• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.239-245
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• 2021

Due to the fourth industrial revolution and the Corona19, the availability of digital devices such as kiosks is a matter directly related to survival for older adults with digital divide. This paper analyzes the problems of existing information service education for older adults and proposes an IT education strategy tailored to aging and life cycle of older adults. The proposed in this study is an educational application-based IT training method that supports repetitive learning regardless of time and place, developing kiosk function as a functional game-type educational application and experimenting with post-training effectiveness. The research method proposed UI usability evaluation frame for older adults, and developed educational applications based on proposed evaluation frame, and conducted kiosk education. As a result of the experiment, the mission success rate after using the IT application was 80.6%, which is a 55.1%P improvement compared to the pre-use(25.5%). This study confirmed that the proposed education for older adults is a way to overcome the limitations of existing IT education in the current situation.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1171-1180
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• 2019

Emotion is a psycho-physiological process that plays an important role in human interactions. Affective computing is centered on the development of human-aware artificial intelligence that can understand and regulate emotions. This field of study is also critical as mental diseases such as depression, autism, attention deficit hyperactivity disorder, and game addiction are associated with emotion. Despite the efforts in emotions recognition and emotion detection from nonstationary, detecting emotions from abnormal EEG signals requires sophisticated learning algorithms because they require a high level of abstraction. In this paper, we investigated LSTM hyperparameters for an optimal emotion EEG classification. Results of several experiments are hereby presented. From the results, optimal LSTM hyperparameter configuration was achieved.

• 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.