• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.572-583
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• 2019

This study sought to find out what the most ideal and appropriate native English speakers-Korean English teacher cooperative class model and the defining factors for organizing effective cooperative classes in the English education environment of our country. To achieve this goal, a total of 165 sixth graders of five elementary schools in Seoul were subject to the study. For about a month from April 1 to April 30, 2019, the survey and statistical analysis were conducted, including multiple return analysis, correlation analysis, cross analysis, and t/F verification. In summary, the results of the study are as follows. First, it was found that among the recognition of cooperative classes between native English speakers and Korean English teachers, it affected the defining factors in the order of class-related skills, task orientation, teaching-learning strategies, and motivation. Second, based on learner characteristics, the difference in perception of cooperative classes between native English speakers and Korean English teachers was verified, and the perception of native-Korean English teachers' cooperative classes was different depending on gender and the type of English cooperative classes currently participating, but the recognition of native-Korean English-Korean English cooperative classes, which were statistically significant, was not confirmed. Third, according to learner characteristics, the difference in the definition factors of the learner was verified and the difference between the sexes occurred, but the learner-defined factors according to the current type of English cooperative class did not occur. Also, there was no difference in the definition factors of scholars according to the type of English cooperative classes desired.

• Journal of the Korea Computer Graphics Society
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• v.25 no.3
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• pp.85-92
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• 2019

A 3D virtual character playing a role in a digital story-telling has a unique style in its appearance and motion. Because the style reflects the unique personality of the character, it is very important to preserve the style and keep its consistency. However, when the character's motion is directly controlled by a user's motion who is wearing motion sensors, the unique style can be discarded. We present a novel character motion control method that uses only a small amount of animation data created only for the character to preserve the style of the character motion. Instead of machine learning approaches requiring a large amount of training data, we suggest a search-based method, which directly searches the most similar character pose from the animation data to the current user's pose. To show the usability of our method, we conducted our experiments with a character model and its animation data created by an expert designer for a virtual reality game. To prove that our method preserves well the original motion style of the character, we compared our result with the result obtained by using general human motion capture data. In addition, to show the scalability of our method, we presented experimental results with different numbers of motion sensors.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.475-482
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• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• Journal of IKEEE
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• v.22 no.4
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• pp.1140-1146
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• 2018

Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.221-230
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• 2018

Most of researchers and business futurists agree that traditional organizational designs are inadequate for coping with today's turbulent and increasingly networked world. Executives in small firms find that their organizations must tap into an extended network of partners to achieve the scale and power needed to succeed in industries dominated by large, global firms. As they attempt to build lean yet agile businesses, these executives are finding that they no longer rely on gut instinct alone. Neither can they simply copy organizational model that worked in the past. They must understand how organizational design choices influence operational efficiency and flexibility and, even more important, how to best align the organization with the environment and the strategy chosen to quickly and effectively sense and respond to opportunities and threats This research examines the capabilities required to build businesses that can survive and prosper in today's fast-faced and uncertain environment. The insights presented in this research have emerged from over 30 years of work with hundreds of executives and entrepreneurs as they struggled to build businesses that could cope with the demands of a rapidly changing, networked global economy. The insights from this research suggest that IT is an important enabler for developing the best capabilities required for success.

• Management & Information Systems Review
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• v.38 no.2
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• pp.83-103
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• 2019

Supply chain management(SCM) requires efforts to search for methods for mutual growth with partner companies and to maintain continuous cooperative relations in order to gain a competitive edge. Because information competencies play a big role within the supply chain, it is essential to examine the relationship of information sharing and partnership quality that can affect information competency. In order to maintain continuous business relations between partner companies, it is necessary to identify the obstacles with partner companies resulting from the imbalance of power within a supply chain and to take on a strategic approach for effectively managing such obstacles. Therefore, there is a significant need to discuss strategic approach methods to enable the logic of mutual growth through the CSV that is worth learning from the partner company and the attributes of non-mediated power. CSV will be reviewed from various aspects as a new management paradigm in the future. This study aims at suggesting a continuous growth model for companies by solving social problems through the integration of CSV and the concept of non-mediated power to advance the information competencies of SCM. A total of 142 copies of survey forms for SCM Implementation Companies were using the PLS structural equation modeling for an analysis, and the following are the findings. Results of this study showed that both CSV and non-mediated power had significant impact on information sharing and partnership qualities, and the conclusion that it is possible to enhance information competency through information sharing and partnership quality. Based on this, this study proposes the implication that it is necessary to elevate awareness of CSV and non-mediated power as variables for the coexistence of SCM participating companies.

• The Mathematical Education
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• v.58 no.1
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• pp.101-120
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• 2019

The goal of this study was to apply an expectancy-value model(Wigfield & Eccles, 2000) to explain changes in six multi-ethnic students' achievement motivation in mathematics during sixth (2012) to eighth (2014) grades. In order to achieve this goal, we used narrative research methods. Although individual students' achievement motivation and mathematics related life experiences differed, there are some common factors influencing their motivation development, especially (a) roles played by parents and teachers; (b) assessment of peers' competencies; (c) past learning experiences related to mathematics curriculum; (d) perception of the relationship between mathematics competency and other subjects; (e) home backgrounds; and (f) perceived task values. In this study, we achieved some insight into why some multi-ethnic students are willing to study hard to get good scores while others are uninterested in mathematics, and why some multi-ethnic students are likely to pursue new mathematical tasks and persist despite challenges, while others easily give up studying mathematics in the face of adversity. We argue that in order to increase and sustain multi-ethnic students' achievement motivation, educators and parents should recognize that motivation is contextually formulated in the intersection of current people, time, and space, not a personal entity formed in an individual's mind. The findings of this study shed light on the development of achievement motivation and can inform efforts to develop multi-ethnic students' positive motivation, which might influence their mathematics achievement and success in school.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.803-812
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• 2021

This study explored the concept of artificial intelligence and the problem-solving process that can be explained through literature research. Through this study, the educational meaning and application plan of artificial intelligence that can be explained were presented. XAI education is a human-centered artificial intelligence education that deals with human-related artificial intelligence problems, and students can cultivate problem-solving skills. In addition, through algorithmic education, it is possible to understand the principles of artificial intelligence, explain artificial intelligence models related to real-life problem situations, and expand to the field of application of artificial intelligence. In order for such XAI education to be applied in elementary schools, examples related to real world must be used, and it is recommended to utilize those that the algorithm itself has interpretability. In addition, various teaching and learning methods and tools should be used for understanding to move toward explanation. Ahead of the introduction of artificial intelligence in the revised curriculum in 2022, we hope that this study will be meaningfully used as the basis for actual classes.