• Journal of Korea Multimedia Society
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• v.24 no.4
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• pp.593-600
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• 2021

In this paper, we present the FlappyBird Competition System (FCS) implementation, a competition-based automated assessment system used in an entry-level artificial intelligence (AI) course at a university. The proposed system provides an evaluation method suitable for AI courses while taking advantage of automated assessment methods. Students are to design a neural network structure, train the weights, and tune hyperparameters using the given reinforcement learning code to improve the overall performance of game AI. Students participate using the resulting trained model during the competition, and the system automatically calculates the final score based on the ranking. The user evaluation conducted after the semester ends shows that our competition-based automated assessment system promotes active participation and inspires students to be interested and motivated to learn AI. Using FCS, the instructor significantly reduces the amount of time required for assessment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.754-774
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• 2023

Wrestling is one of the popular events for modern sports. It is difficult to quantitatively describe a wrestling game between athletes. And deep learning can help wrestling training by human recognition techniques. Based on the characteristics of latest wrestling competition rules and human recognition technologies, a set of wrestling competition video analysis and retrieval system is proposed. This system uses a combination of literature method, observation method, interview method and mathematical statistics to conduct statistics, analysis, research and discussion on the application of technology. Combined the system application in targeted movement technology. A deep learning-based facial recognition psychological feature analysis method for the training and competition of classical wrestling after the implementation of the new rules is proposed. The experimental results of this paper showed that the proportion of natural emotions of male and female wrestlers was about 50%, indicating that the wrestler's mentality was relatively stable before the intense physical confrontation, and the test of the system also proved the stability of the system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.84-88
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• 2011

In this paper, a new competition strategy for learning vector quantization is proposed. The simple competitive strategy used for learning vector quantization moves the winning prototype which is the closest to the newly given data pattern. We propose a new learning strategy based on k-nearest neighbor prototypes as the winning prototypes. The selection of several prototypes as the winning prototypes guarantees that the updating process occurs more frequently. The design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the proposed learning strategy.

• Journal of The Korean Association of Information Education
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• v.23 no.4
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• pp.363-374
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• 2019

Recently, there is an increasing interest in game based learning as a teaching method for digital native learners. This study set kinect game contributes to engaged learning as the competition and cooperation play (achievement goals, interaction), the digital game play (multisensory stimulation, fantasy and curiosity, chance, accurate feedback, control), and the body movement play (embodied cognition, presence). After performing classes using the motion recognition game developed for the elementary school history class, this study conducted semi structured interviews based on engaged learning elements of kinect game based learning for students who were successfully participating in learning. In the result, each element appeared to a successful learner. Based on these results, this study hopes to assist researchers as a basic evidence to introduce kinect game-based learning for engaged learning.

• Journal of KIISE
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• v.42 no.3
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• pp.367-378
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• 2015

This study was directed at the design of a hybrid algorithm for competition relation extraction. Previous works on relation extraction have relied on various lexical and deep parsing indicators and mostly utilize only the machine learning method. We present a new algorithm integrating machine learning with various filtering methods. Some simple but useful features for competition relation extraction are also introduced, and an optimum feature set is proposed. The goal of this paper was to increase the precision of competition relation extraction by combining supervised learning with various filtering methods. Filtering methods were employed for classifying compete relation occurrence, using distance restriction for the filtering of feature pairs, and classifying whether or not the candidate entity pair is spam. For evaluation, a test set consisting of 2,565 sentences was examined. The proposed method was compared with the rule-based method and general relation extraction method. As a result, the rule-based method achieved positive precision of 0.812 and accuracy of 0.568, while the general relation extraction method achieved 0.612 and 0.563, respectively. The proposed system obtained positive precision of 0.922 and accuracy of 0.713. These results demonstrate that the developed method is effective for competition relation extraction.

• The KIPS Transactions:PartA
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• v.10A no.6
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• pp.709-714
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• 2003

It is important that the interaction between learners and contents in Educational Contents. But, there is just simple interaction in traditional WBI or CAI. As it is necessary to study for interaction with learners. There is applied more multimedia elements for the fun of learners. But, it is also necessary to study for Network Educational Game Contents which can give virtual environment to learn easily and funny. In this study, Competition Learning Model is designed for network learning environment. We can look at the new view point of Educational Contents by implementation of Network Educational Game Contents and Competition Learning Model.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.17-24
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• 2023

Based on the problem of 'Tatanic - Machine Learning from Disaster', a representative competition of Kaggle that presents challenges related to data science and solves them, we want to see how data preprocessing and model construction affect prediction accuracy and score. We compare and analyze the features by selecting seven top-ranked solutions with high scores, except when using redundant models or ensemble techniques. It was confirmed that most of the pretreatment has unique and differentiated characteristics, and although the pretreatment process was almost the same, there were differences in scores depending on the type of model. The comparative analysis study in this paper is expected to help participants in the kaggle competition and data science beginners by understanding the characteristics and analysis flow of the preprocessing methods of the top score participants.

• Journal of the Economic Geographical Society of Korea
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• v.9 no.3
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• pp.459-472
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• 2006

This paper examines the implication of competition and collaboration in the innovation process for regional economic development in an increasingly knowledge-based economy. While competition is an important force in securing the competitive advantage of firms, collaboration between firms and organizations should be necessary for promoting the innovative capacity of a region. This study shows that collaboration relations based on trust and stability is important for the long-term development of learning and innovation in competitive environment, and the way how spatial proximity plays an important role in interactive learning processes. It also discusses the reason why the innovative networks facilitating the exchange of tacit knowledge should be embedded in region. Finally, the paper examines the possibility of the networks based on collaboration relationship in less-favored regions such as Chonbuk, and suggests the policy implication of the result for achieving regional innovation systems in the region successfully.

• Korean Journal of Culture and Social Issue
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• v.14 no.1_spc
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• pp.187-221
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• 2008

Although academic interest, the intersection of cognition, emotion, and motivation, is a primary goal of learning and mediates the effects of learning, the present learning environment is full of impeding factors which undermine learner's interests in learning situation. The purpose of this study is to examine current state of academic interests of Korean students and to identify several potential causes of developmental declines in academic interests. It has been consistently found that academic interests in various school subjects decrease with age and grade in school. Three potentially contributing factors to the observed loss of academic interests are mainly discussed: deprived autonomy, severe competition, and normative evaluation. Based on theories on interest and motivation, and empirical findings, various prescriptions are also suggested for designing an interest-based learning environment in order to trigger and enhance learner's academic interests.

• Asia Marketing Journal
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• v.16 no.3
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• pp.35-56
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• 2014

With rapid change and intensive competition in the global economy, the capability to capture, absorb, develop, and transfer new knowledge is a key organizational success factor. Through effective learning, companies are more likely to develop the innovation, quality, and responsiveness essential to meet the growing expectations of customers and the disruptive threats of competitors and new technologies. In the paper the role of technological innovation and its relationship to organizational learning in managing technology-based new products are examined. Several factors which can influence the rate and effectiveness of organizational learning are identified. Barriers to learning also are discussed. Finally, several managerial implications and propositions for future research on learning and technological innovation are advanced.