• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.59-64
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• 2015

In this paper, we found the usefulness of the deep belief network (DBN) in the fields of brain-computer interface (BCI), especially in relation to imagined speech. In recent years, the growth of interest in the BCI field has led to the development of a number of useful applications, such as robot control, game interfaces, exoskeleton limbs, and so on. However, while imagined speech, which could be used for communication or military purpose devices, is one of the most exciting BCI applications, there are some problems in implementing the system. In the previous paper, we already handled some of the issues of imagined speech when using the International Phonetic Alphabet (IPA), although it required complementation for multi class classification problems. In view of this point, this paper could provide a suitable solution for vowel classification for imagined speech. We used the DBN algorithm, which is known as a deep learning algorithm for multi-class vowel classification, and selected four vowel pronunciations:, /a/, /i/, /o/, /u/ from IPA. For the experiment, we obtained the required 32 channel raw electroencephalogram (EEG) data from three male subjects, and electrodes were placed on the scalp of the frontal lobe and both temporal lobes which are related to thinking and verbal function. Eigenvalues of the covariance matrix of the EEG data were used as the feature vector of each vowel. In the analysis, we provided the classification results of the back propagation artificial neural network (BP-ANN) for making a comparison with DBN. As a result, the classification results from the BP-ANN were 52.04%, and the DBN was 87.96%. This means the DBN showed 35.92% better classification results in multi class imagined speech classification. In addition, the DBN spent much less time in whole computation time. In conclusion, the DBN algorithm is efficient in BCI system implementation.

• Journal of Distribution Science
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• v.14 no.10
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• pp.147-152
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• 2016

Purpose - High quality films are affected by both the production stage and various variables such as the size of the movie investment and marketing that changes consumers' perceptions. Consumer preferences should be recognized first to ensure that the movie is successful. If a film is produced without pre-investigation and analysis of consumer demand and taste, the probability of success will be low. This study investigates the balance of production costs, marketing costs, and profits using game theory, suggesting an optimization strategy using the simplex method of linear programming. Research design, data, and methodology - Before the release of the movie, initial demand is assumed to be driven largely by marketing costs. In the next phase, demand is assumed to be driven purely by a movie's production cost and quality, which might also further determine consumer demand. Thus, it is essential to determine how to distribute pure production costs and other costs (marketing) in a limited movie production budget. Moreover, it should be taken into account how to optimally distribute under the assumption that the audience and production company's input resources are limited. This research simplifies the assumptions for large-scale and relatively small-scale movie investments and examines how movie distribution participant profits differ when each cost is invested differently. Results - When first movers or market leaders have to choose both quality and marketing, it has been proven that pursuing a strategy choosing only one is more likely than choosing both. In this situation, market leaders should maximize marketing costs under the premise that market leaders will not lag their quality behind the quality of second movers. Additionally, focusing on movie marketing that produces a quick effect while ceding creative activity to increase movie quality is a natural outcome in the movie distribution environment since a cooperative strategy between market competitors is not feasible. Conclusions - Government film development policy should ignore quality competition between movie production companies and focus on preventing marketing competition. If movie production companies focus on movie production quality improvement then a creative competition would ensue.

• Journal of KIISE
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• v.43 no.9
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• pp.1026-1033
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• 2016

Path-finding on a grid map is a problem generally addressed in the fields of robotics, intelligent agents, and computer games. As technology advances, virtual game worlds tend to be represented more accurately and more realistically, resulting in an excessive increase in the number of grid tiles and in path-search time. In this study, we propose a path-finding algorithm that allows a prompt response to real-time queries by constructing a reduced state space and by precomputing all possible paths in an offline preprocessing stage. In the preprocessing stage, we vertically decompose free space on the grid map, construct a connectivity graph where nodes are the decomposed regions, and store paths between all pairs of nodes in matrix form. In the real-time query stage, we first find the nodes containing the query points and then retrieve the corresponding stored path. The proposed method is simulated for a set of maps that has been used as a benchmark for grid-based path finding. The simulation results show that the state space and the search time decrease significantly.

• Journal of the Korea Computer Industry Society
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• v.3 no.3
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• pp.259-270
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• 2002

In computer Go, the method evaluating the situation of a face is not generalized. To evaluate the situations all the faces accurately, computer Go must judge owners of 361 positions according the changes of the faces. In this paper, we apply the structure of graph as a method analyzing the rules and characters of Go. The Situation Evaluation System(SES) which can evaluate the situation of a face without DB information oかy using strength of stone(SS), influence power(IP), safety(S), position value(PV), and position-value matrix(PM) is proposed. This system is very effective to evaluate the whole situations of Go because it can show the owner of 361 positions between Black and White. As a result, SES can well compute the situations in the opening game of Go. It makes 70.9% hit-ratio as compared with the practical Go games of professional players. According to the results compared with Nemesis, the commercial program which has the joseki(established stones: hewn sequences of moves near the corner which result in near-equal positions for White and Black), SES is superior to Nemesis by 10% higher in the hit-ratio of situation evaluations of professional players.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.3-14
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• 2022

Autonomous equipment requires a large amount of data from various environments. However, it takes a lot of time and cost for an experiment in a real construction sites, which are difficulties in data collection and processing. Therefore, this study aims to develop a virtual environment for autonomous tower cranes technology development and validation. The authors defined automation functions and operation conditions of tower cranes with three performance criteria: operational design domain, object and event detection and response, and minimum functional conditions. Afterward, this study developed a virtual environment for learning and validation for autonomous functions such as recognition, decision making, and control using the Unity game engine. Validation was conducted by construction industry experts with a fidelity which is the representative matrix for virtual environment assessment. Through the virtual environment platform developed in this study, it will be possible to reduce the cost and time for data collection and technology development. Also, it is also expected to contribute to autonomous driving for not only tower cranes but also other construction equipment.

• Journal of Korea Entertainment Industry Association
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• v.14 no.6
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• pp.139-150
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• 2020

The data processing of this study focuses on the textom and social media words about three areas: 'Corona 19 and professional baseball', 'Corona 19 and professional baseball', and 'Corona 19 and professional sports' The data was collected and refined in a web environment and then processed in batch, and the Ucinet6 program was used to visualize it. Specifically, the web environment was collected using Naver, Daum, and Google's channels, and was summarized into 30 words through expert meetings among the extracted words and used in the final study. 30 extracted words were visualized through a matrix, and a CONCOR analysis was performed to identify clusters of similarity and commonality of words. As a result of analysis, the clusters related to Corona 19 and Pro Baseball were composed of one central cluster and five peripheral clusters, and it was found that the contents related to the opening of professional baseball according to the corona 19 wave were mainly searched. The cluster related to Corona 19 and unrelated to professional baseball consisted of one central cluster and five peripheral clusters, and it was found that the keyword of the position of professional baseball related to the professional baseball game according to Corona 19 was mainly searched. Corona 19 and the cluster related to professional sports consisted of one central cluster and five peripheral clusters, and it was found that the keywords related to the start of professional sports according to the aftermath of Corona 19 were mainly searched.

• 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.