• Korean Journal of Culture and Social Issue
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• v.22 no.1
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• pp.41-64
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• 2016

In two experiments, we compared envy with admiration in attentional and motivational benefits. In addition, we tested whether individual differences in working memory capacity (WMC) have consequences for emotion regulation. In Study 1, following WMC tasks, the participants were primed either with envy or with admiration through a recall task, in which they had to recall their own experiences about envy or admiration. The participants in the envy condition considered it more undeserved that another person had an advantage over them, than in the admiration condition. Additionally, in the envy condition, WMC was related to happiness, and anxiety was related to the motivation to study more. In contrast, there were no significant relationships between WMC, emotion and study hours in the admiration condition. Study 2 (N=43) found greater memory for the envy scenario in the envy condition than in the admiration and in the control condition. Additionally, there were significant relationships between WMC, anxiety and recall accuracy in the envy condition. However, these relations were not found in the admiration and in the control condition. Findings implicate that envy may play an important role in memory systems and that WMC is related to emotion regulation abilities.

• Korean Journal of Culture and Social Issue
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• v.22 no.1
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• pp.119-135
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• 2016

We tried to develop the school violence prevention program which focuses on the bystanders. In the context of school violence, most students are bystanders. They can either reinforce the violence or stop the violence. We hypothesized that the students who know their own temperament dimensions such as novelty seeking, harm avoidance, and reward dependence (Cloninger, 1994) will show more tendencies to defend victims from school violence when they learned the methods to prevent school violence in accordance to their temperament dimensions. 351 Korean middle school students participated the 4 sessions of school violence prevention program. The students completed the questionnaires to identify their own behavior in school violence situations. In the experimental group(temperament group), students learned the methods to help the victims that suits with their own temperaments. Whereas, the comparison group just attend the class regardless of their temperaments. Both groups showed significant increase in the trend of defending the victims. However, the temperament group showed more tendency to protect/defend the victims than the comparison group when they faced with school violence. These results show that students can learn the methods of defending victims from school violence more easily by knowing their own temperament dimensions, and can be the prepared and the effective defenders.

• Smart Media Journal
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• v.12 no.10
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• pp.71-84
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• 2023

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.787-799
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• 2023

As IoT devices are widely used at home, IoT automation system that is integrate IoT devices for users' demand are gaining populrity. There is automation rule in IoT automation system that is collecting event and command action. But attacker delay the packet and make time that real state is inconsistent with state recongnized by the system. During the time, the system does not work correctly by predefined automation rule. There is proposed some detection method for delay attack, they have limitations for application to IoT systems that are sensitive to traffic volume and battery consumption. This paper proposes a practical packet delay attack detection technique that can be applied to IoT systems. The proposal scheme in this paper can recognize that, for example, when a sensor transmits an message, an broadcast packet notifying the transmission of a message is sent to the Server recognized that event has occurred. For evaluation purposes, an IoT system implemented using Raspberry Pi was configured, and it was demonstrated that the system can detect packet delay attacks within an average of 2.2 sec. The experimental results showed a power consumption Overhead of an average of 2.5 mA per second and a traffic Overhead of 15%. We demonstrate that our method can detect delay attack efficiently compared to preciously proposed method.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.930-938
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• 2023

Maritime English is a specialized language system designed for ship operations, maritime safety, and external and internal communication onboard. According to the International Maritime Organization's (IMO) International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW), it is imperative that navigational officers engaged in international voyages have a thorough understanding of Maritime English including the use of Standard Marine Communication Phrases (SMCP). This study measured students' proficiency in Maritime English using a learning and testing platform that includes voice recognition, translation, and word entry tasks to evaluate the resulting improvement in Maritime English exam scores. Furthermore, the study aimed to investigate the level of platform use needed for cadets to qualify as junior navigators. The experiment began by examining the correlation between students' overall English skills and their proficiency in SMCP through an initial test, followed by the evaluation of improvements in their scores and changes in exam duration during the mid-term and final exams. The initial test revealed a significant dif erence in Maritime English test scores among groups based on individual factors, such as TOEIC scores and self-assessment of English ability, and both the mid-term and final tests confirmed substantial score improvements for the group using the platform. This study confirmed the efficacy of a learning platform that could be extensively applied in maritime education and potentially expanded beyond the scope of Maritime English education in the future.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.73-80
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• 2023

The recent steep increase in the minimum hourly wage has increased the burden of labor costs, and the share of unmanned stores is increasing in the aftermath of COVID-19. As a result, theft crimes targeting unmanned stores are also increasing, and the "Just Walk Out" system is introduced to prevent such thefts, and LiDAR sensors, weight sensors, etc. are used or manually checked through continuous CCTV monitoring. However, the more expensive sensors are used, the higher the initial cost of operating the store and the higher the cost in many ways, and CCTV verification is difficult for managers to monitor around the clock and is limited in use. In this paper, we would like to propose an AI image processing fusion algorithm that can solve these sensors or human-dependent parts and detect customers who perform abnormal behaviors such as theft at low costs that can be used in unmanned stores and provide cloud-based notifications. In addition, this paper verifies the accuracy of each algorithm based on behavior pattern data collected from unmanned stores through motion capture using mediapipe, object detection using YOLO, and fusion algorithm and proves the performance of the convergence algorithm through various scenario designs.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.433-440
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• 2021

The problem of maritime accidents due to the carelessness of fishing vessels, which is affected by the aging of fishing vessel operators. And there is navigation, communication and fish finder that is installed inside the narrow bridge of a fishing vessel. Therefore these system are monitors as many as of each terminal, which is bad influence on obscuring view of front sea from a fishing vessel bridge. In addition a large problem, it is occurs to reduce of the information recognition ability due to the confusion, which is can not check the display information each of screen equipments. Therefore, there has been demand to simply integrated the equipment, and it has wanted the integrated support system of these equipment. The display must be provided on a fishing vessels such as electronic charts, communications equipments and fish detection into one case. In this paper, the integrated system will be installed the GPS plotter, AIS, VHF-DSC, V-pass, fish finder and power supply in the narrow wheelhouse on a fishing vessel, which is configured in one case and operated by multi function display (MFD). The MFD is integrated to simplify for several multi terminals and provided necessary information on a single screen. This integration fishery support system will has improved in sea safety operation and fishery environment of fishing vessels by this implementation.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.889-897
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• 2023

This study attempted to examine how participation in a cooperative learning extracurricular program affects the improvement of college students' self-directed learning abilities. In order to operate a cooperative learning extracurricular program, students were recruited voluntarily through the school system. A total of 128 students were selected. They formed groups according to the number of participants and participated in the cooperative learning extracurricular program for 9 weeks. Before the program was implemented and after the program was terminated, a survey was conducted on the self-directed learning ability of participating students through a questionnaire. The effectiveness of the program was examined through pre-post tests of the experimental group. The results are as follow. First, the self-directed learning ability scores of students who participated in the cooperative learning extracurricular program significantly improved. Second, in order to closely analyze self-directed learning ability, the sub-elements of self-directed learning ability were examined, and the scores of self-awareness and learning strategy, which are sub-elements of self-directed learning ability, were found to have significantly increased. However, although scores for learning motivation and learning situations improved, the levels were not found to be statistically significant. Based on these results, we presented discussions for improving college students' self-directed learning ability and ways to revitalize cooperative learning in universities.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1321-1330
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• 2023

This study attempts to address the problem of 3D pose estimation for multiple human objects through a single image generated during the character development process that can be used in augmented reality. In the existing top-down method, all objects in the image are first detected, and then each is reconstructed independently. The problem is that inconsistent results may occur due to overlap or depth order mismatch between the reconstructed objects. The goal of this study is to solve these problems and develop a single network that provides consistent 3D reconstruction of all humans in a scene. Integrating a human body model based on the SMPL parametric system into a top-down framework became an important choice. Through this, two types of collision loss based on distance field and loss that considers depth order were introduced. The first loss prevents overlap between reconstructed people, and the second loss adjusts the depth ordering of people to render occlusion inference and annotated instance segmentation consistently. This method allows depth information to be provided to the network without explicit 3D annotation of the image. Experimental results show that this study's methodology performs better than existing methods on standard 3D pose benchmarks, and the proposed losses enable more consistent reconstruction from natural images.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.