• 대한기계학회논문집A
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• 제24권2호
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• pp.455-463
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• 2000

In order to develop nondestructive techniques for the quantitative estimation of creep damage a series of crept copper samples were prepared and their ultrasonic velocities were measured. Velocities measured in three directions with respect to the loading axis decreased nonlinearly and their anisotropy increased as a function of creep-induced porosity. A progressive damage model was described to explain the void-velocity relationship, including the anisotropy. The comparison of modeling study showed that the creep voids evolved from sphere toward flat oblate spheroid with its minor axis aligned along the stress direction. This model allowed us to determine the average aspect ratio of voids for a given porosity content. A novel technique, the back propagation neural network (BPNN), was applied for estimating the porosity content due to the creep damage. The measured velocities were used to train the BP classifier, and its accuracy was tested on another set of creep samples containing 0 to 0.7 % void content. When the void aspect ratio was used as input parameter together with the velocity data, the NN algorithm provided much better estimation of void content.

• 한국정보통신학회논문지
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• 제9권7호
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• pp.1513-1517
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• 2005

본 논문에서는 얼굴영역을 검출하기위해 얼굴 피부색을 보다 효과적으로 모델링하기 위한 피부색 특성을 고려하여 밝기 성분을 제거한 Red, Blue, Green 채널을 모두 사용하는 Hue, Cb, Cg의 M배i-Channel 피부색 모델을 사용한다. 얼굴영역을 분리한 영상에 Harr 웨이블릿을 이용한 에지영상 추출과 얼굴영역의 특징벡터를 구하기 위하여 26개의 특징벡터를 사용한 효율적인 고차 국소 자동 상관함수를 사용하였다. 계산된 특징벡터는 BP 신경망의 학습을 통하여 얼굴인식을 위한 데이터로 사용된다. 시뮬레이션을 통해 제안된 알고리즘에 의한 인식률향상과 속도 향상을 입증한다.

• 대한화장품학회:학술대회논문집
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• 대한화장품학회 2003년도 IFSCC Conference Proceeding Book I
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• pp.285-290
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• 2003

Living cells are continuously subject to all sorts of stress such as ultraviolet rays on skin cells. Tests made in various laboratories show that when young fibroblasts (Le. at the beginning of their proliferate life) were repeatedly put under stress at subletal doses, they acquired a phenotype similar to Senescence Induced Prematurely by Stress (SIPS). The work presented hereafter was made on a new model of senescence induced prematurely by stress from ultraviolet Brays (UVB). The human fibroblast model was put under repeated UVB stress, causing SIPS. Several ageing biomarkers were used in order to characterise the cells that underwent stress:. an increase in the proportion of positive cells with senescence associated $\beta$-galactosidase activity (SA $\beta$-gal) measured by a specific coloration,. the proportion in the different morphological stages that fibroblasts undergo during culture visualised by microscopic observation,. the expression of genes known for overexpressing during senescence, particularly fibronectin and apolipoprotein J, measured by Real Time-PCR,. the common deletion of 4,977 bp in mitochondrial DNA, evaluated by nested PCR. Studying the variation of these 4 biomarkers, we have evaluated the protective effect of a Laminaria digitata extract (LDE) that can be used as a natural active ingredient for anti-ageing cosmetics.

• Structural Engineering and Mechanics
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• 제31권1호
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2004년도 학술발표회
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• pp.1208-1212
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• 2004

다목적댐을 효율적이고 체계적으로 운영하기 위해서는 수문순환에 대한 지역별, 기간별 이해와 더불어 댐저수지로의 정확한 유입량 산정이 필요하다. 수문모델링을 비교하기 위해서는 개념적 모형과 추계학적 모형으로 나눌 수 있는데 개념적 모형은 상당히 많은 입력요소로 말미암아 사용자로 하여금 이해를 하는데 있어서 어려움을 겪을 수 밖에 없는 실정이나 추계학적 모형은 확률적 철상 및 기초적 예측이론을 습득하게 되면 쉽고 간단하여 검토를 용이하게 할 수 있는 장점이 있다. 수자원시스템의 설계, 계획, 운영에 있어서 핵심적인 수문변수의 미래거동의 보다 나은 추정치가 필요하다. 예를 들어, 수력발전, 레크리에이션 이용과 하류지역의 오염희석과 같은 다중 목적을 유지하기 위하여 다목적댐을 운영할 때에, 다가오는 미래시간에 대한 계획된 유입량의 예측이 요구된다. 예측의 목적은 미래에 발생한 정확한 예측을 제공하는 것이다. 따라서 월유입량 예측을 위해 추계학적 모형(ARMA(1,1), ARMAX, TFN, SARIMA)과 신경망 모형(BP, CASCADE 등)의 적용을 통해 한강수게 주요 다목적댐에 가장 적합한 방법을 선정하고자 하는데 본 연구의 목적이 있다.

• Imaging Science in Dentistry
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• 제43권3호
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• pp.153-161
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• 2013

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

• 한국축산식품학회지
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• 제38권2호
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• pp.362-375
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• 2018

This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.

• 한국식품영양과학회지
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• 제22권5호
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• pp.565-569
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• 1993

동결건조한 양조간장의 분말로 부터 갈색물질을 Sephadex G-10으로 겔-여과 크로마토그라피법에 의하여 분획한 다음 이를 동결건조하여 분말로 만든 후, 동갈색물질의 항산화력을 다른 항산화제인 부틸히드록시아니졸(BHA) 및 ${\alpha}-토코패롤과$ 비교하여 보았다. 갈색물질 및 양조간장은 다 같이 지방산의 산화반응에 있어서 과산화물의 생성을 크게 저해시켰고, 갈색물질이 양조간장 자체보다 항산화 효과가 더 높았으며 농도에 비례하여 항산화성이 증대되었다. 그러나 갈색물질의 항산화력은 과산화물 생성억제 능력 그리고 conjugated dienoic acid 생성저해 능력 등에서 BHA 및 ${\alpha}-토코페롤$ 보다는 낮았다. 이와같은 양조간장의 갈색물질의 항산화성은 그 획분이 대량 섭취도고 있는 발효조미식품이라는 점에서 중요한 의의를 가질 것으로 판단된다.

• Structural Engineering and Mechanics
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• 제85권5호
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• pp.635-644
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• 2023

The design of honeycomb sandwich structures is often challenging because these structures can be tailored from a variety of possible cores and face sheets configurations, therefore, the design of sandwich structures is characterized as a time-consuming and complex task. A data-driven computational approach that integrates the analytical method and Artificial Neural Network (ANN) is developed by the authors to rapidly predict the design of sandwich structures for a targeted maximum structural deflection. The elaborated ANN reverse design approach is applied to obtain the thickness of the sandwich core, the thickness of the laminated face sheets, and safety factors for composite sandwich structure. The required data for building ANN model were obtained using the governing equations of sandwich components in conjunction with the Monte Carlo Method. Then, the functional relationship between the input and output features was created using the neural network Backpropagation (BP) algorithm. The input variables were the dimensions of the sandwich structure, the applied load, the core density, and the maximum deflection, which was the reverse input given by the designer. The outstanding performance of reverse ANN model revealed through a low value of mean square error (MSE) together with the coefficient of determination (R2) close to the unity. Furthermore, the output of the model was in good agreement with the analytical solution with a maximum error 4.7%. The combination of reverse concept and ANN may provide a potentially novel approach in designing of sandwich structures. The main added value of this study is the elaboration of a reverse ANN model, which provides a low computational technique as well as savestime in the design or redesign of sandwich structures compared to analytical and finite element approaches.

• Korean Chemical Engineering Research
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• 제54권3호
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• pp.310-314
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• 2016

합성 항산화제에 대한 대체제로 천연 항산화제에 대한 연구가 활발히 진행되고 있으며 미세조류는 천연 항산화제의 원료로 많은 관심을 받고 있다. 본 연구에서는 탈지미세조류에서 총 폴리페놀(TPC) 추출증대를 위해 추출용매, 온도, 시간, 고액비율과 에탄올 첨가 농도 최적화를 수행하였다. 열수와 유기용매 추출성능을 비교했을 때, 열수추출이 유기용매 보다 우수한 성능을 보였으며 온도 증가에 따라 추출성능도 비례하여 증가함을 보였다. 열수에 의한 추출이 에탄올 용액 추출(>98%)에 비해 우수한 성능을 보였으며40% 에탄올 용액을 이용한 열수 추출이 가장 우수한 추출 효과를 보였다. 추출조건10 min, $100^{\circ}C$, 40% 에탄올 열수추출에서 최대 폴리페놀 농도인 3.35 mg GAE (gallic acid equivalent)/g DM을 얻을 수 있었다. 지질 추출을 위한 유기용매 전처리 공정이 선수행 되었음에도 불구하고 탈지미세조류(Tetraselmis KCTC 12236BP)의 폴리페놀 농도가 다른 탈지이전 미세조류와 동등한 수준임을 확인할 수 있어 탈지미세조류가 천연 폴리페놀의 원료로서 적합함을 확인 할 수 있었다. 또한, 고액추출을 모사하기 위해 Peleg 모델을 이용해 예측한 폴리페놀 농도가 실험에 의해 얻어진 값과 높은 일치도를 보임으로 모델을 이용한 모사가 폴리페놀 추출 모사에 유용함을 증명할 수 있었다.