• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.5
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• pp.233-247
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• 2024

The damage to structures during an earthquake can be varied depending on the frequency characteristics of seismic waves and the geological properties of the ground. Therefore, considering such attributes in the design ground motions is crucial. The Korean seismic design standard (KDS 17 10 00) provides design response spectra for various ground classifications. If required for time-domain analysis, ground motion time series can be either selected and adjusted from motions recorded at rock sites in intraplate regions or artificially synthesized. Ground motion time series at soil sites should be obtained from site response analysis. However, in practice, selecting suitable ground motion records is challenging due to the overall lack of large earthquakes in intraplate regions, and artificially synthesized time series often leads to unrealistic responses of structures. As an alternative approach, this study provides a case study of generating ground motion time series based on the hybrid broadband ground motion simulation of selected scenario earthquakes at sites in the Nakdonggang delta region. This research is significant as it provides a novel method for generating ground motion time series that can be used in seismic design and response analysis. For large-magnitude earthquake scenarios close to the epicenter, the simulated response spectra surpassed the 1000-year design response spectra in some specific frequency ranges. Subsequently, the acceleration time series at each location were used as input motions to perform nonlinear 1D site response analysis through the PySeismoSoil Package to account for the site response characteristics at each location. The results of the study revealed a tendency to amplify ground motion in the mid to long-period range in most places within the study area. Additionally, significant amplification in the short-period range was observed in some locations characterized by a thin soil layer and relatively high shear wave velocity soil near the upper bedrock.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.1-6
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• 2024

In response to the increasing demand for unrestricted access to diverse services regardless of location, cost-effective and easily deployable Wireless Mesh Network (WMN) solutions have once again captured attention. This paper primarily addresses the implementation challenges of Autonomous Load-balancing Field-based Anycast routing+ (ALFA+) for three-dimensional (3D) WMNs. Subsequently, we evaluate the performance of ALFA+ in an 802.11-based 3D WMN testbed established within a university campus using commercial devices, thus validating the practical viability of ALFA+. While most prior research has relied on performance evaluation through virtual environment simulations, this study distinguishes itself by performance evaluations in a real-world testbed using commercial devices and providing detailed implementation-related information necessary for such evaluations. This approach holds considerable significance in assessing the actual applicability of ALFA+.

• Information Systems Review
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• v.26 no.2
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• pp.83-93
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• 2024

This study proposes a preprocessing methodology based on Dynamic Time Warping (DTW) and k-Nearest Neighbors (kNN) to effectively represent time series data for predicting the completion quality of electroplating baths. The proposed DTW-based kNN preprocessing approach was applied to various regression models and compared. The results demonstrated a performance improvement of up to 43% in maximum RMSE and 24% in MAE compared to traditional decision tree models. Notably, when integrated with neural network-based regression models, the performance improvements were pronounced. The combined structure of the proposed preprocessing method and regression models appears suitable for situations with long time series data and limited data samples, reducing the risk of overfitting and enabling reasonable predictions even with scarce data. However, as the number of data samples increases, the computational load of the DTW and kNN algorithms also increases, indicating a need for future research to improve computational efficiency.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.437-444
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• 2024

Video games, recognized as the "ninth art," carry not only entertainment functions but also the significant responsibility of disseminating ideas and culture. Increasingly, game developers are integrating specific cultural elements into their games, aiming to foster cultural heritage and emotional resonance. This study explores the modern transformation of traditional artistic concepts in the digital gaming field, starting from the Chinese painting theory of 'Woyou'. By examining game examples, the study analyzes the forms of expression of 'Woyou' in virtual landscape spaces and how player identity influences the 'Woyou' experience. The findings suggest that digital games offer new forms of traditional aesthetic wisdom and that Chinese traditional painting theory can gain modern interpretation in the context of digital game design. This provides theoretical support for optimizing game visual design and promotes the expansive development of traditional art concepts in the new media era.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.157-164
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• 2024

In this paper, we propose a technique that utilizes the physical properties of foam particles to synthesize foam sounds and efficiently control their size. A typical way to represent sound in physics-based simulation environments is to generate and synthesize virtual sounds. In particular, foam particles have a large number of particles, so synthesizing sounds using only particles is computationally expensive, and a way to reduce the amount of computation is to use spatial information, lattices. In this paper, we present a method for reliably mapping and clustering foam particles into a lattice space. Furthermore, we utilize this structure to control the loudness of the sound according to the location of the sound source and the audience. As a result, the method proposed in this paper proposes an efficient way to synthesize the sound of bubble particles, which utilizes the velocity and position of the bubble particles projected in the lattice space, and synthesizes the sound of bubble particles based on the position relationship of the audience and the directionality of the sound.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.165-173
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• 2024

Game engines are essential tools in game development, speeding up processes and simplifying the integration of various modules like physics, graphics, animations, and AI. This study provides a comprehensive overview of modern game engine technologies, including advanced rendering techniques, graphics APIs, physics simulations, AI integration, audio systems, networking, VR/AR, and development tools. It highlights recent advancements such as real-time ray tracing, physically based rendering, machine learning for content generation and intelligent NPCs, cloud gaming, and novel input methods like brain-computer interfaces. The paper also explores future directions, including enhanced cross-platform support and new technologies that will drive the evolution of game engines. This analysis serves as a valuable resource for developers, researchers, and industry professionals.

• The Journal of the Acoustical Society of Korea
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• v.43 no.5
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• pp.547-556
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• 2024

To predict the response of dynamic systems through analysis, it is essential to accurately estimate the system's stiffness and apply it to the analytical model. However, directly measuring the stiffness of actual mechanical systems is challenging. Many existing methods involve decomposing the system into components, obtaining the frequency response for each component, and then reassembling them to determine the overall system response. This process can be cumbersome, and variations in coupling conditions between components can increase errors. In this study, a new method is proposed to estimate system stiffness indirectly through experiments without decomposing the system into components. The approach combines response measurements from the entire system with a theoretical model for analysis. It simplifies the stiffness source into a lumped mass model and constructs the equations of motion based on a reduced-order model of the entire system. Subsequently, the stiffness is quantified by calculating the interface forces between the stiffness source and the receiver using vibration measurements obtained at arbitrary positions through experimentation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.1047-1057
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• 2024

Recently, deep neural network-based reinforcement learning models have been applied in various advanced industrial fields such as autonomous driving, smart factories, and home networks, but it has been shown to be vulnerable to malicious adversarial attack. In this paper, we applied deep reinforcement learning models, DQN and PPO, to the autonomous driving simulation environment HighwayEnv and conducted three adversarial attacks: FGSM(Fast Gradient Sign Method), BIM(Basic Iterative Method), PGD(Projected Gradient Descent) and CW(Carlini and Wagner). In order to respond to adversarial attack, we proposed a method for deep learning models based on reinforcement learning to operate normally by removing noise from adversarial images using a bilateral filter algorithm. Furthermore, we analyzed performance of adversarial attacks using two popular metrics such as average of episode duration and the average of the reward obtained by the agent. In our experiments on a model that removes noise of adversarial images using a bilateral filter, we confirmed that the performance is maintained as good as when no adversarial attack was performed.

• Journal of Digital Forensics
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• v.13 no.4
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• pp.231-244
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• 2019

Digital evidence is crucial to issues that require not only cybercrime but also to prove the facts of the cyber environment. While digital evidence has traditionally been collected from physically specific media, it is often impossible to specify the physical location of the data as cloud and distributed processing technologies evolve, requiring a ways of collecting data remotely. However, existing procedures or rules for collecting data from remote locations only emphasize fundamental conditions such as integrity and audit tracking, but there are many ambiguity when applied to actual collection. In addition, the threat of falsification that occurs when collecting data from remote locations was not considered. In this paper, we propose a framework that can ensure the reliability of the collection environment and the reliability of the network to show that it has not been falsified and that data that exists remotely has been collected as it was originally. It also implements this framework as a virtual environment and collects digital evidence from remote locations through case studies to discuss its effectiveness.

• Journal of Industrial Convergence
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• v.22 no.10
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• pp.37-45
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• 2024

This study aims to compare and analyze the functionality and efficiency of 3D generation AI platforms to evaluate their practical applicability in the 3D content creation process and suggest improvement directions. A total of nine platforms were researched using search, and four platforms were selected based on their utilization of the latest technology, compatibility, and user accessibility. We used the same prompts to create 3D objects on each platform and analyzed the results, focusing on whether they were customizable, beneficial for creating immersive content, efficient in production, free to test, or good value for money. The results showed that Meshy and Tripo performed well with fast generation speeds and efficient polygon optimization, while Spline offered a wide range of media application capabilities but was limited in quality. We found that different 3D generation AI platforms are suitable for different production pipelines and user needs. This study provides practitioners interested in 3D content creation with a practical guide for platform selection and provides insights into the future direction of 3D generative AI technology, which will contribute to future research and industrial applications.