• Journal of the Korea Convergence Society
• /
• v.10 no.8
• /
• pp.21-26
• /
• 2019

This paper proposes a fatigue recognition method using the C4.5 algorithm. Based on domestic and international studies on fatigue evaluation, we have completed the fatigue self - assessment scale in combination with lifestyle and cultural characteristics of Chinese people. The scales used in the text were applied to 58 sub items and were used to assess the type and extent of fatigue. These items fall into four categories that measure physical fatigue, mental fatigue, personal habits, and fatigue outcomes. The purpose of this study is to analyze the leading causes of fatigue formation and to recognize the degree of fatigue, thereby increasing the personal interest in fatigue and reducing the risk of cerebrovascular disease due to excessive fatigue. The recognition rate of the fatigue recognition system using the C4.5 algorithm was 85% on average, confirming the usefulness of this proposal.

• The Journal of the Korea Contents Association
• /
• v.18 no.11
• /
• pp.135-145
• /
• 2018

In online communities, users use comments to exchange their opinions and feelings on various subjects. Communication based on comments is quick and convenient, but sometimes this light-weight characteristic makes users use impolite and aggressive words, which leads to an online conflict. Therefore, it is important to analyze and classify users according to their characteristics in order to predict and take action for this kind of troubles. In this paper, we present several quantitative measures for describing the structures of comments trees based on the assumption that the user characteristics be observed as a form of some structural feature in comment trees of articles in which they posted comments. We examine the distribution of the proposed measures over article posters and commenters, and in addition, we show the effectiveness of the presented structural features by conducting experiments to classify users who have received warnings of the administrator from benign users.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.3
• /
• pp.333-340
• /
• 2019

A combat system performs a given mission enduring various threats. It is important to analyze the reliability of combat systems in order to increase their ability to perform a given mission. Most of studies considered no threat or on threat and didn't analyze all the dependent relationships among the components. In this paper, we analyze the loss probability of the function of the combat system and use it to analyze the reliability. The proposed method is divided into two layers, A lower layer and a upper layer. In lower layer, the failure probability of each components is derived by using FTA to consider various threats. In the upper layer, The loss probability of function is analyzed using the failure probability of the component derived from lower layer and BBN in order to consider the dependent relationships among the components. Using the proposed method, it is possible to analyze considering various threats and the dependency between components.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.3
• /
• pp.282-290
• /
• 2019

Recently child abuse has occurred frequently in educational institutions such as daycare center and kindergarten. Therefore, government made it mandatory to install CCTVs, but it is not easy to inspect the CCTV images. In this paper, we propose a model for judging child abuse using CCTV images. First of all, child abuse is a physical abuse of children by adults, thus a model for classifying adults and children is needed. The existing Haar scheme uses the frontal image to classify adults and children. However, the OpenPose allows to classify adults and children regardless of frontal and side image. In this research, a child abuse judgment model was designed and implemented by applying characteristics of adult and child posture when a child was abused. Since the implemented system utilizes the currently installed CCTV image, it is possible to monitor the child abuse in real time without any additional installation, which enables us to cope with the abuse promptly.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.11
• /
• pp.1343-1350
• /
• 2019

Converting boundary images to time-series makes it feasible to perform boundary matching even on a very large image database, which is very important for interactive and fast matching. In recent research, there has been an attempt to perform fast matching considering partial denoising by converting the boundary image into time series. In this paper, to improve performance, we propose an index-building method considering all possible arbitrary denoising parameters for removing arbitrary partial noises. This is a challenging problem since the partial denoising boundary matching must be considered for all possible denoising parameters. We propose an efficient single index-building algorithm by constructing a minimum bounding rectangle(MBR) according to all possible denoising parameters. The results of extensive experiments conducted show that our index-based matching method improves the search performance up to 46.6 ~ 4023.6 times.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.2
• /
• pp.311-320
• /
• 2020

This study conducted a comparative analysis on domestic and foreign computer science curriculum, in order to introduce the data structure education as a core foundation of computer science. The findings show that the scope and level of data structure contents included in elementary school software education are poorer than those in U.S.A and England. To resolve such a problem, it selected some data structure education factors and a Delphi survey about the importance of contents and the fitness of education periods were administered to experts. Although they responded that 'text information', 'array', 'stack' and 'queue' for linear structures', and 'tree' for non-linear structures are important, their opinions were different in education periods by its factors. The generalization of the findings may be limited, given that the analysis was based on the survey of some experts, but this study has an implication, in that it provides important information for improving elementary school software curriculum for the introduction of data structures.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.5
• /
• pp.576-583
• /
• 2020

This paper proposes a technique to determine the spam comments on YouTube, which have recently seen tremendous growth. On YouTube, the spammers appeared to promote their channels or videos in popular videos or leave comments unrelated to the video, as it is possible to monetize through advertising. YouTube is running and operating its own spam blocking system, but still has failed to block them properly and efficiently. Therefore, we examined related studies on YouTube spam comment screening and conducted classification experiments with six different machine learning techniques (Decision tree, Logistic regression, Bernoulli Naive Bayes, Random Forest, Support vector machine with linear kernel, Support vector machine with Gaussian kernel) and ensemble model combining these techniques in the comment data from popular music videos - Psy, Katy Perry, LMFAO, Eminem and Shakira.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.1
• /
• pp.1-12
• /
• 2021

The International Maritime Organization (IMO) is promoting the introduction of e-Navigation that prevents maritime accidents by fusion of Information & Communication Technology (ICT) with ship operation technology. In Korea, Korean e-Navigation is also being developed for fishing vessels and small vessels sailing offshore, which are vulnerable to maritime accidents. However, for the successful development of Korean e-Navigation, it is necessary to develop an indicator that can evaluate the development performance so that the development performance that has been progressed so far can be evaluated and the development direction can be re-established. Therefore, this study attempted to develop an evaluation index tailored to the development goal of e-Navigation service centering on the collision accident, which is a major maritime accident. In this study, a collision accident evaluation index for e-Navigation service was developed by deriving and quantifying the root cause of maritime collision accidents using Root cause analysis(RCA) and fault tree analysis (FTA) techniques. This indicator is considered to be helpful in reducing maritime accidents as it is used as a development indicator for e-Navigation and an indicator for maritime collision accident analysis.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.4
• /
• pp.510-518
• /
• 2022

The diagnostic evaluation conducted at the national level is very important to detect underachievers in school early. This study used an artificial intelligence method to find the characteristics of underachievers that affect learning development for middle school students. In this study an artificial intelligence model was constructed and analyzed to determine whether the Busan Education Longitudinal Data in 2020 by entering data from the first year of middle school in 2019. A predictive model was developed to predict basic middle school Korean, English, and mathematics education with machine learning algorithms, and it was confirmed that the accuracy was 78%, 82%, and 83%, respectively, in the prediction for the next school year. In addition, by drawing an achievement prediction decision tree for each middle school subject we are analyzing the process of prediction. Finally, we examined what characteristics affect achievement prediction.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.9
• /
• pp.1330-1339
• /
• 2022

3D object detection generally aims to detect relatively large data such as automobiles, buses, persons, furniture, etc, so it is vulnerable to small object detection. In addition, in an environment with limited resources such as embedded devices, it is difficult to apply the model because of the huge amount of computation. In this paper, the accuracy of small object detection was improved by focusing on local features using only one layer, and the inference speed was improved through the proposed knowledge distillation method from large pre-trained network to small network and adaptive quantization method according to the parameter size. The proposed model was evaluated using SUN RGB-D Val and self-made apple tree data set. Finally, it achieved the accuracy performance of 62.04% at mAP@0.25 and 47.1% at mAP@0.5, and the inference speed was 120.5 scenes per sec, showing a fast real-time processing speed.