth of epistemological beliefs may be expanded to include scientists' beliefs in science through disciplinary reading.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.155-162
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• 2020

Recently, with the development of computer vision and deep learning technology, research on human action recognition has been actively conducted for video analysis, video surveillance, interactive multimedia, and human machine interaction applications. Diverse techniques have been introduced for human action understanding and classification by many researchers using RGB image, depth image, skeleton and inertial data. However, skeleton-based action discrimination is still a challenging research topic for human machine-interaction. In this paper, we propose an end-to-end skeleton joints mapping of action for generating spatio-temporal image so-called dynamic image. Then, an efficient deep convolution neural network is devised to perform the classification among the action classes. We use publicly accessible UTD-MHAD skeleton dataset for evaluating the performance of the proposed method. As a result of the experiment, the proposed system shows better performance than the existing methods with high accuracy of 97.45%.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.347-358
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• 2009

In this thesis, we develop a vision based hand motion recognition system using a camera with two rotational motors. Existing systems were implemented using a range camera or multiple cameras and have a limited working area. In contrast, we use an uncalibrated camera and get more wide working area by pan-tilt motion. Given an image sequence provided by the pan-tilt camera, color and pattern information are integrated into a tracking system in order to find the 2D position and direction of the hand. With these pose information, we estimate 3D motion plane on which the gesture motion trajectory from approximately forms. The 3D trajectory of the moving finger tip is projected into the motion plane, so that the resolving power of the linear gesture patterns is enhanced. We have tested the proposed approach in terms of the accuracy of trace angle and the dimension of the working volume.

• Journal of Digital Convergence
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• v.15 no.6
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• pp.83-96
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• 2017

This study aims to explore how to improve students' rights based on their age and development stages. To this end, we analyzed a survey that had been carried out with 1,065 students from elementary, middle and high schools in 2 most populated counties in Korea, focusing on the differences in their perception with regard to the right to survive, develop, be protected and participate. The result of the analysis showed that high school students' sense of rights was at the lowest in general, while being particularly low in their sense of participation rights. However, when it comes to the development rights and protection rights, the level of recognition of middle school students were as low as those of high school students. Based on the results, we suggested that a proactive effort to guarantee adolescents' participation rights is required, and that education of human rights should be emphasized not only for children but also for their supporters. In the follow-up study, it is required to investigate the differences between development stages and regions by including participants with diverse ages and residential areas.