• 대한원격탐사학회지
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• 제35권6_1호
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• pp.959-971
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• 2019

대표적인 딥러닝(deep learning) 기법 중 하나인 Convolutional Neural Network(CNN)은 고수준의 공간-분광 특징을 추출할 수 있어 초분광 영상 분류(Hyperspectral Image Classification)에 적용하는 연구가 활발히 진행되고 있다. 그러나 초분광 영상은 높은 분광 차원이 학습 과정의 시간과 복잡도를 증가시킨다는 문제가 있어 이를 해결하기 위해 기존 딥러닝 기반 초분광 영상 분류 연구들에서는 차원축소의 목적으로 Principal Component Analysis (PCA)를 적용한 바 있다. PCA는 데이터를 독립적인 주성분의 축으로 변환시킬 수 있어 분광 차원을 효율적으로 압축할 수 있으나, 분광 정보의 손실을 초래할 수 있다. PCA의 사용 유무가 CNN 학습의 정확도와 시간에 영향을 미치는 것은 분명하지만 이를 분석한 연구가 부족하다. 본 연구의 목적은 PCA를 통한 분광 차원축소가 CNN에 미치는 영향을 정량적으로 분석하여 효율적인 초분광 영상 분류를 위한 적절한 PCA의 적용 방법을 제안하는 데에 있다. 이를 위해 PCA를 적용하여 초분광 영상을 축소시켰으며, 축소된 차원의 크기를 바꿔가며 CNN 모델에 적용하였다. 또한, 모델 내의 컨볼루션(convolution) 연산 방식에 따른 PCA의 민감도를 분석하기 위해 2D-CNN과 3D-CNN을 적용하여 비교 분석하였다. 실험결과는 분류정확도, 학습시간, 분산 비율, 학습 과정을 통해 분석되었다. 축소된 차원의 크기가 분산 비율이 99.7~8%인 주성분 개수일 때 가장 효율적이었으며, 3차원 커널 경우 2D-CNN과는 다르게 원 영상의 분류정확도가 PCA-CNN보다 더 높았으며, 이를 통해 PCA의 차원축소 효과가 3차원 커널에서 상대적으로 적은 것을 알 수 있었다.

• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1157-1165
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• 2017

2-D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2-D complex Gabor filter bank consisting of the 2-D complex Gabor filtering outputs at multiple orientations and frequencies. Although several approaches for fast 2-D complex Gabor filtering have been proposed, they primarily focus on reducing the runtime of performing the 2-D complex Gabor filtering once at specific orientation and frequency. To obtain the 2-D complex Gabor filter bank output, existing methods are repeatedly applied with respect to multiple orientations and frequencies. In this paper, we propose a novel approach that efficiently computes the 2-D complex Gabor filter bank by reducing the computational redundancy that arises when performing the Gabor filtering at multiple orientations and frequencies. The proposed method first decomposes the Gabor basis kernels to allow a fast convolution with the Gaussian kernel in a separable manner. This enables reducing the runtime of the 2-D complex Gabor filter bank by reusing intermediate results of the 2-D complex Gabor filtering computed at a specific orientation. Experimental results demonstrate that our method runs faster than state-of-the-arts methods for fast 2-D complex Gabor filtering, while maintaining similar filtering quality.

• Journal of Veterinary Science
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• 제23권5호
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• pp.65.1-65.10
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• 2022

Background: Persistent uroliths after a cystotomy in dogs are a common cause of surgical failure. Objectives: This study examined the following: the success rate of retrograde urohydropropulsion in male dogs using non-enhanced computed tomography (CT), whether the CT mean beam attenuation values in Hounsfield Units (mHU) measured in vivo could predict the urolithiasis composition and whether the selected reconstruction kernel may influence the measured mHU. Methods: All dogs and cats that presented with lower urinary tract uroliths and had a non-enhanced CT preceding surgery were included. In male dogs, CT was performed after retrograde urohydropropulsion to detect the remaining urethral calculi. The percentage and location of persistent calculi were recorded. The images were reconstructed using three kernels, from smooth to ultrasharp, and the calculi mHU were measured. Results: Sixty-five patients were included in the study. The success rate of retrograde urohydropropulsion in the 45 male dogs was 55.6% and 86.7% at the first and second attempts, respectively. The predominant components of the calculi were cystine (20), struvite (15), calcium oxalate (8), and urate (7). The convolution kernel influenced the mHU values (p < 0.05). The difference in mHU regarding the calculus composition was better assessed using the smoother kernel. A mHU greater than 1,000 HU was predictive of calcium oxalate calculi. Conclusions: Non-enhanced CT is useful for controlling the success of retrograde urohydropropulsion. The mHU could allow a prediction of the calculus composition, particularly for calcium oxalate, which may help determine the therapeutic strategy.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2000년도 추계 학술발표회 논문집 Proceedings of EESK Conference-Fall 2000
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• pp.143-150
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• 2000

A physical lumped parameter model is proposed for the time domain analysis of dam-reservoir system. The exact solution of transmitting boundary is derived for a semi-infinite 2-D reservoir of constant depth. The characteristics of the solution are examined in both frequency and the domains. Mass and damping coefficient are obtained from asymptotic behavior of the frequency domain solution. Further refinement to the lumped model is made by approximating the kernel function of the convolution integral in the exact solution. Finally a new physical lumped parameter model is proposed that consists of two masses, a spring and two dampers for each mode. It is demonstrated that new lumped parameter model of transmitting boundary can give excellent results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1443-1448
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• 2003

Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impounded by linear models due to the lack of a similarly accepted nonlinear modelling or data based technique. The authors have recently developed a new method for obtaining Volterra kernels of up to third order by use of pseudorandom M-sequence. By use of this method, nonparametric NMPC is derived in discrete-time using multi-dimensional convolution between plant data and Volterra kernel measurements. This approach is applied to an industrial polymerisation process using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC.

• 대한수학회보
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• 제51권4호
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• pp.965-978
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• 2014

In this paper we establish sharp $L^p$-regularity estimates for averaging operators with convolution kernel associated to hypersurfaces in $\mathbb{R}^d(d{\geq}2)$ of the form $y{\mapsto}(y,{\gamma}(y))$ where $y{\in}\mathbb{R}^{d-1}$ and ${\gamma}(y)={\sum}^{d-1}_{i=1}{\pm}{\mid}y_i{\mid}^{m_i}$ with $2{\leq}m_1{\leq}{\cdots}{\leq}m_{d-1}$.

• Journal of information and communication convergence engineering
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• 제8권5호
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• pp.581-585
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• 2010

A grid was sandwiched between two cascaded imaging plates. Using a fan-beam X-ray tube and a single exposure scheme, the two imaging plates, respectively, recorded grid-less and grid type information of the object. Referring to the mathematical model of the Grid-less and grid technique, it was explained that the collected components whereas that of imaging plates with grid was of high together with large scattered components whereas that of imaging plate with grid was of low and suppressed scattered components. Based on this assumption and using a Gaussian convolution kernel representing the effect of scattering, the related data of the imaging plates were simulated by computer. These observed data were then employed in the developed post-processing estimation and restoration (kalman-filter) algorithms and accordingly, the quality of the resultant image was effectively improved.

• Interaction and multiscale mechanics
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• 제3권3호
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• pp.267-276
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• 2010

A nonlocal finite element model is developed for solving elasto-static frictional contact problems of nanostructures and nanoscale devices. A two dimensional Eringen-type nonlocal elasticity model is adopted. The material is characterized by a stress-strain constitutive relation of a convolution integral form whose kernel is capable to take into account both the diffusion process of nonlocal elasticity and the scale ratio effects. The incremental convex programming procedure is exploited as a solver. Two examples of different nature are presented, the first one presents the behavior of a nanoscale contacting system and the second example discusses the nano-indentation problem.

• 한국산업융합학회 논문집
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• 제17권3호
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• pp.184-193
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• 2014

Machine vision systems is applied for various industries such as optimize your spending, automate your production and maximize your efficiency. This research is effective for most optimal light condition of machine vision that technology was applied bald outside human visual acuity. Image processing converts a target image captured by a CCD camera into a digital signal and then performs various arithmetic operations on the signal to extract the characteristics of the target, such as points, lines, circles, area and length. The mathematical concepts of convolution and the kernel matrix are used to apply filters to signals, to perform functions such as extracting edges and reducing unwanted noise. This research analyze and compares matching ratio with reference image and search for optimal lighting condition in accuracy that user wants coming input image according to brightness change of lighting.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2005년도 가을 학술발표논문집 Vol.32 No.2 (2)
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• pp.796-798
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• 2005

본 연구에서는 엔트로피 이론을 사용하여 ICA(Independent Component Analysis) 점수함수를 생성하는 새로운 밀도추정자(Density Estimator)를 제안한다. 원 신호에 대한 밀도함수의 추정은 적당한 점수함수를 생성하기 위해 필요하고, 미분 가능한 밀도함수인 커널을 이용한 밀도추정법(Kernel Density Estimation)을 이용하여 점수함수를 생성하였다. 보다 빠른 점수함수의 생성을 위해서 식의 형태를 convolution 형태로 표현하였으며, ICA 학습을 위해서 결합엔트로피를 최대화(Joint Entropy Maximization)하는 방향으로 커널의 폭을 학습하였다. 이를 위해서 기울기 강하법(Gradient descent method)를 사용하였으며, 이러한 제약 사항은 새로운 밀도 추정자를 설계하기 위한 기본적인 개념을 나타낸다. 실험결과, 커널의 폭을 담당하는 smoothing parameters들이 일정한 값으로 학습함을 알 수 있었다.