• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.337-345
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• 2020

The Weapon-Target Assignment(WTA) problem can be formulated as an optimization problem that minimize the threat of targets. Existing methods consider the trade-off between optimality and execution time to meet the various mission objectives. We propose a multi-agent reinforcement learning algorithm for WTA based on mean field game to solve the problem in real-time with nearly optimal accuracy. Mean field game is a recent method introduced to relieve the curse of dimensionality in multi-agent learning algorithm. In addition, previous reinforcement learning models for WTA generally do not consider weapon interference, which may be critical in real world operations. Therefore, we modify the reward function to discourage the crossing of weapon trajectories. The feasibility of the proposed method was verified through simulation of a WTA problem with multiple targets in realtime and the proposed algorithm can assign the weapons to all targets without crossing trajectories of weapons.

• Archives of design research
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• v.16 no.4
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• pp.141-150
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• 2003

In 21 century, Game industry became one field of the cultural content industries. Also, It is one of the most important technologies as a Multimedia, 3-D sound, virtual reality field, and character industry and its scale and effect are getting big. But Korea still lags behind America and Japan in developing game. For that, many support business progressed. However, Special importance〔emphasis〕 is attached to the developing of software engines. In the mean time, The game market of children(under 8) is the biggest one, which form over 30% of all the markets in the PC game market of America. The Children's game, which has both entertaining and educating, is a content-concerned industry. So with a short period of the developing time, they make a high value added. In this paper, 1 study the existing computer games and propose for game contents and the game factor's abstraction suited to the character by kid age. It is concluded that 'Asports', 'Asim,' 'U.J RPG', 'S＋RPG' are beneficial to the child growth on each stage, through serveys analysis of protocol, and research of the cjaracters of the growth on the each age.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.33-36
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• 2000

To create a more realistic soccer game derived from TV images, we are developing an image synthesis system that generates 3D image sequence from TV images. We propose the method for the team and the pose recognition of players in TV images. The representation includes camera calibration method, team recognition method and pose recognition method. To find the location of a player on the field, a field model is constructed and a player's field position is transformed by 4-feature points. To recognize the team information of players, we compute RGB mean values and standard deviations of a player in TV images. Finally, to recognize pose of a player, this system computes the velocity and the ratio of player(height/width). Experimental results are included to evaluate the performance of the team and the pose recognition.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.169-185
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• 2017

Given that nowadays things get very fast due to the pervasive use of the Internet and mobile devices, decision-making time can be an important variable in the online economic decisions. Although in experimental and behavioral economics, measures like scores or earnings are usually preferred, this study argues that the time variable can be dealt with as a new decision outcome. Thus, by selecting some psychological factors presumably impactful in the online context (i.e., incidental emotions, psychological distances, and individual's impulsivity), this study tested their effect on decision time in the online centipede game. As a result, the mean decision time in the game was longer (1) in the happiness condition than in the anger condition and (2) in the friend condition than in the stranger condition. The people with attention difficulties spent a short time in the decision and the people who dislike complex problems spent a short time in explaining their decision. This study can contribute to the field as it used the decision time as the dependent variable and it tested the effect of psychological factors in the context of online decision-making. Future studies can be conducted in other online decision situations or by considering other psychological variables.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.268-282
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• 2022

One of the most important technologies today is augmented reality technology, it allows users to experience the real world using virtual objects that are combined with the real world. This technology is interesting and has become applied in many sectors such as the shopping and medicine, also it has been included in the sector of education. In the field of education, AR technology has become widely used due to its effectiveness. It has many benefits, such as arousing students' interest in learning imaginative concepts that are difficult to understand. On the other hand, studies have proven that collaborative between students increases learning opportunities by exchanging information, and this is known as Collaborative Learning. The use of multimodal creates a distinctive and interesting experience, especially for students, as it increases the interaction of users with the technologies. The research aims at developing collaborative framework for developing achievement of 6th graders through designing a framework that integrated a collaborative framework with a multimodal input "hand-gesture and touch", considering the development of an effective, fun and easy to use framework with a multimodal interaction in AR technology that was applied to reformulate the genetics and traits lesson from the science textbook for the 6th grade, the first semester, the second lesson, in an interactive manner by creating a video based on the science teachers' consultations and a puzzle game in which the game images were inserted. As well, the framework adopted the cooperative between students to solve the questions. The finding showed a significant difference between post-test and pre-test of the experimental group on the mean scores of the science course at the level of remembering, understanding, and applying. Which indicates the success of the framework, in addition to the fact that 43 students preferred to use the framework over traditional education.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.16 no.4
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• pp.334-345
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• 2006

The objectives of this study were to analyze and compare 24 hrs personal exposure levels of MF at microenvironments such as home, school, educational institute, internet pc game room, transportation, and other places according to time activity patterns using various metrics for children attending the primary schools located near and away from the power lines, and to characterize the major microenvironments and impact factors attributed personal exposure level. The study was carried out for 44 children attending a primary school away from the lines(school A) and 125 children attending a school away from 154 kV power lines(school B), all who aged 12 years and were 6 grade, from July 2003 to December 2003. All participants filled in a questionnaire about characteristics, residence, use of electrical appliances and others. Children wore a small satchel in which EMDEX II and Lite (Enertech, Co. Ltd) and a diary of activity list for period of registration in 20 minutes blocks. All statistical calculations were made with the SAS System, Releas 6.12. The summary of results was presented below. First, about the characteristics of subjects, there no differences between two groups. The subject almost spent about 56 % of their time at home and about 20~25 % of their time at school. Fifty percent of children spent 2 hours at private educational institutes. Second, the personal exposure measurements of children in school B was statistically higher than those of children in school A by various metrics such as arithmetic mean, geometric mean, percentile(5, 25, 50, 75, 95), maximum, rate of change metric, constant field metric. The arithmetic and geometric mean magnetic fields during the time the children were at school B were 0.98 and $0.86{\mu}T$ and were about 23 times higher than those of children were at school A. In conclusion, the significant major determinants of personal exposure level is the distance from the power line to microenvironments.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.51-59
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• 1999

In this paper we propose the technique for tracking players and a ball and for obtaining players' ground traces using image mosaic in general soccer sequences. Here, general soccer sequences mean the case that there is no extreme zoom-in or zoom-out of TV camera. Obtaining player's ground traces requires that the following three main problems be solved. There main problems: (1) ground field extraction (2) player and ball tracking and team indentification (3) player positioning. The region of ground field is extracted on the basis of color information. Players are tracked by template matching and Kalman filtering. Occlusion reasoning between overlapped players in done by color histogram back-projection. To find the location of a player, a ground model is constructed and transformation between the input images and the field model is computed using four or more feature points. But, when feature points extracted are insufficient, image-based mosaic technique is applied. By this image-to-model transformation, the traces of players on the ground model can be determined. We tested our method on real TV soccer sequence and the experimental results are given.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.