• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.369-372
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• 2018

This paper suggests non-parallel-voice-conversion network conversing voice between unmapped voice pair as source voice and target voice. Conventional voice conversion researches used learning methods that minimize spectrogram's distance error. Not only these researches have some problem that is lost spectrogram resolution by methods averaging pixels. But also have used parallel data that is hard to collect. This research uses PPGs that is input voice's phonetic data and a GAN learning method to generate more clear voices. To evaluate the suggested method, we conduct MOS test with GMM based Model. We found that the performance is improved compared to the conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.21-28
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• 2015

Feature normalization as a pre-processing step has been widely used to reduce the effect of different scale in each feature dimension and error rate in classification. Most of the existing normalization methods, however, do not use the class labels of data points and, as a result, do not guarantee the optimality of normalization in classification aspect. A supervised rank normalization method, combination of rank normalization and supervised learning technique, was proposed and demonstrated better result than others. In this paper, another technique, training sample selection, is introduced in supervised feature normalization to reduce classification error more. Training sample selection is a common technique for increasing classification accuracy by removing noisy samples and can be applied in supervised normalization method. Two sample selection measures based on the classes of neighboring samples and the distance to neighboring samples were proposed and both of them showed better results than previous supervised rank normalization method.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.14-25
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• 2021

The purpose of this study is to explore what are the interactive factors of synchronous online education in university and identify the level of interaction. This study used mixed research method. As a result of the interaction level, it was recognized that face-to-face education could be more interactive than synchronous online education. Synchronous online education could have better interactive between instructor and learner, and among learners than asynchronous online education. Factors which influencing the interaction were as follows: small group activities and scaffolding, diversification of communication channels and integration of learner's question in learning content. Detrimental elements were distance felt between instructor and learners, low intimacy among learners, content-focused lecture, restrictions on non-verbal communication, unstable systems and misusing microphones. The necessary factors to promote interaction are planning interactive class activities, etc. Based on the results, it was to suggest what kinds of efforts are needed to make interaction more effective in terms of teaching and learning method & activity, tool & system, and environment.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.493-501
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• 2022

This paper is a study on context-sensitive spelling error correction and uses the Korean WordNet (KorLex)[1] that defines the relationship between words as a graph to improve the performance of the correction[2] based on the vector information of the word embedded in the correction technique. The Korean WordNet replaced WordNet[3] developed at Princeton University in the United States and was additionally constructed for Korean. In order to learn a semantic network in graph form or to use it for learned vector information, it is necessary to transform it into a vector form by embedding learning. For transformation, we list the nodes (limited number) in a line format like a sentence in a graph in the form of a network before the training input. One of the learning techniques that use this strategy is Deepwalk[4]. DeepWalk is used to learn graphs between words in the Korean WordNet. The graph embedding information is used in concatenation with the word vector information of the learned language model for correction, and the final correction word is determined by the cosine distance value between the vectors. In this paper, In order to test whether the information of graph embedding affects the improvement of the performance of context- sensitive spelling error correction, a confused word pair was constructed and tested from the perspective of Word Sense Disambiguation(WSD). In the experimental results, the average correction performance of all confused word pairs was improved by 2.24% compared to the baseline correction performance.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.307-318
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• 2022

Virtual reality is actively used in the medical field as a learning effectiveness that induces immersion, but research on the factors that induce learning immersion is insufficient. Therefore, this study extracted the usability and cognitive affordance evaluation elements of the interface through literature research and selected four types of virtual reality medical contents to conduct a Useability-Test for experts. Based on this, an interface design method according to virtual reality medical technology content was proposed. In summary, it can be seen that the information-providing interface affects immersion due to visibility, distance from the experience's gaze, color harmony, uniform visualization, feedback, reaction speed, and expected changes resulting from manipulation, and does not impair immersion. This study has limitations in generalization using limited content, so it is expected that continuous research will discuss the development of standardized guides in interface design and the precision of interface design research from a user perspective.

• Education of Primary School Mathematics
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• v.25 no.3
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• pp.233-249
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• 2022

Measurement plays an important role in both school mathematics and real life. Among the measurement areas, length is the first to learn and is the basis for measurement. Time is measured in its own way and is characterized by being the most abstract. This study attempted to analyze elementary mathematics textbooks and representations in real life to examine how the length and time of learning in school mathematics differ from those represented in real life. Based on this, we tried to derive implications for the direction of measurement education and elementary math textbooks. As a result of the analysis, the concept of length was used the same in real life and school mathematics. However, terms such as distance, depth, and height were not defined, and the representation of the approximate value was presented in a fragmentary form. In addition, there were parts where students were likely to feel confused in school mathematics and real life, such as the same units such as 'minutes and seconds' were used in time. Therefore, considering these differences, it is necessary to consider the direction of composition of math textbooks and teaching and learning so that students can connect school mathematics and real life and understand widely about measurement concepts.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.55-63
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• 2024

This paper discusses the concept and continuously evolving applications of metaverse technology in the field of safety education, and proposes methodologies for employing metaverse technology in safety education. Additionally, it analyzes educational cases using the metaverse to explore specific directions for the advancement of safety education. Therefore, this study aims to propose methodologies for safety education utilizing metaverse technology. Recently, the metaverse has emerged as a new platform in various fields, including education. In particular, safety education using metaverse technology is carried out because it can provide an engaging learning experience by fostering understanding and immersion through interaction, moving away from one-way didactic teaching. This paper proposes a three-stage educational process for safety education using metaverse technology and presents various implementable projects and activity examples for each stage. This approach can contribute to developing practical response skills for various situations, going beyond traditional safety education methods. Future research is expected to deeply explore the long-term effectiveness of this educational methodology and its practical applicability in educational settings.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.245-251
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• 2021

As the trend of steadily increasing the number of single or double household, there is a growing demand to see who is the outsider visiting the home during the free time. Various models of face recognition technology have been proposed through many studies, and Harr Cascade of OpenCV and Hog of Dlib are representative open source models. Among the two modes, Dlib's Hog has strengths in front of the indoor and at a limited distance, which is the focus of this study. In this paper, a face recognition visitor access system based on Dlib was designed and implemented. The whole system consists of a front module, a server module, and a mobile module, and in detail, it includes face registration, face recognition, real-time visitor verification and remote control, and video storage functions. The Precision, Specificity, and Accuracy according to the change of the distance threshold value were calculated using the error matrix with the photos published on the Internet, and compared with the results of previous studies. As a result of the experiment, it was confirmed that the implemented system was operating normally, and the result was confirmed to be similar to that reported by Dlib.

• Journal of the Korea Computer Graphics Society
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• v.14 no.4
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• pp.35-39
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• 2008

A computer supported collaborative system provides with a shared virtual workspace over the Internet where its remote users cooperate in order to achieve their goals by overcoming problems caused by distance and time. VRMMS (Virtual Reality Molecular Modeling System) [1] is a VR based collaborative system where biologists can remotely participate in and exercise molecular modeling tasks such as viewing three dimensional structures of molecular models, confirming results of molecular simulations and providing with feedbacks for the next simulations. Biologists can utilize VRMMS in executing molecular simulations. However, first-time users and beginners need to spend some time for studying and practicing in order to skillfully manipulate molecular models and the system. The best way to resolve the problem is to have a face-to-face session of teaching and learning VRMMS. However, it is not practically recommended in the sense that the users are remotely located. It follows that the learning time could last longer than desired. In this paper, we propose to use Second Life [2] combining with VRMMS for removing the problem. It can be used in building a shared workplace over the Internet where molecular simulations using VRMMS can be exercised, taught, learned and practiced. Through the web, users can collaborate with each other using VRMMS. Their avatars and tools of molecular simulations can be remotely utilized in order to provide with senses of 'being there' to the remote users. The users can discuss, teach and learn over the Internet. The shared workspaces for discussion and education are designed and implemented in Second Life. Since the activities in Second Life and VRMMS are designed to realistic, the system is expected to help users in improving their learning and experimental performances.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.936-939
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• 2022

As air combat system technologies developed in recent years, the development of air defense systems is required. In the operating concept of the anti-aircraft defense system, selecting an appropriate armament for the target is one of the system's capabilities in efficiently responding to threats using limited anti-aircraft power. Much of the flying threat identification relies on the operator's visual identification. However, there are many limitations in visually discriminating a flying object maneuvering high speed from a distance. In addition, as the demand for unmanned and intelligent weapon systems on the modern battlefield increases, it is essential to develop a technology that automatically identifies and classifies the aircraft instead of the operator's visual identification. Although some examples of weapon system identification with deep learning-based models by collecting video data for tanks and warships have been presented, aerial vehicle identification is still lacking. Therefore, in this paper, we present a model for classifying fighters, helicopters, and drones using a convolutional neural network model and analyze the performance of the presented model.