• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.11-19
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• 2024

Multi-agent systems can be utilized in various real-world cooperative environments such as battlefield engagements and unmanned transport vehicles. In the context of battlefield engagements, where dense reward design faces challenges due to limited domain knowledge, it is crucial to consider situations that are learned through explicit sparse rewards. This paper explores the collaborative potential among allied agents in a battlefield scenario. Utilizing the Multi-Robot Warehouse Environment(RWARE) as a sparse reward environment, we define analogous problems and establish evaluation criteria. Constructing a learning environment with the QMIX algorithm from the reinforcement learning library Ray RLlib, we enhance the Agent Network of QMIX and integrate Random Network Distillation(RND). This enables the extraction of patterns and temporal features from partial observations of agents, confirming the potential for improving the acquisition of sparse reward experiences through intrinsic rewards.

• Journal of Korean Clinical Health Science
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• v.11 no.1
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• pp.1607-1615
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• 2023

purpose: In addition to evaluating the quality of sleep of college students, the effect on non-face-to-face online learning satisfaction is identified and used as basic data for improving the quality of remote lectures. Methods: From June 1 to June 24, 2022, a self-entry survey was conducted on students enrolled in the dental hygiene department of D University in Daegu. To evaluate the non-face-to-face online learning satisfaction and sleep quality of the study subjects using the lBM SPSS Statistics 21 program, ANOVA analysis was conducted on the difference between individual stress levels and non-face-to-face online learning satisfaction. The correlation between sleep quality, stress, and non-face-to-face online learning satisfaction was analyzed using Pearson's correlation coefficient. Results: The lower the quality of sleep, the higher the stress, resulting in statistically significant results (p<0.001). The higher the quality of sleep, the higher the learning satisfaction, resulting in statistically significant results (p<0.001). There was a statistically significant positive correlation between learning satisfaction and stress (r=0.591, p<0.01). Conciussions: Through the above results, in order to improve the satisfaction of non-face-to-face online learning, it is necessary to manage the individual's learning environment and health to relieve stress. Instructors also need to communicate with learners and apply teaching methods considering learners' academic abilities.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.16-19
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• 2011

최근에 Smart Phone 보급의 급격한 확산에 따라 2012년경에는 국내에서 약 2천만명 정도가 Smart Phone을 사용할 것이며 전 세계적으로도 약 3억5천만대 정도의 사용자가 Smart Phone을 사용할 것으로 예상되고 있다. 이러한 Smart Phone에서 시작된 u-Device 변혁은 Smart Phone, Tablet-PC, Smart TV, Desk Top Computer를 연계한 Seamless 학습 환경 및 최근의 N-Screen 환경의 구현을 가능하게 하고 있다.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.911-923
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• 2016

Machine learning becomes increasingly important in the big data era. Logistic regression is a type of classification in machine leaning, and has been widely used in various fields, including medicine, economics, marketing, and social sciences. Rhipe that integrates R and Hadoop environment, has not been discussed by many researchers owing to the difficulty of its installation and MapReduce implementation. In this paper, we present the MapReduce implementation of Gradient Descent algorithm and Newton-Raphson algorithm for logistic regression using Rhipe. The Newton-Raphson algorithm does not require a learning rate, while Gradient Descent algorithm needs to manually pick a learning rate. We choose the learning rate by performing the mixed procedure of grid search and binary search for processing big data efficiently. In the performance study, our Newton-Raphson algorithm outpeforms Gradient Descent algorithm in all the tested data.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• Journal of Korean Dental Hygiene Science
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• v.4 no.2
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• pp.31-41
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• 2021

Background: To investigate the mediating effect of learning flow in the relationship between blended learning achievement and learning satisfaction in dental hygiene students, and to identify whether face-to-face studies should be substituted with non-face-to-face studies. Methods: Total 134 dental hygiene students, who underwent blended learning in the dental hygiene class during the first semester of 2021, were recruited. The research tools were blended learning achievement, learning flow, and learning satisfaction, comprising 15 questions in total. Mediation regression analysis was used to analyze the mediating effect of learning flow in the relationship between learning achievement and learning satisfaction, and that between each variable. Results: Learning flow and learning satisfaction (r=0.490, p<0.001) were positively interrelated, and the interrelation between variables was statistically significant. Class environment had the biggest effect as a subfactor of class achievement, and it appeared to have an effect on class attitude and learning motive. The effect of learning flow on learning satisfaction was statistically significant. Learning flow was partially mediated by both blended learning achievement and learning satisfaction. Learning flow was directly related to blended learning satisfaction and learning achievement. Conclusion: The results showed the possible use of blended learning in dental hygiene theory and practical subjects.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.161-166
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• 2022

In this paper, we analyze the problems that occur when performing the big data analysis using R as a data analysis tool, and present the usefulness of the data analysis with SparkR which connects R and Spark to support distributed processing of big data effectively. First, we study the memory allocation problem of R which occurs when loading large amounts of data and performing operations, and the characteristics and programming environment of SparkR. And then, we perform the comparison analysis of the execution performance when linear regression analysis is performed in each environment. As a result of the analysis, it was shown that R can be used for data analysis through SparkR without additional language learning, and the code written in R can be effectively processed distributedly according to the increase in the number of nodes in the cluster.

• Journal of Korean Society for Quality Management
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• v.36 no.1
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• pp.72-79
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• 2008

The policy for credit card approval/disapproval is based on the applier's personal and financial information. In this paper, we will analyze 2 credit card approval data with several classification methods. We identify which variables are important factors to decide the approval of credit card. Our main tool is an open-source statistical programming environment R which is freely available from http://www.r-project.org. It is getting popular recently because of its flexibility and a lot of packages (libraries) made by R-users in the world. We will use most widely used methods, LDNQDA, Logistic Regression, CART (Classification and Regression Trees), neural network, and SVM (Support Vector Machines) for comparisons.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.255-260
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• 2022

This study aimed to develop a service model that uses a deep learning algorithm for detecting diseases and pests in strawberries through image data. In addition, the pest detection performance of deep learning models was further improved by proposing segmented image data sets specialized in disease and pest symptoms. The CNN-based YOLO deep learning model was selected to enhance the existing R-CNN-based model's slow learning speed and inference speed. A general image data set and a proposed segmented image dataset was prepared to train the pest and disease detection model. When the deep learning model was trained with the general training data set, the pest detection rate was 81.35%, and the pest detection reliability was 73.35%. On the other hand, when the deep learning model was trained with the segmented image dataset, the pest detection rate increased to 91.93%, and detection reliability was increased to 83.41%. This study concludes with the possibility of improving the performance of the deep learning model by using a segmented image dataset instead of a general image dataset.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.71-73
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• 2019

How much and where vehicles exist is an essential element in the implementation of a GeoAI-based urban environment that reflects traffic information. In this paper, we trained vehicle data using Mask R-CNN that deep learning model useful for object detection and extraction, and verified vehicle detection in actual aerial images taken with drones.