• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.897-903
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• 2021

Purpose: Recently, ransomware damage that encrypts victim's data through hacking and demands money in exchange for releasing it is increasing domestically and internationally. Accordingly, research and development on various response technologies and solutions are in progress. Method: A secure storage area and a general storage area were created in the same virtual environment, and the sample data was saved by registering the access process. In order to check whether the stored sample data is infringed, the ransomware sample was executed and the hash function of the sample data was checked to see if it was infringed. The access control performance checked whether the sample data was accessed through the same name and storage location as the registered access process. Result: As a result of the experiment, the sample data in the secure storage area maintained data integrity from ransomware and unauthorized processes. Conclusion: Through this study, the creation of a secure storage area and the whitelist-based access control method are evaluated as suitable as a method to protect important data, and it is possible to provide a more secure computing environment through future technology scalability and convergence with existing solutions.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.63-71
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• 2021

Among drone autonomous flight technologies, obstacle avoidance is a very important technology that can prevent damage to drones or surrounding environments and prevent danger. Although the LiDAR sensor-based obstacle avoidance method shows relatively high accuracy and is widely used in recent studies, it has disadvantages of high unit price and limited processing capacity for visual information. Therefore, this paper proposes an obstacle avoidance algorithm for drones using camera-based PPO(Proximal Policy Optimization) reinforcement learning, which is relatively inexpensive and highly scalable using visual information. Drone, obstacles, target points, etc. are randomly located in a learning environment in the three-dimensional space, stereo images are obtained using a Unity camera, and then YOLov4Tiny object detection is performed. Next, the distance between the drone and the detected object is measured through triangulation of the stereo camera. Based on this distance, the presence or absence of obstacles is determined. Penalties are set if they are obstacles and rewards are given if they are target points. The experimennt of this method shows that a camera-based obstacle avoidance algorithm can be a sufficiently similar level of accuracy and average target point arrival time compared to a LiDAR-based obstacle avoidance algorithm, so it is highly likely to be used.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.95-107
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• 2022

This study attempted to develop a bodice prototype suitable for the 20's obese male's trunk with a BMI of 25kg/m² or more, which is distinguished from the standard body type. Through this, it was intended to provide data to help the development of clothing for obese males. Patterns such as front bodice shoulder line and front sagging were modified through primary appearance and garment pressure evaluation. Through the second evaluation, corrections such as back armhole, back waistline, and front sagging were performed. Through the third evaluation, the final pattern drafting method was developed by removing the front sagging added through the second evaluation. In the case of obese male body types in their 20s, a drafting method distinguished from the standard body type was required in the method of setting the front and back waist lines, back armhole darts, and front shoulder lines due to protruding and posture of the abdomen. This study was meaningful in that it presented a bodice prototype drafting method suitable for the 20s obese males. In the follow-up study, it is thought that actual clothing experiments and studies to develop clothing patterns by applying them to obese male tops in their 20s should be conducted.

• The Journal of the Acoustical Society of Korea
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• v.42 no.1
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• pp.7-15
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• 2023

Recently, as interest in air quality in vehicles increases, the use of fine dust detection sensors for air quality measurement is becoming common. An axial-flow fan is inserted in the fine dust sensor installed in the air conditioning system in the vehicle to prevent dust from sinking directly on the sensor. When the sensor operates, the flow noise caused by the rotation of the axial-flow fan acts as a major noise source of the fine dust sensor. flow noise is recognized as one of the product competitiveness of fine dust sensors. In this study, the noise was gradually reduced at the same flow rate by improving the flow performance of the small axial flow fan. First, a virtual fan performance tester consisting of about 20 million grids was developed to analyze the aerodynamic performance of the target small axial-flow fan. In addition, the flow field was simulated by using compressible Large Eddy Simulation for direct computation of flow noise as well as high-accurate prediction of flow rate. The validity of numerical method are confirmed through the comparison of predicted results with experimental ones. After the effects of pitch angle on flow performance were analyzed using the verified numerical method, the pitch angle was determined to maximize the flow rate. It was found that the flow rate was increased by 8.1 % and noise was reduced by 0.8 dBA when the axial-flow fan with the optimum pitch angle was used.

• Science of Emotion and Sensibility
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• v.26 no.2
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• pp.53-66
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• 2023

In recent times, more fashion brands have been seeking ways to use metaverse platforms, in which users can actively participate, as their new brand touch-points. This study aims to compare the brand experiences of the fashion brand Gentle Monster's offline store and its equivalent metaverse store. By changing the order of offline and metaverse visits, two groups participated in the field study that allowed them to experience directly the offline and metaverse stores. As a result of the analysis, the following findings were discovered: (1) In the overall experiential response, the frequency of sensory modules responding to new information was much higher than that of feeling experiences; (2) Experiential responses were more active in the offline store where the subjects could touch and use products directly rather than in the metaverse; (3) Among the four types of theme space, the experiential response was the most frequent in the product space; (4) The first group that visited the metaverse store before the offline store showed a more active experience than the second group that visited the offline store first. Finally, the results of this study show that metaverse brand stores in virtual space not only provide differentiated experiences beyond the spatiotemporal constraints of real space but can also be used as a strategic tool to make offline store experiences more meaningful and rich.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.667-674
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• 2023

In the event of a disaster occurring within a building, the prompt and efficient evacuation and rescue of occupants within the building becomes the foremost priority to minimize casualties. For the purpose of such rescue operations, it is essential to ascertain the distribution of individuals within the building. Nevertheless, there is a primary dependence on accounts provided by pertinent individuals like building proprietors or security staff, alongside fundamental data encompassing floor dimensions and maximum capacity. Consequently, accurate determination of the number of occupants within the building holds paramount significance in reducing uncertainties at the site and facilitating effective rescue activities during the golden hour. This research introduces a methodology employing computer vision algorithms to count the number of occupants within distinct building locations based on images captured by installed multiple CCTV cameras. The counting methodology consists of three stages: (1) establishing virtual Lines of Interest (LOI) for each camera to construct a multi-camera network environment, (2) detecting and tracking people within the monitoring area using deep learning, and (3) aggregating counts across the multi-camera network. The proposed methodology was validated through experiments conducted in a five-story building with the average accurary of 89.9% and the average MAE of 0.178 and RMSE of 0.339, and the advantages of using multiple cameras for occupant counting were explained. This paper showed the potential of the proposed methodology for more effective and timely disaster management through common surveillance systems by providing prompt occupancy information.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.311-326
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• 2023

This paper proposes a plan to improve the installation and operation of traffic safety facilities using a microscopic simulation by confirming the difference in the perception method and range of autonomous vehicles and human vehicles. In this study, the existing 『Traffic Safety Sign Installation·Management Guidelines』 was reviewed, and safety signs among traffic safety facilities were classified according to changes in vehicle behavior. Subsequently, for the classified facilities, the installation location of the traffic sign was changed through simulation experiments, and the optimal location was inferred to suggest an improvement plan. This study confirmed how traffic safety facilities installed based on the visibility of human drivers affect road efficiency and safety in mixed traffic flow with autonomous vehicles and human-controlled vehicles. The optimal location derived through this study is meaningful because it can be used as the basis for revising the guidelines on the installation and management of traffic safety facilities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.273-275
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• 2022

The use of drugs by pregnant women poses a potential risk to the fetus. Therefore, it is essential to classify drugs that pregnant women should prohibit. However, the fetal toxicity of most drugs has not been identified. This takes a lot of time and cost. In silico approaches, such as virtual screening, can identify compounds that may present a high risk to the fetus for a wide range of compounds at the low cost and time. We collected class information of each drug from the hazard classification lists for prescribing drugs in pregnancy by the government of Korea and Australia. Using the structural and chemical features of each drug, various machine learning models were constructed to predict fetal toxicity of drugs. For all models, the quantitative performance evaluation was performed. Based on the attention algorithm, important molecular substructures of compounds were identified in the process of predicting the fetal toxicity of the drug by the proposed model. From the results, we confirmed that drugs with a high risk of fetal toxicity can be predicted for a wide range of compounds by machine learning. This study can be used as a pre-screening tool for fetal toxicity predictions, as it provides key molecular substructures associated with the fetal toxicity of compounds.

• Korean Journal of Culture and Social Issue
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• v.24 no.4
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• pp.541-562
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• 2018

Conflict of interest (COI) is one of the dominant circumstantial factors of moral corruption across various fields. Several management strategies have been proposed to prevent self-interested decision making in COIs. Among these strategies, message persuasion has been considered as a practical and effective approach. Prior studies have found that framing and repetition are two of the major factors in the persuasion effect of message. However, their effect on moral decision making in COI has not been well explored. The purpose of this study was to compare the differential effects of positively framed message and negatively framed message, and secondly, to investigate how the effectiveness of persuasive message changes through repetitive exposures. A total of 63 participants were randomly assigned to one of 3 framing conditions: positive framing, negative framing, and no-message condition. Prior to the on-line experiment involving a consultation task, differently framed persuasive message were presented to the participants. This process was repeated four times in a row. The results showed that participants with positive-framing message were less likely to provide self-interested consultation than participants in the no-message condition. Also, a U-shaped quadric relation between repetition and self-interest consultation was found. Implications and limitations are further discussed.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.523-533
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• 2022

During the COVID-19 pandemic, the use of video classes and real-time online education has increased, but the lack of interaction between instructors and learners remains a challenging problem to be resolved. This paper designs and implements a learning support system that utilizes a digital human to improve faculty-student interaction, which plays an important role in increasing the educational effect and satisfaction of real-time online classes. In this paper, a digital human participates in a class as a virtual learner and asks questions raised by other learners through an anonymous chat system to the instructor on behalf of the learners. In addition, as a class facilitator, the digital human analyzes the lecturer's speech in real time and provides it to the learner in the form of a summary of the class, thereby facilitating faculty-student interaction. In order to confirm that the proposed system can be used in actual online real-time classes, we apply our system to Zoom classes. Experimental results show that facilitated Q&A and real-time class summaries are successfully provided through our digital human-based learning support system.