• Journal of Information Processing Systems
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• v.19 no.1
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• pp.118-129
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• 2023

The expansion of smart cities drives the growth of data generated from sensor devices, benefitting citizens with enhanced governance, intelligent decision-making, optimized and sustainable management of available resources. The exposure of user data during its collection from sensors, storage in databases, and processing by artificial intelligence-based solutions presents significant security and privacy challenges. In this paper, we investigate the various threats and attacks affecting the growth of future smart cities and discuss the available countermeasures using artificial intelligence and blockchain-based solutions. Open challenges in existing literature due to the lack of countermeasures against quantum-inspired attacks are discussed, focusing on postquantum security solutions for resource-constrained sensor devices. Additionally, we discuss future research and challenges for the growing smart city environment and suggest possible solutions.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.407-414
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• 2014

The aim of the present study was to carry out quantitative analysis of spatial resolution for the influence of the focus size and digital image post-processing on the Computed Radiography (CR). The modulation transfer functions of an edge measuring method (MTF) was used for the evaluation of the spatial resolution. The focus size of X-ray tube was used the small focus (0.6 mm) and the large focus (1.2 mm). We evaluated the 50% and 10% of MTF for the enhancement of edge and contrast by using multi-scale image contrast amplification (MUSICA) in digital image post-processing. As a results, the edge enhancement than the contrast enhancement were significantly higher the spatial resolution of MTF 50% in all focus. Also the spatial resolution of the obtained images in a large focus were improved by digital image processing. In conclusion, the results of this study should serve as a basic data for obtain the high resolution clinical images, such as skeletal and chest images on the CR.

• Spatial Information Research
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• v.22 no.4
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• pp.39-47
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• 2014

The Yeongsan river estuary has a serious water quality problem due to the water stagnation and it is imperative to predict the changes of water quality for mitigating water pollution. EFDC(Environmental Fluid Dynamics Code) model was mainly utilized to predict the changes of water quality for the estuary. The EFDC modeling normally accompanies the large volume of modeling output. For checking the spatial distribution of the modeling results, post-processing for converting of the output is prerequisite and mainly post-processing program is EFDC_Explorer. However, EFDC_Explorer only shows the spatial distribution of the time series and this doesn't support overlay function with other thematic maps. This means the impossible to the connection analysis with a various GIS data and high dimensional analysis. Therefore, this study aims to develop a post-processing system of a EFDC output to use them as GIS layers. For achieving this purpose, a editing module for main input files, and a module for converting binary format into an ASCII format, and a module for converting it into a layer format to use in a GIS based environment, and a module for visualizing the reconfigured model result efficiently were developed. Using the developed system, result file is possible to automatically convert the GIS based layer and it is possible to utilize for water quality management.

• Geophysics and Geophysical Exploration
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• v.13 no.4
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• pp.336-348
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• 2010

The Single-channel seismic survey with the source of bubble pulser and drilling survey was carried out in 2008 and 2009 for the site survey of Daecheon-Wonsando area, which was a proposed area of Korea-China subsea tunnel. The goal of this study is to analyze the depth and characteristics of acoustic basement for the stability assessment and tunnel design in this proposed area through combining drilling data with this single-channel seismic data after detailed processing. For this purpose, among the data processing schemes which are usually applied to multi-channel seismic data, we applied the F-K filtering to eliminate the AC(alternating current) noise and the post-stack depth migration to produce depth section. As a result, we verified that the improved depth section could be obtained from single-channel seismic data, and the distribution and characteristics of basement could be analyzed in survey area through the combined analysis with drilling data. However, we could not interpret the detailed structures, fault and fracture zone, due to the quality of bubble pulser source and single-channel data. We expect that those detailed structures can be analyzed when higher resolution seismic data is provided. Therefore, we recommend some items for future seismic survey of subsea tunnel to obtain the high resolution seismic data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.157-166
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• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• Composites Research
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• v.26 no.2
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• pp.123-128
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• 2013

The PVDF (polyvinylidene fluoride) fibers were prepared using the wet spinning processing. To improve ${\beta}$-phase crystalline which closely related piezoelectric property PVDF wet spun fibers conducted post treatment. Post treatment is consisted of heat stretching and annealing process. The heat stretching and annealing conditions were controlled by changing temperature between glass transition temperature and melting temperature. From these experimental data, the resulting crystal structure of the ${\beta}$-phase crystalline was confirmed by FT-IR and XRD experiments. From these analysis results, optimum stretching and annealing conditions of the wet spun PVDF fibers were founded to increase high ${\beta}$-phase crystalline. Furthermore results showed that thermal processing had a direct effect on modifying the crystalline microstructure and also confirmed that heat stretching and annealing could increase the degree of crystallinity and ${\beta}$-phase crystalline. Finally, piezoelectric constant ($d_{11}$) of the post heat treated PVDF fibers reinforced composite were measured to investigate the feasibility for the sensing materials.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.240-243
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• 2022

Tracking the location of program defects is an essential task for software maintenance and repair. When a bug report is submitted, bug localization is a costly task because of the developer's manual effort. Many researchers have tried to automate the task, but according to the reported results, the performance is still insufficient in practice. Therefore, in this study, we analyzed a large amount of bug report data and the latest research and found that the existing studies used only one preprocessing without considering the characteristics of the bug report. In this paper, to solve the problems mentioned earlier, we propose a pre/post-processing operator selection approach for bug localization.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.01a
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• pp.175-178
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• 2015

최근 모바일 장비 및 시스템의 발달로 위치정보와 이동 객체에 관련된 많은 서비스들이 연구되고 있다. 궤적은 이동 객체가 시간에 따라 변하는 위치정보들의 모음이다. 기존의 데이터베이스 시스템에서는 이동 객체 데이터 타입을 지원하지 않는다. 이 논문에서는 공간 데이터베이스로 많이 활용되고 있는 PostgreSQL/PostGIS 상에서 궤적 데이터의 연산 및 질의 처리가 가능한 확장형 모듈로써 PostTrajectory를 제안하고 있다. 또한, 궤적 데이터에 대한 다양한 질의 요구 조건들을 충족시킬 수 있는 함수들을 구현하여 궤적 데이터의 효율적인 질의 처리를 가능하게 하였다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.96-106
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• 2005

An integrated single-wafer processing tool, composed of multiple single wafer processing modules, transfer robots, and load locks, has complex routing sequences, and often has critical post-processing residency constraints. Scheduling of these tools is an intricate problem, and testing schedulers with actual tools requires too much time and cost. The Single Wafer Processor (SWP) simulator presented in this paper is to validate an on-line scheduler, and evaluate performance of integrated single-wafer processing tools before the scheduler is actually deployed into real systems. The data transfer between the scheduler and the simulator is carried out with TCP/IP communication using messages and files. The developed simulator consists of six modules, i.e., GUI (Graphic User Interface), emulators, execution system, module managers, analyzer, and 3D animator. The overall framework is built using Microsoft Visual C++, and the animator is embodied using OpenGL API (Application Programming Interface).

• Journal of The Korean Association of Information Education
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• v.26 no.6
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• pp.481-490
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• 2022

With the development of computing technology, the big data era has arrived, and we live with a lot of data around us. Elementary school students are no exception. Therefore, it is very important to learn to process data from elementary school. Since elementary school students have intuitive thinking, data visualization, which expresses data directly in pictures, is an important learning element. In this study, we study how effective elementary school students can visualize data in their daily lives to improve their information processing capabilities. Adata visualization program was developed by organizing and visualizing data using data visualization tools for the 8th class, which can be done by third graders in elementary school, and then experiencing the process of interaction. As a result of applying the developed program to 186 students in 7 classes, knowledge information processing competency factors were evaluated before and after class. As a result of the pre- and post-test, there was a significant difference in knowledge information processing capabilities. Therefore, the data visualization program developed in this study is effective.