• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.31-38
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• 2009

Lens distortion is inevitable phenomenon in machine vision system. More and more distortion phenomenon is occurring in order to choice of lens for minimizing cost and system size. As shown above, correction of lens distortion is critical issue. However previous lens correction methods using camera model have problem such as nonlinear property and complicated operation. And recent lens correction methods using neural network also have accuracy and efficiency problem. In this study, I propose new algorithms for correction of lens distortion. Distorted image is divided based on the distortion quantity using k-means. And each divided image region is corrected by using neural network. As a result, the proposed algorithms have better accuracy than previous methods without image division.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.83-92
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• 2009

In this paper, an extended snake algorithm using color variance energy is proposed for segmenting an interest object in color image. General snake algorithm makes use of energy in image to segment images into a interesting area and background. There are many kinds of energy that can be used by the snake algorithm. The efficiency of the snake algorithm is depend on what kind of energy is used. A general snake algorithm based on active contour model uses the intensity value as an image energy that can be implemented and analyzed easily. But it is sensitive to noises because the image gradient uses a differential operator to get its image energy. And it is difficult for the general snake algorithm to be applied on the complex image background. Therefore, the proposed snake algorithm efficiently segment an interest object on the color image by adding a color variance of the segmented area to the image energy. This paper executed various experiments to segment an interest object on color images with simple or complex background for verifying the performance of the proposed extended snake algorithm. It shows improved accuracy performance about 12.42 %.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.23-29
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• 2022

Recently, due to the increase in the use of Internet of Things (IoT) devices, the amount of data transmitted and processed to cloud computing servers has increased rapidly. As a result, network problems (delay, server overload and security threats) are emerging. In particular, edge computing with lower computational capabilities than cloud computing requires a lightweight authentication algorithm that can easily authenticate numerous IoT devices.In this paper, we proposed a key-agreement protocol of a lightweight algorithm that guarantees anonymity and forward and backward secrecy between IoT and edge devices. and the proposed algorithm is stable in MITM and replay attacks for edge device and IoT. As a result of comparing and analyzing the proposed key-agreement protocol with previous studies, it was shown that a lightweight protocol that can be efficiently used in IoT and edge devices.

• Annual Conference of KIPS
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• 2021.05a
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• pp.163-166
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• 2021

ICISC'20에서 발표된 경량 블록암호 PIPO는 비트 슬라이스 기법 적용으로 효율적인 구현이 되었으며, 부채널 내성을 지니기에 안전하지 않은 환경에서도 안정적으로 사용 가능한 경량 블록암호이다. 본 논문에서는 ARM 프로세서를 대상으로 PIPO의 병렬 최적 구현을 제안한다. 제안하는 구현물은 8평문, 16평문의 병렬 암호화가 가능하다. 구현에는 최적의 명령어 활용, 레지스터 내부 정렬, 로테이션 연산 최적화 기법을 사용하였다. 구현은 A10x fusion 프로세서를 대상으로 한다. 대상 프로세서상에서, 기존 레퍼런스 PIPO 코드는 64/128, 64/256 규격에서 각각 34.6 cpb, 44.7 cpb의 성능을 가지나, 제안하는 기법은 8평문 64/128, 64/256 규격에서 각각 12.0 cpb, 15.6 cpb, 16평문 64/128, 64/256 규격에서 각각 6.3 cpb, 8.1 cpb의 성능을 보여준다. 이는 기존 대비 각 규격별로 8평문 병렬 구현물은 약 65.3%, 66.4%, 16평문 병렬 구현물은 약 81.8%, 82.1% 더 좋은 성능을 보인다.

• The Journal of Bigdata
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• v.7 no.1
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• pp.1-14
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• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.279-279
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• 2022

최근 이미지분석 기술은 하드웨어 및 소프트웨어 기술의 급격한 발전으로 인해 의학, 생물학, 지리학, 재료공학 등에서 수많은 연구 분야에서 광범위하게 활용되고 있으며, 이미지분석은 다량의 토사에 대하여 입경을 포함한 형상학적 특성을 간편하게 정량화 할 수 있기 때문에 매우 효과적인 분석 방법으로 판단된다. 현재 모래의 입도분석 방법으로는 신뢰성 있는 체가름 시험법(KSF2302) 등이 있으나, 번거로운 처리과정과 많은 시간이 소요된다. 또한 입자형상은 입경이 세립 할수록 직접 측정이 어렵기 때문에, 최근에는 이미지 분석을 이용하는 방법이 시도되고 있다. 본 연구에서는 75㎛ 이상의 조립질 하상 토사 이미지를 취득하여, 입자들의 장·축단 길이, 면적, 둘레, 공칭직경 및 종횡비 등의 형상학적 특성인자를 자동으로 측정하는 프로그램 개발을 수행하였다. 프로그램은 이미지 분석에 특화된 라이브러리인 OpenCV(Open Source Computer Vision)를 적용하였다. 이미지 분석 절차는 크게 이미지 취득, 기하보정, 노이즈제거, 객체추출 및 형상인자 측정 단계로 구성되며, 이미지 취득시 패널의 하단에 Back light를 부착해 시료에 의해 발생되는 음영을 제거하였다. 기하보정은 원근변환(perspective transform)을 적용했으며, 노이즈 제거는 모폴로지 연산과 입자간의 중첩으로 인한 뭉침을 제거하기 위해 watershed 알고리즘을 적용하였다. 최종적으로 객체의 외곽선 추출하여 입자들의 다양한 정보(장축, 단축, 둘레, 면적, 공칭직경, 종횡비)를 산출하고, 분포형으로 제시하였다. 본 연구에서 제안하는 이미지분석을 적용한 토사의 형상학적 특성 측정 방법은 시간과 비용의 측면에서 보다 효율적으로 하상 토사에 대한 다양한 정보를 획득 할 수 있을 것으로 기대한다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.110-110
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• 2022

본 연구는 자율운항 선박의 원격 고장 진단 기법 개발의 일부로 수행되었다. 특히, 엔진 연료 계통 장비로부터 계측된 시계열 데이터로부터 상태 진단을 위한 알고리즘 구현 결과를 제시하였다. 엔진 연료 펌프와 청정기를 가진 육상 실험 장비로부터 진동 시계열 데이터 계측하였으며, 이상 감지, 고장 분류 및 고장 예측이 가능한 심층 학습(Deep Learning) 및 기계 학습(Machine Learning) 알고리즘을 구현하였다. 육상 실험 장비에 고장 유형 별로 인위적인 고장을 발생시켜 특징적인 진동 신호를 계측하여, 인공 지능 학습에 이용하였다. 계측된 신호 데이터는 선행 발생한 사건의 신호가 후행 사건에 영향을 미치는 특성을 가지고 있으므로, 시계열에 내포된 고장 상태는 시간 간의 선후 종속성을 반영할 수 있는 학습 알고리즘을 제시하였다. 고장 사건의 시간 종속성을 반영할 수 있도록 순환(Recurrent) 계열의 RNN(Recurrent Neural Networks), LSTM(Long Short-Term Memory models)의 모델과 합성곱 연산 (Convolution Neural Network)을 기반으로 하는 Conv1D 모델을 적용하여 예측 정확성을 비교하였다. 특히, 합성곱 계열의 RNN LSTM 모델이 고차원의 순차적 자연어 언어 처리에 장점을 보이는 모델임을 착안하여, 신호의 시간 종속성을 학습에 반영할 수 있는 합성곱 계열의 Conv1 알고리즘을 고장 예측에 사용하였다. 또한 기계 학습 모델의 효율성을 감안하여 XGBoost를 추가로 적용하여 고장 예측을 시도하였다. 최종적으로 연료 펌프와 청정기의 진동 신호로부터 Conv1D 모델과 XGBoost 모델의 고장 예측 성능 결과를 비교하였다

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1707-1720
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• 2023

With the increase in satellite time series data, the utility of remote sensing data is growing. In the analysis of time series data, the relative positional accuracy between images has a significant impact on the results, making image registration essential for correction. In recent years, research on image registration has been increasing by applying deep learning, which outperforms existing image registration algorithms. To train deep learning-based registration models, a large number of image pairs are required. Additionally, creating a correlation map between the data of existing deep learning models and applying additional computations to extract registration points is inefficient. To overcome these drawbacks, this study developed a data augmentation technique for training image registration models and applied it to OffsetNet, a registration model that predicts the offset amount itself, to perform image registration for KOMSAT-2, -3, and -3A. The results of the model training showed that OffsetNet accurately predicted the offset amount for the test data, enabling effective registration of the master and slave images.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.15-20
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• 2023

Until now, Pollard's Rho algorithm has been known as the most efficient way for discrete algebraic problems to decrypt symmetric keys. However, the algorithm is being studied on how to further reduce the complexity of O(${\sqrt{p}}$) performance, along with the disadvantage of having to store the giant stride m=⌈${\sqrt{p}}$⌉ data. This paper proposes an array method for cycle detection in discrete logarithms. The proposed method reduces the number of updates of stack memory by at least 73%. This is done by only updating the array when (x_i<0.5x_i-1)∩(x_i<0.5(p-1)). The proposed array method undergoes the same number of modular calculation as stack method, but significantly reduces the number of updates and the execution time for array through the use of a binary search method.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.23-29
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• 2024

In this paper, we propose a fast GPU-based method for representing pore flow, absorption, emission, and diffusion effects represented by cloth-liquid interactions using smoothed particle hydrodynamics (SPH), a particle-based fluid solver: 1) a unified framework for GPU-based representation of various physical effects represented by cloth-liquid interactions; 2) a method for efficiently calculating the saturation of a node based on SPH and transferring it to the surrounding porous particles; 3) a method for improving the stability based on Darcy's law to reliably calculate the direction of fluid absorption and release; 4) a method for controlling the amount of fluid absorbed by the porous particles according to the direction of flow; and finally, 5) a method for releasing the SPH particles without exceeding their maximum mass. The main advantage of the proposed method is that all computations are computed and run on the GPU, allowing us to quickly model porous materials, porous flows, absorption, reflection, diffusion, etc. represented by the interaction of cloth and fluid.