• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권3호
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.132-132
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• 2021

Access to accurate spatial precipitation in many hydrological studies is necessary. Existence of many mountains with diverse topography in South Korea causes different spatial distribution of precipitation. Rain gauge stations show accurate precipitation information in points, but due to the limited use of rain gauge stations and the difficulty of accessing them, there is not enough accurate information in the whole area. Weather radars can provide an integrated precipitation information spatially. Despite this, weather radar data have some errors that can not provide accurate data, especially in heavy rainfall. In this study, some location-based variable like aspect, elevation, plan curvature, profile curvature, slope and distance from the sea which has most effect on rainfall was considered. Then Automatic Weather Station data was used for spatial training of variables in each event. According to this, K-fold cross-validation method was combined with Adaptive Neuro-Fuzzy Inference System. Based on this, 80% of Automatic Weather Station data was used for training and validation of model and 20% was used for testing and evaluation of model. Finally, spatial distribution of precipitation for 1×1 km resolution in Gwangdeoksan radar station was estimates. The results showed a significant decrease in RMSE and an increase in correlation with the observed amount of precipitation.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.142-142
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• 2022

Adjusting weather radar data is a prerequisite for its use in various hydrological studies. Effect of spatial variables are considered to adjust weather radar data in many of these researches. The existence of diverse topography in South Korea has increased the importance of analyzing these variables. In this study, some spatial variable like slope, elevation, aspect, distance from the sea, plan and profile curvature was considered. To investigate different topographic conditions, tried to use three radar station of Gwanaksan, Gwangdeoksan and Gudeoksan which are located in northwest, north and southeast of South Korea, respectively. To form the suitable fuzzy model and create the best membership functions of variables, ANFIS-PSO model was applied. After optimizing the model, the correlation coefficient and sensitivity of adjusted Quantitative Precipitation Estimation (QPE) based on spatial variables was calculated to find how variables work in adjusted QPE process. The results showed that the variable of elevation causes the most change in rainfall and consequently in the adjustment of radar data in model. Accordingly, the sensitivity ratio calculated for variables shows that with increasing rainfall duration, the effects of these variables on rainfall adjustment increase. The approach of this study, due to the simplicity and accuracy of this method, can be used to adjust the weather radar data and other required models.

• Steel and Composite Structures
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• 제49권6호
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• pp.645-666
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• 2023

Due to the steadily declining supply of natural coarse aggregates, the concrete industry has shifted to substituting coarse aggregates generated from byproducts and industrial waste. Oil palm shell is a substantial waste product created during the production of palm oil (OPS). When considering the usage of OPSC, building engineers must consider its uniaxial compressive strength (UCS). Obtaining UCS is expensive and time-consuming, machine learning may help. This research established five innovative hybrid AI algorithms to predict UCS. Aquila optimizer (AO) is used with methods to discover optimum model parameters. Considered models are artificial neural network (AO - ANN), adaptive neuro-fuzzy inference system (AO - ANFIS), support vector regression (AO - SVR), random forest (AO - RF), and extreme gradient boosting (AO - XGB). To achieve this goal, a dataset of OPS-produced concrete specimens was compiled. The outputs depict that all five developed models have justifiable accuracy in UCS estimation process, showing the remarkable correlation between measured and estimated UCS and models' usefulness. All in all, findings depict that the proposed AO - XGB model performed more suitable than others in predicting UCS of OPSC (with R², RMSE, MAE, VAF and A_15-index at 0.9678, 1.4595, 1.1527, 97.6469, and 0.9077). The proposed model could be utilized in construction engineering to ensure enough mechanical workability of lightweight concrete and permit its safe usage for construction aims.

• 한국자원식물학회:학술대회논문집
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• 한국자원식물학회 2023년도 임시총회 및 춘계학술대회
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• pp.38-38
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• 2023

Sorghum (Sorghum bicolor L.) is a promising biomass crop with a high lignocellulose content. This study aimed to select high salt-tolerance sorghum lines for cultivation on reclaimed land. Using 7-day seedlings of the sorghum population consisted of 71 radiation-derived mutants (M₂ to M₆) and 33 genetic resources, survival rate (SR), plant height (PH), root length (RL), fresh weight (FW), and chlorophyll content (CC) were measured for two weeks after 102 mM (0.6%) NaCl treatment. Furthermore, the characteristics of the sorghum population were confirmed using correlation analysis, PCA (principal component analysis), and the FCE (fuzzy comprehensive evaluation) method. Under 102 mM NaCl conditions, SR ranged from 4.9 (IS645-200-6) to 82.4% (KLSo79125-200-1), with an average of 49.9%. PH varied from 7.5 (Mesusu-100-2) to 33.2 cm (DINE-A-MITE-100-2-10), with an average of 20.4 cm. RL ranged from 1.0 (IS645-200-1) to 17.0 cm (30-100-2), with an average of 7.7 cm. FW varied from 0.1 (IS645-200-6) to 4.5 g/plant (DINE-A-MITE-100-2-10), with an average of 2.1 g/plant. CC ranged from 0.9 (DINE-A-MITE-100-2-2) to 3.1 mg/g (IS12937), with an average of 1.7 mg/g. An overall positive correlation, with SR and FW (r = 0.86, P < 0.01), and FW and CC (r = 0.79, P < 0.01), was shown by correlation analysis. Among the five traits, two principal components were extracted by PCA analysis. PC1 was significantly associated with FW, while PC2 was highly involved with RL. To evaluate the salt tolerance level of the sorghum population when an FCE based on trait data was performed, MFV (membership function value) was 0.68. As a result of compiling the MFV of each line, eight lines with MFV > 0.68 were selected. Ultimately, the radiation-derived mutant lines, DINE-A-MITE-100-2-10 and DINE-A-MITE-100-2-12 were selected as salt-tolerant sorghum lines. The results are expected to inform salt-tolerant sorghum breeding programs, and the high salt-tolerance sorghum lines might be advantageous for cultivation on reclaimed land.

• 한국컴퓨터정보학회논문지
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• 제18권4호
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• pp.35-43
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• 2013

텍스처 이미지가 다양한 산업 애플리케이션 분야에 널리 사용됨에 따라, 이러한 이미지들의 저작권 보호는 중요한 이슈가 되어왔다. 이러한 이유로, 본 논문은 이미지에 내재한 텍스처 특성을 이용한 칼라 텍스처 이미지 워터마킹 알고리즘을 제안한다. 제안한 알고리즘은 퍼지 클러스터링을 위한 입력으로써 그레이 레벨 동시발생 행렬의 에너지와 동질성 특징을 사용하여 워터마크를 삽입하기 위한 적당한 블록들을 선택한다. 워터마크를 삽입하기 위해 먼저 선택된 블록들에 이산 웨이블릿 변환을 수행하고, 이산 웨이블릿 변환의 서버밴드들의 하나를 선택한다. 그런후에 이 워터마크를 중간 대역의 이산 코사인 변환 계수에 삽입한다. 또한, 본 논문은 워터마크 삽입 후 비인지성과 다양한 형태의 워커마킹 공격에 대해 강인성이 뛰어난 이득 계수들과 이산 웨이블릿 변환의 서버밴드들의 효과를 탐색한다. 모의실험 결과, 제안한 알고리즘은 이득 계수가 42이고 HH 밴드에 워터마크를 삽입하였을 때 높은 PSNR 값 (47.66 dB to 48.04 dB) 및 낮은 M-SVD 값 (8.84 to 15.6)을 얻었다. 또한 제안한 알고리즘은 노이즈 첨가, 필터링, 잘라내기 및 JPEG 압축과 같은 다양한 이미지 처리 공격에서도 높은 상관 값 (0.7193 to 1)을 보였다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제2권8호
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• pp.579-588
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• 2013

본 논문에서는 화재 비디오 영상의 저작권 보호를 위해 효과적인 워터마킹 기법을 제안한다. 제안하는 워터마킹 기법은 명암도 동시발생 행렬과 퍼지 클러스터링 알고리즘을 이용하여 화재의 색상과 텍스처의 특징을 효율적으로 이용한다. 명암도 동시발생 행렬은 각 후보 화재 영상의 블록에 대한 에너지와 동질성을 계산하여 텍스처 데이터 셋을 만드는데 사용하며, 퍼지 클러스터링은 화재 비디오 영상의 색상 분할과 워터마커 삽입을 위한 텍스처 블록을 결정하기 위해 사용된다. 선택된 텍스처 블록은 이산 웨이블릿 변환을 통해 네 가지 서브밴드 (LL, LH, HL, HH)를 가지는 1차 레벨 웨이블릿 구조로 분해되고, 워터마커는 사람의 시각에 영향을 주지 않는 LH 영역에 삽입된다. 모의실험결과, 제안한 워터마킹 기법은 약 48 데시벨의 높은 첨부 신호 대 잡음 비와 1.6-2.0의 낮은 M-특이치 분해 값을 보였다. 또한, 제안한 워터마킹 기법은 노이즈 첨가, 필터링, 크로핑, JPEG 압축과 같은 영상처리 공격에서도 기존 이미지 워터마킹 알고리즘보다 정규화된 상관 값에서 높은 성능을 보였다.

• 복식
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• 제58권10호
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• pp.65-77
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• 2008

The purpose of this study is to analyze the correlation between the density, the Count and the width of cross section in 2D function through comparison the difference of simulated fabrics based on the various yarns, and to compare the 3D effect by Lay Mapping of diverse fabrics. The method of research is to weave the eight fabrics composed of cotton, linen, worsted, slender yarn, loop, $m{\acute{e}}lange$, woolen, and yarn twist with Hi-Tex program, and to practice 3D mapping with Hi-Print program. As a mapping object, the flared skirt which is a basic costume item is selected. As a result, the thickness of yarn in CAD system was fixed by the width of cross section rather than Count, especially by the width of core section not including the fluff section. The type of yarn such as cotton yarn, linen yarn, and worsted had effect on the shape of texture, but had few interrelations with dimension. In the case of 3D mapping, the textural characteristic and the dimension were presented precisely, whereas there were several limitations. First, the thickness of tissue has not been represented. Secondly, the effect of texture such as fuzzy look, loop was not expressed on the skirt outline including sideline and hemline. Thirdly, the difference of silhouette was not distinct. The common point in 2D and 3D operations is that the representation of texture is relatively accurate and that is difficult to measure and manifest of thickness, the side. For more professional digitalizing in fashion industry, above all in the domain of 3D, it must be supplement the subdivided and differentiated mapping process according to the texture, deviating from the existing analog-based organization which has to designate the form and silhouette suitable for tissue.

• 디자인학연구
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• 제20권3호
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• pp.203-214
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• 2007

본 논문은 이동(Navigation)지원을 목적으로 하는 정보기기 콘텐츠를 개발하기 위하여 보행자의 감성이 반영된 감성정보처리법을 퍼지이론에 기초하여 제안하였다. 먼저, 가상의 목적지로 설정한 상업공간의 감성평가를 실시하고, 시각적 특징과 감성적 특징 사이의 인과관계를 규명하여 내비게이션 지식베이스로 구축하는 방법을 제안하였다. 지식베이스는 감성적 특징 사이의 상관관계모델, 시각적 특징량과 감성적 특징량 사이의 인과관계모델, 그리고 시각적 특징량과 물리적 특징량 사이의 변환모델로 구축된다. 다음으로, 보행자의 목적지에 대한 감성적 취향과 내비게이션에 주어지는 시간적 제약조건에 따라 목적지 탐색을 4가지 유형으로 분류하고 각각의 유형에 적합한 목적지 탐색방법을 제시하였다. 마지막으로, 구축된 지식베이스와 감성검색 알고리즘을 이용하여 내비게이션 유형별로 목적지 탐색을 시뮬레이션 하여 보행자의 감성을 고려한 정보탐색방법으로서의 유효성을 검증하였다. 본 연구에서 제안한 감성정보처리법은 다양한 분야의 정보기기 콘텐츠를 개발함에 있어 사용자의 감성기능을 적용하는 방법론으로 활용될 수 있을 것으로 기대된다.

• Smart Structures and Systems
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• 제14권5호
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• pp.785-809
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• 2014

In this paper, an Adaptive nerou-based inference system (ANFIS) is being used for the prediction of shear strength of high strength concrete (HSC) beams without stirrups. The input parameters comprise of tensile reinforcement ratio, concrete compressive strength and shear span to depth ratio. Additionally, 122 experimental datasets were extracted from the literature review on the HSC beams with some comparable cross sectional dimensions and loading conditions. A comparative analysis has been carried out on the predicted shear strength of HSC beams without stirrups via the ANFIS method with those from the CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94 codes of design. The shear strength prediction with ANFIS is discovered to be superior to CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94. The predictions obtained from the ANFIS are harmonious with the test results not accounting for the shear span to depth ratio, tensile reinforcement ratio and concrete compressive strength; the data of the average, variance, correlation coefficient and coefficient of variation (CV) of the ratio between the shear strength predicted using the ANFIS method and the real shear strength are 0.995, 0.014, 0.969 and 11.97%, respectively. Taking a look at the CV index, the shear strength prediction shows better in nonlinear iterations such as the ANFIS for shear strength prediction of HSC beams without stirrups.