• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.709-719
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• 2023

Recently, Post-Quantum Cryptography (PQC), which is secure against attacks using quantum computers, has been actively studied. In 2022, the KpqC competition, a competition for domestic PQC standardization, was launched, and a total of 16 candidate algorithms were received, and the first round is underway. In this paper, we apply Alexander May's Meet-LWE attack to TiGER, a lattice-based key encapsulation mechanism that is a candidate for the first round of the KpqC competition, and analyze its concrete attack complexity. The computational results of applying the Meet-LWE attack to each of the proposed parameters of TiGER show that the proposed TiGER192 parameter, which targets 192-bit quantum security, actually achieves 170-bit classical security. In addition, we propose a parameter setting to increase the attack complexity against the Meet-LWE attack.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.131-136
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• 2023

Local youth outmigration is increasingly growing. Various studies are being conducted to identify the factors contributing to this problem, but there is a lack of research analyzing each region individually. Therefore, this study aims to analyze the factors influencing youth outmigration in Busan and predict the risk levels of youth population outflow using machine learning techniques. By utilizing district-level data collected from the KOSIS, we divided the population into three groups based on age (the early 20s, late 20s, and early 30s) and employed Decision Tree and Random Forest algorithms to classify and predict the risk levels of youth population outmigration. The results indicate that the predictive model for youth outmigration risk levels achieves the highest accuracies of 0.93, 0.75, and 0.63 for each age group, respectively.

• Journal of Digital Policy
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• v.3 no.1
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• pp.27-32
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• 2024

In this paper, proposes a super resolution method that enhances the quality of results by refining texture features, contrasting each, and utilizing the results as weights. For the improvement of quality, a precise and clear restoration result in details such as boundary areas is crucial in super resolution, along with minimizing unnecessary artifacts like noise. The proposed method constructs a residual block structure with multiple paths and skip-connections for feature estimation in conventional Convolutional Neural Network (CNN)-based super resolution methods to enhance quality. Additional learning is performed for sharpened and blurred image results for further texture analysis. By contrasting each super resolution result and allocating weights through this process, the proposed method achieves improved quality in detailed and smoothed areas of the image. The experimental results of the proposed method, evaluated using the PSNR and SSIM values as quality metrics, show higher results compared to existing algorithms, confirming the enhancement in quality.

• Tunnel and Underground Space
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• v.34 no.2
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• pp.143-153
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• 2024

Tunnel Boring Machine (TBM) method is a tunnel excavation method that produces lower levels of noise and vibration during excavation compared to drilling and blasting methods, and it offers higher stability. It is increasingly being applied to tunnel projects worldwide. The disc cutter is an excavation tool mounted on the cutterhead of a TBM, which constantly interacts with the ground at the tunnel face, inevitably leading to wear. In this study quantitatively predicted disc cutter wear using geological conditions, TBM operational parameters, and machine learning algorithms. Among the input variables for predicting disc cutter wear, the Uniaxial Compressive Strength (UCS) is considerably limited compared to machine and wear data, so the UCS estimation for the entire section was first conducted using TBM machine data, and then the prediction of the Coefficient of Wearing rate(CW) was performed with the completed data. Comparing the performance of CW prediction models, the XGBoost model showed the highest performance, and SHapley Additive exPlanation (SHAP) analysis was conducted to interpret the complex prediction model.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2409-2421
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• 2011

This paper will present hybrid tests to a one bay-one story steel frame structure under ground excitation. A structure used in this paper for hybrid test, to evaluate performance and behavior, is divided into two models; one is numerical model with one column element, and a truss or a beam element, the other is physical substructural model with one beam-column element. All tests considered one or three degrees of freedom to implement real-time hybrid test, and two control algorithms to control hardware are used; one using MATLAB/Simulink, the other using OpenSees, OpenFresco and xPCTarget. In addition, for real-time data communication between numerical and physical substructural models SCRAMNet was used. The results of hybrid tests were compared with one of numerical analysis of numerical model with fiber force-based beam-column elements using OpenSees. Real-time hybrid tests were implemented for the validation of control system with simple structure, and then it will be extended to hybrid test for higher nonlinear or complex structure later on.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.531-536
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• 2002

In this paper, we have applied Genetic Algorithms(GAs) to Intrusion Detection System(TDS), and then proposed and simulated the misuse detection model firstly. We have implemented with the KBD contest data, and tried to simulated in the same environment. In the experiment, the set of record is regarded as a chromosome, and GAs are used to produce the intrusion patterns. That is, the intrusion rules are generated. We have concentrated on the simulation and analysis of classification among the Data Mining techniques and then the intrusion patterns are produced. The generated rules are represented by intrusion data and classified between abnormal and normal users. The different rules are generated separately from three models "Time Based Traffic Model", "Host Based Traffic Model", and "Content Model". The proposed system has generated the update and adaptive rules automatically and continuously on the misuse detection method which is difficult to update the rule generation. The generated rules are experimented on 430M test data and almost 94.3% of detection rate is shown.3% of detection rate is shown.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.105-116
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• 2001

Moving objects are spatiotemporal data that location and shape of spatial objects are changed continuously over time. If spatiotemporal moving objects are managed by conventional database system, moving objects management systems have two problems as follows. First, update for location information changed over time is occurred frequently. Second, past and future information of moving objects are not provided by system because only current state of objects is stored in the system. Therefore, in this paper, we propose a spatiotemporal moving objects management system which is able to not only manage historical information of moving objects without frequent update, but also provide all location information about past, current, and near future. In the proposed system, information of moving objects are divided into location information for representing location and motion information for representing moving habits. Especially, we propose the method which can search location information all objects by use of changing process algorithms with minimum history information. Finally, we applied the proposed method to battlefield analysis system, as the result of experiment, we knew that past, current, and near future location information for moving objects are managed by relational database and GIS system.

• Journal of Satellite, Information and Communications
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• v.12 no.4
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• pp.100-105
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• 2017

In this paper, we study an interference avoidance scenario in the presence of a interferer which can rapidly observe the transmit power of backscatter communications and effectively interrupt backscatter signals. We consider a power control with a sub-channel allocation to avoid interference attacks and a power-splitting ratio for backscattering and RF energy harvesting in sensors. We formulate the problem based on a Stackelberg game theory and compute the optimal transmit power, power-splitting ratio, and sub-channel allocation parameter to maximize a utility function against the interferer. We propose the utility maximization using Lagrangian dual decomposition for the backscatter communications and the interferer to prove the existence of the Stackelberg equilibrium. Numerical results show that the proposed algorithms effectively maximize the utility, compared to that of the algorithm based on the Nash game, so as to overcome a malicious interference in backscatter communications.

• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.958-966
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• 2007

In this paper, we implement a sound-enhanced MPEG-1 audio decoder on embedded OS Platform. Low bit rate lossy audio codecs such as MP3, OGG, and AAC for mitigating the problems in storage space and network bandwidth suffer a major common problem such as a loss of high frequency fidelity of audio signal. This high frequency loss will reproduce only a band-limited low-frequency part of audio in the standard CD-quality audio. In order to overcome this problem, we embedded a sound enhancement algorithm into the MPEG-1 audio decoder and then the algorithms optimized according to the characteristic of the MPEG-1 audio layer I, II, III were implemented on an embedded OS platform. From the experimental results with spectrum analysis and listening test, we confirm the superiority of the proposed system compared to the standard MPEG-1 audio decoder.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.449-455
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• 2014

Detection of moving objects is a fundamental task in most of the computer vision applications, such as video surveillance, activity recognition and human motion analysis. This is a difficult task due to many challenges in realistic scenarios which include irregular motion in background, illumination changes, objects cast shadows, changes in scene geometry and noise, etc. In this paper, we propose an foreground extraction algorithm based on codebook, a database of information about background pixel obtained from input image sequence. Initially, we suppose a first frame as a background image and calculate difference between next input image and it to detect moving objects. The resulting difference image may contain noises as well as pure moving objects. Second, we investigate a codebook with color and brightness of a foreground pixel in the difference image. If it is matched, it is decided as a fault detected pixel and deleted from foreground. Finally, a background image is updated to process next input frame iteratively. Some pixels are estimated by input image if they are detected as background pixels. The others are duplicated from the previous background image. We apply out algorithm to PETS2009 data and compare the results with those of GMM and standard codebook algorithms.