• The Journal of Korea Robotics Society
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• v.3 no.4
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• pp.270-277
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• 2008

The development of a face robot basically targets very natural human-robot interaction (HRI), especially emotional interaction. So does a face robot introduced in this paper, named Buddy. Since Buddy was developed for a mobile service robot, it doesn't have a living-being like face such as human's or animal's, but a typically robot-like face with hard skin, which maybe suitable for mass production. Besides, its structure and mechanism should be simple and its production cost also should be low enough. This paper introduces the mechanisms and functions of mobile face robot named Buddy which can take on natural and precise facial expressions and make dynamic gestures driven by one laptop PC. Buddy also can perform lip-sync, eye-contact, face-tracking for lifelike interaction. By adopting a customized emotional reaction decision model, Buddy can create own personality, emotion and motive using various sensor data input. Based on this model, Buddy can interact probably with users and perform real-time learning using personality factors. The interaction performance of Buddy is successfully demonstrated by experiments and simulations.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.831-837
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• 2017

The popularization of smart phones is increasing interest in individual customized service and various systems have been changing in order to meet such changes to satisfy facilities such as museums. Museum has the element of view, contained purpose of history education, also has role as institution that should have fun and education. In order to achieve this purpose, it is necessary to arouse sufficient interest in cultural assets, In parallel, to provide various contents of cultural assets is essential. Therefore, to provide an efficient viewing cultural assets services in this study make a study to effective method for combined education and evaluation using Unity3D engine and SDK for Vuforia called Augmented Reality development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.407-420
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• 2015

Natural Language Question Answering (NLQA) and Prescriptive Analytics (PA) have been identified as innovative, emerging technologies in 2015 by the Gartner group. These technologies require knowledge bases that consist of data that has been extracted from unstructured texts. Every business requires a knowledge base for business analytics as it can enhance companies' competitiveness in their industry. Most intelligent or analytic services depend a lot upon on knowledge bases. However, building a qualified knowledge base is very time consuming and requires a considerable amount of effort, especially if it is to be manually created. Another problem that occurs when creating a knowledge base is that it will be outdated by the time it is completed and will require constant updating even when it is ready in use. For these reason, it is more advisable to create a computerized knowledge base. This research focuses on building a computerized knowledge base for business using a supervised learning and rule-based method. The method proposed in this paper is based on information extraction, but it has been specialized and modified to extract information related only to a business. The business knowledge base created by our system can also be used for advanced functions such as presenting the hierarchy of technologies and products, and the relations between technologies and products. Using our method, these relations can be expanded and customized according to business requirements.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1046-1053
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• 2003

Recently various new model of teaching-learning as web based education system has been proposed. The demand for the customized courseware which is required from the learners is increased, the needs of the efficient and automated education agents in the web-based instruction are recognized. But many education systems that had been studied recently did not service fluently the courses which learners had been wanting and could not provide the way for the learners to study the teaming weakness which is observed in the continuous feedback of the course. In this paper we propose a multi-agent system for course scheduling of learner-oriented using weakness analysis algorithm. First proposed system analyze learner's result of evaluation and calculates teaming accomplishment. From this accomplishment the multi-agent schedules the suitable course for the learner The learner achieves an active and complete learning from the repeated and suitable course.

• Journal of the HCI Society of Korea
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• v.13 no.3
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• pp.65-73
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• 2018

Game balance settings are crucial to game design. Game balancing must take into account a large amount of numerical values, configuration data, and the relationship between elements. Once released and served, a game - even for a balanced game - often requires calibration according to the game player's preference. To achieve sustainability, game balance needs adjustment while allowing for small changes. In fact, from the producers' standpoint, game balance issue is a critical success factor in game production. Therefore, they often invest much time and capital in game design. However, if such a costly game cannot provide players with an appropriate level of difficulty, the game is more likely to fail. On the contrary, if the game successfully identifies the game players' propensity and performs self-balancing to provide appropriate difficulty levels, this will significantly reduce the likelihood of game failure, while at the same time increasing the lifecycle of the game. Accordingly, if a novel technology for game balancing is developed using artificial intelligence (AI) that offers personalized, intelligent, and customized service to individual game players, it would bring significant changes to the game production system.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.3
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• pp.1-13
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• 2018

Landscape Architecture is a minority manpower field that requires wide knowledge and experience. Therefore, the service market is narrower than other fields, and education service for practitioners is lacking. The purpose of this study is to propose e-learning content development methodology that can provide customized landscaping practical education according to the level of education and increase the economic efficiency of the development process. First, in theoretical review, the ADDIE model was modified to select the curriculum development model that pursues efficiency and introduced the concept of reusing learning objects in the SCORM-based model. In particular, to overcome the problems presented in the precious studies, the analysis and design stages have been strengthened and faculty designers with integrated knowledge of Landscape Architecture and ICT have led the overall phase. The actual development process is based on a step by step procedure--analysis of landscaping practitioners needs and environments, etc., teaching and learning procedures and the design of activities considering contents reuse, the first development such as actual shooting and editing, and the second development reusing the first development content--and was done in the order of evaluation and revision of professionalism and satisfaction. As a result of the study, the space-based courses composed of modular learning objects were first developed as 216 courses in 8 subjects, as 208 courses in 3 subjects in total, in which the modularized learning object are crossed and combined in units and difficulty-based courses were second developed in 216 courses with 3 subjects in total. As a result of the evaluation the satisfaction assessment of the overall satisfaction was 4.20 and the average value of the eight measures was 3.97, both being close to 4.0. For the professional assessment, the scores of 8 subjects were very high at 84.8 to 96.4 points. in context, the scores of 5 subjects were equal to from 89.9 to 96.4 points. In conclusion, as the study was conducted based on a clear understanding of the digital characteristics of e-learning contents and general characteristic of the landscaping industry, it was possible to develop a curriculum by developing a course composed of modular learning objects and reusing learning objects by unit. In particular, it has been proven to be effective in conveying professional knowledge and experiences via general procedures and provided an opportunity to overcome some analog problems that may occur in offline education. In the future, further studies need to be done by expanding the content and by focusing on segmented subjects.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.15-20
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• 2024

Local governments are conducting smartphone usage training for the elderly to bridge the information gap caused by a super-aged society. However, the one-to-many educational approach has limitations, and the elderly face difficulties due to insufficient learning effectiveness. This study proposes an educational service that can be used in offline training settings, considering an environment where the elderly can repeatedly learn to address these issues. This service utilizes generative AI to identify the parts that users find challenging and provides personalized problems for individualized practice. Integrating this app with existing local government training programs is expected to significantly enhance the efficiency of smartphone education in terms of personalized 1:1 training, time management, and the appropriateness of educational content.

• The Journal of Bigdata
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• v.3 no.2
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• pp.59-70
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• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.