• The Journal of Information Technology
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• v.5 no.4
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• pp.45-55
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• 2002

Virtual Reality(VR) is a new technology which makes humans communicate with computer. It allows the user to see, hear, feel and interact in a three-dimensional virtual world created graphically. In this paper, we introduced VR into psychotherapy area and developed VR system for the exposure therapy of acrophobia. Acrophobia is an abnormal fear of heights. Medications or cognitive-behavior methods have been mainly used as a treatment. Lately the virtual reality technology has been applied to that kind of anxiety disorders. A virtual environment provides patient with stimuli which arouses phobia, and exposing to that environment makes him having ability to over come the fear. In this study, the elevator stimulator that composed with a position sensor, head mount display, and audio system, is suggested. To illustrate the physiological difference between a person who has a feel of phobia and without phobia, heart rate was measured during experiment. And also measured a person's HR after the virtual reality training. In this study, we demonstrated the subjective effectiveness of virtual reality psychotherapy through the clinical experiment.

• Journal of Environmental Health Sciences
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• v.40 no.3
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• pp.255-263
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• 2014

Objectives: This study evaluated the fungicidal efficacy of a fumigant containing 20% ortho-phenylphenol against Trichophytone mentagrophytes (T. mentagrophytes), Candida albicans (C. albicans) and Aspergillus niger (A. niger). Methods: Five replicates of each carrier were contaminated by depositing 0.05 mL of each fungal suspension. After drying, two carriers without exposure to the fumigant and three carriers with exposure to the fumigant were left in a sealed room ($25m^3$) at $21{\pm}0.5^{\circ}C$ and $60{\pm}10%$ relative humidity for 15 hours. Immediately after removal from the test room, each carrier was transferred into recovery diluent and suspended, diluted and inoculated. After incubation, the numbers of each colony were counted, and the parameter values (N, T, d) were calculated. Results: The working culture suspension number (N value) of T. mentagrophytes, C. albicans and A. niger were $1.0{\times}10^8$, $1.2{\times}10^8$ and $5.7{\times}10^7CFU/mL$, respectively. All the colony numbers on the carriers exposed to the fumigant (n1, n2, n3) were higher than 0.5N1 (the number of fungal test suspensions by pour plate method), 0.5N2 (the number of fungal test suspensions by filter membrane method) and 0.5N1, respectively. In addition, all mean numbers of test strains recovered on the control-carriers (T value) were over $10^6CFU/mL$. For the fungicidal effect of the fumigant, all numbers of fungal reductions after exposure of the fumigant (d value) were 4 logCFU/mL. Conclusions: The present study showed that fumigant containing 20% ortho-phenylphenol has effective fungicidal activity against T. mentagrophytes, C. albicans and A. niger.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.3 no.1
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• pp.57-72
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• 2008

Serve and do so that graft together specialists' view application field of computer and developmental disability diagnosis estimation data to construct developmental disability diagnosis estimation system in this Paper and constructed developmental disability diagnosis estimation system. Developmental disability diagnosis estimation must supply information of specification area that specialists are having continuously. Developmental disability diagnosis estimation specialist system need multimedia data processing that is specialized little more for developmental disability classification diagnosis and decision-making and is atomized for this. Characteristic of developmental disability diagnosis estimation system that study in this paper can supply quick feedback about result, and can reduce mistake on recording and calculation as well as can shorten examination's enforcement time, and background of training is efficient system fairly in terms of nonprofessional who is not many can use easily. But, as well as when multimedia information that is essential data of system construction for developmental disability diagnosis estimation is having various kinds attribute and a person must achieve description about all developmental disability diagnosis estimation informations, great amount of work done is accompanied, technology about equal data can become different according to management. Because of these problems, applied search technology of contents base (Content-based) that search connection information by contents of edit target data for developmental disability diagnosis estimation data processing multimedia data processing technical development. In the meantime, typical access way for conversation style data processing to support fast image search, after draw special quality of data by N-dimension vector, store to database regarding this as value of N dimension and used data structure of Tree techniques to use index structure that search relevant data based on this costs. But, these are not coincided correctly in purpose of developmental disability diagnosis estimation because is developed focusing in application field that use data of low dimension such as original space DataBase or geography information system. Therefore, studied save structure and index mechanism of new way that support fast search to search bulky good physician data.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.17-28
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• 2005

Recently intelligent characters in computer games have been an important element more and more because they continually stimulate gamers' interests. As a typical method for implementing such intelligent characters, neural networks have been used for training action patterns of opponent's characters and game rules. However, some of the game rules can be abruptly changed and action properties of garners in on-line game environments are quite different according to gamers. In this paper, we address how a neural network adapts to those environmental changes. Our adaptation solution includes two components: an individual adaptation mechanism and a group adaptation mechanism. With the individual adaptation algorithm, an intelligent character steadily checks its game score, assesses the environmental change with taking into consideration of the lastly earned scores, and initiates a new learning process when a change is detected. In multi-user games, including massively multiple on-line games, intelligent characters confront diverse opponents that have various action patterns and strategies depending on the gamers controlling the opponents. The group adaptation algorithm controls the birth of intelligent characters to conserve an equilibrium state of a game world by using a genetic algorithm. To show the performance of the proposed schemes, we implement a simple fighting action game and experiment on it with changing game rules and opponent characters' action patterns. The experimental results show that the proposed algorithms are able to make intelligent characters adapt themselves to the change.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.173-181
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• 2017

In this paper, we have studied tracking as a training stage of considering the position and the scale of a person given its previous position, scale, as well as next and forward image fraction. Unlike other learning methods, CNN is thereby learning combines both time and spatial features from the image for the two consecutive frames. We introduce multiple path ways in CNN to better fuse local and global information. A creative shift-variant CNN architecture is designed so as to alleviate the drift problem when the distracting objects are similar to the target in cluttered environment. Furthermore, we employ CNNs to estimate the scale through the accurate localization of some key points. These techniques are object-independent so that the proposed method can be applied to track other types of object. The capability of the tracker of handling complex situations is demonstrated in many testing sequences. The accuracy of the SVM classifier using the features learnt by the CNN is equivalent to the accuracy of the CNN. This fact confirms the importance of automatically optimized features. However, the computation time for the classification of a person using the convolutional neural network classifier is less than approximately 1/40 of the SVM computation time, regardless of the type of the used features.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.567-578
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• 2014

PEAS (performance enhancement attitude scale) has been used to measure attitude and disposition toward doping in elite athlete. It is constructed of 17-item, 6-point scale. The purpose of this study was to verify validity of the PEAS for Korean elite player through Rasch model. The scale was administered to 438 Korean elite players. Principal component analysis was used to verify unidimensionality using SPSS program. Rasch measurement computer program, WISTEPS, was used to estimate goodness-of-fit of items and category structure. Differenctial item functioning by gender was also estimated by the WINSTEPS program. All alpha level was set at 0.05. First, principal component analysis showed that unidimensionality is satisfied as over 20.0% of variance of eigenvalue. Second, category probabilities curve showed 5-point scale was better than 6-point scaled statistically. Third, seven items (1, 9, 10, 12, 13, 14, 17) in the 17-item were not good model fit and three items (3, 12, 13) were estimated as the differential item functioning. This study showed that 9-item, 5-point scale is better PEAS to Korean elite player.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.59-66
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• 2016

This paper presents an empirical evaluation on dimensionality reduction strategies by which dissimilarity-based classifications (DBC) can be implemented efficiently. In DBC, classification is not based on feature measurements of individual objects (a set of attributes), but rather on a suitable dissimilarity measure among the individual objects (pair-wise object comparisons). One problem of DBC is the high dimensionality of the dissimilarity space when a lots of objects are treated. To address this issue, two kinds of solutions have been proposed in the literature: prototype selection (PS)-based methods and dimension reduction (DR)-based methods. In this paper, instead of utilizing the PS-based or DR-based methods, a way of performing DBC in Eigen spaces (ES) is considered and empirically compared. In ES-based DBC, classifications are performed as follows: first, a set of principal eigenvectors is extracted from the training data set using a principal component analysis; second, an Eigen space is expanded using a subset of the extracted and selected Eigen vectors; third, after measuring distances among the projected objects in the Eigen space using $l_p$-norms as the dissimilarity, classification is performed. The experimental results, which are obtained using the nearest neighbor rule with artificial and real-life benchmark data sets, demonstrate that when the dimensionality of the Eigen spaces has been selected appropriately, compared to the PS-based and DR-based methods, the performance of the ES-based DBC can be improved in terms of the classification accuracy.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.333-349
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• 2021

The fourth industrial revolution based on information and communication technology has increased the need for an environment where contents in new technologies can be learned for the development of lifelong vocational capabilities. To prepare for this, K University's online lifelong education center has established STEP, a smart learning platform. In this study, we conducted a study and other platform case analysis for STEP learner types, a survey of learners, and a comprehensive analysis based on these results to classify characteristics by learner types. It also intended to establish a plan to provide customized services to meet the needs of STEP learners in the future. The derived results are as follows. It is necessary to constantly manage learning content difficulty and learning motivation survey, and also needs to refine the operation of learning content in terms of learning composition. In addition, it is important to secure specialized content, to manage vulnerable learners, to actively introduce a learner support system and various educational methods.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.530-536
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• 2021

Recently, due to the development of deep learning, end-to-end speech recognition, which directly maps graphemes to speech signals, shows good performance. Especially, among the end-to-end models, conformer shows the best performance. However end-to-end models only focuses on the probability of which grapheme will appear at the time. The decoding process uses a greedy search or beam search. This decoding method is easily affected by the final probability output by the model. In addition, the end-to-end models cannot use external pronunciation and language information due to structual problem. Therefore, in this paper conformer with lexicon transducer is proposed. We compare phoneme-based model with lexicon transducer and grapheme-based model with beam search. Test set is consist of words that do not appear in training data. The grapheme-based conformer with beam search shows 3.8 % of CER. The phoneme-based conformer with lexicon transducer shows 3.4 % of CER.